469 lines
20 KiB
Python
469 lines
20 KiB
Python
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from distutils.version import LooseVersion
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import unittest
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import glob
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import os
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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import pyspark
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from pyspark import pandas as pp
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from pyspark.pandas.testing.utils import ReusedSQLTestCase, TestUtils
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class DataFrameSparkIOTest(ReusedSQLTestCase, TestUtils):
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"""Test cases for big data I/O using Spark."""
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@property
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def test_column_order(self):
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return ["i32", "i64", "f", "bhello"]
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@property
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def test_pdf(self):
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pdf = pd.DataFrame(
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{
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"i32": np.arange(20, dtype=np.int32) % 3,
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"i64": np.arange(20, dtype=np.int64) % 5,
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"f": np.arange(20, dtype=np.float64),
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"bhello": np.random.choice(["hello", "yo", "people"], size=20).astype("O"),
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},
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columns=self.test_column_order,
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index=np.random.rand(20),
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)
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return pdf
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def test_parquet_read(self):
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with self.temp_dir() as tmp:
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data = self.test_pdf
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self.spark.createDataFrame(data, "i32 int, i64 long, f double, bhello string").coalesce(
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1
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).write.parquet(tmp, mode="overwrite")
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def check(columns, expected):
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if LooseVersion("0.21.1") <= LooseVersion(pd.__version__):
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expected = pd.read_parquet(tmp, columns=columns)
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actual = pp.read_parquet(tmp, columns=columns)
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self.assertPandasEqual(expected, actual.to_pandas())
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check(None, data)
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check(["i32", "i64"], data[["i32", "i64"]])
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check(["i64", "i32"], data[["i64", "i32"]])
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if LooseVersion(pa.__version__) < LooseVersion("1.0.0"):
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# TODO: `pd.read_parquet()` changed the behavior due to PyArrow 1.0.0.
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# We might want to adjust the behavior. Let's see how pandas handles it.
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check(("i32", "i64"), data[["i32", "i64"]])
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check(["a", "b", "i32", "i64"], data[["i32", "i64"]])
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check([], pd.DataFrame([]))
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check(["a"], pd.DataFrame([]))
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check("i32", pd.DataFrame([]))
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check("float", data[["f"]])
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# check with pyspark patch.
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if LooseVersion("0.21.1") <= LooseVersion(pd.__version__):
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expected = pd.read_parquet(tmp)
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else:
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expected = data
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actual = pp.read_parquet(tmp)
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self.assertPandasEqual(expected, actual.to_pandas())
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# When index columns are known
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pdf = self.test_pdf
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expected = pp.DataFrame(pdf)
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expected_idx = expected.set_index("bhello")[["f", "i32", "i64"]]
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actual_idx = pp.read_parquet(tmp, index_col="bhello")[["f", "i32", "i64"]]
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self.assert_eq(
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actual_idx.sort_values(by="f").to_spark().toPandas(),
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expected_idx.sort_values(by="f").to_spark().toPandas(),
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)
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@unittest.skipIf(
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LooseVersion(pyspark.__version__) < LooseVersion("3.0.0"),
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"The test only works with Spark>=3.0",
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)
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def test_parquet_read_with_pandas_metadata(self):
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with self.temp_dir() as tmp:
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expected1 = self.test_pdf
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path1 = "{}/file1.parquet".format(tmp)
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expected1.to_parquet(path1)
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self.assert_eq(pp.read_parquet(path1, pandas_metadata=True), expected1)
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expected2 = expected1.reset_index()
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path2 = "{}/file2.parquet".format(tmp)
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expected2.to_parquet(path2)
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self.assert_eq(pp.read_parquet(path2, pandas_metadata=True), expected2)
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expected3 = expected2.set_index("index", append=True)
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path3 = "{}/file3.parquet".format(tmp)
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expected3.to_parquet(path3)
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self.assert_eq(pp.read_parquet(path3, pandas_metadata=True), expected3)
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def test_parquet_write(self):
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with self.temp_dir() as tmp:
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pdf = self.test_pdf
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expected = pp.DataFrame(pdf)
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# Write out partitioned by one column
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expected.to_parquet(tmp, mode="overwrite", partition_cols="i32")
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_parquet(tmp)
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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# Write out partitioned by two columns
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expected.to_parquet(tmp, mode="overwrite", partition_cols=["i32", "bhello"])
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_parquet(tmp)
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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def test_table(self):
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with self.table("test_table"):
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pdf = self.test_pdf
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expected = pp.DataFrame(pdf)
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# Write out partitioned by one column
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expected.spark.to_table("test_table", mode="overwrite", partition_cols="i32")
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_table("test_table")
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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# Write out partitioned by two columns
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expected.to_table("test_table", mode="overwrite", partition_cols=["i32", "bhello"])
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_table("test_table")
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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# When index columns are known
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expected_idx = expected.set_index("bhello")[["f", "i32", "i64"]]
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actual_idx = pp.read_table("test_table", index_col="bhello")[["f", "i32", "i64"]]
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self.assert_eq(
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actual_idx.sort_values(by="f").to_spark().toPandas(),
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expected_idx.sort_values(by="f").to_spark().toPandas(),
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)
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expected_idx = expected.set_index(["bhello"])[["f", "i32", "i64"]]
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actual_idx = pp.read_table("test_table", index_col=["bhello"])[["f", "i32", "i64"]]
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self.assert_eq(
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actual_idx.sort_values(by="f").to_spark().toPandas(),
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expected_idx.sort_values(by="f").to_spark().toPandas(),
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)
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expected_idx = expected.set_index(["i32", "bhello"])[["f", "i64"]]
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actual_idx = pp.read_table("test_table", index_col=["i32", "bhello"])[["f", "i64"]]
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self.assert_eq(
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actual_idx.sort_values(by="f").to_spark().toPandas(),
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expected_idx.sort_values(by="f").to_spark().toPandas(),
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)
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def test_spark_io(self):
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with self.temp_dir() as tmp:
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pdf = self.test_pdf
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expected = pp.DataFrame(pdf)
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# Write out partitioned by one column
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expected.to_spark_io(tmp, format="json", mode="overwrite", partition_cols="i32")
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_spark_io(tmp, format="json")
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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# Write out partitioned by two columns
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expected.to_spark_io(
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tmp, format="json", mode="overwrite", partition_cols=["i32", "bhello"]
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)
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# Reset column order, as once the data is written out, Spark rearranges partition
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# columns to appear first.
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actual = pp.read_spark_io(path=tmp, format="json")
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self.assertFalse((actual.columns == self.test_column_order).all())
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actual = actual[self.test_column_order]
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self.assert_eq(
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actual.sort_values(by="f").to_spark().toPandas(),
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expected.sort_values(by="f").to_spark().toPandas(),
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)
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# When index columns are known
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pdf = self.test_pdf
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expected = pp.DataFrame(pdf)
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col_order = ["f", "i32", "i64"]
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expected_idx = expected.set_index("bhello")[col_order]
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actual_idx = pp.read_spark_io(tmp, format="json", index_col="bhello")[col_order]
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self.assert_eq(
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actual_idx.sort_values(by="f").to_spark().toPandas(),
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expected_idx.sort_values(by="f").to_spark().toPandas(),
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)
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@unittest.skip("openpyxl")
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def test_read_excel(self):
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with self.temp_dir() as tmp:
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path1 = "{}/file1.xlsx".format(tmp)
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self.test_pdf[["i32"]].to_excel(path1)
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self.assert_eq(pp.read_excel(open(path1, "rb")), pd.read_excel(open(path1, "rb")))
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self.assert_eq(
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pp.read_excel(open(path1, "rb"), index_col=0),
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pd.read_excel(open(path1, "rb"), index_col=0),
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)
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self.assert_eq(
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pp.read_excel(open(path1, "rb"), index_col=0, squeeze=True),
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pd.read_excel(open(path1, "rb"), index_col=0, squeeze=True),
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)
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if LooseVersion(pyspark.__version__) >= LooseVersion("3.0.0"):
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self.assert_eq(pp.read_excel(path1), pd.read_excel(path1))
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self.assert_eq(pp.read_excel(path1, index_col=0), pd.read_excel(path1, index_col=0))
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self.assert_eq(
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pp.read_excel(path1, index_col=0, squeeze=True),
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pd.read_excel(path1, index_col=0, squeeze=True),
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)
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self.assert_eq(pp.read_excel(tmp), pd.read_excel(path1))
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path2 = "{}/file2.xlsx".format(tmp)
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self.test_pdf[["i32"]].to_excel(path2)
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self.assert_eq(
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pp.read_excel(tmp, index_col=0).sort_index(),
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pd.concat(
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[pd.read_excel(path1, index_col=0), pd.read_excel(path2, index_col=0)]
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).sort_index(),
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)
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self.assert_eq(
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pp.read_excel(tmp, index_col=0, squeeze=True).sort_index(),
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pd.concat(
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[
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pd.read_excel(path1, index_col=0, squeeze=True),
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pd.read_excel(path2, index_col=0, squeeze=True),
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]
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).sort_index(),
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)
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else:
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self.assertRaises(ValueError, lambda: pp.read_excel(tmp))
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with self.temp_dir() as tmp:
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path1 = "{}/file1.xlsx".format(tmp)
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with pd.ExcelWriter(path1) as writer:
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self.test_pdf.to_excel(writer, sheet_name="Sheet_name_1")
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self.test_pdf[["i32"]].to_excel(writer, sheet_name="Sheet_name_2")
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sheet_names = [["Sheet_name_1", "Sheet_name_2"], None]
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pdfs1 = pd.read_excel(open(path1, "rb"), sheet_name=None, index_col=0)
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pdfs1_squeezed = pd.read_excel(
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open(path1, "rb"), sheet_name=None, index_col=0, squeeze=True
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)
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for sheet_name in sheet_names:
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kdfs = pp.read_excel(open(path1, "rb"), sheet_name=sheet_name, index_col=0)
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self.assert_eq(kdfs["Sheet_name_1"], pdfs1["Sheet_name_1"])
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self.assert_eq(kdfs["Sheet_name_2"], pdfs1["Sheet_name_2"])
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kdfs = pp.read_excel(
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open(path1, "rb"), sheet_name=sheet_name, index_col=0, squeeze=True
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)
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self.assert_eq(kdfs["Sheet_name_1"], pdfs1_squeezed["Sheet_name_1"])
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self.assert_eq(kdfs["Sheet_name_2"], pdfs1_squeezed["Sheet_name_2"])
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if LooseVersion(pyspark.__version__) >= LooseVersion("3.0.0"):
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self.assert_eq(
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pp.read_excel(tmp, index_col=0, sheet_name="Sheet_name_2"),
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pdfs1["Sheet_name_2"],
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)
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for sheet_name in sheet_names:
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kdfs = pp.read_excel(tmp, sheet_name=sheet_name, index_col=0)
|
||
|
|
self.assert_eq(kdfs["Sheet_name_1"], pdfs1["Sheet_name_1"])
|
||
|
|
self.assert_eq(kdfs["Sheet_name_2"], pdfs1["Sheet_name_2"])
|
||
|
|
|
||
|
|
kdfs = pp.read_excel(tmp, sheet_name=sheet_name, index_col=0, squeeze=True)
|
||
|
|
self.assert_eq(kdfs["Sheet_name_1"], pdfs1_squeezed["Sheet_name_1"])
|
||
|
|
self.assert_eq(kdfs["Sheet_name_2"], pdfs1_squeezed["Sheet_name_2"])
|
||
|
|
|
||
|
|
path2 = "{}/file2.xlsx".format(tmp)
|
||
|
|
with pd.ExcelWriter(path2) as writer:
|
||
|
|
self.test_pdf.to_excel(writer, sheet_name="Sheet_name_1")
|
||
|
|
self.test_pdf[["i32"]].to_excel(writer, sheet_name="Sheet_name_2")
|
||
|
|
|
||
|
|
pdfs2 = pd.read_excel(path2, sheet_name=None, index_col=0)
|
||
|
|
pdfs2_squeezed = pd.read_excel(path2, sheet_name=None, index_col=0, squeeze=True)
|
||
|
|
|
||
|
|
self.assert_eq(
|
||
|
|
pp.read_excel(tmp, sheet_name="Sheet_name_2", index_col=0).sort_index(),
|
||
|
|
pd.concat([pdfs1["Sheet_name_2"], pdfs2["Sheet_name_2"]]).sort_index(),
|
||
|
|
)
|
||
|
|
self.assert_eq(
|
||
|
|
pp.read_excel(
|
||
|
|
tmp, sheet_name="Sheet_name_2", index_col=0, squeeze=True
|
||
|
|
).sort_index(),
|
||
|
|
pd.concat(
|
||
|
|
[pdfs1_squeezed["Sheet_name_2"], pdfs2_squeezed["Sheet_name_2"]]
|
||
|
|
).sort_index(),
|
||
|
|
)
|
||
|
|
|
||
|
|
for sheet_name in sheet_names:
|
||
|
|
kdfs = pp.read_excel(tmp, sheet_name=sheet_name, index_col=0)
|
||
|
|
self.assert_eq(
|
||
|
|
kdfs["Sheet_name_1"].sort_index(),
|
||
|
|
pd.concat([pdfs1["Sheet_name_1"], pdfs2["Sheet_name_1"]]).sort_index(),
|
||
|
|
)
|
||
|
|
self.assert_eq(
|
||
|
|
kdfs["Sheet_name_2"].sort_index(),
|
||
|
|
pd.concat([pdfs1["Sheet_name_2"], pdfs2["Sheet_name_2"]]).sort_index(),
|
||
|
|
)
|
||
|
|
|
||
|
|
kdfs = pp.read_excel(tmp, sheet_name=sheet_name, index_col=0, squeeze=True)
|
||
|
|
self.assert_eq(
|
||
|
|
kdfs["Sheet_name_1"].sort_index(),
|
||
|
|
pd.concat(
|
||
|
|
[pdfs1_squeezed["Sheet_name_1"], pdfs2_squeezed["Sheet_name_1"]]
|
||
|
|
).sort_index(),
|
||
|
|
)
|
||
|
|
self.assert_eq(
|
||
|
|
kdfs["Sheet_name_2"].sort_index(),
|
||
|
|
pd.concat(
|
||
|
|
[pdfs1_squeezed["Sheet_name_2"], pdfs2_squeezed["Sheet_name_2"]]
|
||
|
|
).sort_index(),
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
self.assertRaises(ValueError, lambda: pp.read_excel(tmp))
|
||
|
|
|
||
|
|
def test_read_orc(self):
|
||
|
|
with self.temp_dir() as tmp:
|
||
|
|
path = "{}/file1.orc".format(tmp)
|
||
|
|
data = self.test_pdf
|
||
|
|
self.spark.createDataFrame(data, "i32 int, i64 long, f double, bhello string").coalesce(
|
||
|
|
1
|
||
|
|
).write.orc(path, mode="overwrite")
|
||
|
|
|
||
|
|
# `spark.write.orc` create a directory contains distributed orc files.
|
||
|
|
# But pandas only can read from file, not directory. Therefore, we need orc file path.
|
||
|
|
orc_file_path = glob.glob(os.path.join(path, "*.orc"))[0]
|
||
|
|
|
||
|
|
expected = data.reset_index()[data.columns]
|
||
|
|
actual = pp.read_orc(path)
|
||
|
|
self.assertPandasEqual(expected, actual.to_pandas())
|
||
|
|
|
||
|
|
# columns
|
||
|
|
columns = ["i32", "i64"]
|
||
|
|
expected = data.reset_index()[columns]
|
||
|
|
actual = pp.read_orc(path, columns=columns)
|
||
|
|
self.assertPandasEqual(expected, actual.to_pandas())
|
||
|
|
|
||
|
|
# index_col
|
||
|
|
expected = data.set_index("i32")
|
||
|
|
actual = pp.read_orc(path, index_col="i32")
|
||
|
|
self.assert_eq(actual, expected)
|
||
|
|
|
||
|
|
expected = data.set_index(["i32", "f"])
|
||
|
|
actual = pp.read_orc(path, index_col=["i32", "f"])
|
||
|
|
self.assert_eq(actual, expected)
|
||
|
|
|
||
|
|
# index_col with columns
|
||
|
|
expected = data.set_index("i32")[["i64", "bhello"]]
|
||
|
|
actual = pp.read_orc(path, index_col=["i32"], columns=["i64", "bhello"])
|
||
|
|
self.assert_eq(actual, expected)
|
||
|
|
|
||
|
|
expected = data.set_index(["i32", "f"])[["bhello", "i64"]]
|
||
|
|
actual = pp.read_orc(path, index_col=["i32", "f"], columns=["bhello", "i64"])
|
||
|
|
self.assert_eq(actual, expected)
|
||
|
|
|
||
|
|
msg = "Unknown column name 'i'"
|
||
|
|
with self.assertRaises(ValueError, msg=msg):
|
||
|
|
pp.read_orc(path, columns="i32")
|
||
|
|
msg = "Unknown column name 'i34'"
|
||
|
|
with self.assertRaises(ValueError, msg=msg):
|
||
|
|
pp.read_orc(path, columns=["i34", "i64"])
|
||
|
|
|
||
|
|
def test_orc_write(self):
|
||
|
|
with self.temp_dir() as tmp:
|
||
|
|
pdf = self.test_pdf
|
||
|
|
expected = pp.DataFrame(pdf)
|
||
|
|
|
||
|
|
# Write out partitioned by one column
|
||
|
|
expected.to_orc(tmp, mode="overwrite", partition_cols="i32")
|
||
|
|
# Reset column order, as once the data is written out, Spark rearranges partition
|
||
|
|
# columns to appear first.
|
||
|
|
actual = pp.read_orc(tmp)
|
||
|
|
self.assertFalse((actual.columns == self.test_column_order).all())
|
||
|
|
actual = actual[self.test_column_order]
|
||
|
|
self.assert_eq(
|
||
|
|
actual.sort_values(by="f").to_spark().toPandas(),
|
||
|
|
expected.sort_values(by="f").to_spark().toPandas(),
|
||
|
|
)
|
||
|
|
|
||
|
|
# Write out partitioned by two columns
|
||
|
|
expected.to_orc(tmp, mode="overwrite", partition_cols=["i32", "bhello"])
|
||
|
|
# Reset column order, as once the data is written out, Spark rearranges partition
|
||
|
|
# columns to appear first.
|
||
|
|
actual = pp.read_orc(tmp)
|
||
|
|
self.assertFalse((actual.columns == self.test_column_order).all())
|
||
|
|
actual = actual[self.test_column_order]
|
||
|
|
self.assert_eq(
|
||
|
|
actual.sort_values(by="f").to_spark().toPandas(),
|
||
|
|
expected.sort_values(by="f").to_spark().toPandas(),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
from pyspark.pandas.tests.test_dataframe_spark_io 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)
|