What is TimescaleDB?
Who uses TimescaleDB?
Why developers like TimescaleDB?
Here are some stack decisions, common use cases and reviews by companies and developers who chose TimescaleDB in their tech stack.
As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.
We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.
PostgreSQL plus TimescaleDB allow us to concentrate the business effort on how to analyze valuable data instead of manage them on IT side. We are now able to ingest thousand of social shares "managed" data without compromise the scalability of the system or the time query. TimescaleDB is transparent to PostgreSQL , so we continue to use the same SQL syntax without any changes. At the same time, because we need to manage few document objects we dismissed the MongoDB cluster.
Python Sanic PostgreSQL TimescaleDB Redis
Simple, yet, astonishingly fast and powerful stack to handle huge load of data feed from cryptocurrency exchanges across the globe.
- Packaged as a PostgreSQL extension
- Full ANSI SQL
- JOINs (e.g., across PostgreSQL tables)
- Complex queries
- Secondary indexes
- Composite indexes
- Support for very high cardinality data
- Ability to ingest out of order data
- Ability to perform accurate rollups
- Data retention policies
- Fast deletes
- Integration with PostGIS and the rest of the PostgreSQL ecosystem