Smooth Validation and Profiling Performance #619
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action before refactor, with associated tests.
eugmandel
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Aug 15, 2019
jcampbell
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abegong
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Aug 16, 2019
| return column.withColumn('__success', success_udf(column[0])) | ||
| if None in value_set: | ||
| # spark isin returns None when any value is compared to None | ||
| logger.error("expect_column_values_to_be_in_set cannot support a None in the value_set in spark") |
| {'expectation_type': 'expect_column_values_to_be_in_set', | ||
| 'kwargs': {'column': 'D', 'value_set': ['e', 'f', 'g', 'h']}} | ||
| ] | ||
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| sub1 = df[:3] | ||
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| sub1.discard_failing_expectations() | ||
| self.assertEqual(sub1.find_expectations(), exp1) | ||
| # PY2 sorting is allowed and order not guaranteed |
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Feels like sorting is a function that ExpectationSuite.get_config (or similar) should eventually handle.
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This PR addresses issues related to performance and ease of profiling larger datasets.
In addition, this PR changes validation by default to detect column-type expectations and group them. This is nearly zero-cost in all circumstances and can speed up cases where iterating over the same column has a cache benefit. It also demonstrates how using a ValidationOperator may affect validation order or other characteristics.