Librato posted a video "SF Metrics - Michael J. Freedman: PostgreSQL as Persistent Storage for Prometheus Metrics" on VIMEO

Time-series data is now everywhere and increasingly used to power core applications. Yet it creates a number of technical challenges around ingesting high volumes of data, supporting complex queries for recent and historical time intervals, and performing time-centric analysis and data management. Meanwhile, Prometheus and Grafana have emerged as a popular duo for collecting, querying, and graphing metrics. But while Prometheus has its own time-series storage subsystem for metrics monitoring, users sometimes need richer time-series analysis as well as the ability to join such data against other relational data to answer key business questions.In this talk, we take a somewhat heretical stance in the monitoring world, and describe why and how we view an enhanced Postgres as an effective Prometheus backend to support those complex questions (and get a proper SQL interface). We present pg_prometheus, a new native Prometheus datatype for Postgres, as well as a remote storage adaptor that allows Prometheus to write directly to a Postgres database. We also describe our work with TimescaleDB, a new open-source database designed for time-series workloads, engineered up as a PostgreSQL extension. In transforming PostgreSQL into a scalable time-series database, TimescaleDB serves as a powerful persistent store for Prometheus.Cast: Librato