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--
-- Licensed to Crate.io GmbH ("Crate") under one or more contributor
-- license agreements. See the NOTICE file distributed with this work for
-- additional information regarding copyright ownership. Crate licenses
-- this file to you under the Apache License, Version 2.0 (the "License");
-- you may not use this file except in compliance with the License. You may
-- obtain a copy of the License at
--
-- http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-- WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-- License for the specific language governing permissions and limitations
-- under the License.
--
-- However, if you have executed another commercial license agreement
-- with Crate these terms will supersede the license and you may use the
-- software solely pursuant to the terms of the relevant commercial agreement.
-- ─────────────────────────────────────────────────────────────────────────────
-- CrateDB Industrial IoT — Schema
-- Run these statements in order before importing data.
-- ─────────────────────────────────────────────────────────────────────────────
-- ── 1. PLANTS ─────────────────────────────────────────────────────────────────
-- ERP master data — one row per industrial facility (5 rows)
CREATE TABLE IF NOT EXISTS rtia.plants (
plant_id TEXT PRIMARY KEY,
plant_name TEXT,
city TEXT,
federal_state TEXT,
industry_segment TEXT,
employee_count INTEGER,
operational_since INTEGER,
plant_manager TEXT,
annual_revenue_eur_m DOUBLE PRECISION,
certifications ARRAY(TEXT),
geo_location GEO_POINT
);
-- ── 2. DEVICES ────────────────────────────────────────────────────────────────
-- CMMS asset registry — one row per physical device (500 rows)
CREATE TABLE IF NOT EXISTS rtia.devices (
device_id TEXT PRIMARY KEY,
device_type TEXT,
plant_id TEXT,
line_id TEXT,
manufacturer TEXT,
model TEXT,
serial_number TEXT,
installation_date TIMESTAMP WITH TIME ZONE,
last_maintenance_date TIMESTAMP WITH TIME ZONE,
next_maintenance_due TIMESTAMP WITH TIME ZONE,
warranty_expiry TIMESTAMP WITH TIME ZONE,
asset_value_eur DOUBLE PRECISION,
responsible_technician TEXT
);
-- ── 3. MAINTENANCE_LOG ────────────────────────────────────────────────────────
-- Work order history — preventive, corrective, and emergency jobs (~1,700 rows)
CREATE TABLE IF NOT EXISTS rtia.maintenance_log (
work_order_id TEXT PRIMARY KEY,
device_id TEXT,
plant_id TEXT,
maintenance_type TEXT, -- 'preventive' | 'corrective' | 'emergency'
technician TEXT,
scheduled_date TIMESTAMP WITH TIME ZONE,
completed_date TIMESTAMP WITH TIME ZONE,
duration_hours DOUBLE PRECISION,
cost_eur DOUBLE PRECISION,
status TEXT, -- 'completed' | 'scheduled'
notes TEXT INDEX USING FULLTEXT WITH (analyzer = 'standard'),
notes_embedding FLOAT_VECTOR(384) -- sentence-transformers/all-MiniLM-L6-v2
);
-- ── 4. IOT_DATA ───────────────────────────────────────────────────────────────
-- Sensor readings — 500,000 rows across 500 devices and 5 plants
-- Partitioned by month; clustered by device_id within each partition.
CREATE TABLE IF NOT EXISTS rtia.iot_data (
device_id TEXT,
device_type TEXT,
plant_id TEXT,
line_id TEXT,
"timestamp" TIMESTAMP WITH TIME ZONE,
month TIMESTAMP WITH TIME ZONE
GENERATED ALWAYS AS DATE_TRUNC('month', "timestamp"),
metric_value DOUBLE PRECISION,
metric_unit TEXT,
status TEXT, -- 'normal' | 'warning' | 'critical' | 'offline'
quality_score DOUBLE PRECISION,
geo_location GEO_POINT,
metadata OBJECT(STRICT) AS (
firmware_version TEXT,
model TEXT,
installation_date TEXT,
last_calibration TEXT
)
)
CLUSTERED BY (device_id) INTO 4 SHARDS
PARTITIONED BY (month);
-- ─────────────────────────────────────────────────────────────────────────────
-- IMPORT DATA
-- Adjust the file path or URL to match your environment.
-- ─────────────────────────────────────────────────────────────────────────────
COPY rtia.plants FROM 'https://iot2-601357753311-eu-west-1-an.s3.eu-west-1.amazonaws.com/plants.json' WITH (format = 'json');
COPY rtia.devices FROM 'https://iot2-601357753311-eu-west-1-an.s3.eu-west-1.amazonaws.com/devices.json' WITH (format = 'json');
COPY rtia.maintenance_log FROM 'https://iot2-601357753311-eu-west-1-an.s3.eu-west-1.amazonaws.com/maintenance_log.json' WITH (format = 'json');
COPY rtia.iot_data FROM 'https://iot2-601357753311-eu-west-1-an.s3.eu-west-1.amazonaws.com/iot_demo_dataset.json' WITH (format = 'json');
-- ── 5. LOCATIONS ──────────────────────────────────────────────────────────────
-- Reference geometries for geo queries (DISTANCE / WITHIN). Lets the advanced
-- queries look a location up by name instead of hand-coding it inline.
-- location_type is 'point' (a coordinate in geo_location, used with DISTANCE)
-- or 'area' (a polygon in geo_area, used with WITHIN).
CREATE TABLE IF NOT EXISTS rtia.locations (
location_name TEXT PRIMARY KEY,
location_type TEXT, -- 'point' | 'area'
geo_location GEO_POINT, -- set when location_type = 'point'
geo_area GEO_SHAPE -- set when location_type = 'area'
);
INSERT INTO rtia.locations (location_name, location_type, geo_location, geo_area) VALUES
('Stuttgart', 'point', [9.1819, 48.7843], NULL), -- PLANT_STUTTGART
('Frankfurt', 'point', [8.6638, 50.1071], NULL), -- Frankfurt (Main) Hauptbahnhof
('Munich', 'point', [11.5586, 48.1402], NULL), -- PLANT_MUNICH
('Hamburg', 'point', [10.0068, 53.5528], NULL), -- PLANT_HAMBURG
('Dortmund', 'point', [7.459, 51.5178], NULL), -- PLANT_DORTMUND
('Leipzig', 'point', [12.3815, 51.3454], NULL), -- PLANT_LEIPZIG
('Bavaria', 'area', NULL, 'POLYGON((10.0 47.3, 13.8 47.3, 13.8 50.6, 10.0 50.6, 10.0 47.3))')
ON CONFLICT (location_name) DO UPDATE SET
location_type = excluded.location_type,
geo_location = excluded.geo_location,
geo_area = excluded.geo_area;
-- ── 6. KNN_SEARCHES ───────────────────────────────────────────────────────────
-- Reference query vectors for the VECTOR SEARCH section (KNN_MATCH). Lets a
-- search look its embedding up by name instead of pasting a 384-element literal
-- inline. query_name is the short slug; search_string is the natural-language
-- query the advanced_queries.sql subqueries match on.
-- Embeddings are FLOAT_VECTOR(384) from sentence-transformers/all-MiniLM-L6-v2,
-- precomputed by generate_embeddings.py (normalized).
CREATE TABLE IF NOT EXISTS rtia.knn_searches (
query_name TEXT,
search_string TEXT PRIMARY KEY,
embedding FLOAT_VECTOR(384)
);
INSERT INTO rtia.knn_searches (query_name, search_string, embedding) VALUES
('thermal_event', 'emergency shutdown thermal runaway critical breach',
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('calibration_drift', 'calibration drift sensor out of range recalibration',
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('mechanical_failure', 'worn parts replaced mechanical failure vibration',
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('signal_cable', 'signal cable damaged transmission restored',
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('routine_inspection', 'routine inspection preventive maintenance nominal',
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ON CONFLICT (search_string) DO UPDATE SET
query_name = excluded.query_name,
embedding = excluded.embedding;