Troubleshoot Tempo on Grafana Labs

Issues with sending traces

Thu, 09 Apr 2026 14:59:14 +0000

Issues with sending traces

Learn about issues related to sending traces.

Distributor refusing spans
Troubleshoot distributor refusing spans
Troubleshoot Grafana Alloy
Gain visibility on how many traces are being pushed to Grafana Alloy and if they are making it to the Tempo backend.

Issues with querying

Thu, 09 Apr 2026 14:59:14 +0000

Issues with querying

Learn about issues related to querying.

Unable to find traces
Troubleshoot missing traces
Too many jobs in the queue
Troubleshoot too many jobs in the queue
Bad blocks
Troubleshoot queries failing with an error message indicating bad blocks.
Tag search
Troubleshoot No options found in Grafana tag search
Response larger than the max
Troubleshoot response larger than the max error message
Long-running traces
Troubleshoot search results when using long-running traces
Too many requests error
Troubleshoot Too many requests error for a Tempo query

Troubleshoot metrics-generator

Thu, 09 Apr 2026 14:59:14 +0000

Troubleshoot metrics-generator

If you’re concerned with data quality issues in the metrics-generator, consider:

Reviewing your telemetry pipeline to determine the number of dropped spans. You are only looking for major issues here.
Reviewing the service graph documentation to understand how they are built.

If everything seems acceptable from these two perspectives, consider the following topics to help resolve general issues with all metrics and span metrics specifically.

All metrics

This section covers metrics for all metrics related to the metrics-generator.

Dropped spans in the distributor

The distributor has a queue of outgoing spans to the metrics-generators. If the queue is full, then the distributor drops spans before they reach the generator. Use the following metric to determine if that’s happening:

sum(rate(tempo_distributor_queue_pushes_failures_total{}[1m]))

Failed pushes to the generator

For any number of reasons, the distributor can fail a push to the generators. Use the following metric to determine if that’s happening:

sum(rate(tempo_distributor_metrics_generator_pushes_failures_total{}[1m]))

Discarded spans in the generator

Spans are rejected from being considered by the metrics-generator by a configurable slack time as well as due to user configurable filters. You can see the number of spans rejected by reason using this metric:

sum(rate(tempo_metrics_generator_spans_discarded_total{}[1m])) by (reason)

If a lot of spans are dropped in the metrics-generator due to your filters, you will need to adjust them. If spans are dropped due to the ingestion slack time, consider adjusting this setting:

metrics_generator:
  metrics_ingestion_time_range_slack: 30s

If spans are regularly exceeding this value you may want to consider reviewing your tracing pipeline to see if you have excessive buffering. Note that increasing this value allows the generator to consume more spans, but does reduce the accuracy of metrics because spans farther away from “now” are included.

Spans could also be discarded if the attributes aren’t valid UTF-8 characters when those attributes are converted to metric labels.

Max active series

The generator protects itself and your remote-write target by having a maximum number of series the generator produces. Use the sum below to determine if series are being dropped due to this limit:

sum(rate(tempo_metrics_generator_registry_series_limited_total{}[1m]))

Use the following setting to update the limit:

overrides:
  defaults:
    metrics_generator:
      max_active_series: 0

Note that this value is per metrics generator. The actual max series remote written will be <# of metrics generators> * <metrics_generator.max_active_series>.

Overflow series

When the active series limit is reached, the metrics-generator produces overflow series instead of dropping new data. These series have the label metric_overflow="true" and capture all data that would otherwise be lost.

To identify overflow series in your metrics:

{metric_overflow="true"}

As existing series become stale and are removed, new series are split out from the overflow bucket until the limit is reached again. To reduce overflow, either increase max_active_series or reduce cardinality by adjusting dimensions or filters.

Entity-based limiting

You can configure entity-based limiting as an alternative to series-based limiting. An entity is a unique label combination (excluding external labels) across multiple metrics. Entity-based limiting ensures the generator always produces the full set of metrics for a given entity, rather than limiting randomly once the series limit is triggered.

To enable entity-based limiting, set limiter_type to entity:

metrics_generator:
  limiter_type: entity

Use the following metric to determine if entities are being limited:

sum(rate(tempo_metrics_generator_registry_entities_limited_total{}[1m]))

Configure the entity limit with:

overrides:
  defaults:
    metrics_generator:
      max_active_entities: 0

Estimate active series demand

When the active series limit is reached, the tempo_metrics_generator_registry_active_series metric no longer reflects the true demand. Use the tempo_metrics_generator_registry_active_series_demand_estimate metric to estimate what the active series count would be without the limit:

tempo_metrics_generator_registry_active_series_demand_estimate{}

This metric uses HyperLogLog estimation and has approximately 3% deviation from the actual cardinality. Use this to determine if you need to increase limits or reduce cardinality.

Remote write failures

For any number of reasons, the generator may fail a write to the remote write target. Use the following metrics to determine if that’s happening:

sum(rate(prometheus_remote_storage_samples_failed_total{}[1m]))
sum(rate(prometheus_remote_storage_samples_dropped_total{}[1m]))
sum(rate(prometheus_remote_storage_exemplars_failed_total{}[1m]))
sum(rate(prometheus_remote_storage_exemplars_dropped_total{}[1m]))

Service graph metrics

Service graphs have additional configuration which can impact the quality of the output metrics.

Expired edges

The following metrics can be used to determine how many edges are failing to find a match. The expired edge only includes those edges that are expired and have no matching information to generate a service graph edge.

Rate of edges that have expired without a match:

sum(rate(tempo_metrics_generator_processor_service_graphs_expired_edges{}[1m]))

Rate of all edges:

sum(rate(tempo_metrics_generator_processor_service_graphs_edges{}[1m]))

If you are seeing a large number of edges expire without a match, consider adjusting the wait setting. This controls how long the metrics generator waits to find a match before it gives up.

metrics_generator:
  processor:
    service_graphs:
      wait: 10s

Service graph max items

The service graph processor has a maximum number of edges it tracks at once to limit the total amount of memory the processor uses. To determine if edges are being dropped due to this limit, check:

sum(rate(tempo_metrics_generator_processor_service_graphs_dropped_spans{}[1m]))

Use max_items to adjust the maximum amount of edges tracked:

metrics_generator:
  processor:
    service_graphs:
      max_items: 10000

Troubleshoot out-of-memory errors

Thu, 09 Apr 2026 14:59:14 +0000

Troubleshoot out-of-memory errors

Learn about out-of-memory (OOM) issues and how to troubleshoot them.

Set the max attribute size to help control out of memory errors

Tempo queriers can run out of memory when fetching traces that have spans with very large attributes. This issue has been observed when trying to fetch a single trace using the tracebyID endpoint.

To avoid these out-of-memory crashes, use max_attribute_bytes to limit the maximum allowable size of any individual attribute. Any key or values that exceed the configured limit are truncated before storing.

Use the tempo_distributor_attributes_truncated_total metric to track how many attributes are truncated.

   # Optional
    # Configures the max size an attribute can be. Any key or value that exceeds this limit will be truncated before storing
    # Setting this parameter to '0' would disable this check against attribute size
    [max_attribute_bytes: <int> | default = '2048']

Refer to the configuration for distributors documentation for more information.

Max trace size

Traces which are long-running (minutes or hours) or large (100K - 1M spans) spike the memory usage of each component when the large trace is encountered. Tempo treats traces as single units, and keeps all data for a trace together to enable features like structural queries and analysis.

Reading a large trace can spike the memory usage of the read components:

query-frontend
querier
ingester
metrics-generator

Writing a large trace can spike the memory usage of the write components:

ingester
compactor
metrics-generator

Start with a smaller trace size limit of 15MB, and increase it as needed. With an average span size of 300 bytes, this allows for 50K spans per trace.

Verify that you’ve configured a limit in max_bytes_per_trace. The largest recommended limit is 60MB.

Configure the limit in the per-tenant overrides:

overrides:
    'tenant123':
        max_bytes_per_trace: 1.5e+07

Refer to the Standard overrides documentation for more information.

If you have long-running batch job traces, consider using span links to break them apart.

Large attributes

Very large attributes, 10KB or longer, can spike the memory usage of each component when they are encountered. Tempo’s Parquet format uses dictionary-encoded columns, which works well for repeated values. However, for very large and high cardinality attributes, this can require a large amount of memory.

A common source of large attributes is auto-instrumentation in these areas:

HTTP
- Request or response bodies
- Large headers
  - http.request.header.<key>
- Large URLs
  - http.url
  - url.full
Databases
- Full query statements
- db.statement
- db.query.text
Queues
- Message bodies

When reading these attributes, they can spike the memory usage of the read components:

query-frontend
querier
ingester
metrics-generator

When writing these attributes, they can spike the memory usage of the write components:

ingester
compactor
metrics-generator

You can automatically limit attribute sizes using max_attribute_bytes. You can also use these options:

Manually update application instrumentation to remove or limit these attributes
Drop the attributes in the tracing pipeline using attribute processor