Exceptions in Google Cloud Dataflow pipelines from BigQuery to Cloud Bigtable

点点圈 提交于 2019-12-04 19:10:54

We spoke offline. The problem here is that you have too many Dataflow workers compared to the number of Cloud Bigtable nodes in your cluster. You need to change that ratio by either reducing Dataflow workers or contacting our team to increase your Cloud Bigtable resources.

Bigtable was performing admirably relative to the amount of Cloud Bigtable Nodes you had, but the load from Dataflow was too high to reliably handle.

You can view your usage in the "CPU Usage" graph in the Google Cloud console. Anything over 80% of your capacity is likely to cause problems. If you get more Bigtable Quota, you can increase the number of nodes you have before you run the Dataflow job, and reduce it after the job is done. For example, Scio does that.

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Regarding "Also what happens to data inside the pipeline? Is it reprocessed? Or is it lost in transit to BigTable?":

Dataflow tries to send the data to Bigtable again. In those cases, Dataflow's retry mechanism will correct for temporary issues.

Unfortunately, when the problem turns out to be Cloud Bigtable overload, the retries compound the problem by sending more traffic to Bigtable, thereby exacerbating the problem.

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