Building Metrics with TimescaleDB

The article discusses building metrics with TimescaleDB to understand system performance better. It highlights common user concerns such as function status, delays, throttling, and misconfigurations. The author explains the choice of TimescaleDB over other tools like Prometheus and InfluxDB due to its compatibility with Postgres, existing feature usage, and local development solution. The requirements for the MVP release include function throughput, SDK throughput, and throttle indicators. The author also explains the choice of storing data in a flat, schema-agnostic structure over other methods like keeping each entry as a record or using Counter, Gauges, Histograms. The article further discusses challenges faced during implementation, such as Timescale's continuous aggregate feature and high cardinality issues. It also touches upon the need to remove tags from metrics due to changes in context. The author concludes by sharing their positive experience with TimescaleDB and its cloud offering.