elasticsearch-analysis

You can analyze Elasticsearch logs to find incident root causes by running Python scripts that build time-bounded queries, perform aggregations, and sample log patterns. This skill identifies top error sources and discovers patterns across large log streams.

Yes, you can use Lucene query syntax for efficient log exploration and Elasticsearch JSON DSL for precise aggregations and boolean queries. Both query methods are supported to generate statistics, samples, and patterns across your ELK or OpenSearch stack.

The best way to investigate an error spike in OpenSearch is to run statistics and sampling scripts that aggregate logs within a specific time range. This identifies top error sources and extracts patterns to pinpoint the incident root cause quickly.

Yes, this log analysis approach works across both ELK and OpenSearch stacks. It uses a proxy-enabled HTTP client to query your cluster, supporting Lucene syntax and JSON DSL to discover incident patterns regardless of your specific stack implementation.

You can sample logs from the last hour by running the provided Python script with a 60-minute time range parameter. The script uses an errors-only sampling strategy to filter and extract relevant log entries for root cause analysis.

Yes, you need Python and the httpx dependency installed to run the log analysis scripts. The scripts use httpx as a proxy-enabled HTTP client to communicate with your Elasticsearch or OpenSearch cluster and retrieve log data.