hequn8128 commented on a change in pull request #13169: URL: https://github.com/apache/flink/pull/13169#discussion_r471353104
########## File path: flink-python/pyflink/datastream/connectors.py ########## @@ -0,0 +1,692 @@ +################################################################################ +# 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. +################################################################################ +from typing import Dict, List, Union + +from pyflink.common.serialization_schemas import DeserializationSchema, SerializationSchema +from pyflink.common.typeinfo import RowTypeInfo +from pyflink.datastream.functions import SourceFunction, SinkFunction +from pyflink.java_gateway import get_gateway +from pyflink.util.utils import to_jarray + + +class FlinkKafkaConsumerBase(SourceFunction): + """ + Base class of all Flink Kafka Consumer data sources. This implements the common behavior across + all kafka versions. + + The Kafka version specific behavior is defined mainly in the specific subclasses. + """ + + def __init__(self, j_flink_kafka_consumer): + super(FlinkKafkaConsumerBase, self).__init__(source_func=j_flink_kafka_consumer) + + def set_commit_offsets_on_checkpoints(self, commit_on_checkpoints: bool): + """ + Specifies whether or not the consumer should commit offsets back to kafka on checkpoints. + This setting will only have effect if checkpointing is enabled for the job. If checkpointing + isn't enabled, only the "auto.commit.enable" (for 0.8) / "enable.auto.commit" (for 0.9+) + property settings will be used. + """ + self._j_function = self._j_function \ + .setCommitOffsetsOnCheckpoints(commit_on_checkpoints) + return self + + def set_start_from_earliest(self): + """ + Specifies the consumer to start reading from the earliest offset for all partitions. This + lets the consumer ignore any committed group offsets in Zookeeper/ Kafka brokers. + + This method does not affect where partitions are read from when the consumer is restored + from a checkpoint or savepoint. When the consumer is restored from a checkpoint or + savepoint, only the offsets in the restored state will be used. + """ + self._j_function = self._j_function.setStartFromEarliest() + return self + + def set_start_from_latest(self): + """ + Specifies the consuer to start reading from the latest offset for all partitions. This lets + the consumer ignore any committed group offsets in Zookeeper / Kafka brokers. + + This method does not affect where partitions are read from when the consumer is restored + from a checkpoint or savepoint. When the consumer is restored from a checkpoint or + savepoint, only the offsets in the restored state will be used. + """ + self._j_function = self._j_function.setStartFromLatest() + return self + + def set_start_from_timestamp(self, startup_offsets_timestamp: int): + """ + Specifies the consumer to start reading partitions from a specified timestamp. The specified + timestamp must be before the current timestamp. This lets the consumer ignore any committed + group offsets in Zookeeper / Kafka brokers. + + The consumer will look up the earliest offset whose timestamp is greater than or equal to + the specific timestamp from Kafka. If there's no such offset, the consumer will use the + latest offset to read data from Kafka. + + This method does not affect where partitions are read from when the consumer is restored + from a checkpoint or savepoint. When the consumer is restored from a checkpoint or + savepoint, only the offsets in the restored state will be used. + + :param startup_offsets_timestamp: timestamp for the startup offsets, as milliseconds for + epoch. + """ + self._j_function = self._j_function.setStartFromTimestamp( + startup_offsets_timestamp) + return self + + def set_start_from_group_offsets(self): + """ + Specifies the consumer to start reading from any committed group offsets found in Zookeeper/ + Kafka brokers. The 'group.id' property must be set in the configuration properties. If no + offset can be found for a partition, the behaviour in 'auto.offset.reset' set in the + configuration properties will be used for the partition. + + This method does not affect where partitions are read from when the consumer is restored + from a checkpoint or savepoint. When the consumer is restored from a checkpoint or + savepoint, only the offsets in the restored state will be used. + """ + self._j_function = self._j_function.setStartFromGroupOffsets() + return self + + def disable_filter_restored_partitions_with_subscribed_topics(self): + """ + By default, when restoring from a checkpoint / savepoint, the consumer always ignores + restored partitions that are no longer associated with the current specified topics or topic + pattern to subscribe to. + + This method does not affect where partitions are read from when the consumer is restored + from a checkpoint or savepoint. When the consumer is restored from a checkpoint or + savepoint, only the offsets in the restored state will be used. + """ + self._j_function = self._j_function \ + .disableFilterRestoredPartitionsWithSubscribedTopics() + return self + + def get_produced_type(self): + return self._j_function.getProducedType() + + +class FlinkKafkaConsumer010(FlinkKafkaConsumerBase): + """ + The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from + Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will + pull data from one or more Kafka partitions. + + The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost + during a failure, and taht the computation processes elements 'exactly once. (These guarantees + naturally assume that Kafka itself does not loose any data.) + + Please note that Flink snapshots the offsets internally as part of its distributed checkpoints. + The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in + sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of + how far the Flink Kafka consumer has consumed a topic. + + Please refer to Kafka's documentation for the available configuration properties: + http://kafka.apache.org/documentation.html#newconsumerconfigs + """ + + def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema, + properties: Dict): + """ + Creates a new Kafka streaming source consumer for Kafka 0.10.x. + + This constructor allows passing multiple topics to the consumer. + + :param topics: The Kafka topics to read from. + :param deserialization_schema: The de-/serializer used to convert between Kafka's byte + messages and Flink's objects. + :param properties: The properties that are used to configure both the fetcher and the offset + handler. + """ + if not isinstance(topics, list): + topics = [topics] + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in properties.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaConsumer010 = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer010 + j_flink_kafka_consumer_010 = \ + JFlinkKafkaConsumer010(topics, + deserialization_schema._j_deserialization_schema, + j_properties) + super(FlinkKafkaConsumer010, self).__init__( + j_flink_kafka_consumer=j_flink_kafka_consumer_010) + + +class FlinkKafkaConsumer011(FlinkKafkaConsumerBase): + """ + The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from + Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will + pull data from one or more Kafka partitions. + + The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost + during a failure, and taht the computation processes elements 'exactly once. (These guarantees + naturally assume that Kafka itself does not loose any data.) + + Please note that Flink snapshots the offsets internally as part of its distributed checkpoints. + The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in + sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of + how far the Flink Kafka consumer has consumed a topic. + + Please refer to Kafka's documentation for the available configuration properties: + http://kafka.apache.org/documentation.html#newconsumerconfigs + """ + + def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema, + properties: Dict): + """ + Creates a new Kafka streaming source consumer for Kafka 0.10.x. + + This constructor allows passing multiple topics to the consumer. + + :param topics: The Kafka topics to read from. + :param deserialization_schema: The de-/serializer used to convert between Kafka's byte + messages and Flink's objects. + :param properties: The properties that are used to configure both the fetcher and the offset + handler. + """ + if not isinstance(topics, list): + topics = [topics] + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in properties.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaConsumer011 = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer011 + j_flink_kafka_consumer_011 = \ + JFlinkKafkaConsumer011(topics, + deserialization_schema._j_deserialization_schema, + j_properties) + super(FlinkKafkaConsumer011, self).__init__(j_flink_kafka_consumer_011) + + +class FlinkKafkaConsumer(FlinkKafkaConsumerBase): + """ + The Flink Kafka Consumer is a streaming data source that pulls a parallel data stream from + Apache Kafka 0.10.x. The consumer can run in multiple parallel instances, each of which will + pull data from one or more Kafka partitions. + + The Flink Kafka Consumer participates in checkpointing and guarantees that no data is lost + during a failure, and taht the computation processes elements 'exactly once. (These guarantees + naturally assume that Kafka itself does not loose any data.) + + Please note that Flink snapshots the offsets internally as part of its distributed checkpoints. + The offsets committed to Kafka / Zookeeper are only to bring the outside view of progress in + sync with Flink's view of the progress. That way, monitoring and other jobs can get a view of + how far the Flink Kafka consumer has consumed a topic. + + Please refer to Kafka's documentation for the available configuration properties: + http://kafka.apache.org/documentation.html#newconsumerconfigs + """ + + def __init__(self, topics: Union[str, List[str]], deserialization_schema: DeserializationSchema, + properties: Dict): + """ + Creates a new Kafka streaming source consumer for Kafka 0.10.x. + + This constructor allows passing multiple topics to the consumer. + + :param topics: The Kafka topics to read from. + :param deserialization_schema: The de-/serializer used to convert between Kafka's byte + messages and Flink's objects. + :param properties: The properties that are used to configure both the fetcher and the offset + handler. + """ + if not isinstance(topics, list): + topics = [topics] + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in properties.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaConsumer = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer + j_flink_kafka_consumer = \ + JFlinkKafkaConsumer(topics, + deserialization_schema._j_deserialization_schema, + j_properties) + super(FlinkKafkaConsumer, self).__init__( + j_flink_kafka_consumer=j_flink_kafka_consumer) + + +class FlinkKafkaProducerBase(SinkFunction): + """ + Flink Sink to produce data into a Kafka topic. + + Please note that this producer provides at-least-once reliability guarantees when checkpoints + are enabled and set_flush_on_checkpoint(True) is set. Otherwise, the producer doesn;t provid any + reliability guarantees. + """ + + def __init__(self, j_flink_kafka_producer): + super(FlinkKafkaProducerBase, self).__init__(sink_func=j_flink_kafka_producer) + + def set_log_failures_only(self, log_failures_only: bool): + """ + Defines whether the producer should fail on errors, or only log them. If this is set to + true, then exceptions will be only logged, if set to false, exceptions will be eventually + thrown and cause the streaming program to fail (and enter recovery). + + :param log_failures_only: The flag to indicate logging-only on exceptions. + """ + self._j_function.setLogFailuresOnly(log_failures_only) + + def set_flush_on_checkpoint(self, flush_on_checkpoint: bool): + """ + If set to true, the Flink producer will wait for all outstanding messages in the Kafka + buffers to be acknowledged by the Kafka producer on a checkpoint. + + This way, the producer can guarantee that messages in the Kafka buffers are part of the + checkpoint. + + :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint) + """ + self._j_function.setFlushOnCheckpoint(flush_on_checkpoint) + + +class FlinkKafkaProducer010(FlinkKafkaProducerBase): + """ + Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.10.x. + """ + + def __init__(self, topic: str, serialization_schema: SerializationSchema, + producer_config: Dict): + """ + Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic. + + Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner. + This default partitioner maps each sink subtask to a single Kafka partition (i.e. all + records received by a sink subtask will end up in the same Kafka partition). + + :param topic: ID of the Kafka topic. + :param serialization_schema: User defined key-less serialization schema. + :param producer_config: Properties with the producer configuration. + """ + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in producer_config.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaProducer010 = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer010 + j_flink_kafka_producer = JFlinkKafkaProducer010( + topic, serialization_schema._j_serialization_schema, j_properties) + super(FlinkKafkaProducer010, self).__init__(j_flink_kafka_producer=j_flink_kafka_producer) + + def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool): + """ + If set to true, Flink will write the (event time) timestamp attached to each record into + Kafka. Timestamps must be positive for Kafka to accept them. + + :param write_timestamp_to_kafka: Flag indicating if Flink's internal timestamps are written + to Kafka. + """ + self._j_function.setWriteTimestampToKafka(write_timestamp_to_kafka) + + +class Semantic(object): + """ + Semantics that can be chosen. + :data: `EXACTLY_ONCE`: + The Flink producer will write all messages in a Kafka transaction that will be committed to + the Kafka on a checkpoint. In this mode FlinkKafkaProducer011 sets up a pool of + FlinkKafkaProducer. Between each checkpoint there is created new Kafka transaction, which is + being committed on FlinkKafkaProducer011.notifyCheckpointComplete(long). If checkpoint + complete notifications are running late, FlinkKafkaProducer011 can run out of + FlinkKafkaProducers in the pool. In that case any subsequent FlinkKafkaProducer011.snapshot- + State() requests will fail and the FlinkKafkaProducer011 will keep using the + FlinkKafkaProducer from previous checkpoint. To decrease chances of failing checkpoints + there are four options: + 1. decrease number of max concurrent checkpoints + 2. make checkpoints mre reliable (so that they complete faster) + 3. increase delay between checkpoints + 4. increase size of FlinkKafkaProducers pool + + :data: `AT_LEAST_ONCE`: + The Flink producer will wait for all outstanding messages in the Kafka buffers to be + acknowledged by the Kafka producer on a checkpoint. + + :data: `NONE`: + Means that nothing will be guaranteed. Messages can be lost and/or duplicated in case of + failure. + """ + + EXACTLY_ONCE = 0, + AT_LEAST_ONCE = 1, + NONE = 2 + + @staticmethod + def _to_j_semantic(semantic): + gateway = get_gateway() + JSemantic = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011.Semantic + if semantic == Semantic.EXACTLY_ONCE: + return JSemantic.EXACTLY_ONCE + elif semantic == Semantic.AT_LEAST_ONCE: + return JSemantic.AT_LEAST_ONCE + elif semantic == Semantic.NONE: + return JSemantic.NONE + else: + raise TypeError("Unsupported semantic: %s, supported semantics are: " + "Semantic.EXACTLY_ONCE, Semantic.AT_LEAST_ONCE, Semantic.NONE" + % semantic) + + +class FlinkKafkaProducer011(SinkFunction): + """ + Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.11.x. By + default producer will use AT_LEAST_ONCE sematic. Before using EXACTLY_ONCE please refer to + Flink's Kafka connector documentation. + """ + + def __init__(self, topic: str, serialization_schema: SerializationSchema, + producer_config: Dict): + """ + Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic. + + Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner. + This default partitioner maps each sink subtask to a single Kafka partition (i.e. all + records received by a sink subtask will end up in the same Kafka partition). + + :param topic: ID of the Kafka topic. + :param serialization_schema: User defined key-less serialization schema. + :param producer_config: Properties with the producer configuration. + """ + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in producer_config.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaProducer011 = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer011 + j_flink_kafka_producer = JFlinkKafkaProducer011( + topic, serialization_schema._j_serialization_schema, j_properties) + super(FlinkKafkaProducer011, self).__init__(sink_func=j_flink_kafka_producer) + + def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool): + """ + If set to true, Flink will write the (event time) timestamp attached to each record into + Kafka. Timestamps must be positive for Kafka to accept them. + + :param write_timestamp_to_kafka: Flag indicating if Flink's internal timestamps are written + to Kafka. + """ + self._j_function.setWriteTimestampToKafka(write_timestamp_to_kafka) + + def ignore_failures_after_transaction_timeout(self): + """ + Disables the propagation of exceptions thrown when committing presumable timed out Kafka + transactions during recovery of the job. If a Kafka transaction is timed out, a commit will + never be successful. Hence, use this feature to avoid recovery loops of the job. Exceptions + will still be logged to inform the user that data loss might have occurred. + + Note that we use current time millis to track the age of a transaction. Moreover, only + exceptions thrown during the recovery are caught, i.e., the producer will attempt at least + one commit of the transaction before giving up. + """ + self._j_function = self._j_function.ignoreFailuresAfterTransactionTimeout() + return self + + def set_log_failures_only(self, log_failures_only: bool): + """ + Defines whether the producer should fail on errors, or only log them. If this is set to + true, then exceptions will be only logged, if set to false, exceptions will be eventually + thrown and cause the streaming program to fail (and enter recovery). + + :param log_failures_only: The flag to indicate logging-only on exceptions. + """ + self._j_function.setLogFailuresOnly(log_failures_only) + + def set_flush_on_checkpoint(self, flush_on_checkpoint: bool): + """ + If set to true, the Flink producer will wait for all outstanding messages in the Kafka + buffers to be acknowledged by the Kafka producer on a checkpoint. + + This way, the producer can guarantee that messages in the Kafka buffers are part of the + checkpoint. + + :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint) + """ + self._j_function.setFlushOnCheckpoint(flush_on_checkpoint) + + +class FlinkKafkaProducer(SinkFunction): + """ + Flink Sink to produce data into a Kafka topic. This producer is compatible with Kafka 0.11.x. By + default producer will use AT_LEAST_ONCE sematic. Before using EXACTLY_ONCE please refer to + Flink's Kafka connector documentation. + """ + + def __init__(self, topic: str, serialization_schema: SerializationSchema, + producer_config: Dict): + """ + Creates a FlinkKafkaProducer for a given topic. The sink produces a DataStream to the topic. + + Using this constructor, the default FlinkFixedPartitioner will be used as the partitioner. + This default partitioner maps each sink subtask to a single Kafka partition (i.e. all + records received by a sink subtask will end up in the same Kafka partition). + + :param topic: ID of the Kafka topic. + :param serialization_schema: User defined key-less serialization schema. + :param producer_config: Properties with the producer configuration. + """ + gateway = get_gateway() + j_properties = gateway.jvm.java.util.Properties() + for key, value in producer_config.items(): + j_properties.setProperty(key, value) + + JFlinkKafkaProducer = gateway.jvm \ + .org.apache.flink.streaming.connectors.kafka.FlinkKafkaProducer + j_flink_kafka_producer = JFlinkKafkaProducer( + topic, serialization_schema._j_serialization_schema, j_properties) + super(FlinkKafkaProducer, self).__init__(sink_func=j_flink_kafka_producer) + + def set_write_timestamp_to_kafka(self, write_timestamp_to_kafka: bool): + """ + If set to true, Flink will write the (event time) timestamp attached to each record into + Kafka. Timestamps must be positive for Kafka to accept them. + + :param write_timestamp_to_kafka: Flag indicating if Flink's internal timestamps are written + to Kafka. + """ + self._j_function.setWriteTimestampToKafka(write_timestamp_to_kafka) + + def ignore_failures_after_transaction_timeout(self): + """ + Disables the propagation of exceptions thrown when committing presumable timed out Kafka + transactions during recovery of the job. If a Kafka transaction is timed out, a commit will + never be successful. Hence, use this feature to avoid recovery loops of the job. Exceptions + will still be logged to inform the user that data loss might have occurred. + + Note that we use current time millis to track the age of a transaction. Moreover, only + exceptions thrown during the recovery are caught, i.e., the producer will attempt at least + one commit of the transaction before giving up. + """ + self._j_function = self._j_function.ignoreFailuresAfterTransactionTimeout() + return self + + def set_log_failures_only(self, log_failures_only: bool): + """ + Defines whether the producer should fail on errors, or only log them. If this is set to + true, then exceptions will be only logged, if set to false, exceptions will be eventually + thrown and cause the streaming program to fail (and enter recovery). + + :param log_failures_only: The flag to indicate logging-only on exceptions. + """ + self._j_function.setLogFailuresOnly(log_failures_only) + + def set_flush_on_checkpoint(self, flush_on_checkpoint: bool): + """ + If set to true, the Flink producer will wait for all outstanding messages in the Kafka + buffers to be acknowledged by the Kafka producer on a checkpoint. + + This way, the producer can guarantee that messages in the Kafka buffers are part of the + checkpoint. + + :param flush_on_checkpoint: Flag indicating the flush mode (true = flush on checkpoint) + """ + self._j_function.setFlushOnCheckpoint(flush_on_checkpoint) + + +class JdbcSink(SinkFunction): + + def __init__(self, j_jdbc_sink): + super(JdbcSink, self).__init__(sink_func=j_jdbc_sink) + + @staticmethod + def sink(sql: str, type_info: RowTypeInfo, jdbc_connection_options: 'JdbcConnectionOptions', Review comment: We can make type_info optional too since we can directly get this type from the input datastream. e.g., ``` def sink(sql: str, jdbc_connection_options: 'JdbcConnectionOptions', type_info: RowTypeInfo = None, jdbc_execution_options: 'JdbcExecutionOptions' = None): ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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