![]() In the example below, we’re creating a metric to track the number of errors experienced by our top-tier enterprise customers ( ). Now, as you’re exploring your spans (and visualizing them as a timeseries graph, top list, or table), you can generate metrics by selecting Export, followed by Generate new metric. If you’ve used Datadog APM, you might be familiar with Trace Search and Analytics, which lets you use tags to query and aggregate spans across any dimension-whether it’s a specific service, endpoint, customer segment, or a combination thereof. Build span-based metrics that are meaningful to your business And because you have full control over your traces, the metrics you create are always accurate and reflective of the state of your system. You can now leverage this stream of traces to generate metrics from any span, using any tag-and track long-term trends in application performance. Datadog Distributed Tracing already allows you to search and analyze your ingested traces live over a 15-minute rolling window and retain only the ones you need by creating highly flexible retention rules. But complex production applications produce an extremely high volume of traces, which are prohibitively expensive to store and nearly impossible to sift through in time-sensitive situations. Tracing has become essential for monitoring today’s increasingly distributed architectures.
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