[BUGFIX] ExpectColumnValuesToBeOfType returns full map-format result for all backends#11868
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EshwarCVS wants to merge 7 commits into
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[BUGFIX] ExpectColumnValuesToBeOfType returns full map-format result for all backends#11868EshwarCVS wants to merge 7 commits into
EshwarCVS wants to merge 7 commits into
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Pydantic coerces False/True to "False"/"True" when str appears before Literal[False]/Literal[True] in a Union. Fix _get_annotation_type to sort bool-like types to the front of generated Union types, so index_col=False (and similar bool flags) are preserved correctly. https://claude.ai/code/session_01ETHG72BuUQaQWWdtWDcQJM
for more information, see https://pre-commit.ci
…-format result
For non-map backends (Pandas non-object dtype, SQLAlchemy, Spark), the
expectation previously returned only {"observed_value": ...}. It now returns
the complete ColumnMapExpectation result structure (element_count,
unexpected_count, unexpected_percent, missing_count, etc.) alongside
observed_value, matching the documented output schema.
Also confirmed fivetran#11026 (accented chars in row_conditions) is already resolved
via alphas8bit in legacy_row_conditions.py.
https://claude.ai/code/session_014PRzTm8HD61UpoyPvE3ciH
👷 Deploy request for niobium-lead-7998 pending review.Visit the deploys page to approve it
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thanks for the PR @EshwarCVS . This expectation is unusual in that the Pandas implementation behaves as a row level check, while the Spark and SQL implementations behave as a schema level check. We'll address the gap, but haven't settled on a solution yet. I'll leave this open, but will hold off reviewing for the time being. |
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Is this PR still relevant? If so, what is blocking it? Is there anything you can do to help move it forward? This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions 🙇 |
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Summary
Fixes #11076 —
ExpectColumnValuesToBeOfTypewas returning only{"observed_value": <type>}for non-object Pandas dtypes, SQLAlchemy,and Spark backends instead of the standard
ColumnMapExpectationresultstructure documented in the expectation.
Also confirms #11026 (accented characters in
row_conditionstrings) isalready resolved via
alphas8bitinlegacy_row_conditions.py.Changes
expect_column_values_to_be_of_type.pytable.row_countandcolumn_values.nonnull.unexpected_countto
get_validation_dependenciesfor all backends so missing/nullstats are always available.
_build_map_result()helper that wraps aggregate type-checkresults through
_format_map_output, computingnonnull_countfromthe null metric and preserving
observed_value._validate()to route non-map paths through_build_map_result.test_expect_column_values_to_be_of_type.pycolumns containing nulls.
Before / After
Before:
{ "success": false, "result": { "observed_value": "StringType" } }After:
{ "success": false, "result": { "element_count": 5, "unexpected_count": 4, "unexpected_percent": 100.0, "partial_unexpected_list": [], "missing_count": 1, "missing_percent": 20.0, "unexpected_percent_total": 80.0, "unexpected_percent_nonmissing": 100.0, "observed_value": "StringType" } }