Your team operates a high-throughput streaming Dataflow pipeline. When a sudden spike in retries occurs, you want to isolate only the ERROR-level log entries that originate from a single failing job so you can inspect the stack traces in Cloud Logging's Logs Explorer. Which advanced filter expression will most quickly return only the relevant error entries for that job?
resource.type="dataflow_step" AND resource.labels.job_id="JOB_ID" AND severity>=ERROR
logName:"dataflow.googleapis.com%2Fjob-message" AND severity>=ERROR
resource.type="dataflow_job" AND resource.labels.job_id="JOB_ID"
Dataflow worker and step logs are exported with the monitored resource type dataflow_step. Each entry includes the job identifier in resource.labels.job_id. Combining that label with a severity filter restricts the result set to the specific job and to messages that are ERROR or higher, eliminating noise from other concurrent pipelines and lower-severity events. The other expressions either lack the job_id constraint or rely on a broader resource type, so they will return unrelated log lines or omit step-level worker errors.
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What is the purpose of severity levels in Cloud Logging?
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What does `resource.type="dataflow_step"` represent in the filter?
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Why is `resource.labels.job_id="JOB_ID"` crucial in the filter?
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What is the difference between 'dataflow_step' and 'dataflow_job' resource types in GCP logging?
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Why is severity filtering important in Cloud Logging advanced filters?
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How does the 'resource.labels.job_id' parameter help in filtering Cloud Logging entries?
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