TimescaleDB is an open-source relational database for time-series data. It
speaks "full SQL" and is correspondingly easy to use like a traditional relational
database, yet scales in ways previously reserved for NoSQL databases.
Compared to the trade-offs demanded by these two alternatives
(relational vs. NoSQL), TimescaleDB offers the best of both
worlds for time-series data:
Easy to use
- Full SQL interface for all SQL natively supported by
PostgreSQL (including secondary indexes, non-time based aggregates,
sub-queries, JOINs, window functions).
- Connects to any client or tool that speaks PostgreSQL, no changes needed.
- Time-oriented features, API functions, and optimizations.
- Robust support for Data retention policies.
- Transparent time/space partitioning for both scaling up (single node)
and scaling out (forthcoming).
- High data write rates (including batched commits, in-memory
indexes, transactional support, support for data backfill).
- Right-sized chunks (two-dimensional data partitions) on single nodes to
ensure fast ingest even at large data sizes.
- Parallelized operations across chunks and servers.
- Engineered up from PostgreSQL, packaged as an extension.
- Proven foundations benefiting from 20+ years of PostgreSQL
research (including streaming replication, backups).
- Flexible management options (compatible with existing PostgreSQL
ecosystem and tooling).
The rest of this section describes the design and motivation around the TimescaleDB
architecture, including why time-series data is different, and how we leverage
its characteristics when building TimescaleDB.
Download the guide
If you want a quick visual intro to TimescaleDB, click on the image below to download the starter guide.
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