Enhancing Observability: Trace Exemplars Bridge Metrics and Traces in Managed Service for Prometheus
Cross-signals correlation is vital for achieving a comprehensive understanding of the behavior and performance of complex modern applications. These applications often require monitoring and troubleshooting with multiple types of signals, such as logs, traces, and metrics. The challenge that developers face is effectively pivoting between these signal types due to isolated backends. Enter trace exemplars: a powerful solution that bridges the gap, providing connectivity between metrics and traces.
Trace Exemplars: Connecting the Dots
A trace exemplar is a specific trace instance that is embedded within metric data, providing a direct link between the two signals. By including trace exemplars in metrics, developers can identify abnormal application performance much easier and faster. This approach streamlines the process of root cause analysis and increases the overall efficiency of observability workflows.
The power of trace exemplars extends to the storage of trace information with metric data. This unified storage improves the accessibility of vital troubleshooting information and minimizes the need to switch between disjointed backends.
Managed Service for Prometheus: Native Support for Exemplars
Recognizing the value of trace exemplars, Managed Service for Prometheus (MSP) now offers native support for Prometheus exemplars. This seamless integration into MSP enables developers to get started with cross-signals correlation more easily, dramatically improving observability workflows.
Measuring High Latency User Journeys
Focusing on tail latency measurements, rather than just average latency, is paramount in identifying problematic high-latency user journeys. Histograms (distributions) can be employed to pinpoint these high-latency events, enabling developers to gain a deeper understanding of why performance issues occur.
Trace exemplars play a critical role in this process, providing comprehensive root cause analysis for latency issues. By offering a clear connection between metrics and traces, developers can diagnose issues more effectively and efficiently.
Using Exemplars with Histograms
To leverage the power of trace exemplars, developers need to know how to integrate them with histograms. This process involves embedding specific trace instances into metric data, allowing developers to quickly pivot from a distribution chart to a detailed example trace for root cause analysis.
The benefits of comparing traces at different latency percentiles become abundantly clear when using trace exemplars with histograms. This comparison technique enables developers to quickly identify patterns and correlations among problematic user journeys, speeding up the resolution of complex performance issues.
Managed Service for Prometheus Exemplars Retention
MSP’s support for exemplars extends to their retention period, where exemplars remain available for querying for an impressive 24 months. In comparison, upstream Prometheus typically retains exemplars for less than 14 days. This vastly increased retention period ensures long-term analysis and historical comparisons.
Compatibility with Tracing Tools
Prometheus exemplars seamlessly work with Cloud Trace and third-party tracing tools, such as Grafana Tempo. This interoperability ensures developers maintain the flexibility to combine and connect their tracing tools, harnessing the full potential of trace exemplars.
In summary, trace exemplars provide a powerful link between metrics and traces, allowing developers to enhance observability and streamline application performance analysis. By adopting trace exemplars and integrating them into their workflows, developers can strengthen their ability to monitor, troubleshoot, and optimize modern applications. Now is the time to explore and master the use of trace exemplars to ensure a more efficient and effective observability approach.
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I honestly can't wait to work in many more projects together!
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