becketqin commented on a change in pull request #6594: [FLINK-9311] [pubsub] Added PubSub source connector with support for checkpointing (ATLEAST_ONCE) URL: https://github.com/apache/flink/pull/6594#discussion_r300306400
########## File path: flink-connectors/flink-connector-gcp-pubsub/src/main/java/org/apache/flink/streaming/connectors/gcp/pubsub/PubSubSource.java ########## @@ -0,0 +1,292 @@ +/* + * 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. + */ + +package org.apache.flink.streaming.connectors.gcp.pubsub; + +import org.apache.flink.api.common.functions.RuntimeContext; +import org.apache.flink.api.common.serialization.DeserializationSchema; +import org.apache.flink.api.common.typeinfo.TypeInformation; +import org.apache.flink.api.java.typeutils.ResultTypeQueryable; +import org.apache.flink.configuration.Configuration; +import org.apache.flink.runtime.state.CheckpointListener; +import org.apache.flink.streaming.api.checkpoint.ListCheckpointed; +import org.apache.flink.streaming.api.functions.source.ParallelSourceFunction; +import org.apache.flink.streaming.api.functions.source.RichSourceFunction; +import org.apache.flink.streaming.api.operators.StreamingRuntimeContext; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.AcknowledgeIdsForCheckpoint; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.AcknowledgeOnCheckpoint; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.Acknowledger; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.PubSubDeserializationSchema; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.PubSubSubscriber; +import org.apache.flink.streaming.connectors.gcp.pubsub.common.PubSubSubscriberFactory; +import org.apache.flink.util.Preconditions; + +import com.google.auth.Credentials; +import com.google.cloud.pubsub.v1.Subscriber; +import com.google.pubsub.v1.ProjectSubscriptionName; +import com.google.pubsub.v1.PubsubMessage; +import com.google.pubsub.v1.ReceivedMessage; +import org.slf4j.Logger; +import org.slf4j.LoggerFactory; + +import java.io.IOException; +import java.io.Serializable; +import java.time.Duration; +import java.util.List; +import java.util.concurrent.CancellationException; + +import static com.google.cloud.pubsub.v1.SubscriptionAdminSettings.defaultCredentialsProviderBuilder; + +/** + * PubSub Source, this Source will consume PubSub messages from a subscription and Acknowledge them on the next checkpoint. + * This ensures every message will get acknowledged at least once. + */ +public class PubSubSource<OUT> extends RichSourceFunction<OUT> + implements ResultTypeQueryable<OUT>, ParallelSourceFunction<OUT>, CheckpointListener, ListCheckpointed<AcknowledgeIdsForCheckpoint<String>> { + public static final int NO_MAX_MESSAGES_TO_ACKNOWLEDGE_LIMIT = -1; + private static final Logger LOG = LoggerFactory.getLogger(PubSubSource.class); + protected final PubSubDeserializationSchema<OUT> deserializationSchema; + protected final PubSubSubscriberFactory pubSubSubscriberFactory; + protected final Credentials credentials; + protected final int maxMessagesToAcknowledge; + protected final AcknowledgeOnCheckpointFactory acknowledgeOnCheckpointFactory; + + protected transient AcknowledgeOnCheckpoint<String> acknowledgeOnCheckpoint; + protected transient PubSubSubscriber subscriber; + + protected transient volatile boolean isRunning; + + PubSubSource(PubSubDeserializationSchema<OUT> deserializationSchema, + PubSubSubscriberFactory pubSubSubscriberFactory, + Credentials credentials, + int maxMessagesToAcknowledge, + AcknowledgeOnCheckpointFactory acknowledgeOnCheckpointFactory) { + this.deserializationSchema = deserializationSchema; + this.pubSubSubscriberFactory = pubSubSubscriberFactory; + this.credentials = credentials; + this.maxMessagesToAcknowledge = maxMessagesToAcknowledge; + this.acknowledgeOnCheckpointFactory = acknowledgeOnCheckpointFactory; + } + + @Override + public void open(Configuration configuration) throws Exception { + super.open(configuration); + if (hasNoCheckpointingEnabled(getRuntimeContext())) { + throw new IllegalArgumentException("The PubSubSource REQUIRES Checkpointing to be enabled and " + + "the checkpointing frequency must be MUCH lower than the PubSub timeout for it to retry a message."); + } + + getRuntimeContext().getMetricGroup().gauge("PubSubMessagesProcessedNotAcked", this::getOutstandingMessagesToAck); + + createAndSetPubSubSubscriber(); + this.isRunning = true; + } + + private boolean hasNoCheckpointingEnabled(RuntimeContext runtimeContext) { + return !(runtimeContext instanceof StreamingRuntimeContext && ((StreamingRuntimeContext) runtimeContext).isCheckpointingEnabled()); + } + + @Override + public void run(SourceContext<OUT> sourceContext) throws Exception { + while (isRunning) { + try { + if (maxMessagesToAcknowledgeLimitReached()) { Review comment: @Xeli Thanks for the explanation. In the case you mentioned, should the checkpoint timeout be set to longer, or should the CP interval be set to smaller? To explain my concern, the motivation and behavior of the current code are following: 1. Continuous checkpoint failure may cause the number of unacknowledged messages to accumulate and grow. 2. To avoid OOM in situation 1, we need to cap the maximum unacknowledged messages. 3. With the while loop, the source may stall forever if checkpoints keep failing. After removing the while loop, the maximum unacknowledged messages will grow slower, i.e. consumption rate is throttled at ~10 pull request per sec, but continuous CP failure may still eventually cause OOM. From what I see in the current Flink code, the design is following: 1. Ideally, checkpoints are performed asynchronously and should not slow down the processing. 2. When there are continuous checkpoint failures, it is a problem by itself, therefore the job can fail by user specifying max tolerable continuous CP failure in checkpoint config. 3. The flow control is done either by explicit throttling or implicitly via backpressure. So while I understand the motivation here, the actual behavior seems not quite align with the overall design here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
