test_integration.py

from __future__ import annotations

import typing as t
import json
from collections import Counter
from datetime import timedelta
from unittest import mock
from unittest.mock import patch
import logging
from textwrap import dedent
import os
import numpy as np  # noqa: TID253
import pandas as pd  # noqa: TID253
import pytest
from pytest import MonkeyPatch
from pathlib import Path
from sqlmesh.core.console import set_console, get_console, TerminalConsole
from sqlmesh.core.config.naming import NameInferenceConfig
from sqlmesh.core.model.common import ParsableSql
from sqlmesh.utils.concurrency import NodeExecutionFailedError
import time_machine
from pytest_mock.plugin import MockerFixture
from sqlglot import exp
from sqlglot.expressions import DataType
import re
from IPython.utils.capture import capture_output


from sqlmesh import CustomMaterialization
from sqlmesh.cli.project_init import init_example_project
from sqlmesh.core import constants as c
from sqlmesh.core import dialect as d
from sqlmesh.core.config import (
    AutoCategorizationMode,
    Config,
    GatewayConfig,
    ModelDefaultsConfig,
    DuckDBConnectionConfig,
    TableNamingConvention,
)
from sqlmesh.core.config.common import EnvironmentSuffixTarget, VirtualEnvironmentMode
from sqlmesh.core.console import Console, get_console
from sqlmesh.core.context import Context
from sqlmesh.core.config.categorizer import CategorizerConfig
from sqlmesh.core.config.plan import PlanConfig
from sqlmesh.core.engine_adapter import EngineAdapter, DuckDBEngineAdapter
from sqlmesh.core.environment import EnvironmentNamingInfo
from sqlmesh.core.macros import macro
from sqlmesh.core.model import (
    FullKind,
    IncrementalByTimeRangeKind,
    IncrementalByUniqueKeyKind,
    IncrementalUnmanagedKind,
    Model,
    ModelKind,
    ModelKindName,
    SqlModel,
    PythonModel,
    ViewKind,
    CustomKind,
    TimeColumn,
    load_sql_based_model,
)
from sqlmesh.core.model.kind import model_kind_type_from_name
from sqlmesh.core.plan import Plan, PlanBuilder, SnapshotIntervals
from sqlmesh.core.snapshot import (
    DeployabilityIndex,
    Snapshot,
    SnapshotChangeCategory,
    SnapshotId,
    SnapshotInfoLike,
    SnapshotTableInfo,
)
from sqlmesh.utils.date import TimeLike, now, to_date, to_datetime, to_timestamp
from sqlmesh.utils.errors import NoChangesPlanError, SQLMeshError, PlanError, ConfigError
from sqlmesh.utils.pydantic import validate_string
from tests.conftest import DuckDBMetadata, SushiDataValidator
from sqlmesh.utils import CorrelationId
from tests.utils.test_helpers import use_terminal_console
from tests.utils.test_filesystem import create_temp_file

if t.TYPE_CHECKING:
    from sqlmesh import QueryOrDF

pytestmark = pytest.mark.slow


@pytest.fixture(autouse=True)
def mock_choices(mocker: MockerFixture):
    mocker.patch("sqlmesh.core.console.TerminalConsole._get_snapshot_change_category")
    mocker.patch("sqlmesh.core.console.TerminalConsole._prompt_backfill")


def plan_choice(plan_builder: PlanBuilder, choice: SnapshotChangeCategory) -> None:
    for snapshot in plan_builder.build().snapshots.values():
        if not snapshot.version:
            plan_builder.set_choice(snapshot, choice)


@time_machine.travel("2023-01-08 15:00:00 UTC")
@pytest.mark.parametrize(
    "context_fixture",
    ["sushi_context", "sushi_no_default_catalog"],
)
def test_forward_only_plan_with_effective_date(context_fixture: Context, request):
    context = request.getfixturevalue(context_fixture)
    model_name = "sushi.waiter_revenue_by_day"
    model = context.get_model(model_name)
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)), start="2023-01-01")
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan_builder = context.plan_builder("dev", skip_tests=True, forward_only=True)
    plan = plan_builder.build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.context_diff.snapshots[snapshot.snapshot_id].is_forward_only
    assert plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].is_forward_only

    assert to_timestamp(plan.start) == to_timestamp("2023-01-07")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[(to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))],
        ),
    ]

    plan = plan_builder.set_effective_from("2023-01-05").build()
    # Default start should be set to effective_from
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    plan = plan_builder.set_start("2023-01-06").build()
    # Start override should take precedence
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    plan = plan_builder.set_effective_from("2023-01-04").build()
    # Start should remain unchanged
    assert plan.start == "2023-01-06"
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert dev_df["event_date"].tolist() == [
        pd.to_datetime("2023-01-06"),
        pd.to_datetime("2023-01-07"),
    ]

    prod_plan = context.plan_builder(skip_tests=True).build()
    # Make sure that the previously set effective_from is respected
    assert prod_plan.start == to_timestamp("2023-01-04")
    assert prod_plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(prod_plan)

    prod_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi.waiter_revenue_by_day WHERE one IS NOT NULL ORDER BY event_date"
    )
    assert prod_df["event_date"].tolist() == [
        pd.to_datetime(x) for x in ["2023-01-04", "2023-01-05", "2023-01-06", "2023-01-07"]
    ]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_forward_only_model_regular_plan(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model_name = "sushi.waiter_revenue_by_day"

    model = context.get_model(model_name)
    model = add_projection_to_model(t.cast(SqlModel, model))
    forward_only_kind = model.kind.copy(update={"forward_only": True})
    model = model.copy(update={"kind": forward_only_kind})

    context.upsert_model(model)
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=False).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.context_diff.snapshots[snapshot.snapshot_id].is_forward_only
    assert plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].is_forward_only

    assert plan.start == to_datetime("2023-01-01")
    assert not plan.missing_intervals

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert not dev_df["event_date"].tolist()

    # Run a restatement plan to preview changes
    plan_builder = context.plan_builder(
        "dev", skip_tests=True, restate_models=[model_name], enable_preview=False
    )
    plan_builder.set_start("2023-01-06")
    assert plan_builder.build().missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Make sure that changed start is reflected in missing intervals
    plan_builder.set_start("2023-01-07")
    assert plan_builder.build().missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan_builder.build())

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert dev_df["event_date"].tolist() == [pd.to_datetime("2023-01-07")]

    # Promote changes to prod
    prod_plan = context.plan_builder(skip_tests=True).build()
    assert not prod_plan.missing_intervals

    context.apply(prod_plan)

    # The change was applied in a forward-only manner so no values in the new column should be populated
    prod_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi.waiter_revenue_by_day WHERE one IS NOT NULL ORDER BY event_date"
    )
    assert not prod_df["event_date"].tolist()


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_forward_only_model_regular_plan_preview_enabled(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model_name = "sushi.waiter_revenue_by_day"

    model = context.get_model(model_name)
    model = add_projection_to_model(t.cast(SqlModel, model))
    forward_only_kind = model.kind.copy(update={"forward_only": True})
    model = model.copy(update={"kind": forward_only_kind})

    context.upsert_model(model)
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=True).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.context_diff.snapshots[snapshot.snapshot_id].is_forward_only
    assert plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].is_forward_only

    assert to_timestamp(plan.start) == to_timestamp("2023-01-07")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert dev_df["event_date"].tolist() == [pd.to_datetime("2023-01-07")]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_forward_only_model_restate_full_history_in_dev(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    model_name = "memory.sushi.customer_max_revenue"
    expressions = d.parse(
        f"""
        MODEL (
            name {model_name},
            kind INCREMENTAL_BY_UNIQUE_KEY (
                unique_key customer_id,
                forward_only true,
            ),
        );

        SELECT
          customer_id, MAX(revenue) AS max_revenue
        FROM memory.sushi.customer_revenue_lifetime
        GROUP BY 1;
        """
    )

    model = load_sql_based_model(expressions)
    assert model.forward_only
    assert model.kind.full_history_restatement_only
    context.upsert_model(model)

    context.plan("prod", skip_tests=True, auto_apply=True, enable_preview=False)

    model_kwargs = {
        **model.dict(),
        # Make a breaking change.
        "query": model.query.order_by("customer_id"),  # type: ignore
    }
    context.upsert_model(SqlModel.parse_obj(model_kwargs))

    # Apply the model change in dev
    plan = context.plan_builder(
        "dev",
        skip_tests=True,
        enable_preview=False,
        categorizer_config=CategorizerConfig.all_full(),
    ).build()
    assert not plan.missing_intervals
    context.apply(plan)

    snapshot = context.get_snapshot(model, raise_if_missing=True)
    snapshot_table_name = snapshot.table_name(False)

    # Manually insert a dummy value to check that the table is recreated during the restatement
    context.engine_adapter.insert_append(
        snapshot_table_name,
        pd.DataFrame({"customer_id": [-1], "max_revenue": [100]}),
    )
    df = context.engine_adapter.fetchdf(
        "SELECT COUNT(*) AS cnt FROM sushi__dev.customer_max_revenue WHERE customer_id = -1"
    )
    assert df["cnt"][0] == 1

    # Apply a restatement plan in dev
    plan = context.plan("dev", restate_models=[model.name], auto_apply=True, enable_preview=False)
    assert len(plan.missing_intervals) == 1

    # Check that the dummy value is not present
    df = context.engine_adapter.fetchdf(
        "SELECT COUNT(*) AS cnt FROM sushi__dev.customer_max_revenue WHERE customer_id = -1"
    )
    assert df["cnt"][0] == 0

    # Check that the table is not empty
    df = context.engine_adapter.fetchdf(
        "SELECT COUNT(*) AS cnt FROM sushi__dev.customer_max_revenue"
    )
    assert df["cnt"][0] > 0


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_full_history_restatement_model_regular_plan_preview_enabled(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model_name = "sushi.marketing"  # SCD2 model

    model = context.get_model(model_name)
    model = add_projection_to_model(t.cast(SqlModel, model))

    context.upsert_model(model)
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    customers_snapshot = context.get_snapshot("sushi.customers", raise_if_missing=True)
    active_customers_snapshot = context.get_snapshot(
        "sushi.active_customers", raise_if_missing=True
    )
    waiter_as_customer_snapshot = context.get_snapshot(
        "sushi.waiter_as_customer_by_day", raise_if_missing=True
    )

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=True).build()

    assert len(plan.new_snapshots) == 6
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[customers_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[active_customers_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[waiter_as_customer_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert all(s.is_forward_only for s in plan.new_snapshots)

    assert to_timestamp(plan.start) == to_timestamp("2023-01-07")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_metadata_changed_regular_plan_preview_enabled(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model_name = "sushi.waiter_revenue_by_day"

    model = context.get_model(model_name)
    model = model.copy(update={"owner": "new_owner"})

    context.upsert_model(model)
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=True).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.METADATA
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.METADATA
    )
    assert not plan.missing_intervals
    assert not plan.restatements


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_hourly_model_with_lookback_no_backfill_in_dev(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")

    model_name = "sushi.waiter_revenue_by_day"

    model = context.get_model(model_name)
    model = SqlModel.parse_obj(
        {
            **model.dict(),
            "kind": model.kind.copy(update={"lookback": 1}),
            "cron": "@hourly",
            "audits": [],
        }
    )
    context.upsert_model(model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    top_waiters_model = context.get_model("sushi.top_waiters")
    top_waiters_model = add_projection_to_model(t.cast(SqlModel, top_waiters_model), literal=True)
    context.upsert_model(top_waiters_model)

    context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    with time_machine.travel(now() + timedelta(hours=2)):
        plan = context.plan_builder("dev", skip_tests=True).build()
        # Make sure the waiter_revenue_by_day model is not backfilled.
        assert plan.missing_intervals == [
            SnapshotIntervals(
                snapshot_id=top_waiters_snapshot.snapshot_id,
                intervals=[
                    (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                    (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                    (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                    (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                    (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                    (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                    (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
                ],
            ),
        ]


@time_machine.travel("2023-01-08 00:00:00 UTC", tick=False)
def test_parent_cron_after_child(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.waiter_revenue_by_day")
    model = SqlModel.parse_obj(
        {
            **model.dict(),
            "cron": "50 23 * * *",
        }
    )
    context.upsert_model(model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    waiter_revenue_by_day_snapshot = context.get_snapshot(model.name, raise_if_missing=True)
    assert waiter_revenue_by_day_snapshot.intervals == [
        (to_timestamp("2023-01-01"), to_timestamp("2023-01-07"))
    ]

    top_waiters_model = context.get_model("sushi.top_waiters")
    top_waiters_model = add_projection_to_model(t.cast(SqlModel, top_waiters_model), literal=True)
    context.upsert_model(top_waiters_model)

    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    with time_machine.travel("2023-01-08 23:55:00 UTC"):  # Past parent's cron, but before child's
        plan = context.plan_builder("dev", skip_tests=True).build()
        # Make sure the waiter_revenue_by_day model is not backfilled.
        assert plan.missing_intervals == [
            SnapshotIntervals(
                snapshot_id=top_waiters_snapshot.snapshot_id,
                intervals=[
                    (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                    (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                    (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                    (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                    (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                    (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                    (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
                ],
            ),
        ]


@time_machine.travel("2023-01-08 00:00:00 UTC")
@pytest.mark.parametrize(
    "forward_only, expected_intervals",
    [
        (
            False,
            [
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
            ],
        ),
        (
            True,
            [
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
            ],
        ),
    ],
)
def test_cron_not_aligned_with_day_boundary(
    init_and_plan_context: t.Callable,
    forward_only: bool,
    expected_intervals: t.List[t.Tuple[int, int]],
):
    context, plan = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.waiter_revenue_by_day")
    model = SqlModel.parse_obj(
        {
            **model.dict(),
            "kind": model.kind.copy(update={"forward_only": forward_only}),
            "cron": "0 12 * * *",
        }
    )
    context.upsert_model(model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    waiter_revenue_by_day_snapshot = context.get_snapshot(model.name, raise_if_missing=True)
    assert waiter_revenue_by_day_snapshot.intervals == [
        (to_timestamp("2023-01-01"), to_timestamp("2023-01-07"))
    ]

    model = add_projection_to_model(t.cast(SqlModel, model), literal=True)
    context.upsert_model(model)

    waiter_revenue_by_day_snapshot = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    )

    with time_machine.travel("2023-01-08 00:10:00 UTC"):  # Past model's cron.
        plan = context.plan_builder(
            "dev", select_models=[model.name], skip_tests=True, enable_preview=True
        ).build()
        assert plan.missing_intervals == [
            SnapshotIntervals(
                snapshot_id=waiter_revenue_by_day_snapshot.snapshot_id,
                intervals=expected_intervals,
            ),
        ]


@time_machine.travel("2023-01-08 00:00:00 UTC")
def test_cron_not_aligned_with_day_boundary_new_model(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    existing_model = context.get_model("sushi.waiter_revenue_by_day")
    existing_model = SqlModel.parse_obj(
        {
            **existing_model.dict(),
            "kind": existing_model.kind.copy(update={"forward_only": True}),
        }
    )
    context.upsert_model(existing_model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    # Add a new model and make a change to a forward-only model.
    # The cron of the new model is not aligned with the day boundary.
    new_model = load_sql_based_model(
        d.parse(
            """
        MODEL (
            name memory.sushi.new_model,
            kind FULL,
            cron '0 8 * * *',
            start '2023-01-01',
        );

        SELECT 1 AS one;
        """
        )
    )
    context.upsert_model(new_model)

    existing_model = add_projection_to_model(t.cast(SqlModel, existing_model), literal=True)
    context.upsert_model(existing_model)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=True).build()
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(
                "memory.sushi.new_model", raise_if_missing=True
            ).snapshot_id,
            intervals=[(to_timestamp("2023-01-06"), to_timestamp("2023-01-07"))],
        ),
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(
                "sushi.waiter_revenue_by_day", raise_if_missing=True
            ).snapshot_id,
            intervals=[
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]


@time_machine.travel("2023-01-08 00:00:00 UTC")
def test_forward_only_preview_child_that_runs_before_parent(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    # This model runs at minute 30 of every hour
    upstream_model = load_sql_based_model(
        d.parse(
            """
        MODEL (
            name memory.sushi.upstream_model,
            kind FULL,
            cron '30 * * * *',
            start '2023-01-01',
        );

        SELECT 1 AS a;
        """
        )
    )
    context.upsert_model(upstream_model)

    # This model runs at minute 0 of every hour, so it runs before the upstream model
    downstream_model = load_sql_based_model(
        d.parse(
            """
        MODEL (
            name memory.sushi.downstream_model,
            kind INCREMENTAL_BY_TIME_RANGE(
               time_column event_date,
               forward_only True,
            ),
            cron '0 * * * *',
            start '2023-01-01',
        );

        SELECT a, '2023-01-06' AS event_date FROM memory.sushi.upstream_model;
        """
        )
    )
    context.upsert_model(downstream_model)

    context.plan("prod", skip_tests=True, auto_apply=True)

    with time_machine.travel("2023-01-08 00:05:00 UTC"):
        # The downstream model runs but not the upstream model
        context.run("prod")

    # Now it's time for the upstream model to run but it hasn't run yet
    with time_machine.travel("2023-01-08 00:35:00 UTC"):
        # Make a change to the downstream model.
        downstream_model = add_projection_to_model(t.cast(SqlModel, downstream_model), literal=True)
        context.upsert_model(downstream_model)

        # The plan should only backfill the downstream model despite upstream missing intervals
        plan = context.plan_builder("dev", skip_tests=True, enable_preview=True).build()
        assert plan.missing_intervals == [
            SnapshotIntervals(
                snapshot_id=context.get_snapshot(
                    downstream_model.name, raise_if_missing=True
                ).snapshot_id,
                intervals=[
                    (to_timestamp("2023-01-07 23:00:00"), to_timestamp("2023-01-08 00:00:00"))
                ],
            ),
        ]


@time_machine.travel("2023-01-08 00:00:00 UTC")
def test_forward_only_monthly_model(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.waiter_revenue_by_day")
    model = SqlModel.parse_obj(
        {
            **model.dict(),
            "kind": model.kind.copy(update={"forward_only": True}),
            "cron": "0 0 1 * *",
            "start": "2022-01-01",
            "audits": [],
        }
    )
    context.upsert_model(model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    waiter_revenue_by_day_snapshot = context.get_snapshot(model.name, raise_if_missing=True)
    assert waiter_revenue_by_day_snapshot.intervals == [
        (to_timestamp("2022-01-01"), to_timestamp("2023-01-01"))
    ]

    model = add_projection_to_model(t.cast(SqlModel, model), literal=True)
    context.upsert_model(model)

    waiter_revenue_by_day_snapshot = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    )

    plan = context.plan_builder(
        "dev", select_models=[model.name], skip_tests=True, enable_preview=True
    ).build()
    assert to_timestamp(plan.start) == to_timestamp("2022-12-01")
    assert to_timestamp(plan.end) == to_timestamp("2023-01-08")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot.snapshot_id,
            intervals=[(to_timestamp("2022-12-01"), to_timestamp("2023-01-01"))],
        ),
    ]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_forward_only_parent_created_in_dev_child_created_in_prod(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    waiter_revenue_by_day_model = context.get_model("sushi.waiter_revenue_by_day")
    waiter_revenue_by_day_model = add_projection_to_model(
        t.cast(SqlModel, waiter_revenue_by_day_model)
    )
    forward_only_kind = waiter_revenue_by_day_model.kind.copy(update={"forward_only": True})
    waiter_revenue_by_day_model = waiter_revenue_by_day_model.copy(
        update={"kind": forward_only_kind}
    )
    context.upsert_model(waiter_revenue_by_day_model)

    waiter_revenue_by_day_snapshot = context.get_snapshot(
        waiter_revenue_by_day_model, raise_if_missing=True
    )
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=False).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[waiter_revenue_by_day_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert all(s.is_forward_only for s in plan.new_snapshots)
    assert plan.start == to_datetime("2023-01-01")
    assert not plan.missing_intervals

    context.apply(plan)

    # Update the child to refer to a newly added column.
    top_waiters_model = context.get_model("sushi.top_waiters")
    top_waiters_model = add_projection_to_model(t.cast(SqlModel, top_waiters_model), literal=False)
    context.upsert_model(top_waiters_model)

    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("prod", skip_tests=True, enable_preview=False).build()
    assert len(plan.new_snapshots) == 1
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )

    context.apply(plan)


@time_machine.travel("2023-01-08 00:00:00 UTC")
def test_new_forward_only_model(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    context.plan("dev", skip_tests=True, no_prompts=True, auto_apply=True, enable_preview=False)

    snapshot = context.get_snapshot("sushi.marketing")

    # The deployable table should not exist yet
    assert not context.engine_adapter.table_exists(snapshot.table_name())
    assert context.engine_adapter.table_exists(snapshot.table_name(is_deployable=False))

    context.plan("prod", skip_tests=True, no_prompts=True, auto_apply=True)

    assert context.engine_adapter.table_exists(snapshot.table_name())
    assert context.engine_adapter.table_exists(snapshot.table_name(is_deployable=False))


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_set_choice_is_reflected_in_missing_intervals(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")
    context.upsert_model(context.get_model("sushi.top_waiters").copy(update={"kind": FullKind()}))
    context.plan("prod", skip_tests=True, no_prompts=True, auto_apply=True)

    model_name = "sushi.waiter_revenue_by_day"

    model = context.get_model(model_name)
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))
    snapshot = context.get_snapshot(model, raise_if_missing=True)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan_builder = context.plan_builder("dev", skip_tests=True)
    plan = plan_builder.build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Change the category to BREAKING
    plan = plan_builder.set_choice(
        plan.context_diff.snapshots[snapshot.snapshot_id], SnapshotChangeCategory.BREAKING
    ).build()
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_BREAKING
    )
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Change the category back to NON_BREAKING
    plan = plan_builder.set_choice(
        plan.context_diff.snapshots[snapshot.snapshot_id], SnapshotChangeCategory.NON_BREAKING
    ).build()
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert dev_df["event_date"].tolist() == [
        pd.to_datetime(x)
        for x in [
            "2023-01-01",
            "2023-01-02",
            "2023-01-03",
            "2023-01-04",
            "2023-01-05",
            "2023-01-06",
            "2023-01-07",
        ]
    ]

    # Promote changes to prod
    prod_plan = context.plan_builder(skip_tests=True).build()
    assert not prod_plan.missing_intervals

    context.apply(prod_plan)
    prod_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi.waiter_revenue_by_day WHERE one IS NOT NULL ORDER BY event_date"
    )
    assert prod_df["event_date"].tolist() == [
        pd.to_datetime(x)
        for x in [
            "2023-01-01",
            "2023-01-02",
            "2023-01-03",
            "2023-01-04",
            "2023-01-05",
            "2023-01-06",
            "2023-01-07",
        ]
    ]


@time_machine.travel("2023-01-08 15:00:00 UTC", tick=True)
@pytest.mark.parametrize("has_view_binding", [False, True])
def test_non_breaking_change_after_forward_only_in_dev(
    init_and_plan_context: t.Callable, has_view_binding: bool
):
    context, plan = init_and_plan_context("examples/sushi")
    context.snapshot_evaluator.adapter.HAS_VIEW_BINDING = has_view_binding
    context.apply(plan)

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))
    waiter_revenue_by_day_snapshot = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    )
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, forward_only=True).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[waiter_revenue_by_day_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert all(s.is_forward_only for s in plan.new_snapshots)
    assert to_timestamp(plan.start) == to_timestamp("2023-01-07")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot.snapshot_id,
            intervals=[(to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))],
        ),
    ]

    # Apply the forward-only changes first.
    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert dev_df["event_date"].tolist() == [pd.to_datetime("2023-01-07")]

    # Make a non-breaking change to a model downstream.
    model = context.get_model("sushi.top_waiters")
    # Select 'one' column from the updated upstream model.
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model), literal=False))
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True).build()
    assert len(plan.new_snapshots) == 1
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert to_timestamp(plan.start) == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Apply the non-breaking changes.
    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT waiter_id FROM sushi__dev.top_waiters WHERE one IS NOT NULL"
    )
    assert not dev_df.empty

    prod_df = context.engine_adapter.fetchdf("DESCRIBE sushi.top_waiters")
    assert "one" not in prod_df["column_name"].tolist()

    # Deploy both changes to prod.
    plan = context.plan_builder("prod", skip_tests=True).build()
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)

    prod_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi.waiter_revenue_by_day WHERE one IS NOT NULL ORDER BY event_date"
    )
    assert prod_df.empty

    prod_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT waiter_id FROM sushi.top_waiters WHERE one IS NOT NULL"
    )
    assert prod_df.empty


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_indirect_non_breaking_change_after_forward_only_in_dev(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")
    # Make sure that the most downstream model is a materialized model.
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": FullKind()})
    context.upsert_model(model)
    context.plan("prod", skip_tests=True, auto_apply=True, no_prompts=True)

    # Make sushi.orders a forward-only model.
    model = context.get_model("sushi.orders")
    updated_model_kind = model.kind.copy(update={"forward_only": True})
    model = model.copy(update={"stamp": "force new version", "kind": updated_model_kind})
    context.upsert_model(model)
    snapshot = context.get_snapshot(model, raise_if_missing=True)

    plan = context.plan_builder(
        "dev",
        skip_tests=True,
        enable_preview=False,
        categorizer_config=CategorizerConfig.all_full(),
    ).build()
    assert (
        plan.context_diff.snapshots[snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert plan.context_diff.snapshots[snapshot.snapshot_id].is_forward_only
    assert not plan.requires_backfill
    context.apply(plan)

    # Make a non-breaking change to a model.
    model = context.get_model("sushi.top_waiters")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=False).build()
    assert len(plan.new_snapshots) == 1
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Apply the non-breaking changes.
    context.apply(plan)

    # Make a non-breaking change upstream from the previously modified model.
    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))
    waiter_revenue_by_day_snapshot = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    )
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder("dev", skip_tests=True, enable_preview=False).build()
    assert len(plan.new_snapshots) == 2
    assert (
        plan.context_diff.snapshots[waiter_revenue_by_day_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiters_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    # Apply the upstream non-breaking changes.
    context.apply(plan)
    assert not context.plan_builder("dev", skip_tests=True).build().requires_backfill

    # Deploy everything to prod.
    plan = context.plan_builder("prod", skip_tests=True, enable_preview=False).build()
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiters_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)
    assert (
        not context.plan_builder("prod", skip_tests=True, enable_preview=False)
        .build()
        .requires_backfill
    )


@time_machine.travel("2023-01-08 15:00:00 UTC", tick=True)
def test_metadata_change_after_forward_only_results_in_migration(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Make a forward-only change
    model = context.get_model("sushi.waiter_revenue_by_day")
    model = model.copy(update={"kind": model.kind.copy(update={"forward_only": True})})
    model = add_projection_to_model(t.cast(SqlModel, model))
    context.upsert_model(model)
    plan = context.plan("dev", skip_tests=True, auto_apply=True, no_prompts=True)
    assert len(plan.new_snapshots) == 2
    assert all(s.is_forward_only for s in plan.new_snapshots)

    # Follow-up with a metadata change in the same environment
    model = model.copy(update={"owner": "new_owner"})
    context.upsert_model(model)
    plan = context.plan("dev", skip_tests=True, auto_apply=True, no_prompts=True)
    assert len(plan.new_snapshots) == 2
    assert all(s.change_category == SnapshotChangeCategory.METADATA for s in plan.new_snapshots)

    # Deploy the latest change to prod
    context.plan("prod", skip_tests=True, auto_apply=True, no_prompts=True)

    # Check that the new column was added in prod
    columns = context.engine_adapter.columns("sushi.waiter_revenue_by_day")
    assert "one" in columns


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_indirect_non_breaking_downstream_of_forward_only(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Make sushi.orders a forward-only model.
    forward_only_model = context.get_model("sushi.orders")
    updated_model_kind = forward_only_model.kind.copy(update={"forward_only": True})
    forward_only_model = forward_only_model.copy(
        update={"stamp": "force new version", "kind": updated_model_kind}
    )
    context.upsert_model(forward_only_model)
    forward_only_snapshot = context.get_snapshot(forward_only_model, raise_if_missing=True)

    non_breaking_model = context.get_model("sushi.waiter_revenue_by_day")
    non_breaking_model = non_breaking_model.copy(update={"start": "2023-01-01"})
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, non_breaking_model)))
    non_breaking_snapshot = context.get_snapshot(non_breaking_model, raise_if_missing=True)
    top_waiter_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan = context.plan_builder(
        "dev",
        skip_tests=True,
        enable_preview=False,
        categorizer_config=CategorizerConfig.all_full(),
    ).build()
    assert (
        plan.context_diff.snapshots[forward_only_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert (
        plan.context_diff.snapshots[non_breaking_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiter_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.context_diff.snapshots[forward_only_snapshot.snapshot_id].is_forward_only
    assert not plan.context_diff.snapshots[non_breaking_snapshot.snapshot_id].is_forward_only
    assert not plan.context_diff.snapshots[top_waiter_snapshot.snapshot_id].is_forward_only

    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiter_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=non_breaking_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)
    assert (
        not context.plan_builder("dev", skip_tests=True, enable_preview=False)
        .build()
        .requires_backfill
    )

    # Deploy everything to prod.
    plan = context.plan_builder("prod", skip_tests=True).build()
    assert plan.start == to_timestamp("2023-01-01")
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=top_waiter_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
        SnapshotIntervals(
            snapshot_id=non_breaking_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]

    context.apply(plan)
    assert (
        not context.plan_builder("prod", skip_tests=True, enable_preview=False)
        .build()
        .requires_backfill
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_breaking_only_impacts_immediate_children(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")
    context.upsert_model(context.get_model("sushi.top_waiters").copy(update={"kind": FullKind()}))
    context.plan("prod", skip_tests=True, auto_apply=True, no_prompts=True)

    breaking_model = context.get_model("sushi.orders")
    breaking_model = breaking_model.copy(update={"stamp": "force new version"})
    context.upsert_model(breaking_model)
    breaking_snapshot = context.get_snapshot(breaking_model, raise_if_missing=True)

    non_breaking_model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, non_breaking_model)))
    non_breaking_snapshot = context.get_snapshot(non_breaking_model, raise_if_missing=True)
    top_waiter_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)

    plan_builder = context.plan_builder("dev", skip_tests=True, enable_preview=False)
    plan_builder.set_choice(breaking_snapshot, SnapshotChangeCategory.BREAKING)
    plan = plan_builder.build()
    assert (
        plan.context_diff.snapshots[breaking_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert (
        plan.context_diff.snapshots[non_breaking_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        plan.context_diff.snapshots[top_waiter_snapshot.snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert plan.start == to_timestamp("2023-01-01")
    assert not any(i.snapshot_id == top_waiter_snapshot.snapshot_id for i in plan.missing_intervals)

    context.apply(plan)
    assert (
        not context.plan_builder("dev", skip_tests=True, enable_preview=False)
        .build()
        .requires_backfill
    )

    # Deploy everything to prod.
    plan = context.plan_builder("prod", skip_tests=True).build()
    assert not plan.missing_intervals

    context.apply(plan)
    assert (
        not context.plan_builder("prod", skip_tests=True, enable_preview=False)
        .build()
        .requires_backfill
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_run_with_select_models(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    with time_machine.travel("2023-01-09 00:00:00 UTC"):
        assert context.run(select_models=["*waiter_revenue_by_day"])

        snapshots = context.state_sync.state_sync.get_snapshots(context.snapshots.values())
        # Only waiter_revenue_by_day and its parents should be backfilled up to 2023-01-09.
        assert {s.name: s.intervals[0][1] for s in snapshots.values() if s.intervals} == {
            '"memory"."sushi"."waiter_revenue_by_day"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."order_items"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."orders"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."items"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."customer_revenue_lifetime"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."customer_revenue_by_day"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."latest_order"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."waiter_names"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."raw_marketing"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."marketing"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."waiter_as_customer_by_day"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."top_waiters"': to_timestamp("2023-01-08"),
            '"memory"."raw"."demographics"': to_timestamp("2023-01-08"),
            "assert_item_price_above_zero": to_timestamp("2023-01-08"),
            '"memory"."sushi"."active_customers"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."customers"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."count_customers_active"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."count_customers_inactive"': to_timestamp("2023-01-08"),
        }


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_with_run(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    with time_machine.travel("2023-01-09 00:00:00 UTC"):
        plan = context.plan(run=True)
        assert plan.has_changes
        assert plan.missing_intervals

        context.apply(plan)

        snapshots = context.state_sync.state_sync.get_snapshots(context.snapshots.values())
        assert {s.name: s.intervals[0][1] for s in snapshots.values() if s.intervals} == {
            '"memory"."sushi"."waiter_revenue_by_day"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."order_items"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."orders"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."items"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."customer_revenue_lifetime"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."customer_revenue_by_day"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."latest_order"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."waiter_names"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."raw_marketing"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."marketing"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."waiter_as_customer_by_day"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."top_waiters"': to_timestamp("2023-01-09"),
            '"memory"."raw"."demographics"': to_timestamp("2023-01-09"),
            "assert_item_price_above_zero": to_timestamp("2023-01-09"),
            '"memory"."sushi"."active_customers"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."customers"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."count_customers_active"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."count_customers_inactive"': to_timestamp("2023-01-09"),
        }


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_ignore_cron(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context("examples/sushi")

    expressions = d.parse(
        f"""
        MODEL (
            name memory.sushi.test_allow_partials,
            kind INCREMENTAL_UNMANAGED,
            allow_partials true,
            start '2023-01-01',
        );

        SELECT @end_ts AS end_ts
        """
    )
    model = load_sql_based_model(expressions)

    context.upsert_model(model)
    context.plan("prod", skip_tests=True, auto_apply=True, no_prompts=True)

    assert (
        context.engine_adapter.fetchone("SELECT MAX(end_ts) FROM memory.sushi.test_allow_partials")[
            0
        ]
        == "2023-01-07 23:59:59.999999"
    )

    plan_no_ignore_cron = context.plan_builder(
        "prod", run=True, ignore_cron=False, skip_tests=True
    ).build()
    assert not plan_no_ignore_cron.missing_intervals

    plan = context.plan_builder("prod", run=True, ignore_cron=True, skip_tests=True).build()
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(model, raise_if_missing=True).snapshot_id,
            intervals=[
                (to_timestamp("2023-01-08"), to_timestamp("2023-01-08 15:00:00")),
            ],
        )
    ]
    context.apply(plan)

    assert (
        context.engine_adapter.fetchone("SELECT MAX(end_ts) FROM memory.sushi.test_allow_partials")[
            0
        ]
        == "2023-01-08 14:59:59.999999"
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_run_with_select_models_no_auto_upstream(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.waiter_revenue_by_day")
    model = SqlModel.parse_obj({**model.dict(), "audits": []})
    context.upsert_model(model)

    context.plan("prod", no_prompts=True, skip_tests=True, auto_apply=True)

    with time_machine.travel("2023-01-09 00:00:00 UTC"):
        assert context.run(select_models=["*waiter_revenue_by_day"], no_auto_upstream=True)

        snapshots = context.state_sync.state_sync.get_snapshots(context.snapshots.values())
        # Only waiter_revenue_by_day should be backfilled up to 2023-01-09.
        assert {s.name: s.intervals[0][1] for s in snapshots.values() if s.intervals} == {
            '"memory"."sushi"."waiter_revenue_by_day"': to_timestamp("2023-01-09"),
            '"memory"."sushi"."order_items"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."orders"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."items"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."customer_revenue_lifetime"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."customer_revenue_by_day"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."latest_order"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."waiter_names"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."raw_marketing"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."marketing"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."waiter_as_customer_by_day"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."top_waiters"': to_timestamp("2023-01-08"),
            '"memory"."raw"."demographics"': to_timestamp("2023-01-08"),
            "assert_item_price_above_zero": to_timestamp("2023-01-08"),
            '"memory"."sushi"."active_customers"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."customers"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."count_customers_active"': to_timestamp("2023-01-08"),
            '"memory"."sushi"."count_customers_inactive"': to_timestamp("2023-01-08"),
        }


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_select_models(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Modify 2 models.
    model = context.get_model("sushi.waiter_revenue_by_day")
    kwargs = {
        **model.dict(),
        # Make a breaking change.
        "query": model.query.order_by("waiter_id"),  # type: ignore
    }
    context.upsert_model(SqlModel.parse_obj(kwargs))

    model = context.get_model("sushi.customer_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    expected_intervals = [
        (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
        (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
        (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
        (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
        (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
        (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
        (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
    ]

    waiter_revenue_by_day_snapshot_id = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    ).snapshot_id

    # Select one of the modified models.
    plan_builder = context.plan_builder(
        "dev", select_models=["*waiter_revenue_by_day"], skip_tests=True
    )
    snapshot = plan_builder._context_diff.snapshots[waiter_revenue_by_day_snapshot_id]
    plan_builder.set_choice(snapshot, SnapshotChangeCategory.BREAKING)
    plan = plan_builder.build()

    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot_id,
            intervals=expected_intervals,
        ),
    ]

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert len(dev_df) == 7

    # Make sure that we only create a view for the selected model.
    schema_objects = context.engine_adapter.get_data_objects("sushi__dev")
    assert len(schema_objects) == 1
    assert schema_objects[0].name == "waiter_revenue_by_day"

    # Validate the other modified model.
    assert not context.get_snapshot("sushi.customer_revenue_by_day").change_category
    assert not context.get_snapshot("sushi.customer_revenue_by_day").version

    # Validate the downstream model.
    assert not context.engine_adapter.table_exists(
        context.get_snapshot("sushi.top_waiters").table_name()
    )
    assert not context.engine_adapter.table_exists(
        context.get_snapshot("sushi.top_waiters").table_name(False)
    )

    # Make sure that tables are created when deploying to prod.
    plan = context.plan("prod", skip_tests=True)
    context.apply(plan)
    assert context.engine_adapter.table_exists(
        context.get_snapshot("sushi.top_waiters").table_name()
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_select_unchanged_model_for_backfill(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Modify 2 models.
    model = context.get_model("sushi.waiter_revenue_by_day")
    kwargs = {
        **model.dict(),
        # Make a breaking change.
        "query": d.parse_one(
            f"{model.query.sql(dialect='duckdb')} ORDER BY waiter_id", dialect="duckdb"
        ),
    }
    context.upsert_model(SqlModel.parse_obj(kwargs))

    model = context.get_model("sushi.customer_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    expected_intervals = [
        (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
        (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
        (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
        (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
        (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
        (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
        (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
    ]

    waiter_revenue_by_day_snapshot_id = context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    ).snapshot_id

    # Select one of the modified models.
    plan_builder = context.plan_builder(
        "dev", select_models=["*waiter_revenue_by_day"], skip_tests=True
    )
    snapshot = plan_builder._context_diff.snapshots[waiter_revenue_by_day_snapshot_id]
    plan_builder.set_choice(snapshot, SnapshotChangeCategory.BREAKING)
    plan = plan_builder.build()

    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=waiter_revenue_by_day_snapshot_id,
            intervals=expected_intervals,
        ),
    ]

    context.apply(plan)

    # Make sure that we only create a view for the selected model.
    schema_objects = context.engine_adapter.get_data_objects("sushi__dev")
    assert {o.name for o in schema_objects} == {"waiter_revenue_by_day"}

    # Now select a model downstream from the previously modified one in order to backfill it.
    plan = context.plan_builder("dev", select_models=["*top_waiters"], skip_tests=True).build()

    assert not plan.has_changes
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(
                "sushi.top_waiters", raise_if_missing=True
            ).snapshot_id,
            intervals=expected_intervals,
        ),
    ]

    context.apply(plan)

    # Make sure that a view has been created for the downstream selected model.
    schema_objects = context.engine_adapter.get_data_objects("sushi__dev")
    assert {o.name for o in schema_objects} == {"waiter_revenue_by_day", "top_waiters"}


@time_machine.travel("2023-01-08 00:00:00 UTC")
def test_snapshot_triggers(init_and_plan_context: t.Callable, mocker: MockerFixture):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # auto-restatement triggers
    orders = context.get_model("sushi.orders")
    orders_kind = {
        **orders.kind.dict(),
        "auto_restatement_cron": "@hourly",
    }
    orders_kwargs = {
        **orders.dict(),
        "kind": orders_kind,
    }
    context.upsert_model(PythonModel.parse_obj(orders_kwargs))

    order_items = context.get_model("sushi.order_items")
    order_items_kind = {
        **order_items.kind.dict(),
        "auto_restatement_cron": "@hourly",
    }
    order_items_kwargs = {
        **order_items.dict(),
        "kind": order_items_kind,
    }
    context.upsert_model(PythonModel.parse_obj(order_items_kwargs))

    waiter_revenue_by_day = context.get_model("sushi.waiter_revenue_by_day")
    waiter_revenue_by_day_kind = {
        **waiter_revenue_by_day.kind.dict(),
        "auto_restatement_cron": "@hourly",
    }
    waiter_revenue_by_day_kwargs = {
        **waiter_revenue_by_day.dict(),
        "kind": waiter_revenue_by_day_kind,
    }
    context.upsert_model(SqlModel.parse_obj(waiter_revenue_by_day_kwargs))

    context.plan(auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full())

    scheduler = context.scheduler()

    import sqlmesh

    spy = mocker.spy(sqlmesh.core.scheduler.Scheduler, "run_merged_intervals")

    with time_machine.travel("2023-01-09 00:00:01 UTC"):
        scheduler.run(
            environment=c.PROD,
            start="2023-01-01",
            auto_restatement_enabled=True,
        )

    assert spy.called

    actual_triggers = spy.call_args.kwargs["auto_restatement_triggers"]
    actual_triggers = {k: v for k, v in actual_triggers.items() if v}
    assert len(actual_triggers) == 12

    for id, trigger in actual_triggers.items():
        model_name = id.name.replace('"memory"."sushi".', "").replace('"', "")
        auto_restatement_triggers = [
            t.name.replace('"memory"."sushi".', "").replace('"', "") for t in trigger
        ]

        if model_name in ("orders", "order_items", "waiter_revenue_by_day"):
            assert auto_restatement_triggers == [model_name]
        elif model_name in ("customer_revenue_lifetime", "customer_revenue_by_day"):
            assert sorted(auto_restatement_triggers) == sorted(["orders", "order_items"])
        elif model_name == "top_waiters":
            assert auto_restatement_triggers == ["waiter_revenue_by_day"]
        else:
            assert auto_restatement_triggers == ["orders"]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_max_interval_end_per_model_not_applied_when_end_is_provided(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    with time_machine.travel("2023-01-09 00:00:00 UTC"):
        context.run()

        plan = context.plan_builder(
            restate_models=["*"], start="2023-01-09", end="2023-01-09"
        ).build()
        context.apply(plan)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_select_models_for_backfill(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    expected_intervals = [
        (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
    ]

    plan = context.plan_builder(
        "dev", backfill_models=["+*waiter_revenue_by_day"], skip_tests=True
    ).build()

    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot("sushi.items", raise_if_missing=True).snapshot_id,
            intervals=expected_intervals,
        ),
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(
                "sushi.order_items", raise_if_missing=True
            ).snapshot_id,
            intervals=expected_intervals,
        ),
        SnapshotIntervals(
            snapshot_id=context.get_snapshot("sushi.orders", raise_if_missing=True).snapshot_id,
            intervals=expected_intervals,
        ),
        SnapshotIntervals(
            snapshot_id=context.get_snapshot(
                "sushi.waiter_revenue_by_day", raise_if_missing=True
            ).snapshot_id,
            intervals=expected_intervals,
        ),
    ]

    context.apply(plan)

    dev_df = context.engine_adapter.fetchdf(
        "SELECT DISTINCT event_date FROM sushi__dev.waiter_revenue_by_day ORDER BY event_date"
    )
    assert len(dev_df) == 1

    schema_objects = context.engine_adapter.get_data_objects("sushi__dev")
    assert {o.name for o in schema_objects} == {
        "items",
        "order_items",
        "orders",
        "waiter_revenue_by_day",
    }

    assert not context.engine_adapter.table_exists(
        context.get_snapshot("sushi.customer_revenue_by_day").table_name()
    )

    # Make sure that tables are created when deploying to prod.
    plan = context.plan("prod")
    context.apply(plan)
    assert context.engine_adapter.table_exists(
        context.get_snapshot("sushi.customer_revenue_by_day").table_name()
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dbt_select_star_is_directly_modified(sushi_test_dbt_context: Context):
    context = sushi_test_dbt_context

    model = context.get_model("sushi.simple_model_a")
    context.upsert_model(
        model,
        query_=ParsableSql(sql="SELECT 1 AS a, 2 AS b"),
    )

    snapshot_a_id = context.get_snapshot("sushi.simple_model_a").snapshot_id  # type: ignore
    snapshot_b_id = context.get_snapshot("sushi.simple_model_b").snapshot_id  # type: ignore

    plan = context.plan_builder("dev", skip_tests=True).build()
    assert plan.directly_modified == {snapshot_a_id, snapshot_b_id}
    assert {i.snapshot_id for i in plan.missing_intervals} == {snapshot_a_id, snapshot_b_id}

    assert plan.snapshots[snapshot_a_id].change_category == SnapshotChangeCategory.NON_BREAKING
    assert plan.snapshots[snapshot_b_id].change_category == SnapshotChangeCategory.NON_BREAKING


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dbt_is_incremental_table_is_missing(sushi_test_dbt_context: Context):
    context = sushi_test_dbt_context

    model = context.get_model("sushi.waiter_revenue_by_day_v2")
    model = model.copy(update={"kind": IncrementalUnmanagedKind(), "start": "2023-01-01"})
    context.upsert_model(model)

    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    snapshot = context.get_snapshot("sushi.waiter_revenue_by_day_v2")
    assert snapshot

    # Manually drop the table
    context.engine_adapter.drop_table(snapshot.table_name())

    context.snapshot_evaluator.evaluate(
        snapshot,
        start="2023-01-01",
        end="2023-01-08",
        execution_time="2023-01-08 15:00:00",
        snapshots={s.name: s for s in context.snapshots.values()},
        deployability_index=DeployabilityIndex.all_deployable(),
    )

    # Make sure the table was recreated
    assert context.engine_adapter.table_exists(snapshot.table_name())


def test_model_attr(sushi_test_dbt_context: Context, assert_exp_eq):
    context = sushi_test_dbt_context
    model = context.get_model("sushi.top_waiters")
    assert_exp_eq(
        model.render_query(),
        """
        SELECT
          CAST("waiter_id" AS INT) AS "waiter_id",
          CAST("revenue" AS DOUBLE) AS "revenue",
          3 AS "model_columns"
        FROM "memory"."sushi"."waiter_revenue_by_day_v2" AS "waiter_revenue_by_day_v2"
        WHERE
          "ds" = (
             SELECT
               MAX("ds")
             FROM "memory"."sushi"."waiter_revenue_by_day_v2" AS "waiter_revenue_by_day_v2"
           )
        ORDER BY
          "revenue" DESC NULLS FIRST
        LIMIT 10
        """,
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_incremental_by_partition(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    source_name = "raw.test_incremental_by_partition"
    model_name = "memory.sushi.test_incremental_by_partition"

    expressions = d.parse(
        f"""
        MODEL (
            name {model_name},
            kind INCREMENTAL_BY_PARTITION (disable_restatement false),
            partitioned_by [key],
            allow_partials true,
            start '2023-01-07',
        );

        SELECT key, value FROM {source_name};
        """
    )
    model = load_sql_based_model(expressions)
    context.upsert_model(model)

    context.engine_adapter.ctas(
        source_name,
        d.parse_one("SELECT 'key_a' AS key, 1 AS value"),
    )

    context.plan(auto_apply=True, no_prompts=True)
    assert context.engine_adapter.fetchall(f"SELECT * FROM {model_name}") == [
        ("key_a", 1),
    ]

    context.engine_adapter.replace_query(
        source_name,
        d.parse_one("SELECT 'key_b' AS key, 1 AS value"),
    )
    context.run(ignore_cron=True)
    assert context.engine_adapter.fetchall(f"SELECT * FROM {model_name}") == [
        ("key_a", 1),
        ("key_b", 1),
    ]

    context.engine_adapter.replace_query(
        source_name,
        d.parse_one("SELECT 'key_a' AS key, 2 AS value"),
    )
    # Run 1 minute later.
    with time_machine.travel("2023-01-08 15:01:00 UTC"):
        context.run(ignore_cron=True)
    assert context.engine_adapter.fetchall(f"SELECT * FROM {model_name}") == [
        ("key_b", 1),
        ("key_a", 2),
    ]

    # model should fully refresh on restatement
    context.engine_adapter.replace_query(
        source_name,
        d.parse_one("SELECT 'key_c' AS key, 3 AS value"),
    )
    context.plan(auto_apply=True, no_prompts=True, restate_models=[model_name])
    assert context.engine_adapter.fetchall(f"SELECT * FROM {model_name}") == [
        ("key_c", 3),
    ]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_custom_materialization(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    custom_insert_called = False

    class CustomFullMaterialization(CustomMaterialization):
        NAME = "test_custom_full"

        def insert(
            self,
            table_name: str,
            query_or_df: QueryOrDF,
            model: Model,
            is_first_insert: bool,
            render_kwargs: t.Dict[str, t.Any],
            **kwargs: t.Any,
        ) -> None:
            nonlocal custom_insert_called
            custom_insert_called = True

            self._replace_query_for_model(model, table_name, query_or_df, render_kwargs)

    model = context.get_model("sushi.top_waiters")
    kwargs = {
        **model.dict(),
        # Make a breaking change.
        "kind": dict(name="CUSTOM", materialization="test_custom_full"),
    }
    context.upsert_model(SqlModel.parse_obj(kwargs))

    context.plan(auto_apply=True, no_prompts=True)

    assert custom_insert_called


# needs to be defined at the top level. If its defined within the test body,
# adding to the snapshot cache fails with: AttributeError: Can't pickle local object
class TestCustomKind(CustomKind):
    __test__ = False  # prevent pytest warning since this isnt a class containing tests

    @property
    def custom_property(self) -> str:
        return validate_string(self.materialization_properties.get("custom_property"))


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_custom_materialization_with_custom_kind(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    custom_insert_calls = []

    class CustomFullMaterialization(CustomMaterialization[TestCustomKind]):
        NAME = "test_custom_full_with_custom_kind"

        def insert(
            self,
            table_name: str,
            query_or_df: QueryOrDF,
            model: Model,
            is_first_insert: bool,
            render_kwargs: t.Dict[str, t.Any],
            **kwargs: t.Any,
        ) -> None:
            assert isinstance(model.kind, TestCustomKind)

            nonlocal custom_insert_calls
            custom_insert_calls.append(model.kind.custom_property)

            self._replace_query_for_model(model, table_name, query_or_df, render_kwargs)

    model = context.get_model("sushi.top_waiters")
    kwargs = {
        **model.dict(),
        # Make a breaking change.
        "kind": dict(
            name="CUSTOM",
            materialization="test_custom_full_with_custom_kind",
            materialization_properties={"custom_property": "pytest"},
        ),
    }
    context.upsert_model(SqlModel.parse_obj(kwargs))

    context.plan(auto_apply=True)

    assert custom_insert_calls == ["pytest"]

    # no changes
    context.plan(auto_apply=True)

    assert custom_insert_calls == ["pytest"]

    # change a property on the custom kind, breaking change
    kwargs["kind"]["materialization_properties"]["custom_property"] = "some value"
    context.upsert_model(SqlModel.parse_obj(kwargs))
    context.plan(auto_apply=True)

    assert custom_insert_calls == ["pytest", "some value"]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_indirect_non_breaking_view_model_non_representative_snapshot(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context("examples/sushi")

    # Forward-only parent
    forward_only_model_name = "memory.sushi.test_forward_only_model"
    forward_only_model_expressions = d.parse(
        f"""
        MODEL (
            name {forward_only_model_name},
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only true,
            ),
        );

        SELECT '2023-01-01' AS ds, 'value' AS value;
        """
    )
    forward_only_model = load_sql_based_model(forward_only_model_expressions)
    assert forward_only_model.forward_only
    context.upsert_model(forward_only_model)

    # FULL downstream model.
    full_downstream_model_name = "memory.sushi.test_full_downstream_model"
    full_downstream_model_expressions = d.parse(
        f"""
        MODEL (
            name {full_downstream_model_name},
            kind FULL,
        );

        SELECT ds, value FROM {forward_only_model_name};
        """
    )
    full_downstream_model = load_sql_based_model(full_downstream_model_expressions)
    context.upsert_model(full_downstream_model)

    # VIEW downstream of the previous FULL model.
    view_downstream_model_name = "memory.sushi.test_view_downstream_model"
    view_downstream_model_expressions = d.parse(
        f"""
        MODEL (
            name {view_downstream_model_name},
            kind VIEW,
        );

        SELECT ds, value FROM {full_downstream_model_name};
        """
    )
    view_downstream_model = load_sql_based_model(view_downstream_model_expressions)
    context.upsert_model(view_downstream_model)

    # Apply the initial plan with all 3 models.
    context.plan(auto_apply=True, no_prompts=True)

    # Make a change to the forward-only model and apply it in dev.
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, forward_only_model)))
    forward_only_model_snapshot_id = context.get_snapshot(forward_only_model_name).snapshot_id
    full_downstream_model_snapshot_id = context.get_snapshot(full_downstream_model_name).snapshot_id
    view_downstream_model_snapshot_id = context.get_snapshot(view_downstream_model_name).snapshot_id
    dev_plan = context.plan("dev", auto_apply=True, no_prompts=True, enable_preview=False)
    assert (
        dev_plan.snapshots[forward_only_model_snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        dev_plan.snapshots[full_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert (
        dev_plan.snapshots[view_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert not dev_plan.missing_intervals

    # Make a follow-up breaking change to the downstream full model.
    new_full_downstream_model_expressions = d.parse(
        f"""
        MODEL (
            name {full_downstream_model_name},
            kind FULL,
        );

        SELECT ds, 'new_value' AS value FROM {forward_only_model_name};
        """
    )
    new_full_downstream_model = load_sql_based_model(new_full_downstream_model_expressions)
    context.upsert_model(new_full_downstream_model)
    full_downstream_model_snapshot_id = context.get_snapshot(full_downstream_model_name).snapshot_id
    view_downstream_model_snapshot_id = context.get_snapshot(view_downstream_model_name).snapshot_id
    dev_plan = context.plan(
        "dev",
        categorizer_config=CategorizerConfig.all_full(),
        auto_apply=True,
        no_prompts=True,
        enable_preview=False,
    )
    assert (
        dev_plan.snapshots[full_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert (
        dev_plan.snapshots[view_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_BREAKING
    )
    assert len(dev_plan.missing_intervals) == 2
    assert dev_plan.missing_intervals[0].snapshot_id == full_downstream_model_snapshot_id
    assert dev_plan.missing_intervals[1].snapshot_id == view_downstream_model_snapshot_id

    # Check that the representative view hasn't been created yet.
    assert not context.engine_adapter.table_exists(
        context.get_snapshot(view_downstream_model_name).table_name()
    )

    # Now promote the very first change to prod without promoting the 2nd breaking change.
    context.upsert_model(full_downstream_model)
    context.plan(auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full())

    # Finally, make a non-breaking change to the full model in the same dev environment.
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, new_full_downstream_model)))
    full_downstream_model_snapshot_id = context.get_snapshot(full_downstream_model_name).snapshot_id
    view_downstream_model_snapshot_id = context.get_snapshot(view_downstream_model_name).snapshot_id
    dev_plan = context.plan(
        "dev",
        categorizer_config=CategorizerConfig.all_full(),
        auto_apply=True,
        no_prompts=True,
        enable_preview=False,
    )
    assert (
        dev_plan.snapshots[full_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.NON_BREAKING
    )
    assert (
        dev_plan.snapshots[view_downstream_model_snapshot_id].change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )

    # Deploy changes to prod
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Check that the representative view has been created.
    assert context.engine_adapter.table_exists(
        context.get_snapshot(view_downstream_model_name).table_name()
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_indirect_non_breaking_view_model_non_representative_snapshot_migration(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context("examples/sushi")

    forward_only_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.forward_only_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only TRUE,
                on_destructive_change 'allow',
            ),
        );

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    forward_only_model = load_sql_based_model(forward_only_model_expr)
    context.upsert_model(forward_only_model)

    downstream_view_a_expr = d.parse(
        """
        MODEL (
            name memory.sushi.downstream_view_a,
            kind VIEW,
        );

        SELECT a from memory.sushi.forward_only_model;
        """
    )
    downstream_view_a = load_sql_based_model(downstream_view_a_expr)
    context.upsert_model(downstream_view_a)

    downstream_view_b_expr = d.parse(
        """
        MODEL (
            name memory.sushi.downstream_view_b,
            kind VIEW,
        );

        SELECT a from memory.sushi.downstream_view_a;
        """
    )
    downstream_view_b = load_sql_based_model(downstream_view_b_expr)
    context.upsert_model(downstream_view_b)

    context.plan(auto_apply=True, no_prompts=True, skip_tests=True)

    # Make a forward-only change
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, forward_only_model)))
    # Make a non-breaking change downstream
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, downstream_view_a)))

    context.plan(auto_apply=True, no_prompts=True, skip_tests=True)

    # Make sure the downstrean indirect non-breaking view is available in prod
    count = context.engine_adapter.fetchone("SELECT COUNT(*) FROM memory.sushi.downstream_view_b")[
        0
    ]
    assert count > 0


@time_machine.travel("2023-01-08 15:00:00 UTC")
@pytest.mark.parametrize(
    "parent_a_category,parent_b_category,expected_child_category",
    [
        (
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.INDIRECT_BREAKING,
        ),
        (
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.INDIRECT_NON_BREAKING,
        ),
        (
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.INDIRECT_NON_BREAKING,
        ),
        (
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.INDIRECT_BREAKING,
        ),
        (
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.METADATA,
        ),
        (
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.METADATA,
        ),
        (
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.BREAKING,
            SnapshotChangeCategory.INDIRECT_BREAKING,
        ),
        (
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.NON_BREAKING,
            SnapshotChangeCategory.INDIRECT_NON_BREAKING,
        ),
        (
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.METADATA,
            SnapshotChangeCategory.METADATA,
        ),
    ],
)
def test_rebase_two_changed_parents(
    init_and_plan_context: t.Callable,
    parent_a_category: SnapshotChangeCategory,  # This change is deployed to prod first
    parent_b_category: SnapshotChangeCategory,  # This change is deployed to prod second
    expected_child_category: SnapshotChangeCategory,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    initial_model_a = context.get_model("sushi.orders")
    initial_model_b = context.get_model("sushi.items")

    # Make change A and deploy it to dev_a
    context.upsert_model(initial_model_a.name, stamp="1")
    plan_builder = context.plan_builder("dev_a", skip_tests=True)
    plan_builder.set_choice(context.get_snapshot(initial_model_a.name), parent_a_category)
    context.apply(plan_builder.build())

    # Make change B and deploy it to dev_b
    context.upsert_model(initial_model_a)
    context.upsert_model(initial_model_b.name, stamp="1")
    plan_builder = context.plan_builder("dev_b", skip_tests=True)
    plan_builder.set_choice(context.get_snapshot(initial_model_b.name), parent_b_category)
    context.apply(plan_builder.build())

    # Deploy change A to prod
    context.upsert_model(initial_model_a.name, stamp="1")
    context.upsert_model(initial_model_b)
    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    # Apply change B in addition to A and plan against prod
    context.upsert_model(initial_model_b.name, stamp="1")
    plan = context.plan_builder("prod", skip_tests=True).build()

    # Validate the category of child snapshots
    direct_child_snapshot = plan.snapshots[context.get_snapshot("sushi.order_items").snapshot_id]
    assert direct_child_snapshot.change_category == expected_child_category

    indirect_child_snapshot = plan.snapshots[context.get_snapshot("sushi.top_waiters").snapshot_id]
    assert indirect_child_snapshot.change_category == expected_child_category


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_unaligned_start_snapshot_with_non_deployable_downstream(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    downstream_model_name = "memory.sushi.customer_max_revenue"

    expressions = d.parse(
        f"""
        MODEL (
            name {downstream_model_name},
            kind INCREMENTAL_BY_UNIQUE_KEY (
                unique_key customer_id,
                forward_only true,
            ),
        );

        SELECT
          customer_id, MAX(revenue) AS max_revenue
        FROM memory.sushi.customer_revenue_lifetime
        GROUP BY 1;
        """
    )

    downstream_model = load_sql_based_model(expressions)
    assert downstream_model.forward_only
    context.upsert_model(downstream_model)

    context.plan(auto_apply=True, no_prompts=True)

    customer_revenue_lifetime_model = context.get_model("sushi.customer_revenue_lifetime")
    kwargs = {
        **customer_revenue_lifetime_model.dict(),
        "name": "memory.sushi.customer_revenue_lifetime_new",
        "kind": dict(
            name="INCREMENTAL_UNMANAGED"
        ),  # Make it incremental unmanaged to ensure the depends_on_past behavior.
    }
    context.upsert_model(SqlModel.parse_obj(kwargs))
    context.upsert_model(
        downstream_model_name,
        query_=ParsableSql(
            sql="SELECT customer_id, MAX(revenue) AS max_revenue FROM memory.sushi.customer_revenue_lifetime_new GROUP BY 1"
        ),
    )

    plan = context.plan_builder("dev", enable_preview=True).build()
    assert {s.name for s in plan.new_snapshots} == {
        '"memory"."sushi"."customer_revenue_lifetime_new"',
        '"memory"."sushi"."customer_max_revenue"',
    }
    for snapshot_interval in plan.missing_intervals:
        assert not plan.deployability_index.is_deployable(snapshot_interval.snapshot_id)
        assert snapshot_interval.intervals[0][0] == to_timestamp("2023-01-07")


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )

    assert all(
        s.virtual_environment_mode.is_dev_only or not s.is_model or s.is_symbolic
        for s in context.snapshots.values()
    )

    # Init prod
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Make a change in dev
    original_model = context.get_model("sushi.waiter_revenue_by_day")
    original_fingerprint = context.get_snapshot(original_model.name).fingerprint
    model = original_model.copy(
        update={
            "query_": ParsableSql(
                sql=original_model.query.order_by("waiter_id").sql(dialect=original_model.dialect)
            )
        }
    )
    model = add_projection_to_model(t.cast(SqlModel, model))
    context.upsert_model(model)

    plan_dev = context.plan_builder("dev").build()
    assert to_timestamp(plan_dev.start) == to_timestamp("2023-01-07")
    assert plan_dev.requires_backfill
    assert plan_dev.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot("sushi.top_waiters").snapshot_id,
            intervals=[(to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))],
        ),
        SnapshotIntervals(
            snapshot_id=context.get_snapshot("sushi.waiter_revenue_by_day").snapshot_id,
            intervals=[(to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))],
        ),
    ]
    assert plan_dev.context_diff.snapshots[context.get_snapshot(model.name).snapshot_id].intervals
    assert plan_dev.context_diff.snapshots[
        context.get_snapshot("sushi.top_waiters").snapshot_id
    ].intervals
    assert plan_dev.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].dev_intervals
    assert plan_dev.context_diff.snapshots[
        context.get_snapshot("sushi.top_waiters").snapshot_id
    ].dev_intervals
    context.apply(plan_dev)

    # Make sure the waiter_revenue_by_day model is a table in prod and a view in dev
    table_types_df = context.engine_adapter.fetchdf(
        "SELECT table_schema, table_type FROM INFORMATION_SCHEMA.TABLES WHERE table_name = 'waiter_revenue_by_day'"
    )
    assert table_types_df.to_dict("records") == [
        {"table_schema": "sushi", "table_type": "BASE TABLE"},
        {"table_schema": "sushi__dev", "table_type": "VIEW"},
    ]

    # Check that the specified dates were backfilled
    min_event_date = context.engine_adapter.fetchone(
        "SELECT MIN(event_date) FROM sushi__dev.waiter_revenue_by_day"
    )[0]
    assert min_event_date == to_date("2023-01-07")

    # Make sure the changes are applied without backfill in prod
    plan_prod = context.plan_builder("prod").build()
    assert not plan_prod.requires_backfill
    assert not plan_prod.missing_intervals
    context.apply(plan_prod)
    assert "one" in context.engine_adapter.columns("sushi.waiter_revenue_by_day")

    # Make sure the revert of a breaking changes results in a full rebuild
    context.upsert_model(original_model)
    assert context.get_snapshot(original_model.name).fingerprint == original_fingerprint

    plan_prod = context.plan_builder(
        "prod", allow_destructive_models=["sushi.waiter_revenue_by_day"]
    ).build()
    assert not plan_prod.requires_backfill
    assert not plan_prod.missing_intervals
    context.apply(plan_prod)
    assert "one" not in context.engine_adapter.columns("sushi.waiter_revenue_by_day")


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_model_kind_change(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )
    context.apply(plan)

    # Change to full kind
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": FullKind()})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.missing_intervals
    assert prod_plan.requires_backfill
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "table"

    # Change back to view
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": ViewKind()})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "view"

    # Change to incremental
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": IncrementalUnmanagedKind()})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "table"

    # Change back to full
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": FullKind()})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "table"


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_model_kind_change_incremental(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )

    forward_only_model_name = "memory.sushi.test_forward_only_model"
    forward_only_model_expressions = d.parse(
        f"""
        MODEL (
            name {forward_only_model_name},
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only true,
            ),
        );

        SELECT '2023-01-01' AS ds, 'value' AS value;
        """
    )
    forward_only_model = load_sql_based_model(forward_only_model_expressions)
    forward_only_model = forward_only_model.copy(
        update={"virtual_environment_mode": VirtualEnvironmentMode.DEV_ONLY}
    )
    context.upsert_model(forward_only_model)

    context.plan("prod", auto_apply=True, no_prompts=True)

    # Change to view
    model = context.get_model(forward_only_model_name)
    original_kind = model.kind
    model = model.copy(update={"kind": ViewKind()})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"test_forward_only_model"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "view"

    model = model.copy(update={"kind": original_kind})
    context.upsert_model(model)
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"test_forward_only_model"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "table"


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_model_kind_change_with_follow_up_changes_in_dev(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )
    context.apply(plan)

    # Make sure the initial state is a view
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "view"

    # Change to incremental unmanaged kind
    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": IncrementalUnmanagedKind()})
    context.upsert_model(model)
    dev_plan = context.plan_builder("dev", skip_tests=True).build()
    assert dev_plan.missing_intervals
    assert dev_plan.requires_backfill
    context.apply(dev_plan)

    # Make a follow-up forward-only change
    model = add_projection_to_model(t.cast(SqlModel, model))
    context.upsert_model(model)
    dev_plan = context.plan_builder("dev", skip_tests=True, forward_only=True).build()
    context.apply(dev_plan)

    # Deploy to prod
    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals
    assert not prod_plan.context_diff.snapshots[
        context.get_snapshot(model.name).snapshot_id
    ].intervals
    context.apply(prod_plan)
    data_objects = context.engine_adapter.get_data_objects("sushi", {"top_waiters"})
    assert len(data_objects) == 1
    assert data_objects[0].type == "table"


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_model_kind_change_manual_categorization(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )
    context.apply(plan)

    model = context.get_model("sushi.top_waiters")
    model = model.copy(update={"kind": FullKind()})
    context.upsert_model(model)
    dev_plan_builder = context.plan_builder("dev", skip_tests=True, no_auto_categorization=True)
    dev_plan_builder.set_choice(
        dev_plan_builder._context_diff.snapshots[context.get_snapshot(model.name).snapshot_id],
        SnapshotChangeCategory.NON_BREAKING,
    )
    dev_plan = dev_plan_builder.build()
    assert dev_plan.requires_backfill
    assert len(dev_plan.missing_intervals) == 1
    context.apply(dev_plan)

    prod_plan = context.plan_builder("prod", skip_tests=True).build()
    assert prod_plan.requires_backfill
    assert prod_plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=context.get_snapshot("sushi.top_waiters").snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        ),
    ]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_seed_model_change(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )
    context.load()
    context.plan("prod", auto_apply=True, no_prompts=True)

    seed_model = context.get_model("sushi.waiter_names")
    with open(seed_model.seed_path, "a") as fd:
        fd.write("\n123,New Test Name")

    context.load()
    seed_model_snapshot = context.get_snapshot("sushi.waiter_names")
    plan = context.plan_builder("dev").build()
    assert plan.directly_modified == {seed_model_snapshot.snapshot_id}
    assert len(plan.missing_intervals) == 2
    context.apply(plan)

    actual_seed_df_in_dev = context.fetchdf("SELECT * FROM sushi__dev.waiter_names WHERE id = 123")
    assert actual_seed_df_in_dev.to_dict("records") == [{"id": 123, "name": "New Test Name"}]
    actual_seed_df_in_prod = context.fetchdf("SELECT * FROM sushi.waiter_names WHERE id = 123")
    assert actual_seed_df_in_prod.empty

    plan = context.plan_builder("prod").build()
    assert plan.directly_modified == {seed_model_snapshot.snapshot_id}
    assert len(plan.missing_intervals) == 1
    assert plan.missing_intervals[0].snapshot_id == seed_model_snapshot.snapshot_id
    context.apply(plan)

    actual_seed_df_in_prod = context.fetchdf("SELECT * FROM sushi.waiter_names WHERE id = 123")
    assert actual_seed_df_in_prod.to_dict("records") == [{"id": 123, "name": "New Test Name"}]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_virtual_environment_mode_dev_only_model_change_downstream_of_seed(
    init_and_plan_context: t.Callable,
):
    """This test covers a scenario when a model downstream of a seed model is modified and explicitly selected
    causing an (unhydrated) seed model to sourced from the state. If SQLMesh attempts to create
    a table for the unchanged seed model, it will fail because the seed model is not hydrated.
    """
    context, _ = init_and_plan_context(
        "examples/sushi", config="test_config_virtual_environment_mode_dev_only"
    )
    context.load()
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Make sure that a different version of the seed model is loaded
    seed_model = context.get_model("sushi.waiter_names")
    seed_model = seed_model.copy(update={"stamp": "force new version"})
    context.upsert_model(seed_model)

    # Make a change to the downstream model
    model = context.get_model("sushi.waiter_as_customer_by_day")
    model = model.copy(update={"stamp": "force new version"})
    context.upsert_model(model)

    # It is important to clear the cache so that the hydrated seed model is not sourced from the cache
    context.clear_caches()

    # Make sure to use the selector so that the seed model is sourced from the state
    plan = context.plan_builder("dev", select_models=[model.name]).build()
    assert len(plan.directly_modified) == 1
    assert list(plan.directly_modified)[0].name == model.fqn
    assert len(plan.missing_intervals) == 1
    assert plan.missing_intervals[0].snapshot_id.name == model.fqn

    # Make sure there's no error when applying the plan
    context.apply(plan)
    context.plan("prod", auto_apply=True, no_prompts=True)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_restatement_plan_ignores_changes(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    restated_snapshot = context.get_snapshot("sushi.top_waiters")

    # Simulate a change.
    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    plan = context.plan_builder(restate_models=["sushi.top_waiters"]).build()
    assert plan.snapshots != context.snapshots

    assert not plan.directly_modified
    assert not plan.has_changes
    assert not plan.new_snapshots
    assert plan.requires_backfill
    assert plan.restatements == {
        restated_snapshot.snapshot_id: (to_timestamp("2023-01-01"), to_timestamp("2023-01-09"))
    }
    assert plan.missing_intervals == [
        SnapshotIntervals(
            snapshot_id=restated_snapshot.snapshot_id,
            intervals=[
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        )
    ]

    context.apply(plan)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_restatement_plan_across_environments_snapshot_with_shared_version(
    init_and_plan_context: t.Callable,
):
    context, _ = init_and_plan_context("examples/sushi")

    # Change kind to incremental unmanaged
    model = context.get_model("sushi.waiter_revenue_by_day")
    previous_kind = model.kind.copy(update={"forward_only": True})
    assert isinstance(previous_kind, IncrementalByTimeRangeKind)

    model = model.copy(
        update={
            "kind": IncrementalUnmanagedKind(),
            "physical_version": "pinned_version_12345",
            "partitioned_by_": [exp.column("event_date")],
        }
    )
    context.upsert_model(model)
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Make some change and deploy it to both dev and prod environments
    model = add_projection_to_model(t.cast(SqlModel, model))
    context.upsert_model(model)
    context.plan("dev_a", auto_apply=True, no_prompts=True)
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Change the kind back to incremental by time range and deploy to prod
    model = model.copy(update={"kind": previous_kind})
    context.upsert_model(model)
    context.plan("prod", auto_apply=True, no_prompts=True)

    # Restate the model and verify that the interval hasn't been expanded because of the old snapshot
    # with the same version
    context.plan(
        restate_models=["sushi.waiter_revenue_by_day"],
        start="2023-01-06",
        end="2023-01-08",
        auto_apply=True,
        no_prompts=True,
    )

    assert (
        context.fetchdf(
            "SELECT COUNT(*) AS cnt FROM sushi.waiter_revenue_by_day WHERE one IS NOT NULL AND event_date < '2023-01-06'"
        )["cnt"][0]
        == 0
    )
    plan = context.plan_builder("prod").build()
    assert not plan.missing_intervals


def test_restatement_plan_hourly_with_downstream_daily_restates_correct_intervals(tmp_path: Path):
    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@hourly'
    );

    select account_id, ts from test.external_table;
    """

    model_b = """
    MODEL (
        name test.b,
        kind FULL,
        cron '@daily'
    );

    select account_id, ts from test.a;
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    for path, defn in {"a.sql": model_a, "b.sql": model_b}.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply
    ctx.plan(auto_apply=True, no_prompts=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    # verify initial state
    for tbl in ["test.a", "test.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]

    # restate A
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-01 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01 01:00:00",
        end="2024-01-01 02:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    # verify result
    for tbl in ["test.a", "test.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ], f"Table {tbl} wasnt cleared"

    # Put some data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 01:30:00",
                "2024-01-01 23:30:00",
                "2024-01-02 03:30:00",
                "2024-01-03 12:30:00",
            ],
        }
    )
    engine_adapter.replace_query(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # Restate A across a day boundary with the expectation that two day intervals in B are affected
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01 02:00:00",
        end="2024-01-02 04:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    for tbl in ["test.a", "test.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",  # present already
            # "2024-01-01 02:30:00", #removed in last restatement
            "2024-01-01 23:30:00",  # added in last restatement
            "2024-01-02 03:30:00",  # added in last restatement
        ], f"Table {tbl} wasnt cleared"


def test_restatement_plan_respects_disable_restatements(tmp_path: Path):
    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01',
        cron '@daily'
    );

    select account_id, ts from test.external_table;
    """

    model_b = """
    MODEL (
        name test.b,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts",
            disable_restatement true,
        ),
        start '2024-01-01',
        cron '@daily'
    );

    select account_id, ts from test.a;
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    for path, defn in {"a.sql": model_a, "b.sql": model_b}.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply
    ctx.plan(auto_apply=True, no_prompts=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    def get_snapshot_intervals(snapshot_id):
        return list(ctx.state_sync.get_snapshots([snapshot_id]).values())[0].intervals

    # verify initial state
    for tbl in ["test.a", "test.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]

    # restate A and expect b to be ignored
    starting_b_intervals = get_snapshot_intervals(ctx.snapshots['"memory"."test"."b"'].snapshot_id)
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-01 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01",
        end="2024-01-02",
        auto_apply=True,
        no_prompts=True,
    )

    # verify A was changed and not b
    assert _dates_in_table("test.a") == [
        "2024-01-01 00:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]
    assert _dates_in_table("test.b") == [
        "2024-01-01 00:30:00",
        "2024-01-01 01:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]

    # Verify B intervals were not touched
    b_intervals = get_snapshot_intervals(ctx.snapshots['"memory"."test"."b"'].snapshot_id)
    assert starting_b_intervals == b_intervals


def test_restatement_plan_clears_correct_intervals_across_environments(tmp_path: Path):
    model1 = """
    MODEL (
        name test.incremental_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "date"
        ),
        start '2024-01-01',
        cron '@daily'
    );

    select account_id, date from test.external_table;
    """

    model2 = """
    MODEL (
        name test.downstream_of_incremental,
        kind FULL
    );

    select account_id, date from test.incremental_model;
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "model1.sql", "w") as f:
        f.write(model1)

    with open(models_dir / "model2.sql", "w") as f:
        f.write(model2)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004, 1005],
            "name": ["foo", "bar", "baz", "bing", "bong"],
            "date": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05"],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "name": exp.DataType.build("varchar"),
        "date": exp.DataType.build("date"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # first, create the prod models
    ctx.plan(auto_apply=True, no_prompts=True)
    assert engine_adapter.fetchone("select count(*) from test.incremental_model") == (5,)
    assert engine_adapter.fetchone("select count(*) from test.downstream_of_incremental") == (5,)
    assert not engine_adapter.table_exists("test__dev.incremental_model")

    # then, make a dev version
    model1 = """
    MODEL (
        name test.incremental_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "date"
        ),
        start '2024-01-01',
        cron '@daily'
    );

    select 1 as account_id, date from test.external_table;
    """
    with open(models_dir / "model1.sql", "w") as f:
        f.write(model1)
    ctx.load()

    ctx.plan(environment="dev", auto_apply=True, no_prompts=True)
    assert engine_adapter.table_exists("test__dev.incremental_model")
    assert engine_adapter.fetchone("select count(*) from test__dev.incremental_model") == (5,)

    # drop some source data so when we restate the interval it essentially clears it which is easy to verify
    engine_adapter.execute("delete from test.external_table where date = '2024-01-01'")
    assert engine_adapter.fetchone("select count(*) from test.external_table") == (4,)

    # now, restate intervals in dev and verify prod is NOT affected
    ctx.plan(
        environment="dev",
        start="2024-01-01",
        end="2024-01-02",
        restate_models=["test.incremental_model"],
        auto_apply=True,
        no_prompts=True,
    )
    assert engine_adapter.fetchone("select count(*) from test.incremental_model") == (5,)
    assert engine_adapter.fetchone(
        "select count(*) from test.incremental_model where date = '2024-01-01'"
    ) == (1,)
    assert engine_adapter.fetchone("select count(*) from test__dev.incremental_model") == (4,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-01'"
    ) == (0,)

    # prod still should not be affected by a run because the restatement only happened in dev
    ctx.run()
    assert engine_adapter.fetchone("select count(*) from test.incremental_model") == (5,)
    assert engine_adapter.fetchone(
        "select count(*) from test.incremental_model where date = '2024-01-01'"
    ) == (1,)

    # drop another interval from the source data
    engine_adapter.execute("delete from test.external_table where date = '2024-01-02'")

    # now, restate intervals in prod and verify that dev IS affected
    ctx.plan(
        start="2024-01-01",
        end="2024-01-03",
        restate_models=["test.incremental_model"],
        auto_apply=True,
        no_prompts=True,
    )
    assert engine_adapter.fetchone("select count(*) from test.incremental_model") == (3,)
    assert engine_adapter.fetchone(
        "select count(*) from test.incremental_model where date = '2024-01-01'"
    ) == (0,)
    assert engine_adapter.fetchone(
        "select count(*) from test.incremental_model where date = '2024-01-02'"
    ) == (0,)
    assert engine_adapter.fetchone(
        "select count(*) from test.incremental_model where date = '2024-01-03'"
    ) == (1,)

    # dev not affected yet until `sqlmesh run` is run
    assert engine_adapter.fetchone("select count(*) from test__dev.incremental_model") == (4,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-01'"
    ) == (0,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-02'"
    ) == (1,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-03'"
    ) == (1,)

    # the restatement plan for prod should have cleared dev intervals too, which means this `sqlmesh run` re-runs 2024-01-01 and 2024-01-02
    ctx.run(environment="dev")
    assert engine_adapter.fetchone("select count(*) from test__dev.incremental_model") == (3,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-01'"
    ) == (0,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-02'"
    ) == (0,)
    assert engine_adapter.fetchone(
        "select count(*) from test__dev.incremental_model where date = '2024-01-03'"
    ) == (1,)

    # the downstream full model should always reflect whatever the incremental model is showing
    assert engine_adapter.fetchone("select count(*) from test.downstream_of_incremental") == (3,)
    assert engine_adapter.fetchone("select count(*) from test__dev.downstream_of_incremental") == (
        3,
    )


def test_prod_restatement_plan_clears_correct_intervals_in_derived_dev_tables(tmp_path: Path):
    """
    Scenario:
        I have models A[hourly] <- B[daily] <- C in prod
        I create dev and add 2 new models D and E so that my dev DAG looks like A <- B <- C <- D[daily] <- E
        I prod, I restate *one hour* of A
    Outcome:
        D and E should be restated in dev despite not being a part of prod
        since B and D are daily, the whole day should be restated even though only 1hr of the upstream model was restated
    """

    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@hourly'
    );

    select account_id, ts from test.external_table;
    """

    def _derived_full_model_def(name: str, upstream: str) -> str:
        return f"""
        MODEL (
            name test.{name},
            kind FULL
        );

        select account_id, ts from test.{upstream};
        """

    def _derived_incremental_model_def(name: str, upstream: str) -> str:
        return f"""
        MODEL (
            name test.{name},
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ts
            ),
            cron '@daily'
        );

        select account_id, ts from test.{upstream} where ts between @start_ts and @end_ts;
        """

    model_b = _derived_incremental_model_def("b", upstream="a")
    model_c = _derived_full_model_def("c", upstream="b")

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    for path, defn in {"a.sql": model_a, "b.sql": model_b, "c.sql": model_c}.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply A, B, C in prod
    ctx.plan(auto_apply=True, no_prompts=True)

    # add D[daily], E in dev
    model_d = _derived_incremental_model_def("d", upstream="c")
    model_e = _derived_full_model_def("e", upstream="d")

    for path, defn in {
        "d.sql": model_d,
        "e.sql": model_e,
    }.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    # plan + apply dev
    ctx.load()
    ctx.plan(environment="dev", auto_apply=True, no_prompts=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    # verify initial state
    for tbl in ["test.a", "test.b", "test.c", "test__dev.d", "test__dev.e"]:
        assert engine_adapter.table_exists(tbl)
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]

    for tbl in ["test.d", "test.e"]:
        assert not engine_adapter.table_exists(tbl)

    # restate A in prod
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-01 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01 01:00:00",
        end="2024-01-01 02:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    # verify result
    for tbl in ["test.a", "test.b", "test.c"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ], f"Table {tbl} wasnt cleared"

    # dev shouldnt have been affected yet
    for tbl in ["test__dev.d", "test__dev.e"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ], f"Table {tbl} was prematurely cleared"

    # run dev to trigger the processing of the prod restatement
    ctx.run(environment="dev")

    # data should now be cleared from dev
    # note that D is a daily model, so clearing an hour interval from A should have triggered the full day in D
    for tbl in ["test__dev.d", "test__dev.e"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ], f"Table {tbl} wasnt cleared"


def test_prod_restatement_plan_clears_unaligned_intervals_in_derived_dev_tables(tmp_path: Path):
    """
    Scenario:
        I have a model A[hourly] in prod
        I create dev and add a model B[daily]
        I prod, I restate *one hour* of A

    Outcome:
        The whole day for B should be restated. The restatement plan for prod has no hints about B's cadence because
        B only exists in dev and there are no other downstream models in prod that would cause the restatement intervals
        to be widened.

        Therefore, this test checks that SQLMesh does the right thing when an interval is partially cleared
    """

    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@hourly'
    );

    select account_id, ts from test.external_table;
    """

    model_b = """
        MODEL (
            name test.b,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ts
            ),
            cron '@daily'
        );

        select account_id, ts from test.a where ts between @start_ts and @end_ts;
        """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "a.sql", "w") as f:
        f.write(model_a)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply A[hourly] in prod
    ctx.plan(auto_apply=True, no_prompts=True)

    # add B[daily] in dev
    with open(models_dir / "b.sql", "w") as f:
        f.write(model_b)

    # plan + apply dev
    ctx.load()
    ctx.plan(environment="dev", auto_apply=True, no_prompts=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    # verify initial state
    for tbl in ["test.a", "test__dev.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]

    # restate A in prod
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-01 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01 01:00:00",
        end="2024-01-01 02:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    # verify result
    assert _dates_in_table("test.a") == [
        "2024-01-01 00:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]

    # dev shouldnt have been affected yet
    assert _dates_in_table("test__dev.b") == [
        "2024-01-01 00:30:00",
        "2024-01-01 01:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]

    # mess with A independently of SQLMesh to prove a whole day gets restated for B instead of just 1hr
    snapshot_table_name = ctx.table_name("test.a", "dev")
    engine_adapter.execute(
        f"delete from {snapshot_table_name} where cast(ts as date) == '2024-01-01'"
    )
    engine_adapter.execute(
        f"insert into {snapshot_table_name} (account_id, ts) values (1007, '2024-01-02 01:30:00')"
    )

    assert _dates_in_table("test.a") == ["2024-01-02 00:30:00", "2024-01-02 01:30:00"]

    # run dev to trigger the processing of the prod restatement
    ctx.run(environment="dev")

    # B should now have no data for 2024-01-01
    # To prove a single day was restated vs the whole model, it also shouldnt have the '2024-01-02 01:30:00' record
    assert _dates_in_table("test__dev.b") == ["2024-01-02 00:30:00"]


def test_prod_restatement_plan_causes_dev_intervals_to_be_processed_in_next_dev_plan(
    tmp_path: Path,
):
    """
    Scenario:
        I have a model A[hourly] in prod
        I create dev and add a model B[daily]
        I prod, I restate *one hour* of A
        In dev, I run a normal plan instead of a cadence run

    Outcome:
        The whole day for B should be restated as part of a normal plan
    """

    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@hourly'
    );

    select account_id, ts from test.external_table;
    """

    model_b = """
        MODEL (
            name test.b,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ts
            ),
            cron '@daily'
        );

        select account_id, ts from test.a where ts between @start_ts and @end_ts;
        """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "a.sql", "w") as f:
        f.write(model_a)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply A[hourly] in prod
    ctx.plan(auto_apply=True, no_prompts=True)

    # add B[daily] in dev
    with open(models_dir / "b.sql", "w") as f:
        f.write(model_b)

    # plan + apply dev
    ctx.load()
    ctx.plan(environment="dev", auto_apply=True, no_prompts=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    # verify initial state
    for tbl in ["test.a", "test__dev.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 01:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]

    # restate A in prod
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-01 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-01 01:00:00",
        end="2024-01-01 02:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    # verify result
    assert _dates_in_table("test.a") == [
        "2024-01-01 00:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]

    # dev shouldnt have been affected yet
    assert _dates_in_table("test__dev.b") == [
        "2024-01-01 00:30:00",
        "2024-01-01 01:30:00",
        "2024-01-01 02:30:00",
        "2024-01-02 00:30:00",
    ]

    # plan dev which should trigger the missing intervals to get repopulated
    ctx.plan(environment="dev", auto_apply=True, no_prompts=True)

    # dev should have the restated data
    for tbl in ["test.a", "test__dev.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-01 02:30:00",
            "2024-01-02 00:30:00",
        ]


def test_prod_restatement_plan_causes_dev_intervals_to_be_widened_on_full_restatement_only_model(
    tmp_path,
):
    """
    Scenario:
        I have am INCREMENTAL_BY_TIME_RANGE model A[daily] in prod
        I create dev and add a INCREMENTAL_BY_UNIQUE_KEY model B (which supports full restatement only)
        I prod, I restate one day of A which should cause intervals in dev to be cleared (but not processed)
        In dev, I run a plan

    Outcome:
        In the dev plan, the entire model for B should be rebuilt because it does not support partial restatement
    """

    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@daily'
    );

    select account_id, ts from test.external_table where ts between @start_ts and @end_ts;
    """

    model_b = """
        MODEL (
            name test.b,
            kind INCREMENTAL_BY_UNIQUE_KEY (
                unique_key (account_id, ts)
            ),
            cron '@daily'
        );

        select account_id, ts from test.a where ts between @start_ts and @end_ts;
        """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "a.sql", "w") as f:
        f.write(model_a)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-02 01:30:00",
                "2024-01-03 02:30:00",
                "2024-01-04 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply A[daily] in prod
    ctx.plan(auto_apply=True)

    # add B[daily] in dev
    with open(models_dir / "b.sql", "w") as f:
        f.write(model_b)

    # plan + apply dev
    ctx.load()
    ctx.plan(environment="dev", auto_apply=True)

    def _dates_in_table(table_name: str) -> t.List[str]:
        return [
            str(r[0]) for r in engine_adapter.fetchall(f"select ts from {table_name} order by ts")
        ]

    # verify initial state
    for tbl in ["test.a", "test__dev.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-02 01:30:00",
            "2024-01-03 02:30:00",
            "2024-01-04 00:30:00",
        ]

    # restate A in prod
    engine_adapter.execute("delete from test.external_table where ts = '2024-01-02 01:30:00'")
    ctx.plan(
        restate_models=["test.a"],
        start="2024-01-02 00:00:00",
        end="2024-01-03 00:00:00",
        auto_apply=True,
        no_prompts=True,
    )

    # verify result
    assert _dates_in_table("test.a") == [
        "2024-01-01 00:30:00",
        "2024-01-03 02:30:00",
        "2024-01-04 00:30:00",
    ]

    # dev shouldnt have been affected yet
    assert _dates_in_table("test__dev.b") == [
        "2024-01-01 00:30:00",
        "2024-01-02 01:30:00",
        "2024-01-03 02:30:00",
        "2024-01-04 00:30:00",
    ]

    # plan dev which should trigger the missing intervals to get repopulated
    ctx.plan(environment="dev", auto_apply=True)

    # dev should have fully refreshed
    # this is proven by the fact that INCREMENTAL_BY_UNIQUE_KEY cant propagate deletes, so if the
    # model was not fully rebuilt, the deleted record would still be present
    for tbl in ["test.a", "test__dev.b"]:
        assert _dates_in_table(tbl) == [
            "2024-01-01 00:30:00",
            "2024-01-03 02:30:00",
            "2024-01-04 00:30:00",
        ]


def test_prod_restatement_plan_missing_model_in_dev(
    tmp_path: Path,
):
    """
    Scenario:
        I have a model B in prod but only model A in dev
        I restate B in prod

    Outcome:
        The A model should be ignore and the plan shouldn't fail
    """

    model_a = """
    MODEL (
        name test.a,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column "ts"
        ),
        start '2024-01-01 00:00:00',
        cron '@hourly'
    );

    select account_id, ts from test.external_table;
    """

    model_b = """
        MODEL (
            name test.b,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ts
            ),
            cron '@daily'
        );

        select account_id, ts from test.external_table where ts between @start_ts and @end_ts;
        """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "a.sql", "w") as f:
        f.write(model_a)

    config = Config(model_defaults=ModelDefaultsConfig(dialect="duckdb"))
    ctx = Context(paths=[tmp_path], config=config)

    engine_adapter = ctx.engine_adapter
    engine_adapter.create_schema("test")

    # source data
    df = pd.DataFrame(
        {
            "account_id": [1001, 1002, 1003, 1004],
            "ts": [
                "2024-01-01 00:30:00",
                "2024-01-01 01:30:00",
                "2024-01-01 02:30:00",
                "2024-01-02 00:30:00",
            ],
        }
    )
    columns_to_types = {
        "account_id": exp.DataType.build("int"),
        "ts": exp.DataType.build("timestamp"),
    }
    external_table = exp.table_(table="external_table", db="test", quoted=True)
    engine_adapter.create_table(table_name=external_table, target_columns_to_types=columns_to_types)
    engine_adapter.insert_append(
        table_name=external_table, query_or_df=df, target_columns_to_types=columns_to_types
    )

    # plan + apply A[hourly] in dev
    ctx.plan("dev", auto_apply=True, no_prompts=True)

    # add B[daily] in prod and remove A
    with open(models_dir / "b.sql", "w") as f:
        f.write(model_b)
    Path(models_dir / "a.sql").unlink()

    # plan + apply dev
    ctx.load()
    ctx.plan(auto_apply=True, no_prompts=True)

    # restate B in prod
    ctx.plan(
        restate_models=["test.b"],
        start="2024-01-01",
        end="2024-01-02",
        auto_apply=True,
        no_prompts=True,
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dev_restatement_of_prod_model(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    context.plan("dev", auto_apply=True, no_prompts=True, skip_tests=True)

    restatement_plan = context.plan_builder("dev", restate_models=["*"]).build()
    assert set(restatement_plan.restatements) == {
        context.get_snapshot("sushi.waiter_revenue_by_day").snapshot_id,
        context.get_snapshot("sushi.top_waiters").snapshot_id,
    }


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_snapshot_table_exists_for_promoted_snapshot(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    context.plan("dev", auto_apply=True, no_prompts=True, skip_tests=True)

    # Drop the views and make sure SQLMesh recreates them later
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters", raise_if_missing=True)
    context.engine_adapter.drop_view(top_waiters_snapshot.table_name())
    context.engine_adapter.drop_view(top_waiters_snapshot.table_name(False))

    # Make the environment unfinalized to force recreation of all views in the virtual layer
    context.state_sync.state_sync.engine_adapter.execute(
        "UPDATE sqlmesh._environments SET finalized_ts = NULL WHERE name = 'dev'"
    )

    context.plan(
        "prod",
        restate_models=["sushi.top_waiters"],
        auto_apply=True,
        no_prompts=True,
        skip_tests=True,
    )
    assert context.engine_adapter.table_exists(top_waiters_snapshot.table_name())


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_against_expired_environment(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    modified_models = {model.fqn, context.get_model("sushi.top_waiters").fqn}

    plan = context.plan_builder("dev").build()
    assert plan.has_changes
    assert set(plan.context_diff.modified_snapshots) == modified_models
    assert plan.missing_intervals
    context.apply(plan)

    # Make sure there are no changes when comparing against the existing environment.
    plan = context.plan_builder("dev").build()
    assert not plan.has_changes
    assert not plan.context_diff.modified_snapshots
    assert not plan.missing_intervals

    # Invalidate the environment and make sure that the plan detects the changes.
    context.invalidate_environment("dev")
    plan = context.plan_builder("dev").build()
    assert plan.has_changes
    assert set(plan.context_diff.modified_snapshots) == modified_models
    assert not plan.missing_intervals
    context.apply(plan)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_new_forward_only_model_concurrent_versions(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    new_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only TRUE,
                on_destructive_change 'allow',
            ),
        );

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    new_model = load_sql_based_model(new_model_expr)

    # Add the first version of the model and apply it to dev_a.
    context.upsert_model(new_model)
    snapshot_a = context.get_snapshot(new_model.name)
    plan_a = context.plan_builder("dev_a").build()
    snapshot_a = plan_a.snapshots[snapshot_a.snapshot_id]

    assert snapshot_a.snapshot_id in plan_a.context_diff.new_snapshots
    assert snapshot_a.snapshot_id in plan_a.context_diff.added
    assert snapshot_a.change_category == SnapshotChangeCategory.BREAKING

    context.apply(plan_a)

    new_model_alt_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only TRUE,
                on_destructive_change 'allow',
            ),
        );

        SELECT '2023-01-07' AS ds, 1 AS b;
        """
    )
    new_model_alt = load_sql_based_model(new_model_alt_expr)

    # Add the second version of the model but don't apply it yet
    context.upsert_model(new_model_alt)
    snapshot_b = context.get_snapshot(new_model_alt.name)
    plan_b = context.plan_builder("dev_b").build()
    snapshot_b = plan_b.snapshots[snapshot_b.snapshot_id]

    assert snapshot_b.snapshot_id in plan_b.context_diff.new_snapshots
    assert snapshot_b.snapshot_id in plan_b.context_diff.added
    assert snapshot_b.change_category == SnapshotChangeCategory.BREAKING

    assert snapshot_b.fingerprint != snapshot_a.fingerprint
    assert snapshot_b.version == snapshot_a.version

    # Apply the 1st version to prod
    context.upsert_model(new_model)
    plan_prod_a = context.plan_builder("prod").build()
    assert snapshot_a.snapshot_id in plan_prod_a.snapshots
    assert (
        plan_prod_a.snapshots[snapshot_a.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    context.apply(plan_prod_a)

    df = context.fetchdf("SELECT * FROM memory.sushi.new_model")
    assert df.to_dict() == {"ds": {0: "2023-01-07"}, "a": {0: 1}}

    # Modify the 1st version in prod to trigger a forward-only change
    new_model = add_projection_to_model(t.cast(SqlModel, new_model))
    context.upsert_model(new_model)
    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    # Apply the 2nd version to dev_b.
    # At this point the snapshot of the 2nd version has already been categorized but not
    # persisted in the state. This means that when the snapshot of the 1st version was
    # being unpaused during promotion to prod, the state of the 2nd version snapshot was not updated
    context.apply(plan_b)

    # Apply the 2nd version to prod
    context.upsert_model(new_model_alt)
    plan_prod_b = context.plan_builder("prod").build()
    assert (
        plan_prod_b.snapshots[snapshot_b.snapshot_id].change_category
        == SnapshotChangeCategory.BREAKING
    )
    assert not plan_prod_b.requires_backfill
    context.apply(plan_prod_b)

    df = context.fetchdf("SELECT * FROM memory.sushi.new_model").replace({np.nan: None})
    assert df.to_dict() == {"ds": {0: "2023-01-07"}, "b": {0: None}}


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_new_forward_only_model_same_dev_environment(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    new_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only TRUE,
                on_destructive_change 'allow',
            ),
        );

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    new_model = load_sql_based_model(new_model_expr)

    # Add the first version of the model and apply it to dev.
    context.upsert_model(new_model)
    snapshot_a = context.get_snapshot(new_model.name)
    plan_a = context.plan_builder("dev").build()
    snapshot_a = plan_a.snapshots[snapshot_a.snapshot_id]

    assert snapshot_a.snapshot_id in plan_a.context_diff.new_snapshots
    assert snapshot_a.snapshot_id in plan_a.context_diff.added
    assert snapshot_a.change_category == SnapshotChangeCategory.BREAKING

    context.apply(plan_a)

    df = context.fetchdf("SELECT * FROM memory.sushi__dev.new_model")
    assert df.to_dict() == {"ds": {0: "2023-01-07"}, "a": {0: 1}}

    new_model_alt_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                forward_only TRUE,
                on_destructive_change 'allow',
            ),
        );

        SELECT '2023-01-07' AS ds, 1 AS b;
        """
    )
    new_model_alt = load_sql_based_model(new_model_alt_expr)

    # Add the second version of the model and apply it to the same environment.
    context.upsert_model(new_model_alt)
    snapshot_b = context.get_snapshot(new_model_alt.name)

    context.invalidate_environment("dev", sync=True)
    plan_b = context.plan_builder("dev").build()
    snapshot_b = plan_b.snapshots[snapshot_b.snapshot_id]

    context.apply(plan_b)

    df = context.fetchdf("SELECT * FROM memory.sushi__dev.new_model").replace({np.nan: None})
    assert df.to_dict() == {"ds": {0: "2023-01-07"}, "b": {0: 1}}


@time_machine.travel("2023-01-08 01:00:00 UTC")
def test_run_auto_restatement(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    context.engine_adapter.execute(
        "CREATE TABLE _test_auto_restatement_intervals (name STRING, start_ds STRING, end_ds STRING)"
    )

    @macro()
    def record_intervals(
        evaluator, name: exp.Expression, start: exp.Expression, end: exp.Expression, **kwargs: t.Any
    ) -> None:
        if evaluator.runtime_stage == "evaluating":
            evaluator.engine_adapter.insert_append(
                "_test_auto_restatement_intervals",
                pd.DataFrame({"name": [name.name], "start_ds": [start.name], "end_ds": [end.name]}),
            )

    new_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                auto_restatement_cron '0 6 * * 7',  -- At 6am every Sunday
                auto_restatement_intervals 3,
            ),
            start '2023-01-01',
        );

        @record_intervals('new_model', @start_ds, @end_ds);

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    new_model = load_sql_based_model(new_model_expr)
    context.upsert_model(new_model)

    new_model_downstream_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model_downstream,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
            ),
            cron '@hourly',
        );

        @record_intervals('new_model_downstream', @start_ts, @end_ts);

        SELECT * FROM memory.sushi.new_model;
        """
    )
    new_model_downstream = load_sql_based_model(new_model_downstream_expr)
    context.upsert_model(new_model_downstream)

    plan = context.plan_builder("prod").build()
    context.apply(plan)

    with time_machine.travel("2023-01-08 06:01:00 UTC"):
        assert context.run()

        recorded_intervals_df = context.engine_adapter.fetchdf(
            "SELECT start_ds, end_ds FROM _test_auto_restatement_intervals WHERE name = 'new_model'"
        )
        # The first interval is the first backfill and the second interval should be the 3 auto restated intervals
        assert recorded_intervals_df.to_dict() == {
            "start_ds": {0: "2023-01-01", 1: "2023-01-05"},
            "end_ds": {0: "2023-01-07", 1: "2023-01-07"},
        }
        recorded_intervals_downstream_df = context.engine_adapter.fetchdf(
            "SELECT start_ds, end_ds FROM _test_auto_restatement_intervals WHERE name = 'new_model_downstream'"
        )
        # The first interval is the first backfill, the second interval should be the 3 days of restated intervals, and
        # the third interval should catch up to the current hour
        assert recorded_intervals_downstream_df.to_dict() == {
            "start_ds": {
                0: "2023-01-01 00:00:00",
                1: "2023-01-05 00:00:00",
                2: "2023-01-08 01:00:00",
            },
            "end_ds": {
                0: "2023-01-08 00:59:59.999999",
                1: "2023-01-07 23:59:59.999999",
                2: "2023-01-08 05:59:59.999999",
            },
        }

        snapshot = context.get_snapshot(new_model.name)
        snapshot = context.state_sync.state_sync.get_snapshots([snapshot.snapshot_id])[
            snapshot.snapshot_id
        ]
        assert snapshot.next_auto_restatement_ts == to_timestamp("2023-01-15 06:00:00")
        assert not snapshot.pending_restatement_intervals

        snapshot_downstream = context.get_snapshot(new_model_downstream.name)
        snapshot_downstream = context.state_sync.state_sync.get_snapshots(
            [snapshot_downstream.snapshot_id]
        )[snapshot_downstream.snapshot_id]
        assert not snapshot_downstream.next_auto_restatement_ts
        assert not snapshot_downstream.pending_restatement_intervals


@time_machine.travel("2023-01-08 01:00:00 UTC")
def test_run_auto_restatement_plan_preview(init_and_plan_context: t.Callable):
    context, init_plan = init_and_plan_context("examples/sushi")
    context.apply(init_plan)

    new_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                auto_restatement_cron '0 6 * * 7',
            ),
            start '2023-01-01',
        );

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    new_model = load_sql_based_model(new_model_expr)
    context.upsert_model(new_model)
    snapshot = context.get_snapshot(new_model.name)

    plan_dev = context.plan_builder("dev").build()
    # Make sure that a limited preview is computed by default
    assert to_timestamp(plan_dev.start) == to_timestamp("2023-01-07")
    assert plan_dev.missing_intervals == [
        SnapshotIntervals(
            snapshot.snapshot_id,
            [(to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))],
        )
    ]
    assert not plan_dev.deployability_index.is_deployable(snapshot.snapshot_id)
    context.apply(plan_dev)

    plan_prod = context.plan_builder("prod").build()
    assert plan_prod.missing_intervals == [
        SnapshotIntervals(
            context.get_snapshot(new_model.name).snapshot_id,
            [
                (to_timestamp("2023-01-01"), to_timestamp("2023-01-02")),
                (to_timestamp("2023-01-02"), to_timestamp("2023-01-03")),
                (to_timestamp("2023-01-03"), to_timestamp("2023-01-04")),
                (to_timestamp("2023-01-04"), to_timestamp("2023-01-05")),
                (to_timestamp("2023-01-05"), to_timestamp("2023-01-06")),
                (to_timestamp("2023-01-06"), to_timestamp("2023-01-07")),
                (to_timestamp("2023-01-07"), to_timestamp("2023-01-08")),
            ],
        )
    ]
    context.apply(plan_prod)


@time_machine.travel("2023-01-08 01:00:00 UTC")
def test_run_auto_restatement_failure(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    @macro()
    def fail_auto_restatement(evaluator, start: exp.Expression, **kwargs: t.Any) -> None:
        if evaluator.runtime_stage == "evaluating" and start.name != "2023-01-01":
            raise Exception("Failed")

    new_model_expr = d.parse(
        """
        MODEL (
            name memory.sushi.new_model,
            kind INCREMENTAL_BY_TIME_RANGE (
                time_column ds,
                auto_restatement_cron '0 6 * * 7',  -- At 6am every Sunday
                auto_restatement_intervals 3,
            ),
            start '2023-01-01',
        );

        @fail_auto_restatement(@start_ds);

        SELECT '2023-01-07' AS ds, 1 AS a;
        """
    )
    new_model = load_sql_based_model(new_model_expr)
    context.upsert_model(new_model)

    plan = context.plan_builder("prod").build()
    context.apply(plan)

    with time_machine.travel("2023-01-08 06:01:00 UTC"):
        run_status = context.run()
        assert run_status.is_failure

        snapshot = context.get_snapshot(new_model.name)
        snapshot = context.state_sync.state_sync.get_snapshots([snapshot.snapshot_id])[
            snapshot.snapshot_id
        ]
        assert snapshot.next_auto_restatement_ts == to_timestamp("2023-01-15 06:00:00")
        assert snapshot.pending_restatement_intervals == [
            (to_timestamp("2023-01-05"), to_timestamp("2023-01-08"))
        ]


def test_plan_twice_with_star_macro_yields_no_diff(tmp_path: Path):
    init_example_project(tmp_path, engine_type="duckdb")

    star_model_definition = """
        MODEL (
          name sqlmesh_example.star_model,
          kind FULL
        );

        SELECT @STAR(sqlmesh_example.full_model) FROM sqlmesh_example.full_model
    """

    star_model_path = tmp_path / "models" / "star_model.sql"
    star_model_path.write_text(star_model_definition)

    db_path = str(tmp_path / "db.db")
    config = Config(
        gateways={"main": GatewayConfig(connection=DuckDBConnectionConfig(database=db_path))},
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
    )
    context = Context(paths=tmp_path, config=config)
    context.plan(auto_apply=True, no_prompts=True)

    # Instantiate new context to remove caches etc
    new_context = Context(paths=tmp_path, config=config)

    star_model = new_context.get_model("sqlmesh_example.star_model")
    assert (
        star_model.render_query_or_raise().sql()
        == 'SELECT CAST("full_model"."item_id" AS INT) AS "item_id", CAST("full_model"."num_orders" AS BIGINT) AS "num_orders" FROM "db"."sqlmesh_example"."full_model" AS "full_model"'
    )

    new_plan = new_context.plan_builder().build()
    assert not new_plan.has_changes
    assert not new_plan.new_snapshots


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_create_environment_no_changes_with_selector(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    with pytest.raises(NoChangesPlanError):
        context.plan_builder("dev").build()

    plan = context.plan_builder("dev", select_models=["*top_waiters"]).build()
    assert not plan.missing_intervals
    context.apply(plan)

    schema_objects = context.engine_adapter.get_data_objects("sushi__dev")
    assert {o.name for o in schema_objects} == {"top_waiters"}


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_empty_backfill(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    plan = context.plan_builder("prod", skip_tests=True, empty_backfill=True).build()
    assert plan.missing_intervals
    assert plan.empty_backfill
    assert not plan.requires_backfill

    context.apply(plan)

    for model in context.models.values():
        if model.is_seed or model.kind.is_symbolic:
            continue
        row_num = context.engine_adapter.fetchone(f"SELECT COUNT(*) FROM {model.name}")[0]
        assert row_num == 0

    plan = context.plan_builder("prod", skip_tests=True).build()
    assert not plan.requires_backfill
    assert not plan.has_changes
    assert not plan.missing_intervals

    snapshots = plan.snapshots
    for snapshot in snapshots.values():
        if not snapshot.intervals:
            continue
        assert snapshot.intervals[-1][1] <= to_timestamp("2023-01-08")


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_empty_backfill_new_model(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    new_model = load_sql_based_model(
        d.parse(
            """
        MODEL (
            name memory.sushi.new_model,
            kind FULL,
            cron '0 8 * * *',
            start '2023-01-01',
        );

        SELECT 1 AS one;
        """
        )
    )
    new_model_name = context.upsert_model(new_model).fqn

    with time_machine.travel("2023-01-09 00:00:00 UTC"):
        plan = context.plan_builder("dev", skip_tests=True, empty_backfill=True).build()
        assert plan.end == to_datetime("2023-01-09")
        assert plan.missing_intervals
        assert plan.empty_backfill
        assert not plan.requires_backfill

        context.apply(plan)

        for model in context.models.values():
            if model.is_seed or model.kind.is_symbolic:
                continue
            row_num = context.engine_adapter.fetchone(f"SELECT COUNT(*) FROM sushi__dev.new_model")[
                0
            ]
            assert row_num == 0

        plan = context.plan_builder("prod", skip_tests=True).build()
        assert not plan.requires_backfill
        assert not plan.missing_intervals

        snapshots = plan.snapshots
        for snapshot in snapshots.values():
            if not snapshot.intervals:
                continue
            elif snapshot.name == new_model_name:
                assert snapshot.intervals[-1][1] == to_timestamp("2023-01-09")
            else:
                assert snapshot.intervals[-1][1] <= to_timestamp("2023-01-08")


@time_machine.travel("2023-01-08 15:00:00 UTC")
@pytest.mark.parametrize("forward_only", [False, True])
def test_plan_repairs_unrenderable_snapshot_state(
    init_and_plan_context: t.Callable, forward_only: bool
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    target_snapshot = context.get_snapshot("sushi.waiter_revenue_by_day")
    assert target_snapshot

    # Manually corrupt the snapshot's query
    raw_snapshot = context.state_sync.state_sync.engine_adapter.fetchone(
        f"SELECT snapshot FROM sqlmesh._snapshots WHERE name = '{target_snapshot.name}' AND identifier = '{target_snapshot.identifier}'"
    )[0]  # type: ignore
    parsed_snapshot = json.loads(raw_snapshot)
    parsed_snapshot["node"]["query"] = "SELECT @missing_macro()"
    context.state_sync.state_sync.engine_adapter.update_table(
        "sqlmesh._snapshots",
        {"snapshot": json.dumps(parsed_snapshot)},
        f"name = '{target_snapshot.name}' AND identifier = '{target_snapshot.identifier}'",
    )

    context.clear_caches()
    target_snapshot_in_state = context.state_sync.get_snapshots([target_snapshot.snapshot_id])[
        target_snapshot.snapshot_id
    ]

    with pytest.raises(Exception):
        target_snapshot_in_state.model.render_query_or_raise()

    # Repair the snapshot by creating a new version of it
    context.upsert_model(target_snapshot.model.name, stamp="repair")
    target_snapshot = context.get_snapshot(target_snapshot.name)

    plan_builder = context.plan_builder("prod", forward_only=forward_only)
    plan = plan_builder.build()
    if not forward_only:
        assert target_snapshot.snapshot_id in {i.snapshot_id for i in plan.missing_intervals}
    assert plan.directly_modified == {target_snapshot.snapshot_id}
    plan_builder.set_choice(target_snapshot, SnapshotChangeCategory.NON_BREAKING)
    plan = plan_builder.build()

    context.apply(plan)

    context.clear_caches()
    assert context.get_snapshot(target_snapshot.name).model.render_query_or_raise()
    target_snapshot_in_state = context.state_sync.get_snapshots([target_snapshot.snapshot_id])[
        target_snapshot.snapshot_id
    ]
    assert target_snapshot_in_state.model.render_query_or_raise()


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_no_backfill_for_model_downstream_of_metadata_change(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    # Make sushi.waiter_revenue_by_day a forward-only model.
    forward_only_model = context.get_model("sushi.waiter_revenue_by_day")
    updated_model_kind = forward_only_model.kind.copy(update={"forward_only": True})
    forward_only_model = forward_only_model.copy(update={"kind": updated_model_kind})
    context.upsert_model(forward_only_model)

    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    # Make a metadata change upstream of the forward-only model.
    context.upsert_model("sushi.orders", owner="new_owner")

    plan = context.plan_builder("test_dev").build()
    assert plan.has_changes
    assert not plan.directly_modified
    assert not plan.indirectly_modified
    assert not plan.missing_intervals
    assert all(
        snapshot.change_category == SnapshotChangeCategory.METADATA
        for snapshot in plan.new_snapshots
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_evaluate_uncategorized_snapshot(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Add a new projection
    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    # Downstream model references the new projection
    downstream_model = context.get_model("sushi.top_waiters")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, downstream_model), literal=False))

    df = context.evaluate(
        "sushi.top_waiters", start="2023-01-05", end="2023-01-06", execution_time=now()
    )
    assert set(df["one"].tolist()) == {1}


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_table_name(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    snapshot = context.get_snapshot("sushi.waiter_revenue_by_day")
    assert snapshot
    assert (
        context.table_name("sushi.waiter_revenue_by_day", "prod")
        == f"memory.sqlmesh__sushi.sushi__waiter_revenue_by_day__{snapshot.version}"
    )

    with pytest.raises(SQLMeshError, match="Environment 'dev' was not found."):
        context.table_name("sushi.waiter_revenue_by_day", "dev")

    with pytest.raises(
        SQLMeshError, match="Model 'sushi.missing' was not found in environment 'prod'."
    ):
        context.table_name("sushi.missing", "prod")

    # Add a new projection
    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    context.plan("dev_a", auto_apply=True, no_prompts=True, skip_tests=True)

    new_snapshot = context.get_snapshot("sushi.waiter_revenue_by_day")
    assert new_snapshot.version != snapshot.version

    assert (
        context.table_name("sushi.waiter_revenue_by_day", "dev_a")
        == f"memory.sqlmesh__sushi.sushi__waiter_revenue_by_day__{new_snapshot.version}"
    )

    # Make a forward-only change
    context.upsert_model(model, stamp="forward_only")

    context.plan("dev_b", auto_apply=True, no_prompts=True, skip_tests=True, forward_only=True)

    forward_only_snapshot = context.get_snapshot("sushi.waiter_revenue_by_day")
    assert forward_only_snapshot.version == snapshot.version
    assert forward_only_snapshot.dev_version != snapshot.version

    assert (
        context.table_name("sushi.waiter_revenue_by_day", "dev_b")
        == f"memory.sqlmesh__sushi.sushi__waiter_revenue_by_day__{forward_only_snapshot.dev_version}__dev"
    )

    assert (
        context.table_name("sushi.waiter_revenue_by_day", "dev_b", prod=True)
        == f"memory.sqlmesh__sushi.sushi__waiter_revenue_by_day__{snapshot.version}"
    )


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_full_model_change_with_plan_start_not_matching_model_start(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    model = context.get_model("sushi.top_waiters")
    context.upsert_model(model, kind=model_kind_type_from_name("FULL")())  # type: ignore

    # Apply the change with --skip-backfill first and no plan start
    context.plan("dev", skip_tests=True, skip_backfill=True, no_prompts=True, auto_apply=True)

    # Apply the plan again but this time don't skip backfill and set start
    # to be later than the model start
    context.plan("dev", skip_tests=True, no_prompts=True, auto_apply=True, start="1 day ago")

    # Check that the number of rows is not 0
    row_num = context.engine_adapter.fetchone(f"SELECT COUNT(*) FROM sushi__dev.top_waiters")[0]
    assert row_num > 0


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_indirect_non_breaking_view_is_updated_with_new_table_references(
    init_and_plan_context: t.Callable,
):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Add a new projection to the base model
    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(add_projection_to_model(t.cast(SqlModel, model)))

    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    # Run the janitor to delete the old snapshot record
    context.run_janitor(ignore_ttl=True)

    # Check the downstream view and make sure it's still queryable
    assert context.get_model("sushi.top_waiters").kind.is_view
    row_num = context.engine_adapter.fetchone(f"SELECT COUNT(*) FROM sushi.top_waiters")[0]
    assert row_num > 0


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_plan_explain(init_and_plan_context: t.Callable):
    old_console = get_console()
    set_console(TerminalConsole())

    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    waiter_revenue_by_day_model = context.get_model("sushi.waiter_revenue_by_day")
    waiter_revenue_by_day_model = add_projection_to_model(
        t.cast(SqlModel, waiter_revenue_by_day_model)
    )
    context.upsert_model(waiter_revenue_by_day_model)

    waiter_revenue_by_day_snapshot = context.get_snapshot(waiter_revenue_by_day_model.name)
    top_waiters_snapshot = context.get_snapshot("sushi.top_waiters")

    common_kwargs = dict(skip_tests=True, no_prompts=True, explain=True)

    # For now just making sure the plan doesn't error
    context.plan("dev", **common_kwargs)
    context.plan("dev", **common_kwargs, skip_backfill=True)
    context.plan("dev", **common_kwargs, empty_backfill=True)
    context.plan("dev", **common_kwargs, forward_only=True, enable_preview=True)
    context.plan("prod", **common_kwargs)
    context.plan("prod", **common_kwargs, forward_only=True)
    context.plan("prod", **common_kwargs, restate_models=[waiter_revenue_by_day_model.name])

    set_console(old_console)

    # Make sure that the now changes were actually applied
    for target_env in ("dev", "prod"):
        plan = context.plan_builder(target_env, skip_tests=True).build()
        assert plan.has_changes
        assert plan.missing_intervals
        assert plan.directly_modified == {waiter_revenue_by_day_snapshot.snapshot_id}
        assert len(plan.new_snapshots) == 2
        assert {s.snapshot_id for s in plan.new_snapshots} == {
            waiter_revenue_by_day_snapshot.snapshot_id,
            top_waiters_snapshot.snapshot_id,
        }


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dbt_requirements(sushi_dbt_context: Context):
    assert set(sushi_dbt_context.requirements) == {"dbt-core", "dbt-duckdb"}
    assert sushi_dbt_context.requirements["dbt-core"].startswith("1.")
    assert sushi_dbt_context.requirements["dbt-duckdb"].startswith("1.")


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dbt_dialect_with_normalization_strategy(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context(
        "tests/fixtures/dbt/sushi_test", config="test_config_with_normalization_strategy"
    )
    assert context.default_dialect == "duckdb,normalization_strategy=LOWERCASE"


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_dbt_before_all_with_var_ref_source(init_and_plan_context: t.Callable):
    _, plan = init_and_plan_context(
        "tests/fixtures/dbt/sushi_test", config="test_config_with_normalization_strategy"
    )
    environment_statements = plan.to_evaluatable().environment_statements
    assert environment_statements
    rendered_statements = [e.render_before_all(dialect="duckdb") for e in environment_statements]
    assert rendered_statements[0] == [
        "CREATE TABLE IF NOT EXISTS analytic_stats (physical_table TEXT, evaluation_time TEXT)",
        "CREATE TABLE IF NOT EXISTS to_be_executed_last (col TEXT)",
        "SELECT 1 AS var, 'items' AS src, 'waiters' AS ref",
    ]


@pytest.mark.parametrize(
    "context_fixture",
    ["sushi_context", "sushi_dbt_context", "sushi_test_dbt_context", "sushi_no_default_catalog"],
)
def test_model_add(context_fixture: Context, request):
    initial_add(request.getfixturevalue(context_fixture), "dev")


def test_model_removed(sushi_context: Context):
    environment = "dev"
    initial_add(sushi_context, environment)

    top_waiters_snapshot_id = sushi_context.get_snapshot(
        "sushi.top_waiters", raise_if_missing=True
    ).snapshot_id

    sushi_context._models.pop('"memory"."sushi"."top_waiters"')

    def _validate_plan(context, plan):
        validate_plan_changes(plan, removed=[top_waiters_snapshot_id])
        assert not plan.missing_intervals

    def _validate_apply(context):
        assert not sushi_context.get_snapshot("sushi.top_waiters", raise_if_missing=False)
        assert sushi_context.state_reader.get_snapshots([top_waiters_snapshot_id])
        env = sushi_context.state_reader.get_environment(environment)
        assert env
        assert all(snapshot.name != '"memory"."sushi"."top_waiters"' for snapshot in env.snapshots)

    apply_to_environment(
        sushi_context,
        environment,
        SnapshotChangeCategory.BREAKING,
        plan_validators=[_validate_plan],
        apply_validators=[_validate_apply],
    )


def test_non_breaking_change(sushi_context: Context):
    environment = "dev"
    initial_add(sushi_context, environment)
    validate_query_change(sushi_context, environment, SnapshotChangeCategory.NON_BREAKING, False)


def test_breaking_change(sushi_context: Context):
    environment = "dev"
    initial_add(sushi_context, environment)
    validate_query_change(sushi_context, environment, SnapshotChangeCategory.BREAKING, False)


def test_logical_change(sushi_context: Context):
    environment = "dev"
    initial_add(sushi_context, environment)
    previous_sushi_items_version = sushi_context.get_snapshot(
        "sushi.items", raise_if_missing=True
    ).version

    change_data_type(
        sushi_context,
        "sushi.items",
        DataType.Type.DOUBLE,
        DataType.Type.FLOAT,
    )
    apply_to_environment(sushi_context, environment, SnapshotChangeCategory.NON_BREAKING)

    change_data_type(
        sushi_context,
        "sushi.items",
        DataType.Type.FLOAT,
        DataType.Type.DOUBLE,
    )
    apply_to_environment(sushi_context, environment, SnapshotChangeCategory.NON_BREAKING)

    assert (
        sushi_context.get_snapshot("sushi.items", raise_if_missing=True).version
        == previous_sushi_items_version
    )


def validate_query_change(
    context: Context,
    environment: str,
    change_category: SnapshotChangeCategory,
    logical: bool,
):
    versions = snapshots_to_versions(context.snapshots.values())

    change_data_type(
        context,
        "sushi.items",
        DataType.Type.DOUBLE,
        DataType.Type.FLOAT,
    )

    directly_modified = ['"memory"."sushi"."items"']
    indirectly_modified = [
        '"memory"."sushi"."order_items"',
        '"memory"."sushi"."waiter_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_lifetime"',
        '"memory"."sushi"."top_waiters"',
        "assert_item_price_above_zero",
    ]
    not_modified = [
        snapshot.name
        for snapshot in context.snapshots.values()
        if snapshot.name not in directly_modified and snapshot.name not in indirectly_modified
    ]

    if change_category == SnapshotChangeCategory.BREAKING and not logical:
        models_same = not_modified
        models_different = directly_modified + indirectly_modified
    elif change_category == SnapshotChangeCategory.FORWARD_ONLY:
        models_same = not_modified + directly_modified + indirectly_modified
        models_different = []
    else:
        models_same = not_modified + indirectly_modified
        models_different = directly_modified

    def _validate_plan(context, plan):
        validate_plan_changes(plan, modified=directly_modified + indirectly_modified)
        assert bool(plan.missing_intervals) != logical

    def _validate_apply(context):
        current_versions = snapshots_to_versions(context.snapshots.values())
        validate_versions_same(models_same, versions, current_versions)
        validate_versions_different(models_different, versions, current_versions)

    apply_to_environment(
        context,
        environment,
        change_category,
        plan_validators=[_validate_plan],
        apply_validators=[_validate_apply],
    )


@pytest.mark.parametrize(
    "from_, to",
    [
        (ModelKindName.INCREMENTAL_BY_TIME_RANGE, ModelKindName.FULL),
        (ModelKindName.FULL, ModelKindName.INCREMENTAL_BY_TIME_RANGE),
    ],
)
def test_model_kind_change(from_: ModelKindName, to: ModelKindName, sushi_context: Context):
    environment = f"test_model_kind_change__{from_.value.lower()}__{to.value.lower()}"
    incremental_snapshot = sushi_context.get_snapshot("sushi.items", raise_if_missing=True).copy()

    if from_ != ModelKindName.INCREMENTAL_BY_TIME_RANGE:
        change_model_kind(sushi_context, from_)
        apply_to_environment(sushi_context, environment, SnapshotChangeCategory.NON_BREAKING)

    if to == ModelKindName.INCREMENTAL_BY_TIME_RANGE:
        sushi_context.upsert_model(incremental_snapshot.model)
    else:
        change_model_kind(sushi_context, to)

    logical = to in (ModelKindName.INCREMENTAL_BY_TIME_RANGE, ModelKindName.EMBEDDED)
    validate_model_kind_change(to, sushi_context, environment, logical=logical)


def change_model_kind(context: Context, kind: ModelKindName):
    if kind in (ModelKindName.VIEW, ModelKindName.EMBEDDED, ModelKindName.FULL):
        context.upsert_model(
            "sushi.items",
            partitioned_by=[],
        )
    context.upsert_model("sushi.items", kind=model_kind_type_from_name(kind)())  # type: ignore


def validate_model_kind_change(
    kind_name: ModelKindName,
    context: Context,
    environment: str,
    *,
    logical: bool,
):
    directly_modified = ['"memory"."sushi"."items"']
    indirectly_modified = [
        '"memory"."sushi"."order_items"',
        '"memory"."sushi"."waiter_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_lifetime"',
        '"memory"."sushi"."top_waiters"',
        "assert_item_price_above_zero",
    ]
    if kind_name == ModelKindName.INCREMENTAL_BY_TIME_RANGE:
        kind: ModelKind = IncrementalByTimeRangeKind(time_column=TimeColumn(column="event_date"))
    elif kind_name == ModelKindName.INCREMENTAL_BY_UNIQUE_KEY:
        kind = IncrementalByUniqueKeyKind(unique_key="id")
    else:
        kind = model_kind_type_from_name(kind_name)()  # type: ignore

    def _validate_plan(context, plan):
        validate_plan_changes(plan, modified=directly_modified + indirectly_modified)
        assert (
            next(
                snapshot
                for snapshot in plan.snapshots.values()
                if snapshot.name == '"memory"."sushi"."items"'
            ).model.kind.name
            == kind.name
        )
        assert bool(plan.missing_intervals) != logical

    apply_to_environment(
        context,
        environment,
        SnapshotChangeCategory.NON_BREAKING,
        plan_validators=[_validate_plan],
    )


def test_environment_isolation(sushi_context: Context):
    prod_snapshots = sushi_context.snapshots.values()

    change_data_type(
        sushi_context,
        "sushi.items",
        DataType.Type.DOUBLE,
        DataType.Type.FLOAT,
    )
    directly_modified = ['"memory"."sushi"."items"']
    indirectly_modified = [
        '"memory"."sushi"."order_items"',
        '"memory"."sushi"."waiter_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_by_day"',
        '"memory"."sushi"."customer_revenue_lifetime"',
        '"memory"."sushi"."top_waiters"',
        "assert_item_price_above_zero",
    ]

    apply_to_environment(sushi_context, "dev", SnapshotChangeCategory.BREAKING)

    # Verify prod unchanged
    validate_apply_basics(sushi_context, "prod", prod_snapshots)

    def _validate_plan(context, plan):
        validate_plan_changes(plan, modified=directly_modified + indirectly_modified)
        assert not plan.missing_intervals

    apply_to_environment(
        sushi_context,
        "prod",
        SnapshotChangeCategory.BREAKING,
        plan_validators=[_validate_plan],
    )


def test_environment_promotion(sushi_context: Context):
    initial_add(sushi_context, "dev")

    # Simulate prod "ahead"
    change_data_type(sushi_context, "sushi.items", DataType.Type.DOUBLE, DataType.Type.FLOAT)
    apply_to_environment(sushi_context, "prod", SnapshotChangeCategory.BREAKING)

    # Simulate rebase
    apply_to_environment(sushi_context, "dev", SnapshotChangeCategory.BREAKING)

    # Make changes in dev
    change_data_type(sushi_context, "sushi.items", DataType.Type.FLOAT, DataType.Type.DECIMAL)
    apply_to_environment(sushi_context, "dev", SnapshotChangeCategory.NON_BREAKING)

    change_data_type(sushi_context, "sushi.top_waiters", DataType.Type.DOUBLE, DataType.Type.INT)
    apply_to_environment(sushi_context, "dev", SnapshotChangeCategory.BREAKING)

    change_data_type(
        sushi_context,
        "sushi.customer_revenue_by_day",
        DataType.Type.DOUBLE,
        DataType.Type.FLOAT,
    )
    apply_to_environment(
        sushi_context,
        "dev",
        SnapshotChangeCategory.FORWARD_ONLY,
        allow_destructive_models=['"memory"."sushi"."customer_revenue_by_day"'],
    )

    # Promote to prod
    def _validate_plan(context, plan):
        sushi_items_snapshot = context.get_snapshot("sushi.items", raise_if_missing=True)
        sushi_top_waiters_snapshot = context.get_snapshot(
            "sushi.top_waiters", raise_if_missing=True
        )
        sushi_customer_revenue_by_day_snapshot = context.get_snapshot(
            "sushi.customer_revenue_by_day", raise_if_missing=True
        )

        assert (
            plan.context_diff.modified_snapshots[sushi_items_snapshot.name][0].change_category
            == SnapshotChangeCategory.NON_BREAKING
        )
        assert (
            plan.context_diff.modified_snapshots[sushi_top_waiters_snapshot.name][0].change_category
            == SnapshotChangeCategory.BREAKING
        )
        assert (
            plan.context_diff.modified_snapshots[sushi_customer_revenue_by_day_snapshot.name][
                0
            ].change_category
            == SnapshotChangeCategory.NON_BREAKING
        )
        assert plan.context_diff.snapshots[
            sushi_customer_revenue_by_day_snapshot.snapshot_id
        ].is_forward_only

    apply_to_environment(
        sushi_context,
        "prod",
        SnapshotChangeCategory.NON_BREAKING,
        plan_validators=[_validate_plan],
        allow_destructive_models=['"memory"."sushi"."customer_revenue_by_day"'],
    )


def test_no_override(sushi_context: Context) -> None:
    change_data_type(
        sushi_context,
        "sushi.items",
        DataType.Type.INT,
        DataType.Type.BIGINT,
    )

    change_data_type(
        sushi_context,
        "sushi.order_items",
        DataType.Type.INT,
        DataType.Type.BIGINT,
    )

    plan_builder = sushi_context.plan_builder("prod")
    plan = plan_builder.build()

    sushi_items_snapshot = sushi_context.get_snapshot("sushi.items", raise_if_missing=True)
    sushi_order_items_snapshot = sushi_context.get_snapshot(
        "sushi.order_items", raise_if_missing=True
    )
    sushi_water_revenue_by_day_snapshot = sushi_context.get_snapshot(
        "sushi.waiter_revenue_by_day", raise_if_missing=True
    )

    items = plan.context_diff.snapshots[sushi_items_snapshot.snapshot_id]
    order_items = plan.context_diff.snapshots[sushi_order_items_snapshot.snapshot_id]
    waiter_revenue = plan.context_diff.snapshots[sushi_water_revenue_by_day_snapshot.snapshot_id]

    plan_builder.set_choice(items, SnapshotChangeCategory.BREAKING).set_choice(
        order_items, SnapshotChangeCategory.NON_BREAKING
    )
    plan_builder.build()
    assert items.is_new_version
    assert waiter_revenue.is_new_version
    plan_builder.set_choice(items, SnapshotChangeCategory.NON_BREAKING)
    plan_builder.build()
    assert not waiter_revenue.is_new_version


@pytest.mark.parametrize(
    "change_categories, expected",
    [
        ([SnapshotChangeCategory.NON_BREAKING], SnapshotChangeCategory.BREAKING),
        ([SnapshotChangeCategory.BREAKING], SnapshotChangeCategory.BREAKING),
        (
            [SnapshotChangeCategory.NON_BREAKING, SnapshotChangeCategory.NON_BREAKING],
            SnapshotChangeCategory.BREAKING,
        ),
        (
            [SnapshotChangeCategory.NON_BREAKING, SnapshotChangeCategory.BREAKING],
            SnapshotChangeCategory.BREAKING,
        ),
        (
            [SnapshotChangeCategory.BREAKING, SnapshotChangeCategory.NON_BREAKING],
            SnapshotChangeCategory.BREAKING,
        ),
        (
            [SnapshotChangeCategory.BREAKING, SnapshotChangeCategory.BREAKING],
            SnapshotChangeCategory.BREAKING,
        ),
    ],
)
def test_revert(
    sushi_context: Context,
    change_categories: t.List[SnapshotChangeCategory],
    expected: SnapshotChangeCategory,
):
    environment = "prod"
    original_snapshot_id = sushi_context.get_snapshot("sushi.items", raise_if_missing=True)

    types = (DataType.Type.DOUBLE, DataType.Type.FLOAT, DataType.Type.DECIMAL)
    assert len(change_categories) < len(types)

    for i, category in enumerate(change_categories):
        change_data_type(sushi_context, "sushi.items", *types[i : i + 2])
        apply_to_environment(sushi_context, environment, category)
        assert (
            sushi_context.get_snapshot("sushi.items", raise_if_missing=True) != original_snapshot_id
        )

    change_data_type(sushi_context, "sushi.items", types[len(change_categories)], types[0])

    def _validate_plan(_, plan):
        snapshot = next(s for s in plan.snapshots.values() if s.name == '"memory"."sushi"."items"')
        assert snapshot.change_category == expected
        assert not plan.missing_intervals

    apply_to_environment(
        sushi_context,
        environment,
        change_categories[-1],
        plan_validators=[_validate_plan],
    )
    assert sushi_context.get_snapshot("sushi.items", raise_if_missing=True) == original_snapshot_id


def test_revert_after_downstream_change(sushi_context: Context):
    environment = "prod"
    change_data_type(sushi_context, "sushi.items", DataType.Type.DOUBLE, DataType.Type.FLOAT)
    apply_to_environment(sushi_context, environment, SnapshotChangeCategory.BREAKING)

    change_data_type(
        sushi_context,
        "sushi.waiter_revenue_by_day",
        DataType.Type.DOUBLE,
        DataType.Type.FLOAT,
    )
    apply_to_environment(sushi_context, environment, SnapshotChangeCategory.NON_BREAKING)

    change_data_type(sushi_context, "sushi.items", DataType.Type.FLOAT, DataType.Type.DOUBLE)

    def _validate_plan(_, plan):
        snapshot = next(s for s in plan.snapshots.values() if s.name == '"memory"."sushi"."items"')
        assert snapshot.change_category == SnapshotChangeCategory.BREAKING
        assert plan.missing_intervals

    apply_to_environment(
        sushi_context,
        environment,
        SnapshotChangeCategory.BREAKING,
        plan_validators=[_validate_plan],
    )


def test_auto_categorization(sushi_context: Context):
    environment = "dev"
    for config in sushi_context.configs.values():
        config.plan.auto_categorize_changes.sql = AutoCategorizationMode.FULL
    initial_add(sushi_context, environment)

    version = sushi_context.get_snapshot(
        "sushi.waiter_as_customer_by_day", raise_if_missing=True
    ).version
    fingerprint = sushi_context.get_snapshot(
        "sushi.waiter_as_customer_by_day", raise_if_missing=True
    ).fingerprint

    model = t.cast(SqlModel, sushi_context.get_model("sushi.customers", raise_if_missing=True))
    sushi_context.upsert_model(
        "sushi.customers",
        query_=ParsableSql(sql=model.query.select("'foo' AS foo").sql(dialect=model.dialect)),  # type: ignore
    )
    apply_to_environment(sushi_context, environment)

    assert (
        sushi_context.get_snapshot(
            "sushi.waiter_as_customer_by_day", raise_if_missing=True
        ).change_category
        == SnapshotChangeCategory.INDIRECT_NON_BREAKING
    )
    assert (
        sushi_context.get_snapshot(
            "sushi.waiter_as_customer_by_day", raise_if_missing=True
        ).fingerprint
        != fingerprint
    )
    assert (
        sushi_context.get_snapshot("sushi.waiter_as_customer_by_day", raise_if_missing=True).version
        == version
    )


@use_terminal_console
def test_multi(mocker):
    context = Context(paths=["examples/multi/repo_1", "examples/multi/repo_2"], gateway="memory")

    with patch.object(get_console(), "log_warning") as mock_logger:
        context.plan_builder(environment="dev")
        warnings = mock_logger.call_args[0][0]
        repo1_path, repo2_path = context.configs.keys()
        assert f"Linter warnings for {repo1_path}" in warnings
        assert f"Linter warnings for {repo2_path}" not in warnings

    assert (
        context.render("bronze.a").sql()
        == '''SELECT 1 AS "col_a", 'b' AS "col_b", 1 AS "one", 'repo_1' AS "dup"'''
    )
    assert (
        context.render("silver.d").sql()
        == '''SELECT "c"."col_a" AS "col_a", 2 AS "two", 'repo_2' AS "dup" FROM "memory"."silver"."c" AS "c"'''
    )
    context._new_state_sync().reset(default_catalog=context.default_catalog)
    plan = context.plan_builder().build()
    assert len(plan.new_snapshots) == 5
    context.apply(plan)

    # Ensure before_all, after_all statements for multiple repos have executed
    environment_statements = context.state_reader.get_environment_statements(c.PROD)
    assert len(environment_statements) == 2
    assert context.fetchdf("select * from before_1").to_dict()["1"][0] == 1
    assert context.fetchdf("select * from before_2").to_dict()["2"][0] == 2
    assert context.fetchdf("select * from after_1").to_dict()["repo_1"][0] == "repo_1"
    assert context.fetchdf("select * from after_2").to_dict()["repo_2"][0] == "repo_2"

    old_context = context
    context = Context(
        paths=["examples/multi/repo_1"],
        state_sync=old_context.state_sync,
        gateway="memory",
    )
    context._engine_adapter = old_context.engine_adapter
    del context.engine_adapters

    model = context.get_model("bronze.a")
    assert model.project == "repo_1"
    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(sql=model.query.select("'c' AS c").sql(dialect=model.dialect))
            }
        )
    )
    plan = context.plan_builder().build()

    assert set(snapshot.name for snapshot in plan.directly_modified) == {
        '"memory"."bronze"."a"',
        '"memory"."bronze"."b"',
        '"memory"."silver"."e"',
    }
    assert sorted([x.name for x in list(plan.indirectly_modified.values())[0]]) == [
        '"memory"."silver"."c"',
        '"memory"."silver"."d"',
    ]
    assert len(plan.missing_intervals) == 3
    context.apply(plan)
    validate_apply_basics(context, c.PROD, plan.snapshots.values())

    # Ensure that before_all and after_all statements of both repos are there despite planning with repo_1
    environment_statements = context.state_reader.get_environment_statements(c.PROD)
    assert len(environment_statements) == 2

    # Ensure that environment statements have the project field set correctly
    sorted_env_statements = sorted(environment_statements, key=lambda es: es.project)
    assert sorted_env_statements[0].project == "repo_1"
    assert sorted_env_statements[1].project == "repo_2"

    # Assert before_all and after_all for each project
    assert sorted_env_statements[0].before_all == [
        "CREATE TABLE IF NOT EXISTS before_1 AS select @one()"
    ]
    assert sorted_env_statements[0].after_all == [
        "CREATE TABLE IF NOT EXISTS after_1 AS select @dup()"
    ]
    assert sorted_env_statements[1].before_all == [
        "CREATE TABLE IF NOT EXISTS before_2 AS select @two()"
    ]
    assert sorted_env_statements[1].after_all == [
        "CREATE TABLE IF NOT EXISTS after_2 AS select @dup()"
    ]


@use_terminal_console
def test_multi_repo_single_project_environment_statements_update(copy_to_temp_path):
    paths = copy_to_temp_path("examples/multi")
    repo_1_path = f"{paths[0]}/repo_1"
    repo_2_path = f"{paths[0]}/repo_2"

    context = Context(paths=[repo_1_path, repo_2_path], gateway="memory")
    context._new_state_sync().reset(default_catalog=context.default_catalog)

    initial_plan = context.plan_builder().build()
    context.apply(initial_plan)

    # Get initial statements
    initial_statements = context.state_reader.get_environment_statements(c.PROD)
    assert len(initial_statements) == 2

    # Modify repo_1's config to add a new before_all statement
    repo_1_config_path = f"{repo_1_path}/config.yaml"
    with open(repo_1_config_path, "r") as f:
        config_content = f.read()

    # Add a new before_all statement to repo_1 only
    modified_config = config_content.replace(
        "CREATE TABLE IF NOT EXISTS before_1 AS select @one()",
        "CREATE TABLE IF NOT EXISTS before_1 AS select @one()\n  - CREATE TABLE IF NOT EXISTS before_1_modified AS select 999",
    )

    with open(repo_1_config_path, "w") as f:
        f.write(modified_config)

    # Create new context with modified config but only for repo_1
    context_repo_1_only = Context(
        paths=[repo_1_path], state_sync=context.state_sync, gateway="memory"
    )

    # Plan with only repo_1, this should preserve repo_2's statements from state
    repo_1_plan = context_repo_1_only.plan_builder(environment="dev").build()
    context_repo_1_only.apply(repo_1_plan)
    updated_statements = context_repo_1_only.state_reader.get_environment_statements("dev")

    # Should still have statements from both projects
    assert len(updated_statements) == 2

    # Sort by project
    sorted_updated = sorted(updated_statements, key=lambda es: es.project or "")

    # Verify repo_1 has the new statement
    repo_1_updated = sorted_updated[0]
    assert repo_1_updated.project == "repo_1"
    assert len(repo_1_updated.before_all) == 2
    assert "CREATE TABLE IF NOT EXISTS before_1_modified" in repo_1_updated.before_all[1]

    # Verify repo_2 statements are preserved from state
    repo_2_preserved = sorted_updated[1]
    assert repo_2_preserved.project == "repo_2"
    assert len(repo_2_preserved.before_all) == 1
    assert "CREATE TABLE IF NOT EXISTS before_2" in repo_2_preserved.before_all[0]
    assert "CREATE TABLE IF NOT EXISTS after_2 AS select @dup()" in repo_2_preserved.after_all[0]


@use_terminal_console
def test_multi_virtual_layer(copy_to_temp_path):
    paths = copy_to_temp_path("tests/fixtures/multi_virtual_layer")
    path = Path(paths[0])
    first_db_path = str(path / "db_1.db")
    second_db_path = str(path / "db_2.db")

    config = Config(
        gateways={
            "first": GatewayConfig(
                connection=DuckDBConnectionConfig(database=first_db_path),
                variables={"overriden_var": "gateway_1"},
            ),
            "second": GatewayConfig(
                connection=DuckDBConnectionConfig(database=second_db_path),
                variables={"overriden_var": "gateway_2"},
            ),
        },
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        model_naming=NameInferenceConfig(infer_names=True),
        default_gateway="first",
        gateway_managed_virtual_layer=True,
        variables={"overriden_var": "global", "global_one": 88},
    )

    context = Context(paths=paths, config=config)
    assert context.default_catalog_per_gateway == {"first": "db_1", "second": "db_2"}
    assert len(context.engine_adapters) == 2

    # For the model without gateway the default should be used and the gateway variable should overide the global
    assert (
        context.render("first_schema.model_one").sql()
        == 'SELECT \'gateway_1\' AS "item_id", 88 AS "global_one", 1 AS "macro_one"'
    )

    # For model with gateway specified the appropriate variable should be used to overide
    assert (
        context.render("db_2.second_schema.model_one").sql()
        == 'SELECT \'gateway_2\' AS "item_id", 88 AS "global_one", 1 AS "macro_one"'
    )

    plan = context.plan_builder().build()
    assert len(plan.new_snapshots) == 4
    context.apply(plan)

    # Validate the tables that source from the first tables are correct as well with evaluate
    assert (
        context.evaluate(
            "first_schema.model_two", start=now(), end=now(), execution_time=now()
        ).to_string()
        == "     item_id  global_one\n0  gateway_1          88"
    )
    assert (
        context.evaluate(
            "db_2.second_schema.model_two", start=now(), end=now(), execution_time=now()
        ).to_string()
        == "     item_id  global_one\n0  gateway_2          88"
    )

    assert sorted(set(snapshot.name for snapshot in plan.directly_modified)) == [
        '"db_1"."first_schema"."model_one"',
        '"db_1"."first_schema"."model_two"',
        '"db_2"."second_schema"."model_one"',
        '"db_2"."second_schema"."model_two"',
    ]

    model = context.get_model("db_1.first_schema.model_one")

    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'c' AS extra").sql(dialect=model.dialect)
                )
            }
        )
    )
    plan = context.plan_builder().build()
    context.apply(plan)

    state_environments = context.state_reader.get_environments()
    state_snapshots = context.state_reader.get_snapshots(context.snapshots.values())

    assert state_environments[0].gateway_managed
    assert len(state_snapshots) == len(state_environments[0].snapshots)
    assert [snapshot.name for snapshot in plan.directly_modified] == [
        '"db_1"."first_schema"."model_one"'
    ]
    assert [x.name for x in list(plan.indirectly_modified.values())[0]] == [
        '"db_1"."first_schema"."model_two"'
    ]

    assert len(plan.missing_intervals) == 1
    assert (
        context.evaluate(
            "db_1.first_schema.model_one", start=now(), end=now(), execution_time=now()
        ).to_string()
        == "     item_id  global_one  macro_one extra\n0  gateway_1          88          1     c"
    )

    # Create dev environment with changed models
    model = context.get_model("db_2.second_schema.model_one")
    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'d' AS extra").sql(dialect=model.dialect)
                )
            }
        )
    )
    model = context.get_model("first_schema.model_two")
    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'d2' AS col").sql(dialect=model.dialect)
                )
            }
        )
    )
    plan = context.plan_builder("dev").build()
    context.apply(plan)

    dev_environment = context.state_sync.get_environment("dev")
    assert dev_environment is not None

    metadata_engine_1 = DuckDBMetadata.from_context(context)
    start_schemas_1 = set(metadata_engine_1.schemas)
    assert sorted(start_schemas_1) == sorted(
        {"first_schema__dev", "sqlmesh", "first_schema", "sqlmesh__first_schema"}
    )

    metadata_engine_2 = DuckDBMetadata(context._get_engine_adapter("second"))
    start_schemas_2 = set(metadata_engine_2.schemas)
    assert sorted(start_schemas_2) == sorted(
        {"sqlmesh__second_schema", "second_schema", "second_schema__dev"}
    )

    # Invalidate dev environment
    context.invalidate_environment("dev")
    invalidate_environment = context.state_sync.get_environment("dev")
    assert invalidate_environment is not None
    assert invalidate_environment.expiration_ts < dev_environment.expiration_ts  # type: ignore
    assert sorted(start_schemas_1) == sorted(set(metadata_engine_1.schemas))
    assert sorted(start_schemas_2) == sorted(set(metadata_engine_2.schemas))

    # Run janitor
    context._run_janitor()
    assert context.state_sync.get_environment("dev") is None
    removed_schemas = start_schemas_1 - set(metadata_engine_1.schemas)
    assert removed_schemas == {"first_schema__dev"}
    removed_schemas = start_schemas_2 - set(metadata_engine_2.schemas)
    assert removed_schemas == {"second_schema__dev"}
    prod_environment = context.state_sync.get_environment("prod")

    # Remove the second gateway's second model and apply plan
    second_model = path / "models/second_schema/model_two.sql"
    os.remove(second_model)
    assert not second_model.exists()
    context = Context(paths=paths, config=config)
    plan = context.plan_builder().build()
    context.apply(plan)
    prod_environment = context.state_sync.get_environment("prod")
    assert len(prod_environment.snapshots_) == 3

    # Changing the flag should show a diff
    context.config.gateway_managed_virtual_layer = False
    plan = context.plan_builder().build()
    assert not plan.requires_backfill
    assert (
        plan.context_diff.previous_gateway_managed_virtual_layer
        != plan.context_diff.gateway_managed_virtual_layer
    )
    assert plan.context_diff.has_changes

    # This should error since the default_gateway won't have access to create the view on a non-shared catalog
    with pytest.raises(NodeExecutionFailedError, match=r"Execution failed for node SnapshotId*"):
        context.apply(plan)


def test_multi_dbt(mocker):
    context = Context(paths=["examples/multi_dbt/bronze", "examples/multi_dbt/silver"])
    context._new_state_sync().reset(default_catalog=context.default_catalog)
    plan = context.plan_builder().build()
    assert len(plan.new_snapshots) == 4
    context.apply(plan)
    validate_apply_basics(context, c.PROD, plan.snapshots.values())

    environment_statements = context.state_sync.get_environment_statements(c.PROD)
    assert len(environment_statements) == 2
    bronze_statements = environment_statements[0]
    assert bronze_statements.before_all == [
        "JINJA_STATEMENT_BEGIN;\nCREATE TABLE IF NOT EXISTS analytic_stats (physical_table VARCHAR, evaluation_time VARCHAR);\nJINJA_END;"
    ]
    assert not bronze_statements.after_all
    silver_statements = environment_statements[1]
    assert not silver_statements.before_all
    assert silver_statements.after_all == [
        "JINJA_STATEMENT_BEGIN;\n{{ store_schemas(schemas) }}\nJINJA_END;"
    ]
    assert "store_schemas" in silver_statements.jinja_macros.root_macros
    analytics_table = context.fetchdf("select * from analytic_stats;")
    assert sorted(analytics_table.columns) == sorted(["physical_table", "evaluation_time"])
    schema_table = context.fetchdf("select * from schema_table;")
    assert sorted(schema_table.all_schemas[0]) == sorted(["bronze", "silver"])


def test_multi_hybrid(mocker):
    context = Context(
        paths=["examples/multi_hybrid/dbt_repo", "examples/multi_hybrid/sqlmesh_repo"]
    )
    context._new_state_sync().reset(default_catalog=context.default_catalog)
    plan = context.plan_builder().build()

    assert len(plan.new_snapshots) == 5
    assert context.dag.roots == {'"memory"."dbt_repo"."e"'}
    assert context.dag.graph['"memory"."dbt_repo"."c"'] == {'"memory"."sqlmesh_repo"."b"'}
    assert context.dag.graph['"memory"."sqlmesh_repo"."b"'] == {'"memory"."sqlmesh_repo"."a"'}
    assert context.dag.graph['"memory"."sqlmesh_repo"."a"'] == {'"memory"."dbt_repo"."e"'}
    assert context.dag.downstream('"memory"."dbt_repo"."e"') == [
        '"memory"."sqlmesh_repo"."a"',
        '"memory"."sqlmesh_repo"."b"',
        '"memory"."dbt_repo"."c"',
        '"memory"."dbt_repo"."d"',
    ]

    sqlmesh_model_a = context.get_model("sqlmesh_repo.a")
    dbt_model_c = context.get_model("dbt_repo.c")
    assert sqlmesh_model_a.project == "sqlmesh_repo"

    sqlmesh_rendered = (
        'SELECT "e"."col_a" AS "col_a", "e"."col_b" AS "col_b" FROM "memory"."dbt_repo"."e" AS "e"'
    )
    dbt_rendered = 'SELECT DISTINCT ROUND(CAST(("b"."col_a" / NULLIF(100, 0)) AS DECIMAL(16, 2)), 2) AS "rounded_col_a" FROM "memory"."sqlmesh_repo"."b" AS "b"'
    assert sqlmesh_model_a.render_query().sql() == sqlmesh_rendered
    assert dbt_model_c.render_query().sql() == dbt_rendered

    context.apply(plan)
    validate_apply_basics(context, c.PROD, plan.snapshots.values())


def test_incremental_time_self_reference(
    mocker: MockerFixture, sushi_context: Context, sushi_data_validator: SushiDataValidator
):
    start_ts = to_timestamp("1 week ago")
    start_date, end_date = to_date("1 week ago"), to_date("yesterday")
    if to_timestamp(start_date) < start_ts:
        # The start date must be aligned by the interval unit.
        start_date += timedelta(days=1)

    df = sushi_context.engine_adapter.fetchdf(
        "SELECT MIN(event_date) FROM sushi.customer_revenue_lifetime"
    )
    assert df.iloc[0, 0] == pd.to_datetime(start_date)
    df = sushi_context.engine_adapter.fetchdf(
        "SELECT MAX(event_date) FROM sushi.customer_revenue_lifetime"
    )
    assert df.iloc[0, 0] == pd.to_datetime(end_date)
    results = sushi_data_validator.validate("sushi.customer_revenue_lifetime", start_date, end_date)
    plan = sushi_context.plan_builder(
        restate_models=["sushi.customer_revenue_lifetime", "sushi.customer_revenue_by_day"],
        start=start_date,
        end="5 days ago",
    ).build()
    revenue_lifeteime_snapshot = sushi_context.get_snapshot(
        "sushi.customer_revenue_lifetime", raise_if_missing=True
    )
    revenue_by_day_snapshot = sushi_context.get_snapshot(
        "sushi.customer_revenue_by_day", raise_if_missing=True
    )
    assert sorted(plan.missing_intervals, key=lambda x: x.snapshot_id) == sorted(
        [
            SnapshotIntervals(
                snapshot_id=revenue_lifeteime_snapshot.snapshot_id,
                intervals=[
                    (to_timestamp(to_date("7 days ago")), to_timestamp(to_date("6 days ago"))),
                    (to_timestamp(to_date("6 days ago")), to_timestamp(to_date("5 days ago"))),
                    (to_timestamp(to_date("5 days ago")), to_timestamp(to_date("4 days ago"))),
                    (to_timestamp(to_date("4 days ago")), to_timestamp(to_date("3 days ago"))),
                    (to_timestamp(to_date("3 days ago")), to_timestamp(to_date("2 days ago"))),
                    (to_timestamp(to_date("2 days ago")), to_timestamp(to_date("1 days ago"))),
                    (to_timestamp(to_date("1 day ago")), to_timestamp(to_date("today"))),
                ],
            ),
            SnapshotIntervals(
                snapshot_id=revenue_by_day_snapshot.snapshot_id,
                intervals=[
                    (to_timestamp(to_date("7 days ago")), to_timestamp(to_date("6 days ago"))),
                    (to_timestamp(to_date("6 days ago")), to_timestamp(to_date("5 days ago"))),
                ],
            ),
        ],
        key=lambda x: x.snapshot_id,
    )
    sushi_context.console = mocker.Mock(spec=Console)
    sushi_context.apply(plan)
    num_batch_calls = Counter(
        [x[0][0] for x in sushi_context.console.update_snapshot_evaluation_progress.call_args_list]  # type: ignore
    )
    # Validate that we made 7 calls to the customer_revenue_lifetime snapshot and 1 call to the customer_revenue_by_day snapshot
    assert num_batch_calls == {
        sushi_context.get_snapshot("sushi.customer_revenue_lifetime", raise_if_missing=True): 7,
        sushi_context.get_snapshot("sushi.customer_revenue_by_day", raise_if_missing=True): 1,
    }
    # Validate that the results are the same as before the restate
    assert results == sushi_data_validator.validate(
        "sushi.customer_revenue_lifetime", start_date, end_date
    )


def test_invalidating_environment(sushi_context: Context):
    apply_to_environment(sushi_context, "dev")
    start_environment = sushi_context.state_sync.get_environment("dev")
    assert start_environment is not None
    metadata = DuckDBMetadata.from_context(sushi_context)
    start_schemas = set(metadata.schemas)
    assert "sushi__dev" in start_schemas
    sushi_context.invalidate_environment("dev")
    invalidate_environment = sushi_context.state_sync.get_environment("dev")
    assert invalidate_environment is not None
    schemas_prior_to_janitor = set(metadata.schemas)
    assert invalidate_environment.expiration_ts < start_environment.expiration_ts  # type: ignore
    assert start_schemas == schemas_prior_to_janitor
    sushi_context._run_janitor()
    schemas_after_janitor = set(metadata.schemas)
    assert sushi_context.state_sync.get_environment("dev") is None
    assert start_schemas - schemas_after_janitor == {"sushi__dev"}


def test_environment_suffix_target_table(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context(
        "examples/sushi", config="environment_suffix_table_config"
    )
    context.apply(plan)
    metadata = DuckDBMetadata.from_context(context)
    environments_schemas = {"sushi"}
    internal_schemas = {"sqlmesh", "sqlmesh__sushi"}
    starting_schemas = environments_schemas | internal_schemas
    # Make sure no new schemas are created
    assert set(metadata.schemas) - starting_schemas == {"raw"}
    prod_views = {x for x in metadata.qualified_views if x.db in environments_schemas}
    # Make sure that all models are present
    assert len(prod_views) == 16
    apply_to_environment(context, "dev")
    # Make sure no new schemas are created
    assert set(metadata.schemas) - starting_schemas == {"raw"}
    dev_views = {
        x for x in metadata.qualified_views if x.db in environments_schemas and "__dev" in x.name
    }
    # Make sure that there is a view with `__dev` for each view that exists in prod
    assert len(dev_views) == len(prod_views)
    assert {x.name.replace("__dev", "") for x in dev_views} - {x.name for x in prod_views} == set()
    context.invalidate_environment("dev")
    context._run_janitor()
    views_after_janitor = metadata.qualified_views
    # Make sure that the number of views after the janitor is the same as when you subtract away dev views
    assert len(views_after_janitor) == len(
        {x.sql(dialect="duckdb") for x in views_after_janitor}
        - {x.sql(dialect="duckdb") for x in dev_views}
    )
    # Double check there are no dev views
    assert len({x for x in views_after_janitor if "__dev" in x.name}) == 0
    # Make sure prod views were not removed
    assert {x.sql(dialect="duckdb") for x in prod_views} - {
        x.sql(dialect="duckdb") for x in views_after_janitor
    } == set()


def test_environment_suffix_target_catalog(tmp_path: Path, monkeypatch: MonkeyPatch) -> None:
    monkeypatch.chdir(tmp_path)

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(catalogs={"main_warehouse": ":memory:"}),
        environment_suffix_target=EnvironmentSuffixTarget.CATALOG,
    )

    assert config.default_connection

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    (models_dir / "model.sql").write_text("""
    MODEL (
        name example_schema.test_model,
        kind FULL
    );

    SELECT '1' as a""")

    (models_dir / "fqn_model.sql").write_text("""
    MODEL (
        name memory.example_fqn_schema.test_model_fqn,
        kind FULL
    );

    SELECT '1' as a""")

    ctx = Context(config=config, paths=tmp_path)

    metadata = DuckDBMetadata.from_context(ctx)
    assert ctx.default_catalog == "main_warehouse"
    assert metadata.catalogs == {"main_warehouse", "memory"}

    ctx.plan(auto_apply=True)

    # prod should go to the default catalog and not be overridden to a catalog called 'prod'
    assert (
        ctx.engine_adapter.fetchone("select * from main_warehouse.example_schema.test_model")[0]  # type: ignore
        == "1"
    )
    assert (
        ctx.engine_adapter.fetchone("select * from memory.example_fqn_schema.test_model_fqn")[0]  # type: ignore
        == "1"
    )
    assert metadata.catalogs == {"main_warehouse", "memory"}
    assert metadata.schemas_in_catalog("main_warehouse") == [
        "example_schema",
        "sqlmesh__example_schema",
    ]
    assert metadata.schemas_in_catalog("memory") == [
        "example_fqn_schema",
        "sqlmesh__example_fqn_schema",
    ]

    # dev should be overridden to go to a catalogs called 'main_warehouse__dev' and 'memory__dev'
    ctx.plan(environment="dev", include_unmodified=True, auto_apply=True)
    assert (
        ctx.engine_adapter.fetchone("select * from main_warehouse__dev.example_schema.test_model")[
            0
        ]  # type: ignore
        == "1"
    )
    assert (
        ctx.engine_adapter.fetchone("select * from memory__dev.example_fqn_schema.test_model_fqn")[
            0
        ]  # type: ignore
        == "1"
    )
    assert metadata.catalogs == {"main_warehouse", "main_warehouse__dev", "memory", "memory__dev"}

    # schemas in dev envs should match prod and not have a suffix
    assert metadata.schemas_in_catalog("main_warehouse") == [
        "example_schema",
        "sqlmesh__example_schema",
    ]
    assert metadata.schemas_in_catalog("main_warehouse__dev") == ["example_schema"]
    assert metadata.schemas_in_catalog("memory") == [
        "example_fqn_schema",
        "sqlmesh__example_fqn_schema",
    ]
    assert metadata.schemas_in_catalog("memory__dev") == ["example_fqn_schema"]

    ctx.invalidate_environment("dev", sync=True)

    # dev catalogs cleaned up
    assert metadata.catalogs == {"main_warehouse", "memory"}

    # prod catalogs still contain physical layer and views still work
    assert metadata.schemas_in_catalog("main_warehouse") == [
        "example_schema",
        "sqlmesh__example_schema",
    ]
    assert metadata.schemas_in_catalog("memory") == [
        "example_fqn_schema",
        "sqlmesh__example_fqn_schema",
    ]

    assert (
        ctx.engine_adapter.fetchone("select * from main_warehouse.example_schema.test_model")[0]  # type: ignore
        == "1"
    )
    assert (
        ctx.engine_adapter.fetchone("select * from memory.example_fqn_schema.test_model_fqn")[0]  # type: ignore
        == "1"
    )


def test_environment_catalog_mapping(init_and_plan_context: t.Callable):
    environments_schemas = {"raw", "sushi"}

    def get_prod_dev_views(metadata: DuckDBMetadata) -> t.Tuple[t.Set[exp.Table], t.Set[exp.Table]]:
        views = metadata.qualified_views
        prod_views = {
            x for x in views if x.catalog == "prod_catalog" if x.db in environments_schemas
        }
        dev_views = {x for x in views if x.catalog == "dev_catalog" if x.db in environments_schemas}
        return prod_views, dev_views

    def get_default_catalog_and_non_tables(
        metadata: DuckDBMetadata, default_catalog: t.Optional[str]
    ) -> t.Tuple[t.Set[exp.Table], t.Set[exp.Table]]:
        tables = metadata.qualified_tables
        user_default_tables = {
            x for x in tables if x.catalog == default_catalog and x.db != "sqlmesh"
        }
        non_default_tables = {x for x in tables if x.catalog != default_catalog}
        return user_default_tables, non_default_tables

    context, plan = init_and_plan_context(
        "examples/sushi", config="environment_catalog_mapping_config"
    )
    context.apply(plan)
    metadata = DuckDBMetadata(context.engine_adapter)
    state_metadata = DuckDBMetadata.from_context(context.state_sync.state_sync)
    prod_views, dev_views = get_prod_dev_views(metadata)
    (
        user_default_tables,
        non_default_tables,
    ) = get_default_catalog_and_non_tables(metadata, context.default_catalog)
    assert len(prod_views) == 16
    assert len(dev_views) == 0
    assert len(user_default_tables) == 15
    assert state_metadata.schemas == ["sqlmesh"]
    assert {x.sql() for x in state_metadata.qualified_tables}.issuperset(
        {
            "physical.sqlmesh._environments",
            "physical.sqlmesh._intervals",
            "physical.sqlmesh._plan_dags",
            "physical.sqlmesh._snapshots",
            "physical.sqlmesh._versions",
        }
    )
    apply_to_environment(context, "dev")
    prod_views, dev_views = get_prod_dev_views(metadata)
    (
        user_default_tables,
        non_default_tables,
    ) = get_default_catalog_and_non_tables(metadata, context.default_catalog)
    assert len(prod_views) == 16
    assert len(dev_views) == 16
    assert len(user_default_tables) == 16
    assert len(non_default_tables) == 0
    assert state_metadata.schemas == ["sqlmesh"]
    assert {x.sql() for x in state_metadata.qualified_tables}.issuperset(
        {
            "physical.sqlmesh._environments",
            "physical.sqlmesh._intervals",
            "physical.sqlmesh._plan_dags",
            "physical.sqlmesh._snapshots",
            "physical.sqlmesh._versions",
        }
    )
    apply_to_environment(context, "prodnot")
    prod_views, dev_views = get_prod_dev_views(metadata)
    (
        user_default_tables,
        non_default_tables,
    ) = get_default_catalog_and_non_tables(metadata, context.default_catalog)
    assert len(prod_views) == 16
    assert len(dev_views) == 32
    assert len(user_default_tables) == 16
    assert len(non_default_tables) == 0
    assert state_metadata.schemas == ["sqlmesh"]
    assert {x.sql() for x in state_metadata.qualified_tables}.issuperset(
        {
            "physical.sqlmesh._environments",
            "physical.sqlmesh._intervals",
            "physical.sqlmesh._plan_dags",
            "physical.sqlmesh._snapshots",
            "physical.sqlmesh._versions",
        }
    )
    context.invalidate_environment("dev")
    context._run_janitor()
    prod_views, dev_views = get_prod_dev_views(metadata)
    (
        user_default_tables,
        non_default_tables,
    ) = get_default_catalog_and_non_tables(metadata, context.default_catalog)
    assert len(prod_views) == 16
    assert len(dev_views) == 16
    assert len(user_default_tables) == 16
    assert len(non_default_tables) == 0
    assert state_metadata.schemas == ["sqlmesh"]
    assert {x.sql() for x in state_metadata.qualified_tables}.issuperset(
        {
            "physical.sqlmesh._environments",
            "physical.sqlmesh._intervals",
            "physical.sqlmesh._plan_dags",
            "physical.sqlmesh._snapshots",
            "physical.sqlmesh._versions",
        }
    )


@pytest.mark.parametrize(
    "context_fixture",
    ["sushi_context", "sushi_no_default_catalog"],
)
def test_unaligned_start_snapshots(context_fixture: Context, request):
    context = request.getfixturevalue(context_fixture)
    environment = "dev"
    apply_to_environment(context, environment)
    # Make breaking change to model upstream of a depends_on_self model
    context.upsert_model("sushi.order_items", stamp="1")
    # Apply the change starting at a date later then the beginning of the downstream depends_on_self model
    plan = apply_to_environment(
        context,
        environment,
        choice=SnapshotChangeCategory.BREAKING,
        plan_start="2 days ago",
        enable_preview=True,
    )
    revenue_lifetime_snapshot = context.get_snapshot(
        "sushi.customer_revenue_lifetime", raise_if_missing=True
    )
    # Validate that the depends_on_self model is non-deployable
    assert not plan.deployability_index.is_deployable(revenue_lifetime_snapshot)


class OldPythonModel(PythonModel):
    kind: ModelKind = ViewKind()


def test_python_model_default_kind_change(init_and_plan_context: t.Callable):
    """
    Around 2024-07-17 Python models had their default Kind changed from VIEW to FULL in order to
    avoid some edge cases where the views might not get updated in certain situations.

    This test ensures that if a user had a Python `kind: VIEW` model stored in state,
    it can still be loaded without error and just show as a breaking change from `kind: VIEW`
    to `kind: FULL`
    """

    # note: we deliberately dont specify a Kind here to allow the defaults to be picked up
    python_model_file = """import typing as t
import pandas as pd  # noqa: TID253
from sqlmesh import ExecutionContext, model

@model(
    "sushi.python_view_model",
    columns={
        "id": "int",
    }
)
def execute(
    context: ExecutionContext,
    **kwargs: t.Any,
) -> pd.DataFrame:
    return pd.DataFrame([
        {"id": 1}
    ])
"""

    context: Context
    context, _ = init_and_plan_context("examples/sushi")

    with open(context.path / "models" / "python_view_model.py", mode="w", encoding="utf8") as f:
        f.write(python_model_file)

    # monkey-patch PythonModel to default to kind: View again
    # and ViewKind to allow python models again
    with (
        mock.patch.object(ViewKind, "supports_python_models", return_value=True),
        mock.patch("sqlmesh.core.model.definition.PythonModel", OldPythonModel),
    ):
        context.load()

    # check the monkey-patching worked
    model = context.get_model("sushi.python_view_model")
    assert model.kind.name == ModelKindName.VIEW
    assert model.source_type == "python"

    # apply plan
    plan: Plan = context.plan(auto_apply=True)

    # check that run() still works even though we have a Python model with kind: View in the state
    snapshot_ids = [s for s in plan.directly_modified if "python_view_model" in s.name]
    snapshot_from_state = list(context.state_sync.get_snapshots(snapshot_ids).values())[0]
    assert snapshot_from_state.model.kind.name == ModelKindName.VIEW
    assert snapshot_from_state.model.source_type == "python"
    context.run()

    # reload context to load model with new defaults
    # this also shows the earlier monkey-patching is no longer in effect
    context.load()
    model = context.get_model("sushi.python_view_model")
    assert model.kind.name == ModelKindName.FULL
    assert model.source_type == "python"

    plan = context.plan(
        categorizer_config=CategorizerConfig.all_full()
    )  # the default categorizer_config doesnt auto-categorize python models

    assert plan.has_changes
    assert not plan.indirectly_modified

    assert len(plan.directly_modified) == 1
    snapshot_id = list(plan.directly_modified)[0]
    assert snapshot_id.name == '"memory"."sushi"."python_view_model"'
    assert plan.modified_snapshots[snapshot_id].change_category == SnapshotChangeCategory.BREAKING

    context.apply(plan)

    df = context.engine_adapter.fetchdf("SELECT id FROM sushi.python_view_model")
    assert df["id"].to_list() == [1]


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_restatement_of_full_model_with_start(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    restatement_plan = context.plan(
        restate_models=["sushi.customers"],
        start="2023-01-07",
        auto_apply=True,
        no_prompts=True,
    )

    sushi_customer_interval = restatement_plan.restatements[
        context.get_snapshot("sushi.customers").snapshot_id
    ]
    assert sushi_customer_interval == (to_timestamp("2023-01-01"), to_timestamp("2023-01-09"))
    waiter_by_day_interval = restatement_plan.restatements[
        context.get_snapshot("sushi.waiter_as_customer_by_day").snapshot_id
    ]
    assert waiter_by_day_interval == (to_timestamp("2023-01-07"), to_timestamp("2023-01-08"))


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_restatement_should_not_override_environment_statements(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")
    context.config.before_all = ["SELECT 'test_before_all';", *context.config.before_all]
    context.load()

    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)

    prod_env_statements = context.state_reader.get_environment_statements(c.PROD)
    assert prod_env_statements[0].before_all[0] == "SELECT 'test_before_all';"

    context.plan(
        restate_models=["sushi.waiter_revenue_by_day"],
        start="2023-01-07",
        auto_apply=True,
        no_prompts=True,
    )

    prod_env_statements = context.state_reader.get_environment_statements(c.PROD)
    assert prod_env_statements[0].before_all[0] == "SELECT 'test_before_all';"


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_restatement_shouldnt_backfill_beyond_prod_intervals(init_and_plan_context: t.Callable):
    context, _ = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.top_waiters")
    context.upsert_model(SqlModel.parse_obj({**model.dict(), "cron": "@hourly"}))

    context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)
    context.run()

    with time_machine.travel("2023-01-09 02:00:00 UTC"):
        # It's time to backfill the waiter_revenue_by_day model but it hasn't run yet
        restatement_plan = context.plan(
            restate_models=["sushi.waiter_revenue_by_day"],
            no_prompts=True,
            skip_tests=True,
        )
        intervals_by_id = {i.snapshot_id: i for i in restatement_plan.missing_intervals}
        # Make sure the intervals don't go beyond the prod intervals
        assert intervals_by_id[context.get_snapshot("sushi.top_waiters").snapshot_id].intervals[-1][
            1
        ] == to_timestamp("2023-01-08 15:00:00 UTC")
        assert intervals_by_id[
            context.get_snapshot("sushi.waiter_revenue_by_day").snapshot_id
        ].intervals[-1][1] == to_timestamp("2023-01-08 00:00:00 UTC")


@time_machine.travel("2023-01-08 15:00:00 UTC")
@use_terminal_console
def test_audit_only_metadata_change(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Add a new audit
    model = context.get_model("sushi.waiter_revenue_by_day")
    audits = model.audits.copy()
    audits.append(("number_of_rows", {"threshold": exp.Literal.number(1)}))
    model = model.copy(update={"audits": audits})
    context.upsert_model(model)

    plan = context.plan_builder("prod", skip_tests=True).build()
    assert len(plan.new_snapshots) == 2
    assert all(s.change_category.is_metadata for s in plan.new_snapshots)
    assert not plan.missing_intervals

    with capture_output() as output:
        context.apply(plan)

    assert "Auditing models" in output.stdout
    assert model.name in output.stdout


def initial_add(context: Context, environment: str):
    assert not context.state_reader.get_environment(environment)

    plan = context.plan(environment, start=start(context), create_from="nonexistent_env")
    validate_plan_changes(plan, added={x.snapshot_id for x in context.snapshots.values()})

    context.apply(plan)
    validate_apply_basics(context, environment, plan.snapshots.values())


def test_plan_production_environment_statements(tmp_path: Path):
    model_a = """
    MODEL (
        name test_schema.a,
        kind FULL,
    );

    @IF(
        @runtime_stage IN ('evaluating', 'creating'),
        INSERT INTO schema_names_for_prod (physical_schema_name) VALUES (@resolve_template('@{schema_name}'))
    );

    SELECT 1 AS account_id
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    for path, defn in {"a.sql": model_a}.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    before_all = [
        "CREATE TABLE IF NOT EXISTS schema_names_for_@this_env (physical_schema_name VARCHAR)",
        "@IF(@runtime_stage = 'before_all', CREATE TABLE IF NOT EXISTS should_create AS SELECT @runtime_stage)",
    ]
    after_all = [
        "@IF(@this_env = 'prod', CREATE TABLE IF NOT EXISTS after_t AS SELECT @var_5)",
        "@IF(@runtime_stage = 'before_all', CREATE TABLE IF NOT EXISTS not_create AS SELECT @runtime_stage)",
    ]
    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        before_all=before_all,
        after_all=after_all,
        variables={"var_5": 5},
    )
    ctx = Context(paths=[tmp_path], config=config)
    ctx.plan(auto_apply=True, no_prompts=True)

    before_t = ctx.fetchdf("select * from schema_names_for_prod").to_dict()
    assert before_t["physical_schema_name"][0] == "sqlmesh__test_schema"

    after_t = ctx.fetchdf("select * from after_t").to_dict()
    assert after_t["5"][0] == 5

    environment_statements = ctx.state_reader.get_environment_statements(c.PROD)
    assert environment_statements[0].before_all == before_all
    assert environment_statements[0].after_all == after_all
    assert environment_statements[0].python_env.keys() == {"__sqlmesh__vars__"}
    assert environment_statements[0].python_env["__sqlmesh__vars__"].payload == "{'var_5': 5}"

    should_create = ctx.fetchdf("select * from should_create").to_dict()
    assert should_create["before_all"][0] == "before_all"

    with pytest.raises(
        Exception, match=r"Catalog Error: Table with name not_create does not exist!"
    ):
        ctx.fetchdf("select * from not_create")


def test_environment_statements_error_handling(tmp_path: Path):
    model_a = """
    MODEL (
        name test_schema.a,
        kind FULL,
    );

    SELECT 1 AS account_id
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    for path, defn in {"a.sql": model_a}.items():
        with open(models_dir / path, "w") as f:
            f.write(defn)

    before_all = [
        "CREATE TABLE identical_table (physical_schema_name VARCHAR)",
        "CREATE TABLE identical_table (physical_schema_name VARCHAR)",
    ]

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        before_all=before_all,
    )
    ctx = Context(paths=[tmp_path], config=config)

    expected_error_message = re.escape(
        """An error occurred during execution of the following 'before_all' statement:

CREATE TABLE identical_table (physical_schema_name TEXT)

Catalog Error: Table with name "identical_table" already exists!"""
    )

    with pytest.raises(SQLMeshError, match=expected_error_message):
        ctx.plan(auto_apply=True, no_prompts=True)

    after_all = [
        "@bad_macro()",
    ]

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        after_all=after_all,
    )
    ctx = Context(paths=[tmp_path], config=config)

    expected_error_message = re.escape(
        """An error occurred during rendering of the 'after_all' statements:

Failed to resolve macros for

@bad_macro()

Macro 'bad_macro' does not exist."""
    )

    with pytest.raises(SQLMeshError, match=expected_error_message):
        ctx.plan(auto_apply=True, no_prompts=True)


def test_before_all_after_all_execution_order(tmp_path: Path, mocker: MockerFixture):
    model = """
    MODEL (
        name test_schema.model_that_depends_on_before_all,
        kind FULL,
    );

    SELECT id, value FROM before_all_created_table
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    with open(models_dir / "model.sql", "w") as f:
        f.write(model)

    # before_all statement that creates a table that the above model depends on
    before_all_statement = (
        "CREATE TABLE IF NOT EXISTS before_all_created_table AS SELECT 1 AS id, 'test' AS value"
    )

    # after_all that depends on the model
    after_all_statement = "CREATE TABLE IF NOT EXISTS after_all_created_table AS SELECT id, value FROM test_schema.model_that_depends_on_before_all"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        before_all=[before_all_statement],
        after_all=[after_all_statement],
    )

    execute_calls: t.List[str] = []

    original_duckdb_execute = DuckDBEngineAdapter.execute

    def track_duckdb_execute(self, expression, **kwargs):
        sql = expression if isinstance(expression, str) else expression.sql(dialect="duckdb")
        state_tables = [
            "_snapshots",
            "_environments",
            "_versions",
            "_intervals",
            "_auto_restatements",
            "_environment_statements",
            "_plan_dags",
        ]

        # to ignore the state queries
        if not any(table in sql.lower() for table in state_tables):
            execute_calls.append(sql)

        return original_duckdb_execute(self, expression, **kwargs)

    ctx = Context(paths=[tmp_path], config=config)

    # the plan would fail if the execution order ever changes and before_all statements dont execute first
    ctx.plan(auto_apply=True, no_prompts=True)

    mocker.patch.object(DuckDBEngineAdapter, "execute", track_duckdb_execute)

    # run with the patched execute
    ctx.run("prod", start="2023-01-01", end="2023-01-02")

    # validate explicitly that the first execute is for the before_all
    assert "before_all_created_table" in execute_calls[0]

    # and that the last is the sole after all that depends on the model
    assert "after_all_created_table" in execute_calls[-1]


@time_machine.travel("2025-03-08 00:00:00 UTC")
def test_tz(init_and_plan_context):
    context, _ = init_and_plan_context("examples/sushi")

    model = context.get_model("sushi.waiter_revenue_by_day")
    context.upsert_model(
        SqlModel.parse_obj(
            {**model.dict(), "cron_tz": "America/Los_Angeles", "start": "2025-03-07"}
        )
    )

    def assert_intervals(plan, intervals):
        assert (
            next(
                intervals.intervals
                for intervals in plan.missing_intervals
                if intervals.snapshot_id.name == model.fqn
            )
            == intervals
        )

    plan = context.plan_builder("prod", skip_tests=True).build()

    # we have missing intervals but not waiter_revenue_by_day because it's not midnight pacific yet
    assert plan.missing_intervals

    with pytest.raises(StopIteration):
        assert_intervals(plan, [])

    # now we're ready 8AM UTC == midnight PST
    with time_machine.travel("2025-03-08 08:00:00 UTC"):
        plan = context.plan_builder("prod", skip_tests=True).build()
        assert_intervals(plan, [(to_timestamp("2025-03-07"), to_timestamp("2025-03-08"))])

    with time_machine.travel("2025-03-09 07:00:00 UTC"):
        plan = context.plan_builder("prod", skip_tests=True).build()

        assert_intervals(
            plan,
            [
                (to_timestamp("2025-03-07"), to_timestamp("2025-03-08")),
            ],
        )

    with time_machine.travel("2025-03-09 08:00:00 UTC"):
        plan = context.plan_builder("prod", skip_tests=True).build()

        assert_intervals(
            plan,
            [
                (to_timestamp("2025-03-07"), to_timestamp("2025-03-08")),
                (to_timestamp("2025-03-08"), to_timestamp("2025-03-09")),
            ],
        )

        context.apply(plan)

        plan = context.plan_builder("prod", skip_tests=True).build()
        assert not plan.missing_intervals


def apply_to_environment(
    context: Context,
    environment: str,
    choice: t.Optional[SnapshotChangeCategory] = None,
    plan_validators: t.Optional[t.Iterable[t.Callable]] = None,
    apply_validators: t.Optional[t.Iterable[t.Callable]] = None,
    plan_start: t.Optional[TimeLike] = None,
    allow_destructive_models: t.Optional[t.List[str]] = None,
    enable_preview: bool = False,
):
    plan_validators = plan_validators or []
    apply_validators = apply_validators or []

    plan_builder = context.plan_builder(
        environment,
        start=plan_start or start(context) if environment != c.PROD else None,
        forward_only=choice == SnapshotChangeCategory.FORWARD_ONLY,
        include_unmodified=True,
        allow_destructive_models=allow_destructive_models if allow_destructive_models else [],
        enable_preview=enable_preview,
    )
    if environment != c.PROD:
        plan_builder.set_start(plan_start or start(context))

    if choice:
        if choice == SnapshotChangeCategory.FORWARD_ONLY:
            # FORWARD_ONLY is deprecated, fallback to NON_BREAKING to keep the existing tests
            choice = SnapshotChangeCategory.NON_BREAKING
        plan_choice(plan_builder, choice)
    for validator in plan_validators:
        validator(context, plan_builder.build())

    plan = plan_builder.build()
    context.apply(plan)

    validate_apply_basics(context, environment, plan.snapshots.values(), plan.deployability_index)
    for validator in apply_validators:
        validator(context)
    return plan


def change_data_type(
    context: Context, model_name: str, old_type: DataType.Type, new_type: DataType.Type
) -> None:
    model = context.get_model(model_name)
    assert model is not None

    if isinstance(model, SqlModel):
        query = model.query.copy()
        data_types = query.find_all(DataType)
        for data_type in data_types:
            if data_type.this == old_type:
                data_type.set("this", new_type)
        context.upsert_model(model_name, query_=ParsableSql(sql=query.sql(dialect=model.dialect)))
    elif model.columns_to_types_ is not None:
        for k, v in model.columns_to_types_.items():
            if v.this == old_type:
                model.columns_to_types_[k] = DataType.build(new_type)
        context.upsert_model(model_name, columns=model.columns_to_types_)


def validate_plan_changes(
    plan: Plan,
    *,
    added: t.Optional[t.Iterable[SnapshotId]] = None,
    modified: t.Optional[t.Iterable[str]] = None,
    removed: t.Optional[t.Iterable[SnapshotId]] = None,
) -> None:
    added = added or []
    modified = modified or []
    removed = removed or []
    assert set(added) == plan.context_diff.added
    assert set(modified) == set(plan.context_diff.modified_snapshots)
    assert set(removed) == set(plan.context_diff.removed_snapshots)


def validate_versions_same(
    model_names: t.List[str],
    versions: t.Dict[str, str],
    other_versions: t.Dict[str, str],
) -> None:
    for name in model_names:
        assert versions[name] == other_versions[name]


def validate_versions_different(
    model_names: t.List[str],
    versions: t.Dict[str, str],
    other_versions: t.Dict[str, str],
) -> None:
    for name in model_names:
        assert versions[name] != other_versions[name]


def validate_apply_basics(
    context: Context,
    environment: str,
    snapshots: t.Iterable[Snapshot],
    deployability_index: t.Optional[DeployabilityIndex] = None,
) -> None:
    validate_snapshots_in_state_sync(snapshots, context)
    validate_state_sync_environment(snapshots, environment, context)
    validate_tables(snapshots, context, deployability_index)
    validate_environment_views(snapshots, environment, context, deployability_index)


def validate_snapshots_in_state_sync(snapshots: t.Iterable[Snapshot], context: Context) -> None:
    snapshot_infos = map(to_snapshot_info, snapshots)
    state_sync_table_infos = map(
        to_snapshot_info, context.state_reader.get_snapshots(snapshots).values()
    )
    assert set(snapshot_infos) == set(state_sync_table_infos)


def validate_state_sync_environment(
    snapshots: t.Iterable[Snapshot], env: str, context: Context
) -> None:
    environment = context.state_reader.get_environment(env)
    assert environment
    snapshot_infos = map(to_snapshot_info, snapshots)
    environment_table_infos = map(to_snapshot_info, environment.snapshots)
    assert set(snapshot_infos) == set(environment_table_infos)


def validate_tables(
    snapshots: t.Iterable[Snapshot],
    context: Context,
    deployability_index: t.Optional[DeployabilityIndex] = None,
) -> None:
    adapter = context.engine_adapter
    deployability_index = deployability_index or DeployabilityIndex.all_deployable()
    for snapshot in snapshots:
        is_deployable = deployability_index.is_representative(snapshot)
        if not snapshot.is_model or snapshot.is_external:
            continue
        table_should_exist = not snapshot.is_embedded
        assert adapter.table_exists(snapshot.table_name(is_deployable)) == table_should_exist
        if table_should_exist:
            assert select_all(snapshot.table_name(is_deployable), adapter)


def validate_environment_views(
    snapshots: t.Iterable[Snapshot],
    environment: str,
    context: Context,
    deployability_index: t.Optional[DeployabilityIndex] = None,
) -> None:
    adapter = context.engine_adapter
    deployability_index = deployability_index or DeployabilityIndex.all_deployable()
    for snapshot in snapshots:
        is_deployable = deployability_index.is_representative(snapshot)
        if not snapshot.is_model or snapshot.is_symbolic:
            continue
        view_name = snapshot.qualified_view_name.for_environment(
            EnvironmentNamingInfo.from_environment_catalog_mapping(
                context.config.environment_catalog_mapping,
                name=environment,
                suffix_target=context.config.environment_suffix_target,
            )
        )

        assert adapter.table_exists(view_name)
        assert select_all(snapshot.table_name(is_deployable), adapter) == select_all(
            view_name, adapter
        )


def select_all(table: str, adapter: EngineAdapter) -> t.Iterable:
    return adapter.fetchall(f"select * from {table} order by 1")


def snapshots_to_versions(snapshots: t.Iterable[Snapshot]) -> t.Dict[str, str]:
    return {snapshot.name: snapshot.version or "" for snapshot in snapshots}


def to_snapshot_info(snapshot: SnapshotInfoLike) -> SnapshotTableInfo:
    return snapshot.table_info


def start(context: Context) -> TimeLike:
    env = context.state_sync.get_environment("prod")
    assert env
    return env.start_at


def add_projection_to_model(model: SqlModel, literal: bool = True) -> SqlModel:
    one_expr = exp.Literal.number(1).as_("one") if literal else exp.column("one")
    kwargs = {
        **model.dict(),
        "query": model.query.select(one_expr),  # type: ignore
    }
    return SqlModel.parse_obj(kwargs)


def test_plan_environment_statements_doesnt_cause_extra_diff(tmp_path: Path):
    model_a = """
    MODEL (
        name test_schema.a,
        kind FULL,
    );

    SELECT 1;
    """

    models_dir = tmp_path / "models"
    models_dir.mkdir()

    (models_dir / "a.sql").write_text(model_a)

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        before_all=["select 1 as before_all"],
        after_all=["select 2 as after_all"],
    )
    ctx = Context(paths=[tmp_path], config=config)

    # first plan - should apply changes
    assert ctx.plan(auto_apply=True, no_prompts=True).has_changes

    # second plan - nothing has changed so should report no changes
    assert not ctx.plan(auto_apply=True, no_prompts=True).has_changes


def test_janitor_cleanup_order(mocker: MockerFixture, tmp_path: Path):
    def setup_scenario():
        models_dir = tmp_path / "models"

        if not models_dir.exists():
            models_dir.mkdir()

        model1_path = models_dir / "model1.sql"

        with open(model1_path, "w") as f:
            f.write("MODEL(name test.model1, kind FULL); SELECT 1 AS col")

        config = Config(
            model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        )
        ctx = Context(paths=[tmp_path], config=config)

        ctx.plan("dev", no_prompts=True, auto_apply=True)

        model1_snapshot = ctx.get_snapshot("test.model1")

        # Delete the model file to cause a snapshot expiration
        model1_path.unlink()

        ctx.load()

        ctx.plan("dev", no_prompts=True, auto_apply=True)

        # Invalidate the environment to cause an environment cleanup
        ctx.invalidate_environment("dev")

        try:
            ctx._run_janitor(ignore_ttl=True)
        except:
            pass

        return ctx, model1_snapshot

    # Case 1: Assume that the snapshot cleanup yields an error, the snapshot records
    # should still exist in the state sync so the next janitor can retry
    mocker.patch(
        "sqlmesh.core.snapshot.evaluator.SnapshotEvaluator.cleanup",
        side_effect=Exception("snapshot cleanup error"),
    )
    ctx, model1_snapshot = setup_scenario()

    # - Check that the snapshot record exists in the state sync
    state_snapshot = ctx.state_sync.state_sync.get_snapshots([model1_snapshot.snapshot_id])
    assert state_snapshot

    # - Run the janitor again, this time it should succeed
    mocker.patch("sqlmesh.core.snapshot.evaluator.SnapshotEvaluator.cleanup")
    ctx._run_janitor(ignore_ttl=True)

    # - Check that the snapshot record does not exist in the state sync anymore
    state_snapshot = ctx.state_sync.state_sync.get_snapshots([model1_snapshot.snapshot_id])
    assert not state_snapshot

    # Case 2: Assume that the view cleanup yields an error, the enviroment
    # record should still exist
    mocker.patch(
        "sqlmesh.core.context.cleanup_expired_views", side_effect=Exception("view cleanup error")
    )
    ctx, model1_snapshot = setup_scenario()

    views = ctx.fetchdf("FROM duckdb_views() SELECT * EXCLUDE(sql) WHERE NOT internal")
    assert views.empty

    # - Check that the environment record exists in the state sync
    assert ctx.state_sync.get_environment("dev")

    # - Run the janitor again, this time it should succeed
    mocker.patch("sqlmesh.core.context.cleanup_expired_views")
    ctx._run_janitor(ignore_ttl=True)

    # - Check that the environment record does not exist in the state sync anymore
    assert not ctx.state_sync.get_environment("dev")


@use_terminal_console
def test_destroy(copy_to_temp_path):
    # Testing project with two gateways to verify cleanup is performed across engines
    paths = copy_to_temp_path("tests/fixtures/multi_virtual_layer")
    path = Path(paths[0])
    first_db_path = str(path / "db_1.db")
    second_db_path = str(path / "db_2.db")

    config = Config(
        gateways={
            "first": GatewayConfig(
                connection=DuckDBConnectionConfig(database=first_db_path),
                variables={"overriden_var": "gateway_1"},
            ),
            "second": GatewayConfig(
                connection=DuckDBConnectionConfig(database=second_db_path),
                variables={"overriden_var": "gateway_2"},
            ),
        },
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        model_naming=NameInferenceConfig(infer_names=True),
        default_gateway="first",
        gateway_managed_virtual_layer=True,
        variables={"overriden_var": "global", "global_one": 88},
    )

    context = Context(paths=paths, config=config)
    plan = context.plan_builder().build()
    assert len(plan.new_snapshots) == 4
    context.apply(plan)

    # Confirm cache exists
    cache_path = Path(path) / ".cache"
    assert cache_path.exists()
    assert len(list(cache_path.iterdir())) > 0

    model = context.get_model("db_1.first_schema.model_one")

    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'c' AS extra").sql(dialect=model.dialect)
                )
            }
        )
    )
    plan = context.plan_builder().build()
    context.apply(plan)

    state_environments = context.state_reader.get_environments()
    state_snapshots = context.state_reader.get_snapshots(context.snapshots.values())

    assert len(state_snapshots) == len(state_environments[0].snapshots)

    # Create dev environment with changed models
    model = context.get_model("db_2.second_schema.model_one")
    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'d' AS extra").sql(dialect=model.dialect)
                )
            }
        )
    )
    model = context.get_model("first_schema.model_two")
    context.upsert_model(
        model.copy(
            update={
                "query_": ParsableSql(
                    sql=model.query.select("'d2' AS col").sql(dialect=model.dialect)
                )
            }
        )
    )
    plan = context.plan_builder("dev").build()
    context.apply(plan)

    dev_environment = context.state_sync.get_environment("dev")
    assert dev_environment is not None

    state_environments = context.state_reader.get_environments()
    state_snapshots = context.state_reader.get_snapshots(context.snapshots.values())
    assert (
        len(state_snapshots)
        == len(state_environments[0].snapshots)
        == len(state_environments[1].snapshots)
    )

    # The state tables at this point should be able to be retrieved
    state_tables = {
        "_environments",
        "_snapshots",
        "_intervals",
        "_auto_restatements",
        "_environment_statements",
        "_intervals",
        "_plan_dags",
        "_versions",
    }
    for table_name in state_tables:
        context.fetchdf(f"SELECT * FROM db_1.sqlmesh.{table_name}")

    # The actual tables as well
    context.engine_adapters["second"].fetchdf(f"SELECT * FROM db_2.second_schema.model_one")
    context.engine_adapters["second"].fetchdf(f"SELECT * FROM db_2.second_schema.model_two")
    context.fetchdf(f"SELECT * FROM db_1.first_schema.model_one")
    context.fetchdf(f"SELECT * FROM db_1.first_schema.model_two")

    # Use the destroy command to remove all data objects and state
    # Mock the console confirmation to automatically return True
    with patch.object(context.console, "_confirm", return_value=True):
        context._destroy()

    # Ensure all tables have been removed
    for table_name in state_tables:
        with pytest.raises(
            Exception, match=f"Catalog Error: Table with name {table_name} does not exist!"
        ):
            context.fetchdf(f"SELECT * FROM db_1.sqlmesh.{table_name}")

    # Validate tables have been deleted as well
    with pytest.raises(
        Exception, match=r"Catalog Error: Table with name model_two does not exist!"
    ):
        context.fetchdf("SELECT * FROM db_1.first_schema.model_two")
    with pytest.raises(
        Exception, match=r"Catalog Error: Table with name model_one does not exist!"
    ):
        context.fetchdf("SELECT * FROM db_1.first_schema.model_one")

    with pytest.raises(
        Exception, match=r"Catalog Error: Table with name model_two does not exist!"
    ):
        context.engine_adapters["second"].fetchdf("SELECT * FROM db_2.second_schema.model_two")
    with pytest.raises(
        Exception, match=r"Catalog Error: Table with name model_one does not exist!"
    ):
        context.engine_adapters["second"].fetchdf("SELECT * FROM db_2.second_schema.model_one")

    # Ensure the cache has been removed
    assert not cache_path.exists()


@use_terminal_console
def test_audits_running_on_metadata_changes(tmp_path: Path):
    def setup_senario(model_before: str, model_after: str):
        models_dir = Path("models")
        create_temp_file(tmp_path, models_dir / "test.sql", model_before)

        # Create first snapshot
        context = Context(paths=tmp_path, config=Config())
        context.plan("prod", no_prompts=True, auto_apply=True)

        # Create second (metadata) snapshot
        create_temp_file(tmp_path, models_dir / "test.sql", model_after)
        context.load()

        with capture_output() as output:
            with pytest.raises(PlanError):
                context.plan("prod", no_prompts=True, auto_apply=True)

        assert 'Failed models\n\n  "model"' in output.stdout

        return output

    # Ensure incorrect audits (bad data, incorrect definition etc) are evaluated immediately
    output = setup_senario(
        "MODEL (name model); SELECT NULL AS col",
        "MODEL (name model, audits (not_null(columns=[col]))); SELECT NULL AS col",
    )
    assert "'not_null' audit error: 1 row failed" in output.stdout

    output = setup_senario(
        "MODEL (name model); SELECT NULL AS col",
        "MODEL (name model, audits (not_null(columns=[this_col_does_not_exist]))); SELECT NULL AS col",
    )
    assert (
        'Binder Error: Referenced column "this_col_does_not_exist" not found in \nFROM clause!'
        in output.stdout
    )


@pytest.mark.set_default_connection(disable=True)
def test_missing_connection_config():
    # This is testing the actual implementation of Config.get_connection
    # To make writing tests easier, it's patched by the autouse fixture provide_sqlmesh_default_connection
    # Case 1: No default_connection or gateways specified should raise a ConfigError
    with pytest.raises(ConfigError):
        ctx = Context(config=Config())

    # Case 2: No connection specified in the gateway should raise a ConfigError
    with pytest.raises(ConfigError):
        ctx = Context(config=Config(gateways={"incorrect": GatewayConfig()}))

    # Case 3: Specifying a default_connection or connection in the gateway should work
    ctx = Context(config=Config(default_connection=DuckDBConnectionConfig()))
    ctx = Context(
        config=Config(gateways={"default": GatewayConfig(connection=DuckDBConnectionConfig())})
    )


@use_terminal_console
def test_render_path_instead_of_model(tmp_path: Path):
    create_temp_file(tmp_path, Path("models/test.sql"), "MODEL (name test_model); SELECT 1 AS col")
    ctx = Context(paths=tmp_path, config=Config())

    # Case 1: Fail gracefully when the user is passing in a path instead of a model name
    for test_model in ["models/test.sql", "models/test.py"]:
        with pytest.raises(
            SQLMeshError,
            match="Resolving models by path is not supported, please pass in the model name instead.",
        ):
            ctx.render(test_model)

    # Case 2: Fail gracefully when the model name is not found
    with pytest.raises(SQLMeshError, match="Cannot find model with name 'incorrect_model'"):
        ctx.render("incorrect_model")

    # Case 3: Render the model successfully
    assert ctx.render("test_model").sql() == 'SELECT 1 AS "col"'


@use_terminal_console
def test_plan_always_recreate_environment(tmp_path: Path):
    def plan_with_output(ctx: Context, environment: str):
        with patch.object(logger, "info") as mock_logger:
            with capture_output() as output:
                ctx.load()
                ctx.plan(environment, no_prompts=True, auto_apply=True)

            # Facade logs info "Promoting environment {environment}"
            assert mock_logger.call_args[0][1] == environment

        return output

    models_dir = tmp_path / "models"

    logger = logging.getLogger("sqlmesh.core.state_sync.db.facade")

    create_temp_file(
        tmp_path, models_dir / "a.sql", "MODEL (name test.a, kind FULL); SELECT 1 AS col"
    )

    config = Config(plan=PlanConfig(always_recreate_environment=True))
    ctx = Context(paths=[tmp_path], config=config)

    # Case 1: Neither prod nor dev exists, so dev is initialized
    output = plan_with_output(ctx, "dev")

    assert """`dev` environment will be initialized""" in output.stdout

    # Case 2: Prod does not exist, so dev is updated
    create_temp_file(
        tmp_path, models_dir / "a.sql", "MODEL (name test.a, kind FULL); SELECT 5 AS col"
    )

    output = plan_with_output(ctx, "dev")
    assert "`dev` environment will be initialized" in output.stdout

    # Case 3: Prod is initialized, so plan comparisons moving forward should be against prod
    output = plan_with_output(ctx, "prod")
    assert "`prod` environment will be initialized" in output.stdout

    # Case 4: Dev is updated with a breaking change. Prod exists now so plan comparisons moving forward should be against prod
    create_temp_file(
        tmp_path, models_dir / "a.sql", "MODEL (name test.a, kind FULL); SELECT 10 AS col"
    )
    ctx.load()

    plan = ctx.plan_builder("dev").build()

    assert (
        next(iter(plan.context_diff.snapshots.values())).change_category
        == SnapshotChangeCategory.BREAKING
    )

    output = plan_with_output(ctx, "dev")
    assert "New environment `dev` will be created from `prod`" in output.stdout
    assert "Differences from the `prod` environment" in output.stdout

    # Case 5: Dev is updated with a metadata change, but comparison against prod shows both the previous and the current changes
    # so it's still classified as a breaking change
    create_temp_file(
        tmp_path,
        models_dir / "a.sql",
        "MODEL (name test.a, kind FULL, owner 'test'); SELECT 10 AS col",
    )
    ctx.load()

    plan = ctx.plan_builder("dev").build()

    assert (
        next(iter(plan.context_diff.snapshots.values())).change_category
        == SnapshotChangeCategory.BREAKING
    )

    output = plan_with_output(ctx, "dev")
    assert "New environment `dev` will be created from `prod`" in output.stdout
    assert "Differences from the `prod` environment" in output.stdout

    stdout_rstrip = "\n".join([line.rstrip() for line in output.stdout.split("\n")])
    assert (
        """MODEL (
   name test.a,
+  owner test,
   kind FULL
 )
 SELECT
-  5 AS col
+  10 AS col"""
        in stdout_rstrip
    )

    # Case 6: Ensure that target environment and create_from environment are not the same
    output = plan_with_output(ctx, "prod")
    assert not "New environment `prod` will be created from `prod`" in output.stdout

    # Case 7: Check that we can still run Context::diff() against any environment
    for environment in ["dev", "prod"]:
        context_diff = ctx._context_diff(environment)
        assert context_diff.environment == environment


@time_machine.travel("2020-01-01 00:00:00 UTC")
def test_scd_type_2_full_restatement_no_start_date(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    # Initial product catalog of 3 products
    raw_products = d.parse("""
    MODEL (
        name memory.store.raw_products,
        kind FULL
    );

    SELECT * FROM VALUES
        (101, 'Laptop Pro', 1299.99, 'Electronics', '2020-01-01 00:00:00'::TIMESTAMP),
        (102, 'Wireless Mouse', 49.99, 'Electronics', '2020-01-01 00:00:00'::TIMESTAMP),
        (103, 'Office Chair', 199.99, 'Furniture', '2020-01-01 00:00:00'::TIMESTAMP)
    AS t(product_id, product_name, price, category, last_updated);
    """)

    # SCD Type 2 model for product history tracking
    product_history = d.parse("""
    MODEL (
        name memory.store.product_history,
        kind SCD_TYPE_2_BY_TIME (
            unique_key product_id,
            updated_at_name last_updated,
            disable_restatement false
        ),
        owner catalog_team,
        cron '0 */6 * * *',
        grain product_id,
        description 'Product catalog change history'
    );

    SELECT
        product_id::INT AS product_id,
        product_name::TEXT AS product_name,
        price::DECIMAL(10,2) AS price,
        category::TEXT AS category,
        last_updated AS last_updated
    FROM
        memory.store.raw_products;
    """)

    raw_products_model = load_sql_based_model(raw_products)
    product_history_model = load_sql_based_model(product_history)
    context.upsert_model(raw_products_model)
    context.upsert_model(product_history_model)

    # Initial plan and apply
    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    query = "SELECT product_id, product_name, price, category, last_updated, valid_from, valid_to FROM memory.store.product_history ORDER BY product_id, valid_from"
    initial_data = context.engine_adapter.fetchdf(query)

    # Validate initial state of 3 products all active
    assert len(initial_data) == 3
    assert initial_data["valid_to"].isna().all()
    initial_product_names = set(initial_data["product_name"].tolist())
    assert initial_product_names == {"Laptop Pro", "Wireless Mouse", "Office Chair"}

    # Price update and category change
    with time_machine.travel("2020-01-15 12:00:00 UTC"):
        raw_products_v2 = d.parse("""
        MODEL (
            name memory.store.raw_products,
            kind FULL
        );

        SELECT * FROM VALUES
            (101, 'Laptop Pro', 1199.99, 'Electronics', '2020-01-15 00:00:00'::TIMESTAMP),
            (102, 'Wireless Mouse', 49.99, 'Electronics', '2020-01-01 00:00:00'::TIMESTAMP),
            (103, 'Ergonomic Office Chair', 229.99, 'Office Furniture', '2020-01-15 00:00:00'::TIMESTAMP)
        AS t(product_id, product_name, price, category, last_updated);
        """)
        raw_products_v2_model = load_sql_based_model(raw_products_v2)
        context.upsert_model(raw_products_v2_model)
        context.plan(
            auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full()
        )
        context.run()

        data_after_first_change = context.engine_adapter.fetchdf(query)

        # Should have 5 records (3 original closed,  2 new activε, 1 unchanged)
        assert len(data_after_first_change) == 5

    # Second change
    with time_machine.travel("2020-02-01 10:00:00 UTC"):
        raw_products_v3 = d.parse("""
        MODEL (
            name memory.store.raw_products,
            kind FULL
        );

        SELECT * FROM VALUES
            (101, 'Laptop Pro Max', 1399.99, 'Electronics', '2020-02-01 00:00:00'::TIMESTAMP),
            (103, 'Ergonomic Office Chair', 229.99, 'Office Furniture', '2020-01-15 00:00:00'::TIMESTAMP),
            (102, 'Wireless Mouse', 49.99, 'Electronics', '2020-01-01 00:00:00'::TIMESTAMP)
        AS t(product_id, product_name, price, category, last_updated);
        """)
        raw_products_v3_model = load_sql_based_model(raw_products_v3)
        context.upsert_model(raw_products_v3_model)
        context.plan(
            auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full()
        )
        context.run()
        data_after_second_change = context.engine_adapter.fetchdf(query)
        assert len(data_after_second_change) == 6

    # Store the current state before full restatement
    data_before_full_restatement = data_after_second_change.copy()

    # Perform full restatement (no start date provided)
    with time_machine.travel("2020-02-01 15:00:00 UTC"):
        plan = context.plan_builder(
            "prod", skip_tests=True, restate_models=["memory.store.product_history"]
        ).build()
        context.apply(plan)
        data_after_full_restatement = context.engine_adapter.fetchdf(query)
        assert len(data_after_full_restatement) == 3

        # Check that all currently active products before restatement are still active after restatement
        active_before = data_before_full_restatement[
            data_before_full_restatement["valid_to"].isna()
        ]
        active_after = data_after_full_restatement
        assert set(active_before["product_id"]) == set(active_after["product_id"])

        expected_products = {
            101: {
                "product_name": "Laptop Pro Max",
                "price": 1399.99,
                "category": "Electronics",
                "last_updated": "2020-02-01",
            },
            102: {
                "product_name": "Wireless Mouse",
                "price": 49.99,
                "category": "Electronics",
                "last_updated": "2020-01-01",
            },
            103: {
                "product_name": "Ergonomic Office Chair",
                "price": 229.99,
                "category": "Office Furniture",
                "last_updated": "2020-01-15",
            },
        }
        for _, row in data_after_full_restatement.iterrows():
            pid = row["product_id"]
            assert pid in expected_products
            expected = expected_products[pid]
            assert row["product_name"] == expected["product_name"]
            assert float(row["price"]) == expected["price"]
            assert row["category"] == expected["category"]

            # valid_from should be the epoch, valid_to should be NaT
            assert str(row["valid_from"]) == "1970-01-01 00:00:00"
            assert pd.isna(row["valid_to"])


def test_plan_evaluator_correlation_id(tmp_path: Path):
    def _correlation_id_in_sqls(correlation_id: CorrelationId, mock_logger):
        sqls = [call[0][0] for call in mock_logger.call_args_list]
        return any(f"/* {correlation_id} */" in sql for sql in sqls)

    ctx = Context(paths=[tmp_path], config=Config())

    # Case: Ensure that the correlation id (plan_id) is included in the SQL for each plan
    for i in range(2):
        create_temp_file(
            tmp_path,
            Path("models", "test.sql"),
            f"MODEL (name test.a, kind FULL); SELECT {i} AS col",
        )

        with mock.patch("sqlmesh.core.engine_adapter.base.EngineAdapter._log_sql") as mock_logger:
            ctx.load()
            plan = ctx.plan(auto_apply=True, no_prompts=True)

        correlation_id = CorrelationId.from_plan_id(plan.plan_id)
        assert str(correlation_id) == f"SQLMESH_PLAN: {plan.plan_id}"

        assert _correlation_id_in_sqls(correlation_id, mock_logger)


@time_machine.travel("2023-01-08 15:00:00 UTC")
def test_scd_type_2_regular_run_with_offset(init_and_plan_context: t.Callable):
    context, plan = init_and_plan_context("examples/sushi")
    context.apply(plan)

    raw_employee_status = d.parse("""
    MODEL (
        name memory.hr_system.raw_employee_status,
        kind FULL
    );

    SELECT
        1001 AS employee_id,
        'engineering' AS department,
        'EMEA' AS region,
        '2023-01-08 15:00:00 UTC' AS last_modified;
    """)

    employee_history = d.parse("""
    MODEL (
        name memory.hr_system.employee_history,
        kind SCD_TYPE_2_BY_TIME (
            unique_key employee_id,
            updated_at_name last_modified,
            disable_restatement false
        ),
        owner hr_analytics,
        cron '0 7 * * *',
        grain employee_id,
        description 'Historical tracking of employee status changes'
    );

    SELECT
        employee_id::INT AS employee_id,
        department::TEXT AS department,
        region::TEXT AS region,
        last_modified AS last_modified
    FROM
        memory.hr_system.raw_employee_status;
    """)

    raw_employee_status_model = load_sql_based_model(raw_employee_status)
    employee_history_model = load_sql_based_model(employee_history)
    context.upsert_model(raw_employee_status_model)
    context.upsert_model(employee_history_model)

    # Initial plan and apply
    plan = context.plan_builder("prod", skip_tests=True).build()
    context.apply(plan)

    query = "SELECT employee_id, department, region, valid_from, valid_to FROM memory.hr_system.employee_history ORDER BY employee_id, valid_from"
    initial_data = context.engine_adapter.fetchdf(query)

    assert len(initial_data) == 1
    assert initial_data["valid_to"].isna().all()
    assert initial_data["department"].tolist() == ["engineering"]
    assert initial_data["region"].tolist() == ["EMEA"]

    # Apply a future plan with source changes a few hours before the cron time of the SCD Type 2 model BUT on the same day
    with time_machine.travel("2023-01-09 00:10:00 UTC"):
        raw_employee_status_v2 = d.parse("""
        MODEL (
            name memory.hr_system.raw_employee_status,
            kind FULL
        );

        SELECT
            1001 AS employee_id,
            'engineering' AS department,
            'AMER' AS region,
            '2023-01-09 00:10:00 UTC' AS last_modified;
        """)
        raw_employee_status_v2_model = load_sql_based_model(raw_employee_status_v2)
        context.upsert_model(raw_employee_status_v2_model)
        context.plan(
            auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full()
        )

    # The 7th hour of the day the run is kicked off for the SCD Type 2 model
    with time_machine.travel("2023-01-09 07:00:01 UTC"):
        context.run()
        data_after_change = context.engine_adapter.fetchdf(query)

        # Validate the SCD2 records for employee 1001
        assert len(data_after_change) == 2
        assert data_after_change.iloc[0]["employee_id"] == 1001
        assert data_after_change.iloc[0]["department"] == "engineering"
        assert data_after_change.iloc[0]["region"] == "EMEA"
        assert str(data_after_change.iloc[0]["valid_from"]) == "1970-01-01 00:00:00"
        assert str(data_after_change.iloc[0]["valid_to"]) == "2023-01-09 00:10:00"
        assert data_after_change.iloc[1]["employee_id"] == 1001
        assert data_after_change.iloc[1]["department"] == "engineering"
        assert data_after_change.iloc[1]["region"] == "AMER"
        assert str(data_after_change.iloc[1]["valid_from"]) == "2023-01-09 00:10:00"
        assert pd.isna(data_after_change.iloc[1]["valid_to"])

        # Update source model again a bit later on the same day
        raw_employee_status_v2 = d.parse("""
        MODEL (
            name memory.hr_system.raw_employee_status,
            kind FULL
        );

        SELECT
            1001 AS employee_id,
            'sales' AS department,
            'ANZ' AS region,
            '2023-01-09 07:26:00 UTC' AS last_modified;
        """)
        raw_employee_status_v2_model = load_sql_based_model(raw_employee_status_v2)
        context.upsert_model(raw_employee_status_v2_model)
        context.plan(
            auto_apply=True, no_prompts=True, categorizer_config=CategorizerConfig.all_full()
        )

    # A day later the run is kicked off for the SCD Type 2 model again
    with time_machine.travel("2023-01-10 07:00:00 UTC"):
        context.run()
        data_after_change = context.engine_adapter.fetchdf(query)

        # Validate the SCD2 history for employee 1001 after second change with the historical records intact
        assert len(data_after_change) == 3
        assert data_after_change.iloc[0]["employee_id"] == 1001
        assert data_after_change.iloc[0]["department"] == "engineering"
        assert data_after_change.iloc[0]["region"] == "EMEA"
        assert str(data_after_change.iloc[0]["valid_from"]) == "1970-01-01 00:00:00"
        assert str(data_after_change.iloc[0]["valid_to"]) == "2023-01-09 00:10:00"
        assert data_after_change.iloc[1]["employee_id"] == 1001
        assert data_after_change.iloc[1]["department"] == "engineering"
        assert data_after_change.iloc[1]["region"] == "AMER"
        assert str(data_after_change.iloc[1]["valid_from"]) == "2023-01-09 00:10:00"
        assert str(data_after_change.iloc[1]["valid_to"]) == "2023-01-09 07:26:00"
        assert data_after_change.iloc[2]["employee_id"] == 1001
        assert data_after_change.iloc[2]["department"] == "sales"
        assert data_after_change.iloc[2]["region"] == "ANZ"
        assert str(data_after_change.iloc[2]["valid_from"]) == "2023-01-09 07:26:00"
        assert pd.isna(data_after_change.iloc[2]["valid_to"])

    # Now test restatement works (full restatement support currently)
    with time_machine.travel("2023-01-10 07:38:00 UTC"):
        plan = context.plan_builder(
            "prod",
            skip_tests=True,
            restate_models=["memory.hr_system.employee_history"],
            start="2023-01-09 00:10:00",
        ).build()
        context.apply(plan)
        restated_data = context.engine_adapter.fetchdf(query)

        # Validate the SCD2 history after restatement has been wiped bar one
        assert len(restated_data) == 1
        assert restated_data.iloc[0]["employee_id"] == 1001
        assert restated_data.iloc[0]["department"] == "sales"
        assert restated_data.iloc[0]["region"] == "ANZ"
        assert str(restated_data.iloc[0]["valid_from"]) == "1970-01-01 00:00:00"
        assert pd.isna(restated_data.iloc[0]["valid_to"])


def test_engine_adapters_multi_repo_all_gateways_gathered(copy_to_temp_path):
    paths = copy_to_temp_path("examples/multi")
    repo_1_path = paths[0] / "repo_1"
    repo_2_path = paths[0] / "repo_2"

    # Add an extra gateway to repo_2's config
    repo_2_config_path = repo_2_path / "config.yaml"
    config_content = repo_2_config_path.read_text()

    modified_config = config_content.replace(
        "default_gateway: local",
        dedent("""
              extra:
                connection:
                  type: duckdb
                  database: extra.duckdb

            default_gateway: local
        """),
    )

    repo_2_config_path.write_text(modified_config)

    # Create context with both repos but using the repo_1 path first
    context = Context(
        paths=(repo_1_path, repo_2_path),
        gateway="memory",
    )

    # Verify all gateways from both repos are present
    gathered_gateways = context.engine_adapters.keys()
    expected_gateways = {"local", "memory", "extra"}
    assert gathered_gateways == expected_gateways


def test_physical_table_naming_strategy_table_only(copy_to_temp_path: t.Callable):
    sushi_context = Context(
        paths=copy_to_temp_path("examples/sushi"),
        config="table_only_naming_config",
    )

    assert sushi_context.config.physical_table_naming_convention == TableNamingConvention.TABLE_ONLY
    sushi_context.plan(auto_apply=True)

    adapter = sushi_context.engine_adapter

    snapshot_tables = [
        dict(catalog=str(r[0]), schema=str(r[1]), table=str(r[2]))
        for r in adapter.fetchall(
            "select table_catalog, table_schema, table_name from information_schema.tables where table_type='BASE TABLE'"
        )
    ]

    assert all([not t["table"].startswith("sushi") for t in snapshot_tables])

    prod_env = sushi_context.state_reader.get_environment("prod")
    assert prod_env

    prod_env_snapshots = sushi_context.state_reader.get_snapshots(prod_env.snapshots)

    assert all(
        s.table_naming_convention == TableNamingConvention.TABLE_ONLY
        for s in prod_env_snapshots.values()
    )


def test_physical_table_naming_strategy_hash_md5(copy_to_temp_path: t.Callable):
    sushi_context = Context(
        paths=copy_to_temp_path("examples/sushi"),
        config="hash_md5_naming_config",
    )

    assert sushi_context.config.physical_table_naming_convention == TableNamingConvention.HASH_MD5
    sushi_context.plan(auto_apply=True)

    adapter = sushi_context.engine_adapter

    snapshot_tables = [
        dict(catalog=str(r[0]), schema=str(r[1]), table=str(r[2]))
        for r in adapter.fetchall(
            "select table_catalog, table_schema, table_name from information_schema.tables where table_type='BASE TABLE'"
        )
    ]

    assert all([not t["table"].startswith("sushi") for t in snapshot_tables])
    assert all([t["table"].startswith("sqlmesh_md5") for t in snapshot_tables])

    prod_env = sushi_context.state_reader.get_environment("prod")
    assert prod_env

    prod_env_snapshots = sushi_context.state_reader.get_snapshots(prod_env.snapshots)

    assert all(
        s.table_naming_convention == TableNamingConvention.HASH_MD5
        for s in prod_env_snapshots.values()
    )


@pytest.mark.slow
def test_default_audits_applied_in_plan(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir(exist_ok=True)

    # Create a model with data that will pass the audits
    create_temp_file(
        tmp_path,
        models_dir / "orders.sql",
        dedent("""
            MODEL (
                name test.orders,
                kind FULL
            );

            SELECT
                1 AS order_id,
                'customer_1' AS customer_id,
                100.50 AS amount,
                '2024-01-01'::DATE AS order_date
            UNION ALL
            SELECT
                2 AS order_id,
                'customer_2' AS customer_id,
                200.75 AS amount,
                '2024-01-02'::DATE AS order_date
        """),
    )

    config = Config(
        model_defaults=ModelDefaultsConfig(
            dialect="duckdb",
            audits=[
                "not_null(columns := [order_id, customer_id])",
                "unique_values(columns := [order_id])",
            ],
        )
    )

    context = Context(paths=tmp_path, config=config)

    # Create and apply plan, here audits should pass
    plan = context.plan("prod", no_prompts=True)
    context.apply(plan)

    # Verify model has the default audits
    model = context.get_model("test.orders")
    assert len(model.audits) == 2

    audit_names = [audit[0] for audit in model.audits]
    assert "not_null" in audit_names
    assert "unique_values" in audit_names

    # Verify audit arguments are preserved
    for audit_name, audit_args in model.audits:
        if audit_name == "not_null":
            assert "columns" in audit_args
            columns = [col.name for col in audit_args["columns"].expressions]
            assert "order_id" in columns
            assert "customer_id" in columns
        elif audit_name == "unique_values":
            assert "columns" in audit_args
            columns = [col.name for col in audit_args["columns"].expressions]
            assert "order_id" in columns


@pytest.mark.slow
def test_default_audits_fail_on_bad_data(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir(exist_ok=True)

    # Create a model with data that violates NOT NULL constraint
    create_temp_file(
        tmp_path,
        models_dir / "bad_orders.sql",
        dedent("""
            MODEL (
                name test.bad_orders,
                kind FULL
            );

            SELECT
                1 AS order_id,
                NULL AS customer_id,  -- This violates NOT NULL
                100.50 AS amount,
                '2024-01-01'::DATE AS order_date
            UNION ALL
            SELECT
                2 AS order_id,
                'customer_2' AS customer_id,
                200.75 AS amount,
                '2024-01-02'::DATE AS order_date
        """),
    )

    config = Config(
        model_defaults=ModelDefaultsConfig(
            dialect="duckdb", audits=["not_null(columns := [customer_id])"]
        )
    )

    context = Context(paths=tmp_path, config=config)

    # Plan should fail due to audit failure
    with pytest.raises(PlanError):
        context.plan("prod", no_prompts=True, auto_apply=True)


@pytest.mark.slow
def test_default_audits_with_model_specific_audits(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir(exist_ok=True)
    audits_dir = tmp_path / "audits"
    audits_dir.mkdir(exist_ok=True)

    create_temp_file(
        tmp_path,
        audits_dir / "range_check.sql",
        dedent("""
            AUDIT (
                name range_check
            );

            SELECT * FROM @this_model
            WHERE @column < @min_value OR @column > @max_value
        """),
    )

    # Create a model with its own audits in addition to defaults
    create_temp_file(
        tmp_path,
        models_dir / "products.sql",
        dedent("""
            MODEL (
                name test.products,
                kind FULL,
                audits (
                    range_check(column := price, min_value := 0, max_value := 10000)
                )
            );

            SELECT
                1 AS product_id,
                'Widget' AS product_name,
                99.99 AS price
            UNION ALL
            SELECT
                2 AS product_id,
                'Gadget' AS product_name,
                149.99 AS price
        """),
    )

    config = Config(
        model_defaults=ModelDefaultsConfig(
            dialect="duckdb",
            audits=[
                "not_null(columns := [product_id, product_name])",
                "unique_values(columns := [product_id])",
            ],
        )
    )

    context = Context(paths=tmp_path, config=config)

    # Create and apply plan
    plan = context.plan("prod", no_prompts=True)
    context.apply(plan)

    # Verify model has both default and model-specific audits
    model = context.get_model("test.products")
    assert len(model.audits) == 3

    audit_names = [audit[0] for audit in model.audits]
    assert "not_null" in audit_names
    assert "unique_values" in audit_names
    assert "range_check" in audit_names

    # Verify audit execution order, default audits first then model-specific
    assert model.audits[0][0] == "not_null"
    assert model.audits[1][0] == "unique_values"
    assert model.audits[2][0] == "range_check"


@pytest.mark.slow
def test_default_audits_with_custom_audit_definitions(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir(exist_ok=True)
    audits_dir = tmp_path / "audits"
    audits_dir.mkdir(exist_ok=True)

    # Create custom audit definition
    create_temp_file(
        tmp_path,
        audits_dir / "positive_amount.sql",
        dedent("""
            AUDIT (
                name positive_amount
            );

            SELECT * FROM @this_model
            WHERE @column <= 0
        """),
    )

    # Create a model
    create_temp_file(
        tmp_path,
        models_dir / "transactions.sql",
        dedent("""
            MODEL (
                name test.transactions,
                kind FULL
            );

            SELECT
                1 AS transaction_id,
                'TXN001' AS transaction_code,
                250.00 AS amount,
                '2024-01-01'::DATE AS transaction_date
            UNION ALL
            SELECT
                2 AS transaction_id,
                'TXN002' AS transaction_code,
                150.00 AS amount,
                '2024-01-02'::DATE AS transaction_date
        """),
    )

    config = Config(
        model_defaults=ModelDefaultsConfig(
            dialect="duckdb",
            audits=[
                "not_null(columns := [transaction_id, transaction_code])",
                "unique_values(columns := [transaction_id])",
                "positive_amount(column := amount)",
            ],
        )
    )

    context = Context(paths=tmp_path, config=config)

    # Create and apply plan
    plan = context.plan("prod", no_prompts=True)
    context.apply(plan)

    # Verify model has all default audits including custom
    model = context.get_model("test.transactions")
    assert len(model.audits) == 3

    audit_names = [audit[0] for audit in model.audits]
    assert "not_null" in audit_names
    assert "unique_values" in audit_names
    assert "positive_amount" in audit_names

    # Verify custom audit arguments
    for audit_name, audit_args in model.audits:
        if audit_name == "positive_amount":
            assert "column" in audit_args
            assert audit_args["column"].name == "amount"


def test_incremental_by_time_model_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column ds,
            forward_only true,
            on_destructive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                3 as new_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns
        assert updated_df["new_column"].dropna().tolist() == [3]

    with time_machine.travel("2023-01-11 00:00:00 UTC"):
        updated_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                CAST(4 AS STRING) as new_column,
                @start_ds as ds
            FROM
                source_table;
        """
        (models_dir / "test_model.sql").write_text(updated_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True, run=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 3
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns
        # The destructive change was ignored but this change is coercable and therefore we still return ints
        assert updated_df["new_column"].dropna().tolist() == [3, 4]

    with time_machine.travel("2023-01-12 00:00:00 UTC"):
        updated_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                CAST(5 AS STRING) as new_column,
                @start_ds as ds
            FROM
                source_table;
        """
        (models_dir / "test_model.sql").write_text(updated_model)

        context = Context(paths=[tmp_path], config=config)
        # Make the change compatible since that means we will attempt and alter now that is considered additive
        context.engine_adapter.SCHEMA_DIFFER_KWARGS["compatible_types"] = {
            exp.DataType.build("INT"): {exp.DataType.build("STRING")}
        }
        context.plan("prod", auto_apply=True, no_prompts=True, run=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 4
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns
        # The change is now reflected since an additive alter could be performed
        assert updated_df["new_column"].dropna().tolist() == ["3", "4", "5"]

    context.close()


def test_incremental_by_time_model_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column ds,
            forward_only true,
            on_destructive_change allow,
            on_additive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        'other' as other_column,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column to the source table
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                1 as id,
                'other' as other_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("ALTER TABLE source_table ADD COLUMN new_column INT")
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is removed since destructive is allowed
        assert "name" not in updated_df.columns
        # new_column is not added since additive is ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was applied
        assert "name" not in updated_df.columns
        # new_column is still not added since additive is ignored
        assert "new_column" not in updated_df.columns

    with time_machine.travel("2023-01-11 00:00:00 UTC"):
        updated_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                CAST(1 AS STRING) as id,
                'other' as other_column,
                @start_ds as ds
            FROM
                source_table;
        """
        (models_dir / "test_model.sql").write_text(updated_model)

        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.SCHEMA_DIFFER_KWARGS["compatible_types"] = {
            exp.DataType.build("INT"): {exp.DataType.build("STRING")}
        }
        context.plan("prod", auto_apply=True, no_prompts=True, run=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 3
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is still not added since additive is ignored
        assert "new_column" not in updated_df.columns
        # The additive change was ignored since we set the change as compatible therefore
        # instead of getting strings in the result we still return ints
        assert updated_df["id"].tolist() == [1, 1, 1]

    with time_machine.travel("2023-01-12 00:00:00 UTC"):
        updated_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change allow,
                    on_additive_change allow
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                CAST(1 AS STRING) as id,
                'other' as other_column,
                @start_ds as ds
            FROM
                source_table;
        """
        (models_dir / "test_model.sql").write_text(updated_model)

        context = Context(paths=[tmp_path], config=config)
        # Make the change compatible since that means we will attempt and alter now that is considered additive
        context.engine_adapter.SCHEMA_DIFFER_KWARGS["compatible_types"] = {
            exp.DataType.build("INT"): {exp.DataType.build("STRING")}
        }
        context.plan("prod", auto_apply=True, no_prompts=True, run=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 4
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is now added since it is additive is now allowed
        assert "new_column" in updated_df.columns
        # The change is now reflected since an additive alter could be performed
        assert updated_df["id"].dropna().tolist() == ["1", "1", "1", "1"]

    context.close()


def test_incremental_by_unique_key_model_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_UNIQUE_KEY (
            unique_key id,
            forward_only true,
            on_destructive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_UNIQUE_KEY (
                    unique_key id,
                    forward_only true,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                3 as new_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

    context.close()


def test_incremental_by_unique_key_model_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_UNIQUE_KEY (
            unique_key id,
            forward_only true,
            on_destructive_change allow,
            on_additive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_UNIQUE_KEY (
                    unique_key id,
                    forward_only true,
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                3 as new_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still not in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

    context.close()


def test_incremental_unmanaged_model_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_UNMANAGED(
            on_destructive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_UNMANAGED(
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                3 as new_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

    context.close()


def test_incremental_unmanaged_model_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_UNMANAGED(
            on_destructive_change allow,
            on_additive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_ds as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_UNMANAGED(
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                2 as id,
                3 as new_column,
                @start_ds as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

    context.close()


def test_scd_type_2_by_time_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind SCD_TYPE_2_BY_TIME (
            unique_key id,
            updated_at_name ds,
            on_destructive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_dt as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind SCD_TYPE_2_BY_TIME (
                    unique_key id,
                    updated_at_name ds,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                1 as id,
                3 as new_column,
                @start_dt as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

    context.close()


def test_scd_type_2_by_time_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind SCD_TYPE_2_BY_TIME (
            unique_key id,
            updated_at_name ds,
            on_destructive_change allow,
            on_additive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        *,
        1 as id,
        'test_name' as name,
        @start_dt as ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind SCD_TYPE_2_BY_TIME (
                    unique_key id,
                    updated_at_name ds,
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                *,
                1 as id,
                3 as new_column,
                @start_dt as ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

    context.close()


def test_scd_type_2_by_column_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
        MODEL (
            name test_model,
            kind SCD_TYPE_2_BY_COLUMN (
                unique_key id,
                columns [name],
                on_destructive_change ignore
            ),
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            'test_name' as name,
            @start_ds as ds
        FROM
            source_table;
        """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
        MODEL (
            name test_model,
            kind SCD_TYPE_2_BY_COLUMN (
                unique_key id,
                columns [new_column],
                on_destructive_change ignore
            ),
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            3 as new_column,
            @start_ds as ds
        FROM
            source_table;
        """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

    context.close()


def test_scd_type_2_by_column_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
        MODEL (
            name test_model,
            kind SCD_TYPE_2_BY_COLUMN (
                unique_key id,
                columns [stable],
                on_destructive_change allow,
                on_additive_change ignore
            ),
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            'test_name' as name,
            'stable' as stable,
            @start_ds as ds
        FROM
            source_table;
        """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
        MODEL (
            name test_model,
            kind SCD_TYPE_2_BY_COLUMN (
                unique_key id,
                columns [stable],
                on_destructive_change allow,
                on_additive_change ignore
            ),
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            'stable2' as stable,
            3 as new_column,
            @start_ds as ds
        FROM
            source_table;
        """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was ignored
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

    context.close()


def test_incremental_partition_ignore_destructive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
        MODEL (
            name test_model,
            kind INCREMENTAL_BY_PARTITION (
                on_destructive_change ignore
            ),
            partitioned_by [ds],
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            'test_name' as name,
            @start_ds as ds
        FROM
            source_table;
        """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
        MODEL (
            name test_model,
            kind INCREMENTAL_BY_PARTITION (
                on_destructive_change ignore
            ),
            partitioned_by [ds],
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            3 as new_column,
            @start_ds as ds
        FROM
            source_table;
        """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns

    context.close()


def test_incremental_partition_ignore_additive_change(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
        MODEL (
            name test_model,
            kind INCREMENTAL_BY_PARTITION (
                on_destructive_change allow,
                on_additive_change ignore
            ),
            partitioned_by [ds],
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            'test_name' as name,
            @start_ds as ds
        FROM
            source_table;
        """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("CREATE TABLE source_table (source_id INT)")
        context.engine_adapter.execute("INSERT INTO source_table VALUES (1)")

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns

        context.close()

        # remove `name` column and add new column
        initial_model = """
        MODEL (
            name test_model,
            kind INCREMENTAL_BY_PARTITION (
                on_destructive_change allow,
                on_additive_change ignore
            ),
            partitioned_by [ds],
            start '2023-01-01',
            cron '@daily'
        );

        SELECT
            *,
            1 as id,
            3 as new_column,
            @start_ds as ds
        FROM
            source_table;
        """
        (models_dir / "test_model.sql").write_text(initial_model)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True)

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')

        assert len(updated_df) == 1
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.run()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "source_id" in initial_df.columns
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not still in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns

    context.close()


def test_incremental_by_time_model_ignore_destructive_change_unit_test(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"
    test_dir = tmp_path / "tests"
    test_dir.mkdir()
    test_filepath = test_dir / "test_test_model.yaml"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column ds,
            forward_only true,
            on_destructive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        id,
        name,
        ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    initial_test = f"""

test_test_model:
  model: test_model
  inputs:
    source_table:
      - id: 1
        name: 'test_name'
        ds: '2025-01-01'
  outputs:
    query:
      - id: 1
        name: 'test_name'
        ds: '2025-01-01'
"""

    # Write initial test
    test_filepath.write_text(initial_test)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute(
            "CREATE TABLE source_table (id INT, name STRING, new_column INT, ds STRING)"
        )
        context.engine_adapter.execute(
            "INSERT INTO source_table VALUES (1, 'test_name', NULL, '2023-01-01')"
        )

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)
        test_result = context.test()

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                id,
                new_column,
                ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        updated_test = f"""

        test_test_model:
          model: test_model
          inputs:
            source_table:
              - id: 1
                new_column: 3
                ds: '2025-01-01'
          outputs:
            query:
              - id: 1
                new_column: 3
                ds: '2025-01-01'
        """

        # Write initial test
        test_filepath.write_text(updated_test)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)
        test_result = context.test()

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 1
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("INSERT INTO source_table VALUES (2, NULL, 3, '2023-01-09')")
        context.run()
        test_result = context.test()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still in table since destructive was ignored
        assert "name" in updated_df.columns
        # new_column is added since it is additive and allowed
        assert "new_column" in updated_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

    context.close()


def test_incremental_by_time_model_ignore_additive_change_unit_test(tmp_path: Path):
    models_dir = tmp_path / "models"
    models_dir.mkdir()
    data_dir = tmp_path / "data"
    data_dir.mkdir()
    data_filepath = data_dir / "test.duckdb"
    test_dir = tmp_path / "tests"
    test_dir.mkdir()
    test_filepath = test_dir / "test_test_model.yaml"

    config = Config(
        model_defaults=ModelDefaultsConfig(dialect="duckdb"),
        default_connection=DuckDBConnectionConfig(database=str(data_filepath)),
    )

    # Initial model with 3 columns
    initial_model = f"""
    MODEL (
        name test_model,
        kind INCREMENTAL_BY_TIME_RANGE (
            time_column ds,
            forward_only true,
            on_destructive_change allow,
            on_additive_change ignore
        ),
        start '2023-01-01',
        cron '@daily'
    );

    SELECT
        id,
        name,
        ds
    FROM
        source_table;
    """

    # Write initial model
    (models_dir / "test_model.sql").write_text(initial_model)

    initial_test = f"""

test_test_model:
  model: test_model
  inputs:
    source_table:
      - id: 1
        name: 'test_name'
        ds: '2025-01-01'
  outputs:
    query:
      - id: 1
        name: 'test_name'
        ds: '2025-01-01'
"""

    # Write initial test
    test_filepath.write_text(initial_test)

    with time_machine.travel("2023-01-08 00:00:00 UTC"):
        # Create context and apply initial model
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute(
            "CREATE TABLE source_table (id INT, name STRING, new_column INT, ds STRING)"
        )
        context.engine_adapter.execute(
            "INSERT INTO source_table VALUES (1, 'test_name', NULL, '2023-01-01')"
        )

        # Apply initial plan and load data
        context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)
        test_result = context.test()

        # Verify initial data was loaded
        initial_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(initial_df) == 1
        assert "id" in initial_df.columns
        assert "name" in initial_df.columns
        assert "ds" in initial_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

        context.close()

        # remove `name` column and add new column
        initial_model = """
            MODEL (
                name test_model,
                kind INCREMENTAL_BY_TIME_RANGE (
                    time_column ds,
                    forward_only true,
                    on_destructive_change allow,
                    on_additive_change ignore
                ),
                start '2023-01-01',
                cron '@daily'
            );

            SELECT
                id,
                new_column,
                ds
            FROM
                source_table;
            """
        (models_dir / "test_model.sql").write_text(initial_model)

        # `new_column` is in the output since unit tests are based on the model definition that currently
        # exists and doesn't take into account the historical changes to the table. Therefore `new_column` is
        # not actually in the table but it is represented in the test
        updated_test = f"""
        test_test_model:
          model: test_model
          inputs:
            source_table:
              - id: 1
                new_column: 3
                ds: '2025-01-01'
          outputs:
            query:
              - id: 1
                new_column: 3
                ds: '2025-01-01'
        """

        # Write initial test
        test_filepath.write_text(updated_test)

        context = Context(paths=[tmp_path], config=config)
        context.plan("prod", auto_apply=True, no_prompts=True, skip_tests=True)
        test_result = context.test()

        # Verify data loading continued to work
        # The existing data should still be there and new data should be loaded
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 1
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is not in table since destructive was ignored
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

        context.close()

    with time_machine.travel("2023-01-10 00:00:00 UTC"):
        context = Context(paths=[tmp_path], config=config)
        context.engine_adapter.execute("INSERT INTO source_table VALUES (2, NULL, 3, '2023-01-09')")
        context.run()
        test_result = context.test()
        updated_df = context.fetchdf('SELECT * FROM "default"."test_model"')
        assert len(updated_df) == 2
        assert "id" in updated_df.columns
        assert "ds" in updated_df.columns
        # name is still not in table since destructive was allowed
        assert "name" not in updated_df.columns
        # new_column is not added since it is additive and ignored
        assert "new_column" not in updated_df.columns
        assert len(test_result.successes) == 1
        assert test_result.testsRun == len(test_result.successes)

    context.close()