Features and Comparison
Compare TimescaleDB features with the competition
Tiger Data 101 → TimescaleDB | Section: What is TimescaleDB? | ⏱ Time: ~3 min
Key features at a glance
Section titled “Key features at a glance”| Feature | What it does |
|---|---|
| Hypertables | Automatic time-based partitioning under a single table interface |
| Hypercore | Columnar storage + compression for cold data (10–40x savings) |
| Continuous Aggregates | Auto-refreshing materialized rollups for fast analytics |
| Data Tiering | Move old data to cheaper object storage (e.g., S3) automatically |
| Retention Policies | Auto-drop data older than a defined period |
| Full SQL | JOINs, CTEs, window functions, indexes, all of standard Postgres |
| Native time functions | time_bucket(), first(), last(), histogram() and more |
| High ingest performance | Optimized for append-heavy write patterns |
TimescaleDB vs. the alternatives
Section titled “TimescaleDB vs. the alternatives”| TimescaleDB | InfluxDB | ClickHouse | Plain Postgres | |
|---|---|---|---|---|
| SQL support | Full | Limited (Flux/InfluxQL) | SQL-like | Full |
| PostgreSQL compatible | Yes (it is Postgres) | No | No | Yes |
| Time-series optimized | Yes | Yes | Partial | No |
| Columnar storage | Yes (hypercore) | Yes | Yes | No |
| Continuous Aggregates | Yes | Yes | Materialized Views | Manual |
| JOINs with relational data | Yes | Limited | Yes | Yes |
| Ecosystem (ORMs, tools) | Entire Postgres ecosystem | Telegraf/Grafana focused | Growing | Entire Postgres ecosystem |
The key differentiator: TimescaleDB gives you purpose-built time-series performance without abandoning the PostgreSQL ecosystem you (and your team) already know.