jingz-db commented on code in PR #48124:
URL: https://github.com/apache/spark/pull/48124#discussion_r1837410252
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python/pyspark/sql/tests/pandas/test_pandas_transform_with_state.py:
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@@ -241,11 +235,15 @@ def check_results(batch_df, _):
# test list state with ttl has the same behavior as list state when state
doesn't expire.
def test_transform_with_state_in_pandas_list_state_large_ttl(self):
- def check_results(batch_df, _):
- assert set(batch_df.sort("id").collect()) == {
- Row(id="0", countAsString="2"),
- Row(id="1", countAsString="2"),
- }
+ def check_results(batch_df, batch_id):
+ if batch_id == 0:
+ assert set(batch_df.sort("id").collect()) == {
+ Row(id="0", countAsString="2"),
+ Row(id="1", countAsString="2"),
+ }
+ else:
Review Comment:
Trigger.AvailableNow() will also keep making new batches... I had a
discussion with Bo previously here:
https://github.com/apache/spark/pull/48124#discussion_r1790883181
Not sure if we can do this in the in the `after()` as the query will keep
making new batches because `shouldRunAnotherBatch` will always return True for
processingTime mode. Scala side doesn't have the issue because we are using the
`testStream`, `CheckAnswer` framework and it will shut down the query
explicitly.
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