IoT sensors and application metrics generate massive volumes of timestamp-ordered data. Traditional databases struggle with this workload pattern. Time series databases optimize specifically for sequential writes and time-range queries, enabling efficient storage and analysis.
Database Selection
InfluxDB provides a purpose-built time series database with Flux query language. TimescaleDB extends PostgreSQL with time series optimizations, enabling familiar SQL. ClickHouse offers columnar storage excellent for analytics. Prometheus focuses specifically on metrics with pull-based collection.
- InfluxDB excels for IoT workloads with high cardinality and variable schemas
- TimescaleDB suits teams wanting PostgreSQL compatibility with time series performance
- ClickHouse handles analytical queries across massive datasets efficiently
- Prometheus integrates naturally with Kubernetes and cloud-native monitoring
- Consider managed services reducing operational burden for smaller teams
Data Lifecycle Management
Time series data requires retention policies balancing storage costs with query needs. Downsampling aggregates older data reducing storage while preserving trends. Tiered storage moves cold data to cheaper storage automatically. Plan data lifecycle from the start—retrofitting is difficult.