Logos of Jaeger and ClickHouse connected by streams of data, illustrating their new integration for high-performance tracing.

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Jaeger Adds ClickHouse Database Support

TL;DR: The open-source tracing tool Jaeger now supports the ClickHouse database. This new integration is designed for large-scale telemetry, offering significant performance gains. In one test, it achieved an 8.6x compression rate on 10 million spans, helping teams better manage and store observability data.

By Ashish KaleThe New Stack2h ago1 min readupdated just now

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Key facts

Category: Infrastructure
Impact: High
Published: 2h ago
Source: The New Stack

Full summary

Open-source tracing tool Jaeger now supports ClickHouse, delivering significant performance and compression improvements for observability data at scale.

The open-source distributed tracing system Jaeger has officially added support for the ClickHouse database in its latest release. This integration was a highly requested feature from the user community for several years. The new backend storage option is designed to handle the massive data volumes generated by modern, large-scale applications. In performance tests, the combination of Jaeger and ClickHouse demonstrated an impressive 8.6x data compression rate when processing 10 million spans. This update provides a powerful new option for teams looking to optimize their observability infrastructure and manage telemetry data more efficiently.

This development is significant for developers and operations teams managing observability platforms. ClickHouse's column-oriented architecture is exceptionally well-suited for the append-only, high-volume write streams typical of tracing data. By leveraging ClickHouse, Jaeger users can achieve better performance and drastically reduce storage costs. This allows for longer data retention periods, which is crucial for analyzing long-term trends, debugging complex historical issues, and maintaining a comprehensive view of system behavior over time. The integration makes Jaeger a more scalable and cost-effective choice for organizations facing the challenge of ever-growing data from their distributed services.