def _check_specified_offsets_initializer(
self,
source: KafkaSource,
offsets: Dict[KafkaTopicPartition, int],
reset_strategy: KafkaOffsetResetStrategy,
is_start: bool = True):
if is_start:
field_name = 'startingOffsetsInitializer'
else:
field_name = 'stoppingOffsetsInitializer'
offsets_initializer = get_field_value(source.get_java_function(),
field_name)
self.assertEqual(
offsets_initializer.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.initializer'
'.SpecifiedOffsetsInitializer')
initial_offsets = get_field_value(offsets_initializer,
'initialOffsets')
self.assertTrue(
is_instance_of(initial_offsets,
get_gateway().jvm.java.util.Map))
self.assertEqual(initial_offsets.size(), len(offsets))
for j_topic_partition in initial_offsets:
topic_partition = KafkaTopicPartition(
j_topic_partition.topic(), j_topic_partition.partition())
self.assertIsNotNone(offsets.get(topic_partition))
self.assertEqual(initial_offsets[j_topic_partition],
offsets[topic_partition])
offset_reset_strategy = get_field_value(offsets_initializer,
'offsetResetStrategy')
self.assertTrue(
offset_reset_strategy.equals(
reset_strategy._to_j_offset_reset_strategy()))
def _check_reader_handled_offsets_initializer(
self,
source: KafkaSource,
offset: int,
reset_strategy: KafkaOffsetResetStrategy,
is_start: bool = True):
if is_start:
field_name = 'startingOffsetsInitializer'
else:
field_name = 'stoppingOffsetsInitializer'
offsets_initializer = get_field_value(source.get_java_function(),
field_name)
self.assertEqual(
offsets_initializer.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.initializer'
'.ReaderHandledOffsetsInitializer')
starting_offset = get_field_value(offsets_initializer,
'startingOffset')
self.assertEqual(starting_offset, offset)
offset_reset_strategy = get_field_value(offsets_initializer,
'offsetResetStrategy')
self.assertTrue(
offset_reset_strategy.equals(
reset_strategy._to_j_offset_reset_strategy()))
def test_rabbitmq_connectors(self):
connection_config = RMQConnectionConfig.Builder() \
.set_host('localhost') \
.set_port(5672) \
.set_virtual_host('/') \
.set_user_name('guest') \
.set_password('guest') \
.build()
type_info = Types.ROW([Types.INT(), Types.STRING()])
deserialization_schema = JsonRowDeserializationSchema.builder() \
.type_info(type_info=type_info).build()
rmq_source = RMQSource(connection_config, 'source_queue', True,
deserialization_schema)
self.assertEqual(
get_field_value(rmq_source.get_java_function(), 'queueName'),
'source_queue')
self.assertTrue(
get_field_value(rmq_source.get_java_function(),
'usesCorrelationId'))
serialization_schema = JsonRowSerializationSchema.builder().with_type_info(type_info) \
.build()
rmq_sink = RMQSink(connection_config, 'sink_queue',
serialization_schema)
self.assertEqual(
get_field_value(rmq_sink.get_java_function(), 'queueName'),
'sink_queue')
def test_set_delivery_guarantee(self):
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_record_serializer(self._build_serialization_schema()) \
.build()
guarantee = get_field_value(sink.get_java_function(),
'deliveryGuarantee')
self.assertEqual(guarantee.toString(), 'none')
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_delivery_guarantee(DeliveryGuarantee.AT_LEAST_ONCE) \
.set_record_serializer(self._build_serialization_schema()) \
.build()
guarantee = get_field_value(sink.get_java_function(),
'deliveryGuarantee')
self.assertEqual(guarantee.toString(), 'at-least-once')
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_delivery_guarantee(DeliveryGuarantee.EXACTLY_ONCE) \
.set_record_serializer(self._build_serialization_schema()) \
.build()
guarantee = get_field_value(sink.get_java_function(),
'deliveryGuarantee')
self.assertEqual(guarantee.toString(), 'exactly-once')
def test_kinesis_firehose_sink(self):
_load_specific_flink_module_jars('/flink-connectors/'
'flink-sql-connector-aws-kinesis-firehose')
sink_properties = {
'aws.region': 'eu-west-1',
'aws.credentials.provider.basic.accesskeyid': 'aws_access_key_id',
'aws.credentials.provider.basic.secretkey': 'aws_secret_access_key'
}
ds = self.env.from_collection([('ab', 1), ('bdc', 2), ('cfgs', 3), ('deeefg', 4)],
type_info=Types.ROW([Types.STRING(), Types.INT()]))
kinesis_firehose_sink = KinesisFirehoseSink.builder() \
.set_firehose_client_properties(sink_properties) \
.set_serialization_schema(SimpleStringSchema()) \
.set_delivery_stream_name('stream-1') \
.set_fail_on_error(False) \
.set_max_batch_size(500) \
.set_max_in_flight_requests(50) \
.set_max_buffered_requests(10000) \
.set_max_batch_size_in_bytes(5 * 1024 * 1024) \
.set_max_time_in_buffer_ms(5000) \
.set_max_record_size_in_bytes(1 * 1024 * 1024) \
.build()
ds.sink_to(kinesis_firehose_sink).name('kinesis firehose sink')
plan = eval(self.env.get_execution_plan())
self.assertEqual('kinesis firehose sink: Writer', plan['nodes'][1]['type'])
self.assertEqual(get_field_value(kinesis_firehose_sink.get_java_function(), 'failOnError'),
False)
self.assertEqual(
get_field_value(kinesis_firehose_sink.get_java_function(), 'deliveryStreamName'),
'stream-1')
def test_kinesis_streams_sink(self):
sink_properties = {
'aws.region': 'us-east-1',
'aws.credentials.provider.basic.secretkey': 'aws_secret_access_key'
}
ds = self.env.from_collection([('ab', 1), ('bdc', 2), ('cfgs', 3), ('deeefg', 4)],
type_info=Types.ROW([Types.STRING(), Types.INT()]))
kinesis_streams_sink = KinesisStreamsSink.builder() \
.set_kinesis_client_properties(sink_properties) \
.set_serialization_schema(SimpleStringSchema()) \
.set_partition_key_generator(PartitionKeyGenerator.fixed()) \
.set_stream_name("stream-1") \
.set_fail_on_error(False) \
.set_max_batch_size(500) \
.set_max_in_flight_requests(50) \
.set_max_buffered_requests(10000) \
.set_max_batch_size_in_bytes(5 * 1024 * 1024) \
.set_max_time_in_buffer_ms(5000) \
.set_max_record_size_in_bytes(1 * 1024 * 1024) \
.build()
ds.sink_to(kinesis_streams_sink).name('kinesis streams sink')
plan = eval(self.env.get_execution_plan())
self.assertEqual('kinesis streams sink: Writer', plan['nodes'][1]['type'])
self.assertEqual(get_field_value(kinesis_streams_sink.get_java_function(), 'failOnError'),
False)
self.assertEqual(
get_field_value(kinesis_streams_sink.get_java_function(), 'streamName'), 'stream-1')
def startup_loopback_server():
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
jvm = gateway.jvm
j_env = jvm.System.getenv()
get_field_value(j_env, "m").put(
'PYFLINK_LOOPBACK_SERVER_ADDRESS',
BeamFnLoopbackWorkerPoolServicer().start())
def test_es_sink_dynamic(self):
ds = self.env.from_collection([{
'name': 'ada',
'id': '1'
}, {
'name': 'luna',
'id': '2'
}],
type_info=Types.MAP(
Types.STRING(), Types.STRING()))
es_dynamic_index_sink = Elasticsearch7SinkBuilder() \
.set_emitter(ElasticsearchEmitter.dynamic_index('name', 'id')) \
.set_hosts(['localhost:9200']) \
.build()
j_emitter = get_field_value(es_dynamic_index_sink.get_java_function(),
'emitter')
self.assertTrue(
is_instance_of(
j_emitter,
'org.apache.flink.connector.elasticsearch.sink.SimpleElasticsearchEmitter'
))
ds.sink_to(es_dynamic_index_sink).name('es dynamic index sink')
def startup_loopback_server():
jvm = gateway.jvm
env_config = JPythonConfigUtil.getEnvironmentConfig(
self._j_stream_execution_environment)
parallelism = self.get_parallelism()
if parallelism > 1 and env_config.containsKey(jvm.PythonOptions.PYTHON_ARCHIVES.key()):
import logging
logging.warning("Loopback mode is disabled as python archives are used and the "
"parallelism of the job is greater than 1. The Python user-defined "
"functions will be executed in an independent Python process.")
else:
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
j_env = jvm.System.getenv()
get_field_value(j_env, "m").put(
'PYFLINK_LOOPBACK_SERVER_ADDRESS', BeamFnLoopbackWorkerPoolServicer().start())
def test_set_topic_pattern(self):
source = KafkaSource.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_topic_pattern('test_topic*') \
.set_value_only_deserializer(SimpleStringSchema()) \
.build()
kafka_subscriber = get_field_value(source.get_java_function(),
'subscriber')
self.assertEqual(
kafka_subscriber.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.subscriber.TopicPatternSubscriber'
)
topic_pattern = get_field_value(kafka_subscriber, 'topicPattern')
self.assertTrue(
is_instance_of(topic_pattern,
get_gateway().jvm.java.util.regex.Pattern))
self.assertEqual(topic_pattern.toString(), 'test_topic*')
def _check(schema: DeserializationSchema, class_name: str):
source = KafkaSource.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_topics('test_topic') \
.set_value_only_deserializer(schema) \
.build()
deserialization_schema_wrapper = get_field_value(
source.get_java_function(), 'deserializationSchema')
self.assertEqual(
deserialization_schema_wrapper.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.reader.deserializer'
'.KafkaValueOnlyDeserializationSchemaWrapper')
deserialization_schema = get_field_value(
deserialization_schema_wrapper, 'deserializationSchema')
self.assertEqual(
deserialization_schema.getClass().getCanonicalName(),
class_name)
def test_jdbc_sink(self):
ds = self.env.from_collection([('ab', 1), ('bdc', 2), ('cfgs', 3),
('deeefg', 4)],
type_info=Types.ROW(
[Types.STRING(),
Types.INT()]))
jdbc_connection_options = JdbcConnectionOptions.JdbcConnectionOptionsBuilder()\
.with_driver_name('com.mysql.jdbc.Driver')\
.with_user_name('root')\
.with_password('password')\
.with_url('jdbc:mysql://server-name:server-port/database-name').build()
jdbc_execution_options = JdbcExecutionOptions.builder().with_batch_interval_ms(2000)\
.with_batch_size(100).with_max_retries(5).build()
jdbc_sink = JdbcSink.sink("insert into test table", ds.get_type(),
jdbc_connection_options,
jdbc_execution_options)
ds.add_sink(jdbc_sink).name('jdbc sink')
plan = eval(self.env.get_execution_plan())
self.assertEqual('Sink: jdbc sink', plan['nodes'][1]['type'])
j_output_format = get_field_value(jdbc_sink.get_java_function(),
'outputFormat')
connection_options = JdbcConnectionOptions(
get_field_value(
get_field_value(j_output_format, 'connectionProvider'),
'jdbcOptions'))
self.assertEqual(jdbc_connection_options.get_db_url(),
connection_options.get_db_url())
self.assertEqual(jdbc_connection_options.get_driver_name(),
connection_options.get_driver_name())
self.assertEqual(jdbc_connection_options.get_password(),
connection_options.get_password())
self.assertEqual(jdbc_connection_options.get_user_name(),
connection_options.get_user_name())
exec_options = JdbcExecutionOptions(
get_field_value(j_output_format, 'executionOptions'))
self.assertEqual(jdbc_execution_options.get_batch_interval_ms(),
exec_options.get_batch_interval_ms())
self.assertEqual(jdbc_execution_options.get_batch_size(),
exec_options.get_batch_size())
self.assertEqual(jdbc_execution_options.get_max_retries(),
exec_options.get_max_retries())
def _check_timestamp_offsets_initializer(self,
source: KafkaSource,
timestamp: int,
is_start: bool = True):
if is_start:
field_name = 'startingOffsetsInitializer'
else:
field_name = 'stoppingOffsetsInitializer'
offsets_initializer = get_field_value(source.get_java_function(),
field_name)
self.assertEqual(
offsets_initializer.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.initializer'
'.TimestampOffsetsInitializer')
starting_timestamp = get_field_value(offsets_initializer,
'startingTimestamp')
self.assertEqual(starting_timestamp, timestamp)
def test_set_transactional_id_prefix(self):
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_transactional_id_prefix('test-prefix') \
.set_record_serializer(self._build_serialization_schema()) \
.build()
prefix = get_field_value(sink.get_java_function(),
'transactionalIdPrefix')
self.assertEqual(prefix, 'test-prefix')
def test_set_property(self):
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_record_serializer(self._build_serialization_schema()) \
.set_property('test-key', 'test-value') \
.build()
config = get_field_value(sink.get_java_function(),
'kafkaProducerConfig')
self.assertEqual(config.get('test-key'), 'test-value')
def test_pulsar_source(self):
TEST_OPTION_NAME = 'pulsar.source.enableAutoAcknowledgeMessage'
pulsar_source = PulsarSource.builder() \
.set_service_url('pulsar://localhost:6650') \
.set_admin_url('http://localhost:8080') \
.set_topics('ada') \
.set_start_cursor(StartCursor.earliest()) \
.set_unbounded_stop_cursor(StopCursor.never()) \
.set_bounded_stop_cursor(StopCursor.at_publish_time(22)) \
.set_subscription_name('ff') \
.set_subscription_type(SubscriptionType.Exclusive) \
.set_deserialization_schema(
PulsarDeserializationSchema.flink_type_info(Types.STRING())) \
.set_deserialization_schema(
PulsarDeserializationSchema.flink_schema(SimpleStringSchema())) \
.set_config(TEST_OPTION_NAME, True) \
.set_properties({'pulsar.source.autoCommitCursorInterval': '1000'}) \
.build()
ds = self.env.from_source(source=pulsar_source,
watermark_strategy=WatermarkStrategy.for_monotonous_timestamps(),
source_name="pulsar source")
ds.print()
plan = eval(self.env.get_execution_plan())
self.assertEqual('Source: pulsar source', plan['nodes'][0]['type'])
configuration = get_field_value(pulsar_source.get_java_function(), "sourceConfiguration")
self.assertEqual(
configuration.getString(
ConfigOptions.key('pulsar.client.serviceUrl')
.string_type()
.no_default_value()._j_config_option), 'pulsar://localhost:6650')
self.assertEqual(
configuration.getString(
ConfigOptions.key('pulsar.admin.adminUrl')
.string_type()
.no_default_value()._j_config_option), 'http://localhost:8080')
self.assertEqual(
configuration.getString(
ConfigOptions.key('pulsar.consumer.subscriptionName')
.string_type()
.no_default_value()._j_config_option), 'ff')
self.assertEqual(
configuration.getString(
ConfigOptions.key('pulsar.consumer.subscriptionType')
.string_type()
.no_default_value()._j_config_option), SubscriptionType.Exclusive.name)
test_option = ConfigOptions.key(TEST_OPTION_NAME).boolean_type().no_default_value()
self.assertEqual(
configuration.getBoolean(
test_option._j_config_option), True)
self.assertEqual(
configuration.getLong(
ConfigOptions.key('pulsar.source.autoCommitCursorInterval')
.long_type()
.no_default_value()._j_config_option), 1000)
def test_set_topics(self):
source = KafkaSource.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_topics('test_topic1', 'test_topic2') \
.set_value_only_deserializer(SimpleStringSchema()) \
.build()
kafka_subscriber = get_field_value(source.get_java_function(),
'subscriber')
self.assertEqual(
kafka_subscriber.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.subscriber.TopicListSubscriber'
)
topics = get_field_value(kafka_subscriber, 'topics')
self.assertTrue(
is_instance_of(topics,
get_gateway().jvm.java.util.List))
self.assertEqual(topics.size(), 2)
self.assertEqual(topics[0], 'test_topic1')
self.assertEqual(topics[1], 'test_topic2')
def test_set_record_serializer(self):
sink = KafkaSink.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_record_serializer(self._build_serialization_schema()) \
.build()
serializer = get_field_value(sink.get_java_function(),
'recordSerializer')
self.assertEqual(
serializer.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.sink.'
'KafkaRecordSerializationSchemaBuilder.'
'KafkaRecordSerializationSchemaWrapper')
topic_selector = get_field_value(serializer, 'topicSelector')
self.assertEqual(topic_selector.apply(None), 'test-topic')
value_serializer = get_field_value(serializer,
'valueSerializationSchema')
self.assertEqual(
value_serializer.getClass().getCanonicalName(),
'org.apache.flink.api.common.serialization.SimpleStringSchema')
def _generate_stream_graph(self, clear_transformations: bool = False, job_name: str = None) \
-> JavaObject:
gateway = get_gateway()
JPythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
# start BeamFnLoopbackWorkerPoolServicer when executed in MiniCluster
j_configuration = get_j_env_configuration(self._j_stream_execution_environment)
def startup_loopback_server():
jvm = gateway.jvm
env_config = JPythonConfigUtil.getEnvironmentConfig(
self._j_stream_execution_environment)
parallelism = self.get_parallelism()
if parallelism > 1 and env_config.containsKey(jvm.PythonOptions.PYTHON_ARCHIVES.key()):
import logging
logging.warning("Loopback mode is disabled as python archives are used and the "
"parallelism of the job is greater than 1. The Python user-defined "
"functions will be executed in an independent Python process.")
else:
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
j_env = jvm.System.getenv()
get_field_value(j_env, "m").put(
'PYFLINK_LOOPBACK_SERVER_ADDRESS', BeamFnLoopbackWorkerPoolServicer().start())
python_worker_execution_mode = None
if hasattr(self, "_python_worker_execution_mode"):
python_worker_execution_mode = getattr(self, "_python_worker_execution_mode")
if python_worker_execution_mode is None:
if is_local_deployment(j_configuration):
startup_loopback_server()
elif python_worker_execution_mode == 'loopback':
if is_local_deployment(j_configuration):
startup_loopback_server()
else:
raise ValueError("Loopback mode is enabled, however the job wasn't configured to "
"run in local deployment mode")
elif python_worker_execution_mode != 'process':
raise ValueError(
"It only supports to execute the Python worker in 'loopback' mode and 'process' "
"mode, unknown mode '%s' is configured" % python_worker_execution_mode)
JPythonConfigUtil.configPythonOperator(self._j_stream_execution_environment)
gateway.jvm.org.apache.flink.python.chain.PythonOperatorChainingOptimizer.apply(
self._j_stream_execution_environment)
JPythonConfigUtil.setPartitionCustomOperatorNumPartitions(
get_field_value(self._j_stream_execution_environment, "transformations"))
j_stream_graph = self._j_stream_execution_environment.getStreamGraph(clear_transformations)
if job_name is not None:
j_stream_graph.setJobName(job_name)
return j_stream_graph
def test_with_idleness(self):
jvm = get_gateway().jvm
j_watermark_strategy = WatermarkStrategy.no_watermarks().with_idleness(
Duration.of_seconds(5))._j_watermark_strategy
self.assertTrue(
is_instance_of(
j_watermark_strategy, jvm.org.apache.flink.api.common.
eventtime.WatermarkStrategyWithIdleness))
self.assertEqual(
get_field_value(j_watermark_strategy,
"idlenessTimeout").toMillis(), 5000)
def _check_value_serialization_schema(value_serialization_schema,
expected_class):
serialization_schema = KafkaRecordSerializationSchema.builder() \
.set_topic('test-topic') \
.set_value_serialization_schema(value_serialization_schema) \
.build()
schema_field = get_field_value(
serialization_schema._j_serialization_schema,
'valueSerializationSchema')
self.assertIsNotNone(schema_field)
self.assertEqual(schema_field.getClass().getCanonicalName(),
expected_class)
def _generate_stream_graph(self, clear_transformations: bool = False, job_name: str = None) \
-> JavaObject:
gateway = get_gateway()
JPythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
# start BeamFnLoopbackWorkerPoolServicer when executed in MiniCluster
j_configuration = get_j_env_configuration(
self._j_stream_execution_environment)
def startup_loopback_server():
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
jvm = gateway.jvm
j_env = jvm.System.getenv()
get_field_value(j_env, "m").put(
'PYFLINK_LOOPBACK_SERVER_ADDRESS',
BeamFnLoopbackWorkerPoolServicer().start())
python_worker_execution_mode = None
if hasattr(self, "_python_worker_execution_mode"):
python_worker_execution_mode = getattr(
self, "_python_worker_execution_mode")
if python_worker_execution_mode is None:
if is_local_deployment(j_configuration):
startup_loopback_server()
elif python_worker_execution_mode == 'loopback':
if is_local_deployment(j_configuration):
startup_loopback_server()
else:
raise ValueError(
"Loopback mode is enabled, however the job wasn't configured to "
"run in local deployment mode")
elif python_worker_execution_mode != 'process':
raise ValueError(
"It only supports to execute the Python worker in 'loopback' mode and 'process' "
"mode, unknown mode '%s' is configured" %
python_worker_execution_mode)
JPythonConfigUtil.configPythonOperator(
self._j_stream_execution_environment)
gateway.jvm.org.apache.flink.python.chain.PythonOperatorChainingOptimizer.apply(
self._j_stream_execution_environment)
JPythonConfigUtil.setPartitionCustomOperatorNumPartitions(
get_field_value(self._j_stream_execution_environment,
"transformations"))
j_stream_graph = self._j_stream_execution_environment.getStreamGraph(
clear_transformations)
if job_name is not None:
j_stream_graph.setJobName(job_name)
return j_stream_graph
def test_set_partitions(self):
topic_partition_1 = KafkaTopicPartition('test_topic', 1)
topic_partition_2 = KafkaTopicPartition('test_topic', 2)
source = KafkaSource.builder() \
.set_bootstrap_servers('localhost:9092') \
.set_partitions({topic_partition_1, topic_partition_2}) \
.set_value_only_deserializer(SimpleStringSchema()) \
.build()
kafka_subscriber = get_field_value(source.get_java_function(),
'subscriber')
self.assertEqual(
kafka_subscriber.getClass().getCanonicalName(),
'org.apache.flink.connector.kafka.source.enumerator.subscriber.PartitionSetSubscriber'
)
partitions = get_field_value(kafka_subscriber, 'subscribedPartitions')
self.assertTrue(
is_instance_of(partitions,
get_gateway().jvm.java.util.Set))
self.assertTrue(
topic_partition_1._to_j_topic_partition() in partitions)
self.assertTrue(
topic_partition_2._to_j_topic_partition() in partitions)
def test_for_bounded_out_of_orderness(self):
jvm = get_gateway().jvm
j_watermark_strategy = WatermarkStrategy.for_bounded_out_of_orderness(
Duration.of_seconds(3))._j_watermark_strategy
j_watermark_generator = j_watermark_strategy.createWatermarkGenerator(
None)
self.assertTrue(
is_instance_of(
j_watermark_generator, jvm.org.apache.flink.api.common.
eventtime.BoundedOutOfOrdernessWatermarks))
self.assertEqual(
get_field_value(j_watermark_generator, "outOfOrdernessMillis"),
3000)
def test_kinesis_source(self):
consumer_config = {
'aws.region': 'us-east-1',
'aws.credentials.provider.basic.accesskeyid': 'aws_access_key_id',
'aws.credentials.provider.basic.secretkey': 'aws_secret_access_key',
'flink.stream.initpos': 'LATEST'
}
kinesis_source = FlinkKinesisConsumer("stream-1", SimpleStringSchema(), consumer_config)
ds = self.env.add_source(source_func=kinesis_source, source_name="kinesis source")
ds.print()
plan = eval(self.env.get_execution_plan())
self.assertEqual('Source: kinesis source', plan['nodes'][0]['type'])
self.assertEqual(
get_field_value(kinesis_source.get_java_function(), 'streams')[0], 'stream-1')
def _generate_stream_graph(self, clear_transformations: bool = False, job_name: str = None) \
-> JavaObject:
gateway = get_gateway()
JPythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
JPythonConfigUtil.configPythonOperator(self._j_stream_execution_environment)
gateway.jvm.org.apache.flink.python.chain.PythonOperatorChainingOptimizer.apply(
self._j_stream_execution_environment)
JPythonConfigUtil.setPartitionCustomOperatorNumPartitions(
get_field_value(self._j_stream_execution_environment, "transformations"))
j_stream_graph = self._j_stream_execution_environment.getStreamGraph(clear_transformations)
if job_name is not None:
j_stream_graph.setJobName(job_name)
return j_stream_graph
def kafka_connector_assertion(self, flink_kafka_consumer_clz, flink_kafka_producer_clz):
source_topic = 'test_source_topic'
sink_topic = 'test_sink_topic'
props = {'bootstrap.servers': 'localhost:9092', 'group.id': 'test_group'}
type_info = Types.ROW([Types.INT(), Types.STRING()])
# Test for kafka consumer
deserialization_schema = JsonRowDeserializationSchema.builder() \
.type_info(type_info=type_info).build()
flink_kafka_consumer = flink_kafka_consumer_clz(source_topic, deserialization_schema, props)
flink_kafka_consumer.set_start_from_earliest()
flink_kafka_consumer.set_commit_offsets_on_checkpoints(True)
j_properties = get_field_value(flink_kafka_consumer.get_java_function(), 'properties')
self.assertEqual('localhost:9092', j_properties.getProperty('bootstrap.servers'))
self.assertEqual('test_group', j_properties.getProperty('group.id'))
self.assertTrue(get_field_value(flink_kafka_consumer.get_java_function(),
'enableCommitOnCheckpoints'))
j_start_up_mode = get_field_value(flink_kafka_consumer.get_java_function(), 'startupMode')
j_deserializer = get_field_value(flink_kafka_consumer.get_java_function(), 'deserializer')
j_deserialize_type_info = invoke_java_object_method(j_deserializer, "getProducedType")
deserialize_type_info = typeinfo._from_java_type(j_deserialize_type_info)
self.assertTrue(deserialize_type_info == type_info)
self.assertTrue(j_start_up_mode.equals(get_gateway().jvm
.org.apache.flink.streaming.connectors
.kafka.config.StartupMode.EARLIEST))
j_topic_desc = get_field_value(flink_kafka_consumer.get_java_function(),
'topicsDescriptor')
j_topics = invoke_java_object_method(j_topic_desc, 'getFixedTopics')
self.assertEqual(['test_source_topic'], list(j_topics))
# Test for kafka producer
serialization_schema = JsonRowSerializationSchema.builder().with_type_info(type_info) \
.build()
flink_kafka_producer = flink_kafka_producer_clz(sink_topic, serialization_schema, props)
flink_kafka_producer.set_write_timestamp_to_kafka(False)
j_producer_config = get_field_value(flink_kafka_producer.get_java_function(),
'producerConfig')
self.assertEqual('localhost:9092', j_producer_config.getProperty('bootstrap.servers'))
self.assertEqual('test_group', j_producer_config.getProperty('group.id'))
self.assertFalse(get_field_value(flink_kafka_producer.get_java_function(),
'writeTimestampToKafka'))
def test_source_deprecated_method(self):
test_option = ConfigOptions.key('pulsar.source.enableAutoAcknowledgeMessage') \
.boolean_type().no_default_value()
pulsar_source = PulsarSource.builder() \
.set_service_url('pulsar://localhost:6650') \
.set_admin_url('http://localhost:8080') \
.set_topics('ada') \
.set_deserialization_schema(
PulsarDeserializationSchema.flink_type_info(Types.STRING(), None)) \
.set_subscription_name('ff') \
.set_config(test_option, True) \
.set_config_with_dict({'pulsar.source.autoCommitCursorInterval': '1000'}) \
.build()
configuration = get_field_value(pulsar_source.get_java_function(),
"sourceConfiguration")
self.assertEqual(
configuration.getBoolean(test_option._j_config_option), True)
self.assertEqual(
configuration.getLong(
ConfigOptions.key('pulsar.source.autoCommitCursorInterval').
long_type().no_default_value()._j_config_option), 1000)
def _generate_stream_graph(self, clear_transformations: bool = False, job_name: str = None) \
-> JavaObject:
gateway = get_gateway()
JPythonConfigUtil = gateway.jvm.org.apache.flink.python.util.PythonConfigUtil
# start BeamFnLoopbackWorkerPoolServicer when executed in MiniCluster
j_configuration = get_j_env_configuration(self._j_stream_execution_environment)
if not self._remote_mode and is_local_deployment(j_configuration):
from pyflink.common import Configuration
from pyflink.fn_execution.beam.beam_worker_pool_service import \
BeamFnLoopbackWorkerPoolServicer
jvm = gateway.jvm
env_config = JPythonConfigUtil.getEnvironmentConfig(
self._j_stream_execution_environment)
parallelism = self.get_parallelism()
if parallelism > 1 and env_config.containsKey(jvm.PythonOptions.PYTHON_ARCHIVES.key()):
import logging
logging.warning("Lookback mode is disabled as python archives are used and the "
"parallelism of the job is greater than 1. The Python user-defined "
"functions will be executed in an independent Python process.")
else:
config = Configuration(j_configuration=j_configuration)
config.set_string(
"loopback.server.address", BeamFnLoopbackWorkerPoolServicer().start())
JPythonConfigUtil.configPythonOperator(self._j_stream_execution_environment)
gateway.jvm.org.apache.flink.python.chain.PythonOperatorChainingOptimizer.apply(
self._j_stream_execution_environment)
JPythonConfigUtil.setPartitionCustomOperatorNumPartitions(
get_field_value(self._j_stream_execution_environment, "transformations"))
j_stream_graph = self._j_stream_execution_environment.getStreamGraph(clear_transformations)
if job_name is not None:
j_stream_graph.setJobName(job_name)
return j_stream_graph
def __str__(self):
if self._schema_string is None:
self._schema_string = get_field_value(self._j_schema,
'schema').toString()
return self._schema_string