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Posted to issues@flink.apache.org by GitBox <gi...@apache.org> on 2022/07/01 08:04:25 UTC
[GitHub] [flink] dianfu commented on a diff in pull request #20124: [FLINK-28336][python][format] Support parquet-avro format DataStream API
dianfu commented on code in PR #20124:
URL: https://github.com/apache/flink/pull/20124#discussion_r911711605
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flink-python/pyflink/datastream/connectors/tests/test_file_system.py:
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@@ -0,0 +1,308 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+import tempfile
+
+from py4j.java_gateway import java_import
+
+from pyflink.common.watermark_strategy import WatermarkStrategy
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.functions import MapFunction
+from pyflink.datastream.connectors.file_system import FileSource
+from pyflink.datastream.formats.avro import Schema as AvroSchema
+from pyflink.datastream.formats.parquet import AvroParquetReaders
+from pyflink.datastream.tests.test_util import DataStreamTestSinkFunction
+from pyflink.java_gateway import get_gateway
+from pyflink.testing.test_case_utils import PyFlinkTestCase
+
+
+class FileSourceParquetAvroFormatTests(PyFlinkTestCase):
+
+ def setUp(self):
+ assert os.environ.get('HADOOP_CLASSPATH') is not None, 'Hadoop is needed for this test'
+ super().setUp()
+ self.env = StreamExecutionEnvironment.get_execution_environment()
Review Comment:
Extending PyFlinkStreamingTestCase and then we could reuse the env defined in PyFlinkStreamingTestCase
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flink-python/pyflink/datastream/connectors/tests/test_file_system.py:
##########
@@ -0,0 +1,308 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+import tempfile
+
+from py4j.java_gateway import java_import
+
+from pyflink.common.watermark_strategy import WatermarkStrategy
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.functions import MapFunction
+from pyflink.datastream.connectors.file_system import FileSource
+from pyflink.datastream.formats.avro import Schema as AvroSchema
+from pyflink.datastream.formats.parquet import AvroParquetReaders
+from pyflink.datastream.tests.test_util import DataStreamTestSinkFunction
+from pyflink.java_gateway import get_gateway
+from pyflink.testing.test_case_utils import PyFlinkTestCase
+
+
+class FileSourceParquetAvroFormatTests(PyFlinkTestCase):
+
+ def setUp(self):
+ assert os.environ.get('HADOOP_CLASSPATH') is not None, 'Hadoop is needed for this test'
+ super().setUp()
+ self.env = StreamExecutionEnvironment.get_execution_environment()
+ self.env.set_parallelism(2)
+ self.test_sink = DataStreamTestSinkFunction()
+ self._import_avro_classes()
+
+ def test_avro_parquet_basic(self):
+ parquet_file_name = tempfile.mktemp(suffix='.parquet', dir=self.tempdir)
+ record_schema = """
+ {
+ "type": "record",
+ "name": "test",
+ "fields": [
+ { "name": "null", "type": "null" },
+ { "name": "boolean", "type": "boolean" },
+ { "name": "int", "type": "int" },
+ { "name": "long", "type": "long" },
+ { "name": "float", "type": "float" },
+ { "name": "double", "type": "double" },
+ { "name": "string", "type": "string" }
+ ]
+ }
+ """
+ schema = AvroSchema.parse_string(record_schema)
+ records = [self._create_basic_avro_record(schema, True, 0, 1, 2, 3, 's1'),
+ self._create_basic_avro_record(schema, False, 4, 5, 6, 7, 's2')]
+ self._create_avro_parquet_file(parquet_file_name, schema, records)
+
+ self._build_avro_parquet_job(schema, parquet_file_name)
+ self.env.execute("test_avro_parquet_basic")
+ results = self.test_sink.get_results(True, False)
+ result1 = results[0]
+ result2 = results[1]
+ self.assertEqual(result1['null'], None)
+ self.assertEqual(result1['boolean'], True)
+ self.assertEqual(result1['int'], 0)
+ self.assertEqual(result1['long'], 1)
+ self.assertAlmostEqual(result1['float'], 2, delta=1e-3)
+ self.assertAlmostEqual(result1['double'], 3, delta=1e-3)
+ self.assertEqual(result1['string'], 's1')
+ self.assertEqual(result2['null'], None)
+ self.assertEqual(result2['boolean'], False)
+ self.assertEqual(result2['int'], 4)
+ self.assertEqual(result2['long'], 5)
+ self.assertAlmostEqual(result2['float'], 6, delta=1e-3)
+ self.assertAlmostEqual(result2['double'], 7, delta=1e-3)
+ self.assertEqual(result2['string'], 's2')
+
+ def test_avro_parquet_enum(self):
+ parquet_file_name = tempfile.mktemp(suffix='.parquet', dir=self.tempdir)
+ record_schema = """
+ {
+ "type": "record",
+ "name": "test",
+ "fields": [
+ {
+ "name": "suit",
+ "type": {
+ "type": "enum",
+ "name": "Suit",
+ "symbols" : ["SPADES", "HEARTS", "DIAMONDS", "CLUBS"]
+ }
+ }
+ ]
+ }
+ """
+ schema = AvroSchema.parse_string(record_schema)
+ records = [self._create_enum_avro_record(schema, 'SPADES'),
+ self._create_enum_avro_record(schema, 'DIAMONDS')]
+ self._create_avro_parquet_file(parquet_file_name, schema, records)
+
+ self._build_avro_parquet_job(schema, parquet_file_name)
+ self.env.execute("test_avro_record_enum")
+ results = self.test_sink.get_results(True, False)
+ self.assertEqual(results[0]['suit'], 'SPADES')
+ self.assertEqual(results[1]['suit'], 'DIAMONDS')
+
+ def test_avro_parquet_union(self):
+ parquet_file_name = tempfile.mktemp(suffix='.parquet', dir=self.tempdir)
+ record_schema = """
+ {
+ "type": "record",
+ "name": "test",
+ "fields": [
+ {
+ "name": "union",
+ "type": [ "int", "double", "null" ]
+ }
+ ]
+ }
+ """
+ schema = AvroSchema.parse_string(record_schema)
+ records = [self._create_union_avro_record(schema, 1),
+ self._create_union_avro_record(schema, 2.),
+ self._create_union_avro_record(schema, None)]
+ self._create_avro_parquet_file(parquet_file_name, schema, records)
+
+ self._build_avro_parquet_job(schema, parquet_file_name)
+ self.env.execute("test_avro_record_union")
+ results = self.test_sink.get_results(True, False)
+ self.assertEqual(results[0]['union'], 1)
+ self.assertAlmostEqual(results[1]['union'], 2.0, delta=1e-3)
+ self.assertEqual(results[2]['union'], None)
+
+ def test_avro_parquet_array(self):
+ parquet_file_name = tempfile.mktemp(suffix='.parquet', dir=self.tempdir)
+ # It seems there's bug when array item record contains only one field, which throws
+ # java.lang.ClassCastException: required ... is not a group when reading
+ record_schema = """
+ {
+ "type": "record",
+ "name": "test",
+ "fields": [
+ {
+ "name": "array",
+ "type": {
+ "type": "array",
+ "items": {
+ "type": "record",
+ "name": "item",
+ "fields": [
+ { "name": "int", "type": "int" },
+ { "name": "double", "type": "double" }
+ ]
+ }
+ }
+ }
+ ]
+ }
+ """
+ schema = AvroSchema.parse_string(record_schema)
+ records = [self._create_array_avro_record(schema, [(1, 2.), (3, 4.)]),
+ self._create_array_avro_record(schema, [(5, 6.), (7, 8.)])]
+ self._create_avro_parquet_file(parquet_file_name, schema, records)
+
+ self._build_avro_parquet_job(schema, parquet_file_name)
+ self.env.execute("test_avro_record_array")
+ results = self.test_sink.get_results(True, False)
+ result1 = results[0]
+ result2 = results[1]
+ self.assertEqual(result1['array'][0]['int'], 1)
+ self.assertAlmostEqual(result1['array'][0]['double'], 2., delta=1e-3)
+ self.assertEqual(result1['array'][1]['int'], 3)
+ self.assertAlmostEqual(result1['array'][1]['double'], 4., delta=1e-3)
+ self.assertEqual(result2['array'][0]['int'], 5)
+ self.assertAlmostEqual(result2['array'][0]['double'], 6., delta=1e-3)
+ self.assertEqual(result2['array'][1]['int'], 7)
+ self.assertAlmostEqual(result2['array'][1]['double'], 8., delta=1e-3)
+
+ def test_avro_parquet_map(self):
+ parquet_file_name = tempfile.mktemp(suffix='.parquet', dir=self.tempdir)
+ record_schema = """
+ {
+ "type": "record",
+ "name": "test",
+ "fields": [
+ {
+ "name": "map",
+ "type": {
+ "type": "map",
+ "values": "long"
+ }
+ }
+ ]
+ }
+ """
+ schema = AvroSchema.parse_string(record_schema)
+ records = [self._create_map_avro_record(schema, {'a': 1, 'b': 2}),
+ self._create_map_avro_record(schema, {'c': 3, 'd': 4})]
+ self._create_avro_parquet_file(parquet_file_name, schema, records)
+
+ self._build_avro_parquet_job(schema, parquet_file_name)
+ self.env.execute("test_avro_record_map")
+ results = self.test_sink.get_results(True, False)
+ result1 = results[0]
+ result2 = results[1]
+ self.assertEqual(result1['map']['a'], 1)
+ self.assertEqual(result1['map']['b'], 2)
+ self.assertEqual(result2['map']['c'], 3)
+ self.assertEqual(result2['map']['d'], 4)
+
+ def _build_avro_parquet_job(self, record_schema, parquet_file_name):
+ ds = self.env.from_source(
+ FileSource.for_record_stream_format(
+ AvroParquetReaders.for_generic_record(record_schema),
+ parquet_file_name
+ ).build(),
+ WatermarkStrategy.for_monotonous_timestamps(),
+ "parquet-source"
+ )
+ ds.map(self.PassThroughMapFunction()).add_sink(self.test_sink)
+
+ class PassThroughMapFunction(MapFunction):
+
+ def map(self, value):
+ return value
+
+ @staticmethod
+ def _import_avro_classes():
+ jvm = get_gateway().jvm
+ classes = ['org.apache.avro.generic.GenericData']
+ prefix = 'org.apache.flink.avro.shaded.'
Review Comment:
Add the import into import_flink_view?
##########
flink-python/pyflink/datastream/connectors/tests/test_file_system.py:
##########
@@ -0,0 +1,308 @@
+################################################################################
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+################################################################################
+import os
+import tempfile
+
+from py4j.java_gateway import java_import
+
+from pyflink.common.watermark_strategy import WatermarkStrategy
+from pyflink.datastream import StreamExecutionEnvironment
+from pyflink.datastream.functions import MapFunction
+from pyflink.datastream.connectors.file_system import FileSource
+from pyflink.datastream.formats.avro import Schema as AvroSchema
+from pyflink.datastream.formats.parquet import AvroParquetReaders
+from pyflink.datastream.tests.test_util import DataStreamTestSinkFunction
+from pyflink.java_gateway import get_gateway
+from pyflink.testing.test_case_utils import PyFlinkTestCase
+
+
+class FileSourceParquetAvroFormatTests(PyFlinkTestCase):
+
Review Comment:
Could use `@unittest.skipIf` to skip the test cases in certain cases.
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