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[TASK-167] Support Arrow append operations for decimals, temporal types in Python #206

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[TASK-167] Support Arrow append operations for decimals, temporal types in Python #206

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[TASK-167] Support Arrow append operations for decimals, temporal types
fresh-borzoni Jan 23, 2026
c1d3d50
address review
fresh-borzoni Jan 24, 2026
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1 change: 1 addition & 0 deletions bindings/python/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -36,3 +36,4 @@ arrow-schema = "57.0.0"
arrow-array = "57.0.0"
pyo3-async-runtimes = { version = "0.26.0", features = ["tokio-runtime"] }
jiff = { workspace = true }
bigdecimal = "0.4"
53 changes: 43 additions & 10 deletions bindings/python/example/example.py
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Expand Up @@ -17,6 +17,8 @@

import asyncio
import time
from datetime import date, time as dt_time, datetime
from decimal import Decimal

import pandas as pd
import pyarrow as pa
Expand Down Expand Up @@ -45,6 +47,10 @@ async def main():
pa.field("name", pa.string()),
pa.field("score", pa.float32()),
pa.field("age", pa.int32()),
pa.field("birth_date", pa.date32()),
pa.field("check_in_time", pa.time32("ms")),
pa.field("created_at", pa.timestamp("us")), # TIMESTAMP (NTZ)
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@luoyuxia luoyuxia Jan 25, 2026

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nit: can we also have timestamp with local timezone?

pa.field("salary", pa.decimal128(10, 2)),
]

# Create a PyArrow schema
Expand All @@ -60,7 +66,7 @@ async def main():
admin = await conn.get_admin()

# Create a Fluss table
table_path = fluss.TablePath("fluss", "sample_table")
table_path = fluss.TablePath("fluss", "sample_table_types")

try:
await admin.create_table(table_path, table_descriptor, True)
Expand Down Expand Up @@ -96,6 +102,10 @@ async def main():
pa.array(["Alice", "Bob", "Charlie"], type=pa.string()),
pa.array([95.2, 87.2, 92.1], type=pa.float32()),
pa.array([25, 30, 35], type=pa.int32()),
pa.array([date(1999, 5, 15), date(1994, 3, 20), date(1989, 11, 8)], type=pa.date32()),
pa.array([dt_time(9, 0, 0), dt_time(9, 30, 0), dt_time(10, 0, 0)], type=pa.time32("ms")),
pa.array([datetime(2024, 1, 15, 10, 30), datetime(2024, 1, 15, 11, 0), datetime(2024, 1, 15, 11, 30)], type=pa.timestamp("us")),
pa.array([Decimal("75000.00"), Decimal("82000.50"), Decimal("95000.75")], type=pa.decimal128(10, 2)),
],
schema=schema,
)
Expand All @@ -111,22 +121,41 @@ async def main():
pa.array(["David", "Eve"], type=pa.string()),
pa.array([88.5, 91.0], type=pa.float32()),
pa.array([28, 32], type=pa.int32()),
pa.array([date(1996, 7, 22), date(1992, 12, 1)], type=pa.date32()),
pa.array([dt_time(14, 15, 0), dt_time(8, 45, 0)], type=pa.time32("ms")),
pa.array([datetime(2024, 1, 16, 9, 0), datetime(2024, 1, 16, 9, 30)], type=pa.timestamp("us")),
pa.array([Decimal("68000.00"), Decimal("72500.25")], type=pa.decimal128(10, 2)),
],
schema=schema,
)

append_writer.write_arrow_batch(pa_record_batch)
print("Successfully wrote PyArrow RecordBatch")

# Test 3: Append single rows
print("\n--- Testing single row append ---")
# Dict input
await append_writer.append({"id": 8, "name": "Helen", "score": 93.5, "age": 26})
print("Successfully appended row (dict)")

# List input
await append_writer.append([9, "Ivan", 90.0, 31])
print("Successfully appended row (list)")
# Test 3: Append single rows with Date, Time, Timestamp, Decimal
print("\n--- Testing single row append with temporal/decimal types ---")
# Dict input with all types including Date, Time, Timestamp, Decimal
await append_writer.append({
"id": 8,
"name": "Helen",
"score": 93.5,
"age": 26,
"birth_date": date(1998, 4, 10),
"check_in_time": dt_time(11, 30, 45),
"created_at": datetime(2024, 1, 17, 14, 0, 0),
"salary": Decimal("88000.00"),
})
print("Successfully appended row (dict with Date, Time, Timestamp, Decimal)")

# List input with all types
await append_writer.append([
9, "Ivan", 90.0, 31,
date(1993, 8, 25),
dt_time(16, 45, 0),
datetime(2024, 1, 17, 15, 30, 0),
Decimal("91500.50"),
])
print("Successfully appended row (list with Date, Time, Timestamp, Decimal)")

# Test 4: Write Pandas DataFrame
print("\n--- Testing Pandas DataFrame write ---")
Expand All @@ -136,6 +165,10 @@ async def main():
"name": ["Frank", "Grace"],
"score": [89.3, 94.7],
"age": [29, 27],
"birth_date": [date(1995, 2, 14), date(1997, 9, 30)],
"check_in_time": [dt_time(10, 0, 0), dt_time(10, 30, 0)],
"created_at": [datetime(2024, 1, 18, 8, 0), datetime(2024, 1, 18, 8, 30)],
"salary": [Decimal("79000.00"), Decimal("85500.75")],
}
)

Expand Down
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