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Documentation Grafana Tempo Manage Configure TraceQL metrics

Configure TraceQL metrics

TraceQL language provides metrics queries as an experimental feature. Metric queries extend trace queries by applying a function to trace query results. This powerful feature creates metrics from traces, much in the same way that LogQL metric queries create metrics from logs.

For more information about available queries, refer to TraceQL metrics queries.

Before you begin

To use the metrics generated from traces, you need to:

• Set the local-blocks processor to active in your metrics-generator configuration
• Configure a Tempo data source in Grafana or Grafana Cloud
• Access Grafana Cloud or Grafana version 10.4 or later

Activate and configure the local-blocks processor

The local-blocks processor must be enabled to start using metrics queries like { } | rate(). If not enabled, then the metrics queries fail with the error localblocks processor not found. Enabling the local-blocks processor can be done either per tenant or in all tenants.

To activate the local-blocks processor for all users, add it to the list of processors in the overrides block of your Tempo configuration.

yaml Copy

# Global overrides configuration.
overrides:
  metrics_generator_processors: ['local-blocks']

To configure the processor per tenant, use the metrics_generator_processor override.

Example for per-tenant in the per-tenant overrides:

yaml Copy

  overrides:
    'tenantID':
      metrics_generator_processors:
        - local-blocks

By default, for all tenants in the main configuration:

yaml Copy

overrides:
  defaults:
    metrics_generator:
      processors: [local-blocks]

Add this configuration to run TraceQL metrics queries against all spans (and not just server spans):

yaml Copy

metrics_generator:
  processor:
    local_blocks:
      filter_server_spans: false

To run metrics queries on historical data, you must configure the local-blocks processor to flush RF1 blocks to object storage:

yaml Copy

metrics_generator:
  processor:
    local_blocks:
      flush_to_storage: true

Setting flush_to_storage to true ensures that metrics blocks are flushed to storage so TraceQL metrics queries against historical data.

For more information about overrides, refer to Standard overrides.

yaml Copy

  overrides:
    'tenantID':
      metrics_generator_processors:
        - local-blocks

By default, for all tenants in the main configuration:

yaml Copy

overrides:
  defaults:
    metrics_generator:
      processors: [local-blocks]

Add this configuration to run TraceQL metrics queries against all spans (and not just server spans):

yaml Copy

metrics_generator:
  processor:
    local_blocks:
      filter_server_spans: false

If you configured Tempo using the tempo-distributed Helm chart, you can also set traces_storage using your values.yaml file. Refer to the Helm chart for an example.

yaml Copy

metrics_generator:
  processor:
    local_blocks:
      flush_to_storage: true

Setting flush_to_storage to true ensures that metrics blocks are flushed to storage so TraceQL metrics queries against historical data.

For more information about overrides, refer to Standard overrides.

Evaluate query timeouts

Because of their expensive nature, these queries can take a long time to run. As such, consider increasing the timeouts in various places of the system to allow enough time for the data to be returned.

Consider these areas when raising timeouts:

• Any proxy in front of Grafana
• Grafana data source for Prometheus pointing at Tempo
• Tempo configuration
  • querier.search.query_timeout
  • server.http_server_read_timeout
  • server.http_server_write_timeout

Set TraceQL metrics query options

The query_frontend.metrics configuration block controls all TraceQL metrics queries. The configuration depends on the environment.

For example, in a cloud environment, smaller jobs with more concurrency may be desired due to the nature of scale on the backend.

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query_frontend:
    metrics:
        concurrent_jobs: 1000
        target_bytes_per_job: 2.25e+08 # ~225MB
        interval: 30m0s

For an on-prem backend, you can improve query times by lowering the concurrency, while increasing the job size.

yaml Copy

query_frontend:
    metrics:
        concurrent_jobs: 8
        target_bytes_per_job: 1.25e+09 # ~1.25GB