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Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time Series Databases (InfluxDB, TimescaleDB, ClickHouse)

Time-Series Data

Time-series data is a sequence of data points indexed by time. Examples include server metrics, IoT sensor readings, and financial tick data.

Database Comparison

| Feature | InfluxDB | TimescaleDB | QuestDB | |---------|----------|-------------|---------| | Architecture | Custom TS engine | PostgreSQL extension | Columnar | | SQL | Flux / SQL | Full SQL | SQL | | Write throughput | Very high | High | Very high | | Compression | Excellent | Good | Excellent |

InfluxDB

Purpose-built time-series with automatic downsampling:

from influxdb_client import InfluxDBClient

client = InfluxDBClient(url="http://localhost:8086", token="my-token")

write_api = client.write_api()

p = Point("cpu").tag("host", "server01").field("usage_user", 45.2)

write_api.write(bucket="metrics", record=p)

TimescaleDB

PostgreSQL extension with hypertables:

CREATE TABLE sensor_data (

time TIMESTAMPTZ NOT NULL,

device_id INTEGER,

temperature DOUBLE PRECISION

);

SELECT create_hypertable('sensor_data', 'time');

SELECT time_bucket('5m', time) AS bucket,

AVG(temperature) FROM sensor_data

WHERE time > NOW() - INTERVAL '24h'

GROUP BY bucket;

Performance

For 1 billion rows, QuestDB and InfluxDB lead on write throughput. TimescaleDB wins on SQL compatibility. Choose based on your query patterns and ecosystem requirements.

Conclusion

Choose InfluxDB for purpose-built time-series, TimescaleDB for SQL compatibility, and QuestDB for maximum performance. All support downsampling and retention policies.

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