log-analytics

You can discover and interpret log data stored in OpenSearch without assuming index names or schemas by applying a discovery-first workflow. This approach identifies indices, mappings, and data formats dynamically, enabling faster troubleshooting and observability.

You can build and run PPL queries to measure log volume, detect errors, extract top messages, and identify anomalies in OpenSearch. The system prioritizes PPL for querying and provides fallbacks for complex analytics when needed.

Yes, you can identify and analyze log data stored in AWS-hosted OpenSearch deployments, including AOS and AOSS. The system supports local and cloud OpenSearch deployments to ensure broad compatibility for log analysis tasks.

The best way to correlate OpenSearch logs with traces is to use discovery workflows that identify log indices and then run targeted PPL queries. This correlation helps investigate incidents and improve system reliability by linking related data sources.

Yes, log analysis with OpenSearch provides fallbacks for complex analytics. While it prioritizes PPL for querying log data, it ensures advanced analytical requirements are met through alternative processing methods within the workflow.

You need a discovery-first workflow for OpenSearch log analysis when you must determine indices, mappings, and data formats across unfamiliar local or AWS-hosted deployments. This process is essential to interpret log data without assuming existing schemas.