using-timeseries-databaseslisted

# Time-Series Databases Implement efficient storage and querying for time-stamped data (metrics, IoT sensors, financial ticks, logs). ## Database Selection Choose based on primary use case: **TimescaleDB** - PostgreSQL extension - Use when: Already on PostgreSQL, need SQL + JOINs, hybrid workloads - Query: Standard SQL - Scale: 100K-1M inserts/sec **InfluxDB** - Purpose-built TSDB - Use when: DevOps metrics, Prometheus integration, Telegraf ecosystem - Query: InfluxQL or Flux - Scale: 500K-1M points/sec **ClickHouse** - Columnar analytics - Use when: Fastest aggregations needed, analytics dashboards, log analysis - Query: SQL - Scale: 1M-10M inserts/sec, 100M-1B rows/sec queries **QuestDB** - High-throughput IoT - Use when: Highest write performance needed, financial tick data - Query: SQL + Line Protocol - Scale: 4M+ inserts/sec ## Core Patterns ### 1. Hypertables (TimescaleDB) Automatic time-based partitioning: ```sql CREATE TABLE sensor_data ( time TIMESTAMPTZ NOT NULL, sensor_id INTEGER NOT NULL, temperature DOUBLE PRECISION, humidity DOUBLE PRECISION ); SELECT create_hypertable('sensor_data', 'time'); ``` Benefits: - Efficient data expiration (drop old chunks) - Parallel query execution - Compression on older chunks (10-20x savings) ### 2. Continuous Aggregates Pre-computed rollups for fast dashboard queries: ```sql -- TimescaleDB: hourly rollup CREATE MATERIALIZED VIEW sensor_data_hourly WITH (timescaledb.continuous) AS SELECT time_bu