Professional Cloud DevOps Engineer
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An e-commerce organization runs a legacy order processing service that outputs structured JSON logs to stdout. The operations team needs to define a Service Level Indicator (SLI) to track the 95th and 99th percentile latency across different checkout categories without refactoring the application code to add OpenTelemetry or Prometheus metric client libraries.
A sample log payload emitted by the service contains:
StudyCerts
StudyCerts{
"message": "Order processed successfully",
"transaction_type": "express_checkout",
"duration_ms": 245.8
}
How should the DevOps engineer configure Google Cloud Observability to generate the required latency SLI metrics?
Deploy the Google-Built OpenTelemetry Collector on the host and configure the transform/aco-gke processor to extract latency histograms from standard output.
Create a counter log-based metric in Cloud Logging with a label extracting jsonPayload.duration_ms and another label extracting jsonPayload.transaction_type.
Create a distribution log-based metric in Cloud Logging, configure the field extractor to extract jsonPayload.duration_ms as the numeric value, and define a metric label extracting jsonPayload.transaction_type.
Export the application logs to BigQuery via a log router sink and configure a scheduled query to calculate percentiles and publish them as custom metrics.
Deploy the Google-Built OpenTelemetry Collector on the host and configure the transform/aco-gke processor to extract latency histograms from standard output.
Create a counter log-based metric in Cloud Logging with a label extracting jsonPayload.duration_ms and another label extracting jsonPayload.transaction_type.
Create a distribution log-based metric in Cloud Logging, configure the field extractor to extract jsonPayload.duration_ms as the numeric value, and define a metric label extracting jsonPayload.transaction_type.
A distribution log-based metric extracts continuous or discrete numeric values from matched log entries and records them into histogram buckets over time series intervals within Google Cloud Monitoring.
jsonPayload.duration_ms in the metric's value extractor, Cloud Logging parses the numeric duration directly from structured JSON payloads.jsonPayload.transaction_type allows operators to filter, group, and slice latency distributions across different checkout paths.Direct metric instrumentation via SDKs requires code modification, testing, and redeployment. A distribution log-based metric leverages existing JSON logging infrastructure to provide histogram-based percentile metrics immediately at the platform ingestion layer.
Export the application logs to BigQuery via a log router sink and configure a scheduled query to calculate percentiles and publish them as custom metrics.