Timescale Announces OpenTelemetry Tracing Support for Promscale
May 16, 2022
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Timescale announces general availability of OpenTelemetry tracing support in Promscale, its unified observability backend for metrics and traces powered by SQL.

Timescale furthers its mission to enable developers worldwide to analyze their time-series data effectively; enabling them to know what is happening, why it is happening, and how to fix it.

Timescale - the creators of TimescaleDB, the relational database for time-series built in PostgreSQL - announced the full availability of OpenTelemetry support in Promscale. Promscale is the unified observability backend for Prometheus metrics and OpenTelemetry traces powered by SQL built on TimescaleDB and PostgreSQL. It provides users of cloud-native applications the power and flexibility to interrogate their observability data using a query language they already know and gain unprecedented insight about their distributed systems to troubleshoot problems faster and build better software. With traces now generally available, Promscale provides a single database and unified query language for metrics, traces, and business data.

In 2021, Timescale announced the beta release for OpenTelemetry traces in Promscale. With this announcement, support for OpenTelemetry traces is now fully available, making tracing data more accessible and valuable to developers so they can better monitor, maintain, and troubleshoot their distributed systems.

OpenTelemetry is an open source observability framework for cloud-native service and infrastructure instrumentation hosted by the Cloud Native Computing Foundation (CNCF). Second only to Kubernetes as the project with the most activity and contributors, It focuses on solving the challenges of operating distributed systems by making telemetry data very easy to collect and universally available.

With tracing support, Promscale equips every developer with observability powered by SQL. Observability is a data analytics problem, and SQL is a powerful language for data analytics: by using SQL to analyze tracing data, developers can get unprecedented insights about their distributed systems, identifying production problems faster and reducing mean time to recovery.

OpenTelemetry tracing support in Promscale includes the following features:

- A fully customizable out-of-the-box experience within Grafana to help users understand the performance of their distributed systems, enabling a deep dive into the behavior of microservices.

- A native ingest endpoint for OpenTelemetry traces that understands the OpenTelemetry Protocol (OTLP) to easily ingest OpenTelemetry data.

- Trace data compression and configurable retention to manage disk usage.

- Seamless integrations to visualize distributed traces stored in Promscale using Jaeger and Grafana so users don’t have to learn new tools or change existing workflows.

- Support for ingesting traces in Jaeger and Zipkin formats via the OpenTelemetry Collector so users can also benefit from the new capabilities.

“Observability is an analytics problem; SQL is the lingua franca of observability. By combining SQL with OpenTelemetry traces, you can get new insights not possible with existing observability query languages. Promscale is the only observability solution that makes this possible, thanks to its full SQL support and its support for OpenTelemetry traces'' said Ramon Guiu, VP, Observability at Timescale. “With this feature launch, users have access to out-of-the-box dashboards that will help them understand the behavior of their applications, so they can start troubleshooting immediately.”

With the release of full OpenTelemetry support, Promscale is now even more robust and suited for simple and enterprise-grade deployments; offering unified storage for Prometheus Metrics and OpenTelemetry traces, full SQL and PromQL support, providing users the ability to create unique dashboards, and ask questions aimed at better understanding the systems being monitored. This is an important step towards the goal of enabling every engineer to store all observability data (metrics, logs, traces, metadata, and other future data types) in a single mature and scalable store, and analyze it through a unified and complete SQL interface.

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