spark-instrumented-optimizer/python/pyspark/pandas/tests/test_frame_spark.py

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from distutils.version import LooseVersion
import os

import pandas as pd
import pyspark

from pyspark import pandas as ps
from pyspark.testing.pandasutils import PandasOnSparkTestCase, TestUtils
from pyspark.testing.sqlutils import SQLTestUtils


class SparkFrameMethodsTest(PandasOnSparkTestCase, SQLTestUtils, TestUtils):
    def test_frame_apply_negative(self):
        with self.assertRaisesRegex(
            ValueError, "The output of the function.* pyspark.sql.DataFrame.*int"
        ):
            ps.range(10).spark.apply(lambda scol: 1)

    def test_hint(self):
        pdf1 = pd.DataFrame(
            {"lkey": ["foo", "bar", "baz", "foo"], "value": [1, 2, 3, 5]}
        ).set_index("lkey")
        pdf2 = pd.DataFrame(
            {"rkey": ["foo", "bar", "baz", "foo"], "value": [5, 6, 7, 8]}
        ).set_index("rkey")
        kdf1 = ps.from_pandas(pdf1)
        kdf2 = ps.from_pandas(pdf2)

        if LooseVersion(pyspark.__version__) >= LooseVersion("3.0"):
            hints = ["broadcast", "merge", "shuffle_hash", "shuffle_replicate_nl"]
        else:
            hints = ["broadcast"]

        for hint in hints:
            self.assert_eq(
                pdf1.merge(pdf2, left_index=True, right_index=True).sort_values(
                    ["value_x", "value_y"]
                ),
                kdf1.merge(kdf2.spark.hint(hint), left_index=True, right_index=True).sort_values(
                    ["value_x", "value_y"]
                ),
                almost=True,
            )
            self.assert_eq(
                pdf1.merge(pdf2 + 1, left_index=True, right_index=True).sort_values(
                    ["value_x", "value_y"]
                ),
                kdf1.merge(
                    (kdf2 + 1).spark.hint(hint), left_index=True, right_index=True
                ).sort_values(["value_x", "value_y"]),
                almost=True,
            )

    def test_repartition(self):
        kdf = ps.DataFrame({"age": [5, 5, 2, 2], "name": ["Bob", "Bob", "Alice", "Alice"]})
        num_partitions = kdf.to_spark().rdd.getNumPartitions() + 1

        num_partitions += 1
        new_kdf = kdf.spark.repartition(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

        # Reserves Index
        kdf = kdf.set_index("age")
        num_partitions += 1
        new_kdf = kdf.spark.repartition(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

        # Reflects internal changes
        kdf = kdf.reset_index()
        kdf = kdf.set_index("name")
        kdf2 = kdf + 1
        num_partitions += 1
        self.assert_eq(kdf2.sort_index(), (kdf + 1).spark.repartition(num_partitions).sort_index())

        # Reserves MultiIndex
        kdf = ps.DataFrame({"a": ["a", "b", "c"]}, index=[[1, 2, 3], [4, 5, 6]])
        num_partitions = kdf.to_spark().rdd.getNumPartitions() + 1
        new_kdf = kdf.spark.repartition(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

    def test_coalesce(self):
        num_partitions = 10
        kdf = ps.DataFrame({"age": [5, 5, 2, 2], "name": ["Bob", "Bob", "Alice", "Alice"]})
        kdf = kdf.spark.repartition(num_partitions)

        num_partitions -= 1
        new_kdf = kdf.spark.coalesce(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

        # Reserves Index
        kdf = kdf.set_index("age")
        num_partitions -= 1
        new_kdf = kdf.spark.coalesce(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

        # Reflects internal changes
        kdf = kdf.reset_index()
        kdf = kdf.set_index("name")
        kdf2 = kdf + 1
        num_partitions -= 1
        self.assert_eq(kdf2.sort_index(), (kdf + 1).spark.coalesce(num_partitions).sort_index())

        # Reserves MultiIndex
        kdf = ps.DataFrame({"a": ["a", "b", "c"]}, index=[[1, 2, 3], [4, 5, 6]])
        num_partitions -= 1
        kdf = kdf.spark.repartition(num_partitions)

        num_partitions -= 1
        new_kdf = kdf.spark.coalesce(num_partitions)
        self.assertEqual(new_kdf.to_spark().rdd.getNumPartitions(), num_partitions)
        self.assert_eq(kdf.sort_index(), new_kdf.sort_index())

    def test_checkpoint(self):
        with self.temp_dir() as tmp:
            self.spark.sparkContext.setCheckpointDir(tmp)
            kdf = ps.DataFrame({"a": ["a", "b", "c"]})
            new_kdf = kdf.spark.checkpoint()
            self.assertIsNotNone(os.listdir(tmp))
            self.assert_eq(kdf, new_kdf)

    def test_local_checkpoint(self):
        kdf = ps.DataFrame({"a": ["a", "b", "c"]})
        new_kdf = kdf.spark.local_checkpoint()
        self.assert_eq(kdf, new_kdf)


if __name__ == "__main__":
    import unittest
    from pyspark.pandas.tests.test_frame_spark 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)