Practice vocabulary for log correlation: trace IDs, correlation IDs, structured log fields, cross-service querying, and log-trace-metric correlation.
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The unique identifier that links all log entries produced during a single distributed request across multiple services is called:
The trace_id links all logs for a request — in distributed tracing (OpenTelemetry), the trace_id is a 128-bit ID shared across all spans for a single end-to-end request.
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When a request identifier is passed through every service call in a chain so logs from all services can be connected, this is described as:
The correlation ID is propagated through service calls — typically as an HTTP header (X-Correlation-ID or traceparent), it must be explicitly passed and logged by each service.
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A log field that contains a specific value (rather than being embedded in a message string) and can be used to filter or group log entries is called:
The structured log field enables filtering — structured logging (JSON logs with explicit fields) allows Loki, Elasticsearch, or Splunk to filter by field='value' efficiently.
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When you search logs using a correlation_id to find every log entry related to a specific request across multiple services, you are performing:
We query logs by correlation_id to find all related events — this is the core operational pattern: start with an error, grab its correlation_id, then find the full request story.
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The observability practice of connecting log entries to their corresponding trace spans and metric data points for a single request is called:
The log-trace-metric correlation vocabulary — modern observability platforms (Grafana, Datadog, Honeycomb) allow jumping from a log entry to its trace span to the metric at that timestamp.