Loki: Maximum active stream limit exceeded, reduce the number of active streams
Every unique set of labels is its own stream in Loki, and the tenant has gone over its stream limit. That nearly always means a high cardinality value got into the labels, such as a request ID, user ID, pod hash or full path. Raising the limit only postpones the problem. Move the value out of the labels and into the log line or structured metadata.
Quick fix
Read the commands before running them. Anything that restarts a service, deletes data or changes permissions should be tried on a non-production system first.
# Which label has exploded? (logcli)
logcli series '{job="app"}' --analyze-labels
# Promtail/Alloy: stop promoting the value to a label
# pipeline_stages:
# - labeldrop: [request_id, user_id]
# Query it from the line instead
{job="app"} | json | request_id="abc123"
# Only if streams are legitimately numerous (limits_config)
limits_config:
max_global_streams_per_user: 10000How to diagnose Logging errors
Logging failures are dangerous because they are silent: the application keeps running while its telemetry disappears. The recurring causes are buffer overflow under backpressure (the destination cannot keep up), permissions on log files or directories, and ingestion rate limits at the cloud provider. Monitoring the logging pipeline itself is the only reliable way to notice.
If the quick fix above does not resolve it, work through these steps. They apply to this whole class of error, not just to this one message, which is usually what saves the time.
- Check the collector's own logs first: Fluentd, Logstash and the CloudWatch agent all log their own failures, usually to a separate destination.
- Look for backpressure metrics: buffer queue length, retry counts, and dropped-record counters. A full buffer means the destination, not the collector, is the bottleneck.
- Verify write permissions and disk space on the buffer path. A full disk silently stops most collectors.
- For cloud ingestion, check the API rate limit for the log group or stream and batch more aggressively rather than retrying harder.
- Add a heartbeat log line and alert on its absence. This is the only way to detect a pipeline that has stopped without erroring.
Tools worth reaching for
collector self-logsbuffer/queue metricsdf -haws logs describe-log-streamslogger / fluent-cat for test events
Authoritative references
Primary documentation for this error, worth reading before applying any fix in production.
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Something missing or wrong?
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