ES_INGEST_GROK_TIMEOUT elasticsearch runtime_error ai_generated true

IngestProcessorException: pipeline [my_pipeline] processor [grok] failed with message [grok pattern [%{GREEDYDATA:message}] timed out after [5000ms]]

ID: elasticsearch/ingest-pipeline-grok-timeout

Also available as: JSON · Markdown · 中文
84%Fix Rate
87%Confidence
1Evidence
2024-11-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
7.17.0 active — — —
8.11.0 active — — —
8.12.0 active — — —

Root Cause

A grok pattern in an ingest pipeline takes too long to match due to complex regex or large input data, exceeding the default timeout.

generic

中文

由于复杂的正则表达式或输入数据过大，摄取管道中的 grok 模式匹配耗时过长，超过默认超时时间。

Official Documentation

https://www.elastic.co/guide/en/elasticsearch/reference/current/grok-processor.html

Workarounds

  1. 85% success Optimize the grok pattern by replacing `%{GREEDYDATA}` with more specific patterns (e.g., `%{DATA}` or `%{TIMESTAMP_ISO8601}`) and using anchors like `^` and `$`. 
    Optimize the grok pattern by replacing `%{GREEDYDATA}` with more specific patterns (e.g., `%{DATA}` or `%{TIMESTAMP_ISO8601}`) and using anchors like `^` and `$`.
  2. 80% success Set `ignore_failure: true` on the grok processor to skip failures on problematic documents, and add a `set` processor to flag unparsed fields. 
    Set `ignore_failure: true` on the grok processor to skip failures on problematic documents, and add a `set` processor to flag unparsed fields.
  3. 90% success Use `POST _ingest/pipeline/_simulate` to test patterns on sample data and identify performance bottlenecks. 
    Use `POST _ingest/pipeline/_simulate` to test patterns on sample data and identify performance bottlenecks.

中文步骤

  1. Optimize the grok pattern by replacing `%{GREEDYDATA}` with more specific patterns (e.g., `%{DATA}` or `%{TIMESTAMP_ISO8601}`) and using anchors like `^` and `$`.
  2. Set `ignore_failure: true` on the grok processor to skip failures on problematic documents, and add a `set` processor to flag unparsed fields.
  3. Use `POST _ingest/pipeline/_simulate` to test patterns on sample data and identify performance bottlenecks.

Dead Ends

Common approaches that don't work:

  1. 70% fail

    Masks the problem but doesn't fix pattern inefficiency; can cause pipeline backpressure and node resource exhaustion.

  2. 90% fail

    Loses parsing functionality, leading to unparsed raw data and downstream analysis issues.