Query Analyze Correlated Traces Logs Bigquery

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Your team manages a multi-tier microservices application running on Google Cloud that is experiencing intermittent latency spikes during high-traffic periods. Distributed tracing is configured across all services, and structured application logs are enriched with standard Google Cloud trace correlation fields.

You need to locate specific slow transactions, inspect the correlated logs for each individual trace span, and perform a historical trend analysis over the past 30 days of log data to identify the downstream database operations causing the degradation.

Which combination of observability tools and query workflows should you implement?

Locate high-latency requests in Cloud Trace, query correlated logs in Cloud Logging using the fully qualified trace="projects/[PROJECT_ID]/traces/[TRACE_ID]" filter, and use BigQuery / Log Analytics to run SQL aggregations across historical log records.

Capture real-time logs by initiating a live tailing session filtered on customer IDs, manually inspect latency distributions in Cloud Monitoring Metrics Explorer, and create a Cloud Monitoring log-based metric for 30-day historical query analysis.

Filter logs in Logs Explorer using custom JSON payload properties such as jsonPayload.trace_id="[TRACE_ID]", inspect trace spans via the Error Reporting console, and export raw logs to Cloud Storage for interactive ad-hoc SQL queries.

Export trace data from Cloud Trace into Cloud Storage as CSV files, use Dataprep to clean the trace spans, and execute gcloud logging read commands in parallel shell scripts to fetch matching historical logs.

Professional Cloud DevOps Engineer

Query Analyze Correlated Traces Logs Bigquery

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