On Tuesday, the team at Timescale announced the official production release of TimescaleDB 1.0. Two months ago, the team released its initial release candidate. With the official release, TimescaleDB 1.0 is now the first enterprise-ready time-series database that supports full SQL and scale. This release has crossed over 1M downloads and production deployments at Comcast, Cray, Cree and more.
Mike Freedman, Co-founder/CTO at TimescaleDB says, “Since announcing our first release candidate in September, Timescale’s engineering team has merged over 50 PRs to harden the database, improving stability and ease-of-use.”
Major updates in TimescaleDB 1.0
- TimescaleDB 1.0 comes with a cleaner management of multiple tablespaces that allows hypertables to elastically grow across many disks. Also, the information about the state of hypertables is easily available which includes their dimensions and chunks.
- It’s important to have a robust cross-operating system availability for better usability. This release brings improvements for supporting Windows, FreeBSD, and NetBSD.
- TimescaleDB 1.0 powers the foundation for a database scheduling framework that manages background jobs.
- Since TimescaleDB is implemented as an extension, a single PostgreSQL instance can have multiple, different versions of TimescaleDB running.
- TimescaleDB 1.0 manages edge cases related to the schema and tablespace modifications.
- It also provides cleaner permissions for backup/recovery in templated databases and includes additional test coverage.
TimescaleDB 1.0 supports Prometheus
Prometheus, a leading open source monitoring and alerting tool, is not arbitrarily scalable or durable in the face of disk or node outages. Whereas, TimescaleDB 1.0 is efficient and can easily handle terabytes of data, and supports high availability and replication which it makes it long-term data storage. It also provides advanced capabilities and features, such as full SQL, joins and replication, which are not available in Prometheus. All the metrics recorded in Prometheus are first written to the local node, and then written to TimescaleDB. So, the metrics are immediately backed up, in case of any disk failure on a Prometheus node, would be still safer.
What’s the future like?
The team at Timescale says that the upcoming releases of TimescaleDB will include more automation around capabilities like automatic data aggregation, retention, and archiving. They will also include automated data management techniques for improving query performance, such as non-blocking reclustering and reindexing of older data.
Read more about this release on Timescale’s official website.