spark-instrumented-optimizer/python/pyspark/sql/tests/test_group.py
HyukjinKwon 30468a9015 [SPARK-34306][SQL][PYTHON][R] Use Snake naming rule across the function APIs
### What changes were proposed in this pull request?

This PR completes snake_case rule at functions APIs across the languages, see also SPARK-10621.

In more details, this PR:
- Adds `count_distinct` in Scala Python, and R, and document that `count_distinct` is encouraged. This was not deprecated because `countDistinct` is pretty commonly used. We could deprecate in the future releases.
- (Scala-specific) adds `typedlit` but doesn't deprecate `typedLit` which is arguably commonly used. Likewise, we could deprecate in the future releases.
- Deprecates and renames:
  - `sumDistinct` -> `sum_distinct`
  - `bitwiseNOT` -> `bitwise_not`
  - `shiftLeft` -> `shiftleft` (matched with SQL name in `FunctionRegistry`)
  - `shiftRight` -> `shiftright` (matched with SQL name in `FunctionRegistry`)
  - `shiftRightUnsigned` -> `shiftrightunsigned` (matched with SQL name in `FunctionRegistry`)
  - (Scala-specific) `callUDF` -> `call_udf`

### Why are the changes needed?

To keep the consistent naming in APIs.

### Does this PR introduce _any_ user-facing change?

Yes, it deprecates some APIs and add new renamed APIs as described above.

### How was this patch tested?

Unittests were added.

Closes #31408 from HyukjinKwon/SPARK-34306.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-02-02 09:29:40 +09:00

48 lines
1.9 KiB
Python

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from pyspark.sql import Row
from pyspark.testing.sqlutils import ReusedSQLTestCase
class GroupTests(ReusedSQLTestCase):
def test_aggregator(self):
df = self.df
g = df.groupBy()
self.assertEqual([99, 100], sorted(g.agg({'key': 'max', 'value': 'count'}).collect()[0]))
self.assertEqual([Row(**{"AVG(key#0)": 49.5})], g.mean().collect())
from pyspark.sql import functions
self.assertEqual((0, u'99'),
tuple(g.agg(functions.first(df.key), functions.last(df.value)).first()))
self.assertTrue(95 < g.agg(functions.approx_count_distinct(df.key)).first()[0])
# test deprecated countDistinct
self.assertEqual(100, g.agg(functions.countDistinct(df.value)).first()[0])
if __name__ == "__main__":
import unittest
from pyspark.sql.tests.test_group import * # noqa: F401
try:
import xmlrunner # type: ignore[import]
testRunner = xmlrunner.XMLTestRunner(output='target/test-reports', verbosity=2)
except ImportError:
testRunner = None
unittest.main(testRunner=testRunner, verbosity=2)