Observability in Amazon OpenSearch Service
Observability is the practice of understanding the internal state and performance of complex systems by examining their outputs. Traditional monitoring tells you a system is down; observability helps you understand why by letting you ask new questions about your telemetry data.
What Amazon OpenSearch Service provides
Amazon OpenSearch Service provides a unified observability solution by collecting, correlating, and visualizing three types of telemetry data:
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Logs – Timestamped records of events.
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Traces – End-to-end journey of requests through distributed services.
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Metrics – Time-series data representing system health, through the Amazon Managed Service for Prometheus direct query integration.
By bringing these together in a single interface, Amazon OpenSearch Service helps operations teams, SREs, and developers detect, diagnose, and resolve issues faster.
The Amazon OpenSearch Service approach to observability
Amazon OpenSearch Service differentiates itself in three key ways:
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OpenTelemetry-native with OpenSearch Ingestion as the last mile – Standardize on OTel for instrumentation and collection. Amazon OpenSearch Ingestion serves as the fully managed pipeline that filters, enriches, transforms, and routes your telemetry data before indexing.
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Unified logs, traces, and metrics in OpenSearch UI – Analyze all three signal types from a single observability workspace. Correlate a slow trace to its application logs, or overlay Prometheus metrics on your service dashboards.
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Purpose-driven query languages – Use Piped Processing Language (PPL)
for logs and traces, and PromQL for metrics. Each language is optimized for its signal type, giving you expressive querying without compromise.
Note
The observability features described in this section are available only in OpenSearch UI. They are not available in OpenSearch Dashboards. For new observability workloads, we recommend setting up an Using OpenSearch UI in Amazon OpenSearch Service with an observability workspace.
