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Cross-signal correlation between traces, logs, and metrics using OTel semantic convention fields for end-to-end observability investigations.

opensearch-project By opensearch-project schedule Updated 3/22/2026
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name: correlation description: Cross-signal correlation between traces, logs, and metrics using OTel semantic convention fields for end-to-end observability investigations. allowed-tools: - Bash - curl

Cross-Signal Correlation

Overview

This skill teaches how to correlate traces, logs, and metrics across all three telemetry signals using shared OTel semantic convention fields. Correlation enables end-to-end investigations: start from a metric spike, trace it to a specific request, and find the associated logs — or start from an error log and reconstruct the full trace that produced it.

All OpenSearch queries use the PPL API at /_plugins/_ppl with HTTPS and basic authentication. Prometheus queries use the HTTP API at localhost:9090. Credentials are read from the .env file (default: admin / My_password_123!@#).

Connection Defaults

Variable Default Description
OPENSEARCH_ENDPOINT https://localhost:9200 OpenSearch base URL
OPENSEARCH_USER admin OpenSearch username
OPENSEARCH_PASSWORD My_password_123!@# OpenSearch password

OTel Correlation Fields Reference

Trace Context Correlation

Traces and logs share traceId and spanId fields. When an application emits a log within an active span, the OTel SDK automatically injects the current trace context into the log record. This creates a direct link between log entries and the spans that produced them.

Field Signal Type Description
traceId Traces, Logs keyword Hex-encoded 128-bit trace identifier shared between spans and log records
spanId Traces, Logs keyword Hex-encoded 64-bit span identifier shared between spans and log records
traceFlags Logs integer W3C trace flags (e.g., 01 = sampled) carried on log records
  • In the Trace_Index (otel-v1-apm-span-*): traceId and spanId identify each span
  • In the Log_Index (logs-otel-v1-*): traceId and spanId link the log to the span that was active when the log was emitted

Metric-to-Trace Correlation (Prometheus Exemplars)

Prometheus exemplars attach trace context to individual metric samples. When the OTel SDK records a metric observation inside an active span, it can attach the trace_id and span_id as exemplar labels. This links a specific metric data point back to the trace that produced it.

Exemplar data model:

Field Description
trace_id Hex-encoded trace identifier from the span active during metric recording
span_id Hex-encoded span identifier from the span active during metric recording
filtered_attributes Additional key-value pairs attached to the exemplar
timestamp Time when the exemplar was recorded
value The metric sample value associated with this exemplar

Resource-Level Correlation

All three signals (traces, logs, metrics) share resource attributes that identify the originating service. These attributes are set by the OTel SDK and propagated through the pipeline:

Resource Attribute Traces Field Logs Field Prometheus Label Description
service.name serviceName resource.attributes.service.name service_name Service that produced the telemetry
service.namespace resource.service.namespace resource.attributes.service.namespace service_namespace Namespace grouping related services
service.version resource.service.version resource.attributes.service.version service_version Service version string
service.instance.id resource.service.instance.id resource.attributes.service.instance.id service_instance_id Unique instance identifier
deployment.environment.name resource.deployment.environment.name resource.attributes.deployment.environment.name deployment_environment_name Deployment environment (e.g., production, staging)

The OTel Collector's resourcedetection processor enriches telemetry with environment context, and the Prometheus promote_resource_attributes configuration (in docker-compose/prometheus/prometheus.yml) promotes these resource attributes to metric labels so they are queryable in PromQL.

GenAI Resource Attributes in Prometheus

The following GenAI resource attributes are promoted to Prometheus metric labels via the promote_resource_attributes configuration, enabling metric queries filtered by agent or model:

Resource Attribute Prometheus Label Description
gen_ai.agent.id gen_ai_agent_id Agent identifier
gen_ai.agent.name gen_ai_agent_name Human-readable agent name (only available if SDK sets this as a resource attribute; most SDKs set it as a span attribute instead)
gen_ai.provider.name gen_ai_provider_name LLM provider (e.g., bedrock, openai)
gen_ai.request.model gen_ai_request_model Model requested for the operation
gen_ai.response.model gen_ai_response_model Model that actually served the response

Trace-to-Log Correlation (PPL)

Find Logs by traceId

Given a trace ID, find all log entries emitted during that trace:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId = '\''<TRACE_ID>'\'' | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort `@timestamp`"}'

Find Logs by spanId

Given a span ID, find all log entries emitted during that specific span:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where spanId = '\''<SPAN_ID>'\'' | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort `@timestamp`"}'

Join Spans and Logs by traceId

Use PPL join to combine trace spans with their correlated logs in a single query:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID>'\'' | join left=s right=l ON s.traceId = l.traceId logs-otel-v1-* | fields s.spanId, s.name, s.serviceName, s.durationInNanos, l.severityText, l.body, l.`@timestamp`"}'

Full Timeline Reconstruction

Reconstruct the complete request timeline by interleaving spans and logs sorted by timestamp. Run both queries and merge results by time:

Step 1 — Get all spans for the trace:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID>'\'' | eval signal = '\''span'\'' | fields traceId, spanId, serviceName, name, startTime, endTime, durationInNanos, `status.code`, signal | sort startTime"}'

Step 2 — Get all logs for the trace:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId = '\''<TRACE_ID>'\'' | eval signal = '\''log'\'' | fields traceId, spanId, `resource.attributes.service.name`, severityText, body, `@timestamp`, signal | sort `@timestamp`"}'

Merge both result sets by timestamp to see the full chronological sequence of spans and log entries for the request.

Log-to-Trace Correlation (PPL)

Find Originating Trace from an Error Log

When you find an error log, extract its traceId and query the Trace_Index to reconstruct the full trace:

Step 1 — Find error logs and get their traceId:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where severityText = '\''ERROR'\'' | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort - `@timestamp` | head 10"}'

Step 2 — Query the Trace_Index with the extracted traceId to get all spans:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID_FROM_LOG>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime"}'

Find Specific Span from a Log Entry

When a log entry has a spanId, query the Trace_Index to find the exact span that was active when the log was emitted:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where spanId = '\''<SPAN_ID_FROM_LOG>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code`, `attributes.gen_ai.operation.name`"}'

Metric-to-Trace Correlation (Prometheus Exemplars)

Query Exemplars from Prometheus

Use the Prometheus exemplars API to retrieve trace context attached to metric samples. This links a metric observation back to the specific trace that produced it:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query_exemplars" \
  --data-urlencode 'query=http_server_duration_seconds_bucket' \
  --data-urlencode 'start=2024-01-01T00:00:00Z' \
  --data-urlencode 'end=2024-01-02T00:00:00Z'

The response contains exemplar objects with trace_id and span_id in the labels field:

{
  "status": "success",
  "data": [
    {
      "seriesLabels": { "service_name": "my-agent", "__name__": "http_server_duration_seconds_bucket" },
      "exemplars": [
        {
          "labels": { "trace_id": "abc123...", "span_id": "def456..." },
          "value": "0.25",
          "timestamp": 1704067200.000
        }
      ]
    }
  ]
}

Query Exemplars for GenAI Metrics

Query exemplars for GenAI operation duration, filtered by agent name:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query_exemplars" \
  --data-urlencode 'query=gen_ai_client_operation_duration_seconds_bucket{gen_ai_operation_name="invoke_agent"}' \
  --data-urlencode 'start=2024-01-01T00:00:00Z' \
  --data-urlencode 'end=2024-01-02T00:00:00Z'

Extract trace_id and Query Trace_Index

After extracting a trace_id from an exemplar response, query the Trace_Index for the full trace:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID_FROM_EXEMPLAR>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime"}'

PromQL Queries with GenAI Resource Labels

Filter metrics by GenAI resource labels before correlating to traces via exemplars:

By agent name:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=rate(gen_ai_client_operation_duration_seconds_count{gen_ai_operation_name="invoke_agent"}[5m])'

By model:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=rate(gen_ai_client_token_usage_count[5m])'

Then query exemplars for the filtered metric to get trace IDs for correlation.

Resource-Level Correlation

service.name Across All Signals

The service.name resource attribute is the primary key for correlating telemetry across all three signals at the service level.

Find all traces from a specific service:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where serviceName = '\''my-service'\'' | stats count() as span_count, avg(durationInNanos) as avg_duration by serviceName"}'

Find all logs from the same service:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where `resource.attributes.service.name` = '\''my-service'\'' | stats count() by severityText"}'

Find all metrics from the same service in Prometheus:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=rate(http_server_duration_seconds_count{service_name="my-service"}[5m])'

GenAI Resource Labels in Prometheus

Query metrics filtered by GenAI resource attributes that are promoted to Prometheus labels:

By agent:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=sum(rate(gen_ai_client_operation_duration_seconds_count{gen_ai_operation_name="invoke_agent"}[5m])) by (gen_ai_operation_name)'

By provider and model:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=sum(rate(gen_ai_client_token_usage_count[5m])) by (gen_ai_request_model)'

How Resource Attributes Flow Through the Stack

  1. The OTel SDK sets resource attributes (service.name, service.version, etc.) on all telemetry
  2. The OTel Collector's resourcedetection processor enriches telemetry with environment context (Docker, system info)
  3. For traces and logs: resource attributes are stored in OpenSearch as part of the document
  4. For metrics: the Prometheus promote_resource_attributes configuration (in docker-compose/prometheus/prometheus.yml) promotes resource attributes to metric labels, making them queryable in PromQL

This ensures the same service.name value appears in traces (serviceName field), logs (resource.attributes.service.name field), and metrics (service_name label) — enabling service-level correlation across all backends.

Correlation Workflows

Workflow 1: Metric Spike Investigation

Investigate a metric anomaly by correlating from metrics → traces → logs.

Step 1 — Detect the spike via PromQL:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=rate(http_server_duration_seconds_count[5m])'

Look for services with unusually high request rates or latency.

Step 2 — Query exemplars to get trace IDs from the spike window:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query_exemplars" \
  --data-urlencode 'query=http_server_duration_seconds_bucket' \
  --data-urlencode 'start=<SPIKE_START>' \
  --data-urlencode 'end=<SPIKE_END>'

Extract trace_id values from the exemplar response.

Step 3 — Query the Trace_Index for those traces:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID_FROM_EXEMPLAR>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime"}'

Step 4 — Query the Log_Index for correlated logs:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId = '\''<TRACE_ID_FROM_EXEMPLAR>'\'' | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort `@timestamp`"}'

Workflow 2: Error Log Investigation

Start from an error log and trace back to the root cause.

Step 1 — Find error logs:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where severityText = '\''ERROR'\'' | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort - `@timestamp` | head 10"}'

Step 2 — Extract the traceId from the error log and reconstruct the full trace tree:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID_FROM_ERROR_LOG>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime"}'

Step 3 — Identify the root cause span (look for error status or exceptions):

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID_FROM_ERROR_LOG>'\'' AND `status.code` = 2 | fields traceId, spanId, serviceName, name, `events.attributes.exception.type`, `events.attributes.exception.message` | sort startTime"}'

Step 4 — Get all logs for the error span to see the full context:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId = '\''<TRACE_ID_FROM_ERROR_LOG>'\'' | fields traceId, spanId, severityText, body, `@timestamp` | sort `@timestamp`"}'

Workflow 3: Slow Agent Investigation

Investigate a slow agent invocation by correlating spans, child operations, logs, and metrics.

Step 1 — Find slow invoke_agent spans:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where `attributes.gen_ai.operation.name` = '\''invoke_agent'\'' AND durationInNanos > 5000000000 | fields traceId, spanId, `attributes.gen_ai.agent.name`, durationInNanos, startTime | sort - durationInNanos | head 10"}'

Step 2 — Get all child spans to identify the bottleneck:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID>'\'' | fields traceId, spanId, parentSpanId, name, `attributes.gen_ai.operation.name`, durationInNanos, startTime | sort startTime"}'

Look for child spans with high durationInNanos — these are the bottleneck operations (e.g., slow tool calls, slow LLM responses).

Step 3 — Check tool calls within the slow trace:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID>'\'' AND `attributes.gen_ai.operation.name` = '\''execute_tool'\'' | fields spanId, `attributes.gen_ai.tool.name`, `attributes.gen_ai.tool.call.arguments`, durationInNanos | sort - durationInNanos"}'

Step 4 — Get correlated logs for the slow spans:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId = '\''<TRACE_ID>'\'' | fields spanId, severityText, body, `@timestamp` | sort `@timestamp`"}'

Step 5 — Check GenAI token usage metrics for the agent via PromQL:

curl -s "$PROMETHEUS_ENDPOINT/api/v1/query" \
  --data-urlencode 'query=sum(rate(gen_ai_client_token_usage_count[5m])) by (gen_ai_operation_name, gen_ai_request_model)'

Check if the agent is consuming unusually high token counts, which may explain slow response times.

Dynamic Field Discovery for Correlation

When correlating across signals, use describe or _mapping to discover available fields dynamically. This is especially useful when index schemas differ from the defaults.

Discover Trace Index Fields

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "describe otel-v1-apm-span-000001"}'

Discover Log Index Fields

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "describe logs-otel-v1-000001"}'

Discover Service Map Fields

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "describe otel-v2-apm-service-map-000001"}'

Use the field names from describe output to construct correlation queries when the default field names don't match your index schema.

Advanced Correlation Patterns

Batch Log Correlation via traceId IN List

When you have a set of traceIds from span queries, use IN to fetch all correlated logs in one query:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where traceId IN ('\''<TRACE_ID_1>'\'', '\''<TRACE_ID_2>'\'', '\''<TRACE_ID_3>'\'') | fields traceId, spanId, severityText, body, `resource.attributes.service.name`, `@timestamp` | sort `@timestamp`"}'

This is more efficient than querying logs one traceId at a time.

Log Correlation with Fallback for Missing Trace Context

Some logs may have empty or null traceId. Include those alongside correlated logs:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=logs-otel-v1-* | where `resource.attributes.service.name` = '\''frontend'\'' | where (traceId IN ('\''<TRACE_ID_1>'\'', '\''<TRACE_ID_2>'\'') OR traceId = '\'''\'' OR isnull(traceId)) | sort - `@timestamp` | head 50"}'

Remote Service Dependency Correlation

Identify which remote services a given service calls using coalesce() across OTel attribute variants. Different instrumentation libraries (Node.js, Go, Python, .NET) use different attributes:

curl -sk -u "$OPENSEARCH_USER:$OPENSEARCH_PASSWORD" \
  -X POST "$OPENSEARCH_ENDPOINT/_plugins/_ppl" \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where serviceName = '\''checkout'\'' | where kind = '\''SPAN_KIND_CLIENT'\'' | eval _remoteService = coalesce(`attributes.net.peer.name`, `attributes.server.address`, `attributes.rpc.service`, `attributes.db.system`, `attributes.gen_ai.system`, '\''unknown'\'') | stats count() as calls, avg(durationInNanos) as avg_latency by _remoteService | sort - calls"}'

Field Reference by Protocol

When correlating across different service types, these are the key fields by protocol:

Protocol Remote Service Field Operation Field
gRPC attributes.net.peer.name or attributes.server.address attributes.rpc.method
HTTP attributes.http.host or attributes.server.address attributes.http.route
Database attributes.db.system + attributes.server.address attributes.db.statement
Envoy/Istio attributes.upstream_cluster span name
LLM/GenAI attributes.gen_ai.system + attributes.server.address attributes.gen_ai.request.model
Message Queue attributes.messaging.destination.name span name

References

  • PPL Language Reference — Official PPL syntax documentation. Fetch this if queries fail due to OpenSearch version differences or new syntax.

AWS Managed Service Variants

Amazon OpenSearch Service (SigV4)

Replace the local OpenSearch endpoint and authentication with AWS SigV4 for all PPL queries in this skill:

curl -s --aws-sigv4 "aws:amz:REGION:es" \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  -X POST https://DOMAIN-ID.REGION.es.amazonaws.com/_plugins/_ppl \
  -H 'Content-Type: application/json' \
  -d '{"query": "source=otel-v1-apm-span-* | where traceId = '\''<TRACE_ID>'\'' | fields traceId, spanId, parentSpanId, serviceName, name, startTime, endTime, durationInNanos, `status.code` | sort startTime"}'
  • Endpoint format: https://DOMAIN-ID.REGION.es.amazonaws.com
  • Auth: --aws-sigv4 "aws:amz:REGION:es" with --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY"
  • The PPL API endpoint (/_plugins/_ppl) and query syntax are identical to the local stack
  • No -k flag needed — AWS managed endpoints use valid TLS certificates

Amazon Managed Service for Prometheus (AMP) (SigV4)

Replace the local Prometheus endpoint and authentication with AWS SigV4 for all PromQL and exemplar queries:

Query exemplars:

curl -s --aws-sigv4 "aws:amz:REGION:aps" \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  'https://aps-workspaces.REGION.amazonaws.com/workspaces/WORKSPACE_ID/api/v1/query_exemplars' \
  --data-urlencode 'query=http_server_duration_seconds_bucket' \
  --data-urlencode 'start=2024-01-01T00:00:00Z' \
  --data-urlencode 'end=2024-01-02T00:00:00Z'

Query metrics:

curl -s --aws-sigv4 "aws:amz:REGION:aps" \
  --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY" \
  'https://aps-workspaces.REGION.amazonaws.com/workspaces/WORKSPACE_ID/api/v1/query' \
  --data-urlencode 'query=rate(http_server_duration_seconds_count{service_name="my-service"}[5m])'
  • Endpoint format: https://aps-workspaces.REGION.amazonaws.com/workspaces/WORKSPACE_ID/api/v1/query
  • Auth: --aws-sigv4 "aws:amz:REGION:aps" with --user "$AWS_ACCESS_KEY_ID:$AWS_SECRET_ACCESS_KEY"
  • PromQL query syntax and exemplar API are identical to local Prometheus; only the endpoint and authentication differ
Install via CLI
npx skills add https://github.com/opensearch-project/observability-stack --skill correlation
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