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https://issues.apache.org/jira/browse/FLINK-40236?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated FLINK-40236:
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Labels: pull-request-available (was: )
> _infer_type infers ARRAY<NULL> for a list whose first element is None
> ---------------------------------------------------------------------
>
> Key: FLINK-40236
> URL: https://issues.apache.org/jira/browse/FLINK-40236
> Project: Flink
> Issue Type: Bug
> Components: API / Python
> Reporter: Nikolaus Schuetz
> Priority: Minor
> Labels: pull-request-available
>
> _infer_type in flink-python/pyflink/table/types.py infers a list's element
> type from obj[0] instead of the first non-None element it scans for. When the
> first element is None, the
> array element type collapses to NULL, so schema inference becomes
> order-dependent:
> {code}
> _infer_type([1, None]) -> ArrayType(BigIntType) # correct
> _infer_type([None, 1]) -> ArrayType(NullType) # wrong; should be
> BigIntType
> {code}
> The dict branch just above handles this correctly (it infers from the found
> non-None value). from_elements([Row(c=[None, 1])]) without an explicit schema
> infers ARRAY<NULL>, which
> then typically errors downstream.
> Fix: infer from the scanned non-None element v rather than obj[0]. Verified
> against the released apache-flink 2.3.0 wheel: the array element type is
> NullType before the fix and
> BigIntType after.
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