Product & Engineering

TimescaleDB 1.3 introduces automated continuous aggregates, which can massively speed up workloads that need to process large amounts of data.

Tuning your TimescaleDB database is essential to leveraging your existing hardware. We built a tool to help you do just that.

How we evaluated several third-party tools and ultimately selected Patroni as our preferred method.

Appropriately ordered indexes can speed up queries, sometimes by orders of magnitude.

As of version 0.12, TimescaleDB will come with opt-out, non-personally identifiable telemetry and version checking.

An examination of the strengths and weaknesses of SQL, and why query planners exist, by way of comparing TimescaleDB and InfluxDB.

How 0.10 introduces 15x improvements in query planning times.

How a 5 node TimescaleDB cluster outperforms 30 Cassandra nodes, with higher inserts, up to 5800x faster queries, 10% the cost, a more flexible data model, and of course, full SQL.

20% higher insert performance, up to 1400x faster queries, and simpler queries when using TimescaleDB vs. MongoDB for time-series data.

PG10 introduces quite a few substantial new features: logical replication, full text search support for JSON / JSONB, cross-column statistics, and better support for parallel queries. Overall, PG10 is a significant milestone in the steady development of a venerable open-source database.