[[["わかりやすい","easyToUnderstand","thumb-up"],["問題の解決に役立った","solvedMyProblem","thumb-up"],["その他","otherUp","thumb-up"]],[["わかりにくい","hardToUnderstand","thumb-down"],["情報またはサンプルコードが不正確","incorrectInformationOrSampleCode","thumb-down"],["必要な情報 / サンプルがない","missingTheInformationSamplesINeed","thumb-down"],["翻訳に関する問題","translationIssue","thumb-down"],["その他","otherDown","thumb-down"]],["最終更新日 2025-09-04 UTC。"],[[["\u003cp\u003eThis page provides guidance on managing configurations for deployed pipelines, including compute profiles, pipeline instrumentation, engine parameters, resource allocation, and alerts.\u003c/p\u003e\n"],["\u003cp\u003eYou can customize the compute profile that runs the pipeline and set parameters, with the option to manage profiles and view Dataproc provisioner properties.\u003c/p\u003e\n"],["\u003cp\u003eInstrumentation can be enabled or disabled to generate metrics for each pipeline node, which can help in performance monitoring, and is recommended unless resources are constrained.\u003c/p\u003e\n"],["\u003cp\u003eConfigurations can also be made to allow for custom Spark parameters, memory and CPU specifications for the driver and executor, and the setting of batch intervals for streaming data.\u003c/p\u003e\n"],["\u003cp\u003ePipeline alerts and post-processing tasks can be set up during pipeline design and viewed after deployment, with the flexibility to enable transformation pushdown for BigQuery execution.\u003c/p\u003e\n"]]],[],null,["# Manage pipeline configurations\n\nThis page describes ways you can manage configurations for deployed\npipelines.\n\nBefore you begin\n----------------\n\nThis page requires some background knowledge about [Compute profiles](/data-fusion/docs/how-to/manage-compute-profiles) and\n[pipeline performance](/data-fusion/docs/concepts/performance-tuning-overview).\n\nCompute profile configuration\n-----------------------------\n\nYou can change the compute profile or customize the parameters of the default\ncompute profile that runs the pipeline. For more information, see\n[Manage compute profiles](/data-fusion/docs/how-to/manage-compute-profiles) and [Dataproc provisioner properties](/data-fusion/docs/concepts/dataproc).\n\nPipeline configuration\n----------------------\n\nFor each pipeline, you can enable or disable instrumentation, such as timing\nmetrics. By default, instrumentation is on. If instrumentation is enabled, when\nyou run the pipeline, Cloud Data Fusion generates metrics for each pipeline\nnode. The following metrics display on the **Metrics** tab of each node. The\nsource, transformation, and sink metrics vary slightly.\n\n- Records out\n- Records in\n- Total number of errors\n- Records out per second\n- Min process time (one record)\n- Max process time (one record)\n- Standard deviation\n- Average processing time\n\nWe recommend you always turn on Instrumentation, unless the environment is short\non resources.\n\nFor streaming pipelines, you can also set the **Batch interval**\n(seconds/minutes) for streaming data.\n\nEngine configuration\n--------------------\n\nApache Spark is the default execution engine. You can pass custom parameters\nfor Spark. For more information, see [Parallel processing](/data-fusion/docs/concepts/parallel-processing).\n\nResources\n---------\n\nYou can specify the memory and number of CPUs for the Spark driver and\nexecutor. The driver orchestrates the Spark job. The executor handles the data\nprocessing in Spark. For more information, see [Resource management](/data-fusion/docs/concepts/resource-management).\n\nPipeline alert\n--------------\n\nYou can configure the pipeline to send alerts and start post processing tasks\nafter the pipeline run finishes. You create pipeline alerts when you design the\npipeline. After you deploy the pipeline, you can view the alerts. You can edit\nthe pipeline to change alert settings. For more information, see\n[Create alerts](/data-fusion/docs/how-to/create-alerts).\n\nTransformation pushdown\n-----------------------\n\nYou can enable Transformation pushdown if you want a pipeline to execute\ncertain transformations in BigQuery. For more information,\nsee the [Transformation Pushdown overview](/data-fusion/docs/concepts/transformation-pushdown).\n\nWhat's next\n-----------\n\n- Learn more about [viewing and downloading pipeline logs in Cloud Data Fusion](/data-fusion/docs/how-to/view-and-download-pipeline-logs)."]]
