Richard Swinbank troubleshoots an issue:
I’m using a stream watermark to handle late arriving data – basically1) my watermark enables the stream to accept data arriving up to 10 seconds late …and that’s where the problem shows up.
When I run this streaming query – in Azure Databricks I can do this simply with
display(df_pivot)– I receive the error:AnalysisException: Detected pattern of possible ‘correctness’ issue due to global watermark. The query contains stateful operation which can emit rows older than the current watermark plus allowed late record delay, which are “late rows” in downstream stateful operations and these rows can be discarded. Please refer the programming guide doc for more details. If you understand the possible risk of correctness issue and still need to run the query, you can disable this check by setting the config `spark.sql.streaming.statefulOperator.checkCorrectness.enabled` to false.
Read on to learn more about the scenario, the issue, and the solution.