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P_work's avatar
P_work
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1 year ago
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Notebook has 98% idle time in pipeline

Notebook in pipeline with custom environment (pre-loaded libraries, 8 driver cores, 8 exe cores, dynamic allocation) Monitor for notebook stats: Spark resource usage Total duration: roughly 5 min ...
  • v-echaithra's avatar
    1 year ago

    HI P_work ,

    When the same notebook behaves differently inside and outside the pipeline. That’s a classic case of orchestration overhead and environment provisioning latency.
    Even though the custom environment seems lightweight, the pipeline engine treats it as a separate provisioning task. That’s why you see the 4-minute delay, it's not just the notebook, it's the orchestration and environment spin-up
    Configure your pipeline to use pre-warmed clusters or instance pools. This avoids cold starts and can shave minutes off startup time.
    Instead of calling child pipelines in a loop, use APIs like dbutils.notebook.run() (Databricks) or mssparkutils.notebook.run() (Microsoft Fabric). These maintain context and reduce orchestration delays.