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When you update a job on the Cloud Dataflow service, you replace the existing job with a new job that runs your updated pipeline code.The Cloud Dataflow service retains the job name, but runs the replacement job with an updated . Idk if it's my connection but applying 1 lvl upgrades from garrison is a real pain. The Apache Beam SDKs provide a way to update an ongoing streaming job on the Cloud Dataflow managed service with new pipeline code."In-flight" data will still be processed by the transforms in your new pipeline.However, additional transforms that you add in your replacement pipeline code may or may not take effect, depending on where the records are buffered.

Please have a look at the article, follow the steps outlined here, and if you continue to experience errors, please open a ticket with the Google Play Services team. Note: The Cloud Dataflow service currently has a limitation in that the error returned from a failed update attempt is only visible in your console or terminal if you use blocking execution.The current workaround consists of the following steps: The compatibility check ensures that the Cloud Dataflow service can transfer intermediate state data from the steps in your prior job to your replacement job, as specified by the transform mapping that you provide.We recommend that you attempt only smaller changes to your pipeline's windowing, such as changing the duration of fixed- or sliding-time windows.Making major changes to windowing or triggers, like changing the windowing algorithm, might have unpredictable results on your pipeline output.

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