The default traces search reviews the whole trace. Inspired by PromQL and LogQL, TraceQL is a query language designed for selecting traces. Use the trace view to quickly diagnose errors and high latency events in your system. Search for traces using common dimensions such as time range, duration, span tags, service names, etc. ![]() Read more about this integration in this blog post. Exemplars let you jump from Prometheus metrics to Tempo traces by clicking on recorded exemplars. Loki, with its powerful query language LogQL v2 allows us to filter down on requests that we care about, and jump to traces using the Derived fields support in Grafana. Grafana ships with native support for Tempo using the built-in Tempo data source. Tempo has strong integrations with a number of existing open source tools, including: Read more about this in the architecture section of the docs. It builds an index on the high cardinality trace-id fieldĪnd uses an object store as backend which allows for high parallelization of queries. Grafana Tempo is a high volume distributed tracing backend that can retrieve a trace when queried for the trace-id. Grafana Cloud Traces lets you search for traces, generate metrics from spans, and link your tracing data with logs and metrics. Tempo can be used with any of the open-source tracing protocols, including Jaeger, Zipkin, and OpenTelemetry. Tempo is cost-efficient, requiring only object storage to operate, and is deeply integrated with Grafana, Prometheus, and Loki. Grafana Cloud Traces is based on Tempo, an open-source, easy-to-use, and high-scale distributed tracing backend. Along the way, logs are written in various nodes with a time stamp showing when the info passed through.įinally, the request and response activity ends and a record of that request is sent to Grafana Cloud. Services respond and data flows back from each, sometimes triggering new events across the system. If it passes the check, the information is stored in a database.Īlong the way an anonymization node strips personally-identifying data from the address and sends metadata collected to a marketing qualifying algorithm to determine whether the request was sent from a targeted part of the internet. The email address may be sent to a verification algorithm sitting in a microservice that exists solely for that purpose. ![]() In a cloud computing world, it is possible that clicking that one button causes data to touch multiple nodes across your cluster of microservices. The user’s email address is data that flows through your system. ![]() A user on your website enters their email address into a form to sign up for your mailing list.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |