long-running job on GCP cloud run
Jan 8
I am reading 10 million records from BigQuery and doing some transformation and creating the .csv file, the same .csv stream data I am uploading to SFTP server using Node.JS.
This job taking approximately 5 to 6 hrs to complete the request locally.
Solution has been delpoyed on GCP Cloud run but after 2 to 3 second cloud run is closing the container with 503 error.
Please find below configuration of GCP Cloud Run.
Autoscaling: Up to 1 container instances CPU allocated: default Memory allocated: 2Gi Concurrency: 10 Request timeout: 900 seconds
Is GCP Cloud Run is good option for long running background process?
1 answer
Accepted answer · original discussion
Jan 8
You can try using an Apache Beam pipeline deployed via Cloud Dataflow. Using Python, you can perform the task with the following steps:
Stage 1. Read the data from BigQuery table.
beam.io.Read(beam.io.BigQuerySource(query=your_query,use_standard_sql=True))
Stage 2. Upload Stage 1 result into a CSV file on a GCS bucket.
beam.io.WriteToText(file_path_prefix="", \
file_name_suffix='.csv', \
header='list of csv file headers')
Stage 3. Call a ParDo function which will then take CSV file created in Stage 2 and upload it to the SFTP server. You can refer this link.
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