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The current workaround consists of the following steps: The compatibility check ensures that the 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.The compatibility check also ensures that your pipeline's can cause the compatibility check to fail because any in-flight data or buffered records may not be correctly serialized in the replacement pipeline.
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.
For example: Transform names are hierarchical, based on the transform hierachy in your pipeline.
If your pipeline has a composite transform, the nested transforms are named in terms of their containing transform.
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The compatibility check ensures that things like intermediate state information and buffered data can be transferred from your prior job to your replacement job.
Note: It is possible to use the Update feature to scale a streaming Dataflow pipeline to use a different number of workers.
The Dataflow SDK for Python does not yet support streaming.
When you update your job, the Dataflow service performs a compatibility check between your currently-running job and your potential replacement job.