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How to find metrics

Job metrics can be found in the Geneva Console UI, by clicking on a job’s ID to get to the “Job details” page.

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Core diagnostic metrics

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Quick diagnosis workflow

  1. Check rows_checkpointed vs rows_ready_for_commit.
    • If rows_checkpointed is high but rows_ready_for_commit is low, fragment writer is usually the bottleneck.
    • This often indicates object storage read/write pressure (for example S3).
  2. Compare read, UDF, and checkpoint timing.
    • High read_io_time_ms: storage or scan bottleneck.
    • High udf_processing_time: UDF compute bottleneck.
    • High batch_checkpointing_time: checkpoint overhead bottleneck.
    • Typical mitigations: increase checkpoint_size, increase max_checkpoint_size, or compact the table to produce larger fragments.
  3. Check writer timing.
    • High writer_write_time is commonly object storage throttling/throughput limit.
    • Typical mitigations: use higher network-bandwidth node types, and keep object storage and compute nodes in the same region.
  4. Check commit pressure.
    • High commit_conflict_retries, commit_backoff_time_ms, or commit_concurrent_writer_retries indicates commit contention.
  5. Check parallelism deficit.
    • If cnt_geneva_workers_pending stays high while cnt_geneva_workers_active stays low, the job is running below desired parallelism due to cluster/resource constraints.

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Notes

  • Timing metrics are cumulative and may overlap; do not sum them as exact wall time.
  • For completed jobs, row counters should settle to stable final values.