IoT Sensor Data & Monitoring

This pattern handles bursty event streams and immediate alerting without long-lived hot retention.

Core pattern

STREAM for short-lived raw readings (TTL_SECONDS)
SHARED for persisted rollups/aggregates
live subscriptions for threshold detection
optional topic worker for downstream alert pipelines

Example schema


CREATE TABLE iot.sensor_data (
  event_id BIGINT PRIMARY KEY DEFAULT SNOWFLAKE_ID(),
  sensor_id TEXT NOT NULL,
  temperature DOUBLE,
  humidity DOUBLE,
  timestamp TIMESTAMP DEFAULT NOW()
) WITH (TYPE = 'STREAM', TTL_SECONDS = 10);
 
-- Aggregated metrics (persisted)
CREATE TABLE iot.metrics (
  sensor_id TEXT PRIMARY KEY,
  avg_temp DOUBLE,
  max_temp DOUBLE,
  min_temp DOUBLE,
  hour TIMESTAMP
) WITH (TYPE = 'SHARED', FLUSH_POLICY = 'interval:3600');

Example implementation


await client.subscribeWithSql(
  'SELECT * FROM iot.sensor_data WHERE temperature > 80 OR humidity > 95',
  (event) => {
    if (event.type === 'change' && event.change_type === 'insert') {
      triggerAlert(event.rows);
    }
  }
);

Code references

STREAM runtime behavior: stream_table_provider.rs
Starter integration scaffold: examples/simple-typescript

IoT Sensor Data & Monitoring

Core pattern

Example schema

Example implementation

Code references

Getting Started

Documentation

Resources

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