Anda dapat mengubah profil komputasi atau menyesuaikan parameter profil komputasi default yang menjalankan pipeline. Untuk mengetahui informasi selengkapnya, lihat
Mengelola profil komputasi dan Properti penyedia Dataproc.
Konfigurasi pipeline
Untuk setiap pipeline, Anda dapat mengaktifkan atau menonaktifkan instrumentasi, seperti metrik
waktu. Secara default, instrumentasi aktif. Jika instrumentasi diaktifkan, saat Anda menjalankan pipeline, Cloud Data Fusion akan menghasilkan metrik untuk setiap node pipeline. Metrik berikut ditampilkan di tab Metrics di setiap node. Metrik
sumber, transformasi, dan sink sedikit bervariasi.
Merekam keluar
Data dalam
Total jumlah error
Data yang direkam per detik
Waktu proses min (satu data)
Waktu proses maks (satu data)
Simpangan baku
Waktu pemrosesan rata-rata
Sebaiknya Anda selalu mengaktifkan Instrumentasi, kecuali jika lingkungan kekurangan
resource.
Untuk pipeline streaming, Anda juga dapat menetapkan Interval batch
(detik/menit) untuk data streaming.
Konfigurasi mesin
Apache Spark adalah mesin eksekusi default. Anda dapat meneruskan parameter kustom
untuk Spark. Untuk mengetahui informasi selengkapnya, lihat Pemrosesan paralel.
Resource
Anda dapat menentukan memori dan jumlah CPU untuk driver dan eksekutor Spark. Driver mengatur tugas Spark. Eksekutor menangani pemrosesan data di Spark. Untuk mengetahui informasi selengkapnya, lihat Pengelolaan resource.
Notifikasi pipeline
Anda dapat mengonfigurasi pipeline untuk mengirim pemberitahuan dan memulai tugas pascapemrosesan
setelah operasi pipeline selesai. Anda membuat pemberitahuan pipeline saat mendesain
pipeline. Setelah men-deploy pipeline, Anda dapat melihat pemberitahuan. Anda dapat mengedit
pipeline untuk mengubah setelan pemberitahuan. Untuk mengetahui informasi selengkapnya, lihat
Membuat pemberitahuan.
Pushdown transformasi
Anda dapat mengaktifkan Pushdown transformasi jika ingin pipeline menjalankan transformasi tertentu di BigQuery. Untuk mengetahui informasi selengkapnya,
lihat Ringkasan Pushdown Transformasi.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 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)."]]
