A collection of documents that detail Tempo architectural decisions and operational implications.
Tempo comprises of the following top-level components.
The distributor accepts spans in multiple formats including Jaeger, OpenTelemetry, Zipkin. It routes spans to ingesters by hashing the
traceID and using a distributed consistent hash ring.
The distributor uses the receiver layer from the OpenTelemetry Collector. For best performance it is recommended to ingest OTel Proto. For this reason the Grafana Agent uses the otlp exporter/receiver to send spans to Tempo.
The Ingester batches trace into blocks, creates bloom filters and indexes, and then flushes it all to the backend. Blocks in the backend are generated in the following layout.
<bucketname> / <tenantID> / <blockID> / <meta.json> / <index> / <data> / <bloom_0> / <bloom_1> ... / <bloom_n>
The Query Frontend is responsible for sharding the search space for an incoming query.
Traces are exposed via a simple HTTP endpoint:
Internally, the Query Frontend splits the blockID space into a configurable number of shards and queues these requests. Queriers connect to the Query Frontend via a streaming gRPC connection to process these sharded queries.
The querier is responsible for finding the requested trace id in either the ingesters or the backend storage. Depending on parameters it will query both the ingesters and pull bloom/indexes from the backend to search blocks in object storage.
The querier exposes an HTTP endpoint at:
GET /querier/api/traces/<traceID>, but its not expected to be used directly.
Queries should be sent to the Query Frontend.
The Compactors stream blocks to and from the backend storage to reduce the total number of blocks.
Using older versions of Grafana
When using older versions of Grafana (7.4.x), you must also use
tempo-query in order to visualize traces. The
tempo-query is Jaeger Query with a GRPC Plugin that allows it to query Tempo.
For more information, refer to these example and these docs.
Related Tempo resources
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Getting started with tracing and Grafana Tempo
In this session, we will use an instrumented application to demonstrate how to use logs and Prometheus exemplars to find traces effectively in Tempo. The demo will hit on the basics of operating Tempo and reveal how Tempo allows us to scale tracing as far as possible with less operational cost and complexity than ever before.