A deprecated unified observability backend for Prometheus, Jaeger, and OpenTelemetry metrics and traces built on PostgreSQL and TimescaleDB.
Promscale was an open-source unified observability backend that consolidated metrics from Prometheus and traces from Jaeger and OpenTelemetry into a single PostgreSQL/TimescaleDB database. It provided a durable, scalable storage solution with full PromQL compliance and SQL querying capabilities, simplifying the observability stack by reducing the need for multiple specialized databases.
DevOps engineers, SREs, and developers managing observability stacks who need a unified backend for metrics and traces, especially those already familiar with PostgreSQL seeking to reduce operational complexity.
Developers chose Promscale for its unified architecture that eliminated the need for separate storage systems, its 100% PromQL compliance, certified Jaeger integration, and the reliability of PostgreSQL/TimescaleDB for durable, scalable storage with advanced data management features.
[DEPRECATED] Promscale is a unified metric and trace observability backend for Prometheus, Jaeger and OpenTelemetry built on PostgreSQL and TimescaleDB.
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Consolidates data from Prometheus, Jaeger, and OpenTelemetry into one PostgreSQL database, reducing the need for multiple specialized systems as highlighted in the README.
Serves as a fully compliant remote storage for Prometheus, supporting remote write/read, exemplars, and high availability, ensuring seamless integration.
Officially certified for Jaeger trace storage with OTLP support, enabling a production-ready alternative to Elasticsearch or Cassandra.
Allows querying metrics and traces with SQL, facilitating advanced correlation and analysis beyond standard observability tools, as demonstrated in the documentation.
Promscale has been deprecated and is no longer maintained, making it unsuitable for production deployments due to lack of security updates and support.
Requires managing both the Connector and a PostgreSQL/TimescaleDB database, adding setup and maintenance overhead compared to turnkey solutions.
While it scales to hundreds of thousands of spans per second, it may not match the horizontal scalability of distributed systems like Cassandra for massive trace volumes.