kafka KafkaConsumer 源码
kafka KafkaConsumer 代码
文件路径：/clients/src/main/java/org/apache/kafka/clients/consumer/KafkaConsumer.java
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* 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
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* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
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package org.apache.kafka.clients.consumer;
import org.apache.kafka.clients.ApiVersions;
import org.apache.kafka.clients.ClientUtils;
import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.GroupRebalanceConfig;
import org.apache.kafka.clients.Metadata;
import org.apache.kafka.clients.NetworkClient;
import org.apache.kafka.clients.consumer.internals.ConsumerCoordinator;
import org.apache.kafka.clients.consumer.internals.ConsumerInterceptors;
import org.apache.kafka.clients.consumer.internals.ConsumerMetadata;
import org.apache.kafka.clients.consumer.internals.ConsumerNetworkClient;
import org.apache.kafka.clients.consumer.internals.Fetch;
import org.apache.kafka.clients.consumer.internals.Fetcher;
import org.apache.kafka.clients.consumer.internals.FetcherMetricsRegistry;
import org.apache.kafka.clients.consumer.internals.KafkaConsumerMetrics;
import org.apache.kafka.clients.consumer.internals.NoOpConsumerRebalanceListener;
import org.apache.kafka.clients.consumer.internals.SubscriptionState;
import org.apache.kafka.common.Cluster;
import org.apache.kafka.common.IsolationLevel;
import org.apache.kafka.common.KafkaException;
import org.apache.kafka.common.Metric;
import org.apache.kafka.common.MetricName;
import org.apache.kafka.common.PartitionInfo;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.errors.InterruptException;
import org.apache.kafka.common.errors.InvalidGroupIdException;
import org.apache.kafka.common.errors.TimeoutException;
import org.apache.kafka.common.internals.ClusterResourceListeners;
import org.apache.kafka.common.metrics.KafkaMetricsContext;
import org.apache.kafka.common.metrics.MetricConfig;
import org.apache.kafka.common.metrics.Metrics;
import org.apache.kafka.common.metrics.MetricsContext;
import org.apache.kafka.common.metrics.MetricsReporter;
import org.apache.kafka.common.metrics.Sensor;
import org.apache.kafka.common.network.ChannelBuilder;
import org.apache.kafka.common.network.Selector;
import org.apache.kafka.common.requests.MetadataRequest;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.utils.AppInfoParser;
import org.apache.kafka.common.utils.LogContext;
import org.apache.kafka.common.utils.Time;
import org.apache.kafka.common.utils.Timer;
import org.apache.kafka.common.utils.Utils;
import org.slf4j.Logger;
import java.net.InetSocketAddress;
import java.time.Duration;
import java.util.Collection;
import java.util.Collections;
import java.util.ConcurrentModificationException;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Locale;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.OptionalLong;
import java.util.Properties;
import java.util.Set;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicLong;
import java.util.concurrent.atomic.AtomicReference;
import java.util.regex.Pattern;
/**
* A client that consumes records from a Kafka cluster.
* <p>
* This client transparently handles the failure of Kafka brokers, and transparently adapts as topic partitions
* it fetches migrate within the cluster. This client also interacts with the broker to allow groups of
* consumers to load balance consumption using <a href="#consumergroups">consumer groups</a>.
* <p>
* The consumer maintains TCP connections to the necessary brokers to fetch data.
* Failure to close the consumer after use will leak these connections.
* The consumer is not thread-safe. See <a href="#multithreaded">Multi-threaded Processing</a> for more details.
*
* <h3>Cross-Version Compatibility</h3>
* This client can communicate with brokers that are version 0.10.0 or newer. Older or newer brokers may not support
* certain features. For example, 0.10.0 brokers do not support offsetsForTimes, because this feature was added
* in version 0.10.1. You will receive an {@link org.apache.kafka.common.errors.UnsupportedVersionException}
* when invoking an API that is not available on the running broker version.
* <p>
*
* <h3>Offsets and Consumer Position</h3>
* Kafka maintains a numerical offset for each record in a partition. This offset acts as a unique identifier of
* a record within that partition, and also denotes the position of the consumer in the partition. For example, a consumer
* which is at position 5 has consumed records with offsets 0 through 4 and will next receive the record with offset 5. There
* are actually two notions of position relevant to the user of the consumer:
* <p>
* The {@link #position(TopicPartition) position} of the consumer gives the offset of the next record that will be given
* out. It will be one larger than the highest offset the consumer has seen in that partition. It automatically advances
* every time the consumer receives messages in a call to {@link #poll(Duration)}.
* <p>
* The {@link #commitSync() committed position} is the last offset that has been stored securely. Should the
* process fail and restart, this is the offset that the consumer will recover to. The consumer can either automatically commit
* offsets periodically; or it can choose to control this committed position manually by calling one of the commit APIs
* (e.g. {@link #commitSync() commitSync} and {@link #commitAsync(OffsetCommitCallback) commitAsync}).
* <p>
* This distinction gives the consumer control over when a record is considered consumed. It is discussed in further
* detail below.
*
* <h3><a name="consumergroups">Consumer Groups and Topic Subscriptions</a></h3>
*
* Kafka uses the concept of <i>consumer groups</i> to allow a pool of processes to divide the work of consuming and
* processing records. These processes can either be running on the same machine or they can be
* distributed over many machines to provide scalability and fault tolerance for processing. All consumer instances
* sharing the same {@code group.id} will be part of the same consumer group.
* <p>
* Each consumer in a group can dynamically set the list of topics it wants to subscribe to through one of the
* {@link #subscribe(Collection, ConsumerRebalanceListener) subscribe} APIs. Kafka will deliver each message in the
* subscribed topics to one process in each consumer group. This is achieved by balancing the partitions between all
* members in the consumer group so that each partition is assigned to exactly one consumer in the group. So if there
* is a topic with four partitions, and a consumer group with two processes, each process would consume from two partitions.
* <p>
* Membership in a consumer group is maintained dynamically: if a process fails, the partitions assigned to it will
* be reassigned to other consumers in the same group. Similarly, if a new consumer joins the group, partitions will be moved
* from existing consumers to the new one. This is known as <i>rebalancing</i> the group and is discussed in more
* detail <a href="#failuredetection">below</a>. Group rebalancing is also used when new partitions are added
* to one of the subscribed topics or when a new topic matching a {@link #subscribe(Pattern, ConsumerRebalanceListener) subscribed regex}
* is created. The group will automatically detect the new partitions through periodic metadata refreshes and
* assign them to members of the group.
* <p>
* Conceptually you can think of a consumer group as being a single logical subscriber that happens to be made up of
* multiple processes. As a multi-subscriber system, Kafka naturally supports having any number of consumer groups for a
* given topic without duplicating data (additional consumers are actually quite cheap).
* <p>
* This is a slight generalization of the functionality that is common in messaging systems. To get semantics similar to
* a queue in a traditional messaging system all processes would be part of a single consumer group and hence record
* delivery would be balanced over the group like with a queue. Unlike a traditional messaging system, though, you can
* have multiple such groups. To get semantics similar to pub-sub in a traditional messaging system each process would
* have its own consumer group, so each process would subscribe to all the records published to the topic.
* <p>
* In addition, when group reassignment happens automatically, consumers can be notified through a {@link ConsumerRebalanceListener},
* which allows them to finish necessary application-level logic such as state cleanup, manual offset
* commits, etc. See <a href="#rebalancecallback">Storing Offsets Outside Kafka</a> for more details.
* <p>
* It is also possible for the consumer to <a href="#manualassignment">manually assign</a> specific partitions
* (similar to the older "simple" consumer) using {@link #assign(Collection)}. In this case, dynamic partition
* assignment and consumer group coordination will be disabled.
*
* <h3><a name="failuredetection">Detecting Consumer Failures</a></h3>
*
* After subscribing to a set of topics, the consumer will automatically join the group when {@link #poll(Duration)} is
* invoked. The poll API is designed to ensure consumer liveness. As long as you continue to call poll, the consumer
* will stay in the group and continue to receive messages from the partitions it was assigned. Underneath the covers,
* the consumer sends periodic heartbeats to the server. If the consumer crashes or is unable to send heartbeats for
* a duration of {@code session.timeout.ms}, then the consumer will be considered dead and its partitions will
* be reassigned.
* <p>
* It is also possible that the consumer could encounter a "livelock" situation where it is continuing
* to send heartbeats, but no progress is being made. To prevent the consumer from holding onto its partitions
* indefinitely in this case, we provide a liveness detection mechanism using the {@code max.poll.interval.ms}
* setting. Basically if you don't call poll at least as frequently as the configured max interval,
* then the client will proactively leave the group so that another consumer can take over its partitions. When this happens,
* you may see an offset commit failure (as indicated by a {@link CommitFailedException} thrown from a call to {@link #commitSync()}).
* This is a safety mechanism which guarantees that only active members of the group are able to commit offsets.
* So to stay in the group, you must continue to call poll.
* <p>
* The consumer provides two configuration settings to control the behavior of the poll loop:
* <ol>
* <li><code>max.poll.interval.ms</code>: By increasing the interval between expected polls, you can give
* the consumer more time to handle a batch of records returned from {@link #poll(Duration)}. The drawback
* is that increasing this value may delay a group rebalance since the consumer will only join the rebalance
* inside the call to poll. You can use this setting to bound the time to finish a rebalance, but
* you risk slower progress if the consumer cannot actually call {@link #poll(Duration) poll} often enough.</li>
* <li><code>max.poll.records</code>: Use this setting to limit the total records returned from a single
* call to poll. This can make it easier to predict the maximum that must be handled within each poll
* interval. By tuning this value, you may be able to reduce the poll interval, which will reduce the
* impact of group rebalancing.</li>
* </ol>
* <p>
* For use cases where message processing time varies unpredictably, neither of these options may be sufficient.
* The recommended way to handle these cases is to move message processing to another thread, which allows
* the consumer to continue calling {@link #poll(Duration) poll} while the processor is still working.
* Some care must be taken to ensure that committed offsets do not get ahead of the actual position.
* Typically, you must disable automatic commits and manually commit processed offsets for records only after the
* thread has finished handling them (depending on the delivery semantics you need).
* Note also that you will need to {@link #pause(Collection) pause} the partition so that no new records are received
* from poll until after thread has finished handling those previously returned.
*
* <h3>Usage Examples</h3>
* The consumer APIs offer flexibility to cover a variety of consumption use cases. Here are some examples to
* demonstrate how to use them.
*
* <h4>Automatic Offset Committing</h4>
* This example demonstrates a simple usage of Kafka's consumer api that relies on automatic offset committing.
* <p>
* <pre>
* Properties props = new Properties();
* props.setProperty("bootstrap.servers", "localhost:9092");
* props.setProperty("group.id", "test");
* props.setProperty("enable.auto.commit", "true");
* props.setProperty("auto.commit.interval.ms", "1000");
* props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
* props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
* KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
* consumer.subscribe(Arrays.asList("foo", "bar"));
* while (true) {
* ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
* for (ConsumerRecord<String, String> record : records)
* System.out.printf("offset = %d, key = %s, value = %s%n", record.offset(), record.key(), record.value());
* }
* </pre>
*
* The connection to the cluster is bootstrapped by specifying a list of one or more brokers to contact using the
* configuration {@code bootstrap.servers}. This list is just used to discover the rest of the brokers in the
* cluster and need not be an exhaustive list of servers in the cluster (though you may want to specify more than one in
* case there are servers down when the client is connecting).
* <p>
* Setting {@code enable.auto.commit} means that offsets are committed automatically with a frequency controlled by
* the config {@code auto.commit.interval.ms}.
* <p>
* In this example the consumer is subscribing to the topics <i>foo</i> and <i>bar</i> as part of a group of consumers
* called <i>test</i> as configured with {@code group.id}.
* <p>
* The deserializer settings specify how to turn bytes into objects. For example, by specifying string deserializers, we
* are saying that our record's key and value will just be simple strings.
*
* <h4>Manual Offset Control</h4>
*
* Instead of relying on the consumer to periodically commit consumed offsets, users can also control when records
* should be considered as consumed and hence commit their offsets. This is useful when the consumption of the messages
* is coupled with some processing logic and hence a message should not be considered as consumed until it is completed processing.
* <p>
* <pre>
* Properties props = new Properties();
* props.setProperty("bootstrap.servers", "localhost:9092");
* props.setProperty("group.id", "test");
* props.setProperty("enable.auto.commit", "false");
* props.setProperty("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
* props.setProperty("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
* KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
* consumer.subscribe(Arrays.asList("foo", "bar"));
* final int minBatchSize = 200;
* List<ConsumerRecord<String, String>> buffer = new ArrayList<>();
* while (true) {
* ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(100));
* for (ConsumerRecord<String, String> record : records) {
* buffer.add(record);
* }
* if (buffer.size() >= minBatchSize) {
* insertIntoDb(buffer);
* consumer.commitSync();
* buffer.clear();
* }
* }
* </pre>
*
* In this example we will consume a batch of records and batch them up in memory. When we have enough records
* batched, we will insert them into a database. If we allowed offsets to auto commit as in the previous example, records
* would be considered consumed after they were returned to the user in {@link #poll(Duration) poll}. It would then be
* possible
* for our process to fail after batching the records, but before they had been inserted into the database.
* <p>
* To avoid this, we will manually commit the offsets only after the corresponding records have been inserted into the
* database. This gives us exact control of when a record is considered consumed. This raises the opposite possibility:
* the process could fail in the interval after the insert into the database but before the commit (even though this
* would likely just be a few milliseconds, it is a possibility). In this case the process that took over consumption
* would consume from last committed offset and would repeat the insert of the last batch of data. Used in this way
* Kafka provides what is often called "at-least-once" delivery guarantees, as each record will likely be delivered one
* time but in failure cases could be duplicated.
* <p>
* <b>Note: Using automatic offset commits can also give you "at-least-once" delivery, but the requirement is that
* you must consume all data returned from each call to {@link #poll(Duration)} before any subsequent calls, or before
* {@link #close() closing} the consumer. If you fail to do either of these, it is possible for the committed offset
* to get ahead of the consumed position, which results in missing records. The advantage of using manual offset
* control is that you have direct control over when a record is considered "consumed."</b>
* <p>
* The above example uses {@link #commitSync() commitSync} to mark all received records as committed. In some cases
* you may wish to have even finer control over which records have been committed by specifying an offset explicitly.
* In the example below we commit offset after we finish handling the records in each partition.
* <p>
* <pre>
* try {
* while(running) {
* ConsumerRecords<String, String> records = consumer.poll(Duration.ofMillis(Long.MAX_VALUE));
* for (TopicPartition partition : records.partitions()) {
* List<ConsumerRecord<String, String>> partitionRecords = records.records(partition);
* for (ConsumerRecord<String, String> record : partitionRecords) {
* System.out.println(record.offset() + ": " + record.value());
* }
* long lastOffset = partitionRecords.get(partitionRecords.size() - 1).offset();
* consumer.commitSync(Collections.singletonMap(partition, new OffsetAndMetadata(lastOffset + 1)));
* }
* }
* } finally {
* consumer.close();
* }
* </pre>
*
* <b>Note: The committed offset should always be the offset of the next message that your application will read.</b>
* Thus, when calling {@link #commitSync(Map) commitSync(offsets)} you should add one to the offset of the last message processed.
*
* <h4><a name="manualassignment">Manual Partition Assignment</a></h4>
*
* In the previous examples, we subscribed to the topics we were interested in and let Kafka dynamically assign a
* fair share of the partitions for those topics based on the active consumers in the group. However, in
* some cases you may need finer control over the specific partitions that are assigned. For example:
* <p>
* <ul>
* <li>If the process is maintaining some kind of local state associated with that partition (like a
* local on-disk key-value store), then it should only get records for the partition it is maintaining on disk.
* <li>If the process itself is highly available and will be restarted if it fails (perhaps using a
* cluster management framework like YARN, Mesos, or AWS facilities, or as part of a stream processing framework). In
* this case there is no need for Kafka to detect the failure and reassign the partition since the consuming process
* will be restarted on another machine.
* </ul>
* <p>
* To use this mode, instead of subscribing to the topic using {@link #subscribe(Collection) subscribe}, you just call
* {@link #assign(Collection)} with the full list of partitions that you want to consume.
*
* <pre>
* String topic = "foo";
* TopicPartition partition0 = new TopicPartition(topic, 0);
* TopicPartition partition1 = new TopicPartition(topic, 1);
* consumer.assign(Arrays.asList(partition0, partition1));
* </pre>
*
* Once assigned, you can call {@link #poll(Duration) poll} in a loop, just as in the preceding examples to consume
* records. The group that the consumer specifies is still used for committing offsets, but now the set of partitions
* will only change with another call to {@link #assign(Collection) assign}. Manual partition assignment does
* not use group coordination, so consumer failures will not cause assigned partitions to be rebalanced. Each consumer
* acts independently even if it shares a groupId with another consumer. To avoid offset commit conflicts, you should
* usually ensure that the groupId is unique for each consumer instance.
* <p>
* Note that it isn't possible to mix manual partition assignment (i.e. using {@link #assign(Collection) assign})
* with dynamic partition assignment through topic subscription (i.e. using {@link #subscribe(Collection) subscribe}).
*
* <h4><a name="rebalancecallback">Storing Offsets Outside Kafka</h4>
*
* The consumer application need not use Kafka's built-in offset storage, it can store offsets in a store of its own
* choosing. The primary use case for this is allowing the application to store both the offset and the results of the
* consumption in the same system in a way that both the results and offsets are stored atomically. This is not always
* possible, but when it is it will make the consumption fully atomic and give "exactly once" semantics that are
* stronger than the default "at-least once" semantics you get with Kafka's offset commit functionality.
* <p>
* Here are a couple of examples of this type of usage:
* <ul>
* <li>If the results of the consumption are being stored in a relational database, storing the offset in the database
* as well can allow committing both the results and offset in a single transaction. Thus either the transaction will
* succeed and the offset will be updated based on what was consumed or the result will not be stored and the offset
* won't be updated.
* <li>If the results are being stored in a local store it may be possible to store the offset there as well. For
* example a search index could be built by subscribing to a particular partition and storing both the offset and the
* indexed data together. If this is done in a way that is atomic, it is often possible to have it be the case that even
* if a crash occurs that causes unsync'd data to be lost, whatever is left has the corresponding offset stored as well.
* This means that in this case the indexing process that comes back having lost recent updates just resumes indexing
* from what it has ensuring that no updates are lost.
* </ul>
* <p>
* Each record comes with its own offset, so to manage your own offset you just need to do the following:
*
* <ul>
* <li>Configure <code>enable.auto.commit=false</code>
* <li>Use the offset provided with each {@link ConsumerRecord} to save your position.
* <li>On restart restore the position of the consumer using {@link #seek(TopicPartition, long)}.
* </ul>
*
* <p>
* This type of usage is simplest when the partition assignment is also done manually (this would be likely in the
* search index use case described above). If the partition assignment is done automatically special care is
* needed to handle the case where partition assignments change. This can be done by providing a
* {@link ConsumerRebalanceListener} instance in the call to {@link #subscribe(Collection, ConsumerRebalanceListener)}
* and {@link #subscribe(Pattern, ConsumerRebalanceListener)}.
* For example, when partitions are taken from a consumer the consumer will want to commit its offset for those partitions by
* implementing {@link ConsumerRebalanceListener#onPartitionsRevoked(Collection)}. When partitions are assigned to a
* consumer, the consumer will want to look up the offset for those new partitions and correctly initialize the consumer
* to that position by implementing {@link ConsumerRebalanceListener#onPartitionsAssigned(Collection)}.
* <p>
* Another common use for {@link ConsumerRebalanceListener} is to flush any caches the application maintains for
* partitions that are moved elsewhere.
*
* <h4>Controlling The Consumer's Position</h4>
*
* In most use cases the consumer will simply consume records from beginning to end, periodically committing its
* position (either automatically or manually). However Kafka allows the consumer to manually control its position,
* moving forward or backwards in a partition at will. This means a consumer can re-consume older records, or skip to
* the most recent records without actually consuming the intermediate records.
* <p>
* There are several instances where manually controlling the consumer's position can be useful.
* <p>
* One case is for time-sensitive record processing it may make sense for a consumer that falls far enough behind to not
* attempt to catch up processing all records, but rather just skip to the most recent records.
* <p>
* Another use case is for a system that maintains local state as described in the previous section. In such a system
* the consumer will want to initialize its position on start-up to whatever is contained in the local store. Likewise
* if the local state is destroyed (say because the disk is lost) the state may be recreated on a new machine by
* re-consuming all the data and recreating the state (assuming that Kafka is retaining sufficient history).
* <p>
* Kafka allows specifying the position using {@link #seek(TopicPartition, long)} to specify the new position. Special
* methods for seeking to the earliest and latest offset the server maintains are also available (
* {@link #seekToBeginning(Collection)} and {@link #seekToEnd(Collection)} respectively).
*
* <h4>Consumption Flow Control</h4>
*
* If a consumer is assigned multiple partitions to fetch data from, it will try to consume from all of them at the same time,
* effectively giving these partitions the same priority for consumption. However in some cases consumers may want to
* first focus on fetching from some subset of the assigned partitions at full speed, and only start fetching other partitions
* when these partitions have few or no data to consume.
*
* <p>
* One of such cases is stream processing, where processor fetches from two topics and performs the join on these two streams.
* When one of the topics is long lagging behind the other, the processor would like to pause fetching from the ahead topic
* in order to get the lagging stream to catch up. Another example is bootstraping upon consumer starting up where there are
* a lot of history data to catch up, the applications usually want to get the latest data on some of the topics before consider
* fetching other topics.
*
* <p>
* Kafka supports dynamic controlling of consumption flows by using {@link #pause(Collection)} and {@link #resume(Collection)}
* to pause the consumption on the specified assigned partitions and resume the consumption
* on the specified paused partitions respectively in the future {@link #poll(Duration)} calls.
*
* <h3>Reading Transactional Messages</h3>
*
* <p>
* Transactions were introduced in Kafka 0.11.0 wherein applications can write to multiple topics and partitions atomically.
* In order for this to work, consumers reading from these partitions should be configured to only read committed data.
* This can be achieved by setting the {@code isolation.level=read_committed} in the consumer's configuration.
*
* <p>
* In <code>read_committed</code> mode, the consumer will read only those transactional messages which have been
* successfully committed. It will continue to read non-transactional messages as before. There is no client-side
* buffering in <code>read_committed</code> mode. Instead, the end offset of a partition for a <code>read_committed</code>
* consumer would be the offset of the first message in the partition belonging to an open transaction. This offset
* is known as the 'Last Stable Offset'(LSO).</p>
*
* <p>
* A {@code read_committed} consumer will only read up to the LSO and filter out any transactional
* messages which have been aborted. The LSO also affects the behavior of {@link #seekToEnd(Collection)} and
* {@link #endOffsets(Collection)} for {@code read_committed} consumers, details of which are in each method's documentation.
* Finally, the fetch lag metrics are also adjusted to be relative to the LSO for {@code read_committed} consumers.
*
* <p>
* Partitions with transactional messages will include commit or abort markers which indicate the result of a transaction.
* There markers are not returned to applications, yet have an offset in the log. As a result, applications reading from
* topics with transactional messages will see gaps in the consumed offsets. These missing messages would be the transaction
* markers, and they are filtered out for consumers in both isolation levels. Additionally, applications using
* {@code read_committed} consumers may also see gaps due to aborted transactions, since those messages would not
* be returned by the consumer and yet would have valid offsets.
*
* <h3><a name="multithreaded">Multi-threaded Processing</a></h3>
*
* The Kafka consumer is NOT thread-safe. All network I/O happens in the thread of the application
* making the call. It is the responsibility of the user to ensure that multi-threaded access
* is properly synchronized. Un-synchronized access will result in {@link ConcurrentModificationException}.
*
* <p>
* The only exception to this rule is {@link #wakeup()}, which can safely be used from an external thread to
* interrupt an active operation. In this case, a {@link org.apache.kafka.common.errors.WakeupException} will be
* thrown from the thread blocking on the operation. This can be used to shutdown the consumer from another thread.
* The following snippet shows the typical pattern:
*
* <pre>
* public class KafkaConsumerRunner implements Runnable {
* private final AtomicBoolean closed = new AtomicBoolean(false);
* private final KafkaConsumer consumer;
*
* public KafkaConsumerRunner(KafkaConsumer consumer) {
* this.consumer = consumer;
* }
*
* {@literal}@Override
* public void run() {
* try {
* consumer.subscribe(Arrays.asList("topic"));
* while (!closed.get()) {
* ConsumerRecords records = consumer.poll(Duration.ofMillis(10000));
* // Handle new records
* }
* } catch (WakeupException e) {
* // Ignore exception if closing
* if (!closed.get()) throw e;
* } finally {
* consumer.close();
* }
* }
*
* // Shutdown hook which can be called from a separate thread
* public void shutdown() {
* closed.set(true);
* consumer.wakeup();
* }
* }
* </pre>
*
* Then in a separate thread, the consumer can be shutdown by setting the closed flag and waking up the consumer.
*
* <p>
* <pre>
* closed.set(true);
* consumer.wakeup();
* </pre>
*
* <p>
* Note that while it is possible to use thread interrupts instead of {@link #wakeup()} to abort a blocking operation
* (in which case, {@link InterruptException} will be raised), we discourage their use since they may cause a clean
* shutdown of the consumer to be aborted. Interrupts are mainly supported for those cases where using {@link #wakeup()}
* is impossible, e.g. when a consumer thread is managed by code that is unaware of the Kafka client.
*
* <p>
* We have intentionally avoided implementing a particular threading model for processing. This leaves several
* options for implementing multi-threaded processing of records.
*
* <h4>1. One Consumer Per Thread</h4>
*
* A simple option is to give each thread its own consumer instance. Here are the pros and cons of this approach:
* <ul>
* <li><b>PRO</b>: It is the easiest to implement
* <li><b>PRO</b>: It is often the fastest as no inter-thread co-ordination is needed
* <li><b>PRO</b>: It makes in-order processing on a per-partition basis very easy to implement (each thread just
* processes messages in the order it receives them).
* <li><b>CON</b>: More consumers means more TCP connections to the cluster (one per thread). In general Kafka handles
* connections very efficiently so this is generally a small cost.
* <li><b>CON</b>: Multiple consumers means more requests being sent to the server and slightly less batching of data
* which can cause some drop in I/O throughput.
* <li><b>CON</b>: The number of total threads across all processes will be limited by the total number of partitions.
* </ul>
*
* <h4>2. Decouple Consumption and Processing</h4>
*
* Another alternative is to have one or more consumer threads that do all data consumption and hands off
* {@link ConsumerRecords} instances to a blocking queue consumed by a pool of processor threads that actually handle
* the record processing.
*
* This option likewise has pros and cons:
* <ul>
* <li><b>PRO</b>: This option allows independently scaling the number of consumers and processors. This makes it
* possible to have a single consumer that feeds many processor threads, avoiding any limitation on partitions.
* <li><b>CON</b>: Guaranteeing order across the processors requires particular care as the threads will execute
* independently an earlier chunk of data may actually be processed after a later chunk of data just due to the luck of
* thread execution timing. For processing that has no ordering requirements this is not a problem.
* <li><b>CON</b>: Manually committing the position becomes harder as it requires that all threads co-ordinate to ensure
* that processing is complete for that partition.
* </ul>
*
* There are many possible variations on this approach. For example each processor thread can have its own queue, and
* the consumer threads can hash into these queues using the TopicPartition to ensure in-order consumption and simplify
* commit.
*/
public class KafkaConsumer<K, V> implements Consumer<K, V> {
private static final String CLIENT_ID_METRIC_TAG = "client-id";
private static final long NO_CURRENT_THREAD = -1L;
private static final String JMX_PREFIX = "kafka.consumer";
static final long DEFAULT_CLOSE_TIMEOUT_MS = 30 * 1000;
static final String DEFAULT_REASON = "rebalance enforced by user";
// Visible for testing
final Metrics metrics;
final KafkaConsumerMetrics kafkaConsumerMetrics;
private Logger log;
private final String clientId;
private final Optional<String> groupId;
private final ConsumerCoordinator coordinator;
private final Deserializer<K> keyDeserializer;
private final Deserializer<V> valueDeserializer;
private final Fetcher<K, V> fetcher;
private final ConsumerInterceptors<K, V> interceptors;
private final IsolationLevel isolationLevel;
private final Time time;
private final ConsumerNetworkClient client;
private final SubscriptionState subscriptions;
private final ConsumerMetadata metadata;
private final long retryBackoffMs;
private final long requestTimeoutMs;
private final int defaultApiTimeoutMs;
private volatile boolean closed = false;
private List<ConsumerPartitionAssignor> assignors;
// currentThread holds the threadId of the current thread accessing KafkaConsumer
// and is used to prevent multi-threaded access
private final AtomicLong currentThread = new AtomicLong(NO_CURRENT_THREAD);
// refcount is used to allow reentrant access by the thread who has acquired currentThread
private final AtomicInteger refcount = new AtomicInteger(0);
// to keep from repeatedly scanning subscriptions in poll(), cache the result during metadata updates
private boolean cachedSubscriptionHasAllFetchPositions;
/**
* A consumer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings
* are documented <a href="http://kafka.apache.org/documentation.html#consumerconfigs" >here</a>. Values can be
* either strings or objects of the appropriate type (for example a numeric configuration would accept either the
* string "42" or the integer 42).
* <p>
* Valid configuration strings are documented at {@link ConsumerConfig}.
* <p>
* Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
*
* @param configs The consumer configs
*/
public KafkaConsumer(Map<String, Object> configs) {
this(configs, null, null);
}
/**
* A consumer is instantiated by providing a {@link java.util.Properties} object as configuration.
* <p>
* Valid configuration strings are documented at {@link ConsumerConfig}.
* <p>
* Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
*
* @param properties The consumer configuration properties
*/
public KafkaConsumer(Properties properties) {
this(properties, null, null);
}
/**
* A consumer is instantiated by providing a {@link java.util.Properties} object as configuration, and a
* key and a value {@link Deserializer}.
* <p>
* Valid configuration strings are documented at {@link ConsumerConfig}.
* <p>
* Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
*
* @param properties The consumer configuration properties
* @param keyDeserializer The deserializer for key that implements {@link Deserializer}. The configure() method
* won't be called in the consumer when the deserializer is passed in directly.
* @param valueDeserializer The deserializer for value that implements {@link Deserializer}. The configure() method
* won't be called in the consumer when the deserializer is passed in directly.
*/
public KafkaConsumer(Properties properties,
Deserializer<K> keyDeserializer,
Deserializer<V> valueDeserializer) {
this(Utils.propsToMap(properties), keyDeserializer, valueDeserializer);
}
/**
* A consumer is instantiated by providing a set of key-value pairs as configuration, and a key and a value {@link Deserializer}.
* <p>
* Valid configuration strings are documented at {@link ConsumerConfig}.
* <p>
* Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
*
* @param configs The consumer configs
* @param keyDeserializer The deserializer for key that implements {@link Deserializer}. The configure() method
* won't be called in the consumer when the deserializer is passed in directly.
* @param valueDeserializer The deserializer for value that implements {@link Deserializer}. The configure() method
* won't be called in the consumer when the deserializer is passed in directly.
*/
public KafkaConsumer(Map<String, Object> configs,
Deserializer<K> keyDeserializer,
Deserializer<V> valueDeserializer) {
this(new ConsumerConfig(ConsumerConfig.appendDeserializerToConfig(configs, keyDeserializer, valueDeserializer)),
keyDeserializer, valueDeserializer);
}
@SuppressWarnings("unchecked")
KafkaConsumer(ConsumerConfig config, Deserializer<K> keyDeserializer, Deserializer<V> valueDeserializer) {
try {
GroupRebalanceConfig groupRebalanceConfig = new GroupRebalanceConfig(config,
GroupRebalanceConfig.ProtocolType.CONSUMER);
this.groupId = Optional.ofNullable(groupRebalanceConfig.groupId);
this.clientId = config.getString(CommonClientConfigs.CLIENT_ID_CONFIG);
LogContext logContext;
// If group.instance.id is set, we will append it to the log context.
if (groupRebalanceConfig.groupInstanceId.isPresent()) {
logContext = new LogContext("[Consumer instanceId=" + groupRebalanceConfig.groupInstanceId.get() +
", clientId=" + clientId + ", groupId=" + groupId.orElse("null") + "] ");
} else {
logContext = new LogContext("[Consumer clientId=" + clientId + ", groupId=" + groupId.orElse("null") + "] ");
}
this.log = logContext.logger(getClass());
boolean enableAutoCommit = config.maybeOverrideEnableAutoCommit();
groupId.ifPresent(groupIdStr -> {
if (groupIdStr.isEmpty()) {
log.warn("Support for using the empty group id by consumers is deprecated and will be removed in the next major release.");
}
});
log.debug("Initializing the Kafka consumer");
this.requestTimeoutMs = config.getInt(ConsumerConfig.REQUEST_TIMEOUT_MS_CONFIG);
this.defaultApiTimeoutMs = config.getInt(ConsumerConfig.DEFAULT_API_TIMEOUT_MS_CONFIG);
this.time = Time.SYSTEM;
this.metrics = buildMetrics(config, time, clientId);
this.retryBackoffMs = config.getLong(ConsumerConfig.RETRY_BACKOFF_MS_CONFIG);
List<ConsumerInterceptor<K, V>> interceptorList = (List) config.getConfiguredInstances(
ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
ConsumerInterceptor.class,
Collections.singletonMap(ConsumerConfig.CLIENT_ID_CONFIG, clientId));
this.interceptors = new ConsumerInterceptors<>(interceptorList);
if (keyDeserializer == null) {
this.keyDeserializer = config.getConfiguredInstance(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, Deserializer.class);
this.keyDeserializer.configure(config.originals(Collections.singletonMap(ConsumerConfig.CLIENT_ID_CONFIG, clientId)), true);
} else {
config.ignore(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG);
this.keyDeserializer = keyDeserializer;
}
if (valueDeserializer == null) {
this.valueDeserializer = config.getConfiguredInstance(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, Deserializer.class);
this.valueDeserializer.configure(config.originals(Collections.singletonMap(ConsumerConfig.CLIENT_ID_CONFIG, clientId)), false);
} else {
config.ignore(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG);
this.valueDeserializer = valueDeserializer;
}
OffsetResetStrategy offsetResetStrategy = OffsetResetStrategy.valueOf(config.getString(ConsumerConfig.AUTO_OFFSET_RESET_CONFIG).toUpperCase(Locale.ROOT));
this.subscriptions = new SubscriptionState(logContext, offsetResetStrategy);
ClusterResourceListeners clusterResourceListeners = configureClusterResourceListeners(keyDeserializer,
valueDeserializer, metrics.reporters(), interceptorList);
this.metadata = new ConsumerMetadata(retryBackoffMs,
config.getLong(ConsumerConfig.METADATA_MAX_AGE_CONFIG),
!config.getBoolean(ConsumerConfig.EXCLUDE_INTERNAL_TOPICS_CONFIG),
config.getBoolean(ConsumerConfig.ALLOW_AUTO_CREATE_TOPICS_CONFIG),
subscriptions, logContext, clusterResourceListeners);
List<InetSocketAddress> addresses = ClientUtils.parseAndValidateAddresses(
config.getList(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG), config.getString(ConsumerConfig.CLIENT_DNS_LOOKUP_CONFIG));
this.metadata.bootstrap(addresses);
String metricGrpPrefix = "consumer";
FetcherMetricsRegistry metricsRegistry = new FetcherMetricsRegistry(Collections.singleton(CLIENT_ID_METRIC_TAG), metricGrpPrefix);
ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(config, time, logContext);
this.isolationLevel = IsolationLevel.valueOf(
config.getString(ConsumerConfig.ISOLATION_LEVEL_CONFIG).toUpperCase(Locale.ROOT));
Sensor throttleTimeSensor = Fetcher.throttleTimeSensor(metrics, metricsRegistry);
int heartbeatIntervalMs = config.getInt(ConsumerConfig.HEARTBEAT_INTERVAL_MS_CONFIG);
ApiVersions apiVersions = new ApiVersions();
NetworkClient netClient = new NetworkClient(
new Selector(config.getLong(ConsumerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG), metrics, time, metricGrpPrefix, channelBuilder, logContext),
this.metadata,
clientId,
100, // a fixed large enough value will suffice for max in-flight requests
config.getLong(ConsumerConfig.RECONNECT_BACKOFF_MS_CONFIG),
config.getLong(ConsumerConfig.RECONNECT_BACKOFF_MAX_MS_CONFIG),
config.getInt(ConsumerConfig.SEND_BUFFER_CONFIG),
config.getInt(ConsumerConfig.RECEIVE_BUFFER_CONFIG),
config.getInt(ConsumerConfig.REQUEST_TIMEOUT_MS_CONFIG),
config.getLong(ConsumerConfig.SOCKET_CONNECTION_SETUP_TIMEOUT_MS_CONFIG),
config.getLong(ConsumerConfig.SOCKET_CONNECTION_SETUP_TIMEOUT_MAX_MS_CONFIG),
time,
true,
apiVersions,
throttleTimeSensor,
logContext);
this.client = new ConsumerNetworkClient(
logContext,
netClient,
metadata,
time,
retryBackoffMs,
config.getInt(ConsumerConfig.REQUEST_TIMEOUT_MS_CONFIG),
heartbeatIntervalMs); //Will avoid blocking an extended period of time to prevent heartbeat thread starvation
this.assignors = ConsumerPartitionAssignor.getAssignorInstances(
config.getList(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG),
config.originals(Collections.singletonMap(ConsumerConfig.CLIENT_ID_CONFIG, clientId))
);
// no coordinator will be constructed for the default (null) group id
if (!groupId.isPresent()) {
config.ignore(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG);
config.ignore(ConsumerConfig.THROW_ON_FETCH_STABLE_OFFSET_UNSUPPORTED);
this.coordinator = null;
} else {
this.coordinator = new ConsumerCoordinator(groupRebalanceConfig,
logContext,
this.client,
assignors,
this.metadata,
this.subscriptions,
metrics,
metricGrpPrefix,
this.time,
enableAutoCommit,
config.getInt(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG),
this.interceptors,
config.getBoolean(ConsumerConfig.THROW_ON_FETCH_STABLE_OFFSET_UNSUPPORTED));
}
this.fetcher = new Fetcher<>(
logContext,
this.client,
config.getInt(ConsumerConfig.FETCH_MIN_BYTES_CONFIG),
config.getInt(ConsumerConfig.FETCH_MAX_BYTES_CONFIG),
config.getInt(ConsumerConfig.FETCH_MAX_WAIT_MS_CONFIG),
config.getInt(ConsumerConfig.MAX_PARTITION_FETCH_BYTES_CONFIG),
config.getInt(ConsumerConfig.MAX_POLL_RECORDS_CONFIG),
config.getBoolean(ConsumerConfig.CHECK_CRCS_CONFIG),
config.getString(ConsumerConfig.CLIENT_RACK_CONFIG),
this.keyDeserializer,
this.valueDeserializer,
this.metadata,
this.subscriptions,
metrics,
metricsRegistry,
this.time,
this.retryBackoffMs,
this.requestTimeoutMs,
isolationLevel,
apiVersions);
this.kafkaConsumerMetrics = new KafkaConsumerMetrics(metrics, metricGrpPrefix);
config.logUnused();
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId, metrics, time.milliseconds());
log.debug("Kafka consumer initialized");
} catch (Throwable t) {
// call close methods if internal objects are already constructed; this is to prevent resource leak. see KAFKA-2121
// we do not need to call `close` at all when `log` is null, which means no internal objects were initialized.
if (this.log != null) {
close(0, true);
}
// now propagate the exception
throw new KafkaException("Failed to construct kafka consumer", t);
}
}
// visible for testing
KafkaConsumer(LogContext logContext,
String clientId,
ConsumerCoordinator coordinator,
Deserializer<K> keyDeserializer,
Deserializer<V> valueDeserializer,
Fetcher<K, V> fetcher,
ConsumerInterceptors<K, V> interceptors,
Time time,
ConsumerNetworkClient client,
Metrics metrics,
SubscriptionState subscriptions,
ConsumerMetadata metadata,
long retryBackoffMs,
long requestTimeoutMs,
int defaultApiTimeoutMs,
List<ConsumerPartitionAssignor> assignors,
String groupId) {
this.log = logContext.logger(getClass());
this.clientId = clientId;
this.coordinator = coordinator;
this.keyDeserializer = keyDeserializer;
this.valueDeserializer = valueDeserializer;
this.fetcher = fetcher;
this.isolationLevel = IsolationLevel.READ_UNCOMMITTED;
this.interceptors = Objects.requireNonNull(interceptors);
this.time = time;
this.client = client;
this.metrics = metrics;
this.subscriptions = subscriptions;
this.metadata = metadata;
this.retryBackoffMs = retryBackoffMs;
this.requestTimeoutMs = requestTimeoutMs;
this.defaultApiTimeoutMs = defaultApiTimeoutMs;
this.assignors = assignors;
this.groupId = Optional.ofNullable(groupId);
this.kafkaConsumerMetrics = new KafkaConsumerMetrics(metrics, "consumer");
}
private static Metrics buildMetrics(ConsumerConfig config, Time time, String clientId) {
Map<String, String> metricsTags = Collections.singletonMap(CLIENT_ID_METRIC_TAG, clientId);
MetricConfig metricConfig = new MetricConfig().samples(config.getInt(ConsumerConfig.METRICS_NUM_SAMPLES_CONFIG))
.timeWindow(config.getLong(ConsumerConfig.METRICS_SAMPLE_WINDOW_MS_CONFIG), TimeUnit.MILLISECONDS)
.recordLevel(Sensor.RecordingLevel.forName(config.getString(ConsumerConfig.METRICS_RECORDING_LEVEL_CONFIG)))
.tags(metricsTags);
List<MetricsReporter> reporters = CommonClientConfigs.metricsReporters(clientId, config);
MetricsContext metricsContext = new KafkaMetricsContext(JMX_PREFIX,
config.originalsWithPrefix(CommonClientConfigs.METRICS_CONTEXT_PREFIX));
return new Metrics(metricConfig, reporters, time, metricsContext);
}
/**
* Get the set of partitions currently assigned to this consumer. If subscription happened by directly assigning
* partitions using {@link #assign(Collection)} then this will simply return the same partitions that
* were assigned. If topic subscription was used, then this will give the set of topic partitions currently assigned
* to the consumer (which may be none if the assignment hasn't happened yet, or the partitions are in the
* process of getting reassigned).
* @return The set of partitions currently assigned to this consumer
*/
public Set<TopicPartition> assignment() {
acquireAndEnsureOpen();
try {
return Collections.unmodifiableSet(this.subscriptions.assignedPartitions());
} finally {
release();
}
}
/**
* Get the current subscription. Will return the same topics used in the most recent call to
* {@link #subscribe(Collection, ConsumerRebalanceListener)}, or an empty set if no such call has been made.
* @return The set of topics currently subscribed to
*/
public Set<String> subscription() {
acquireAndEnsureOpen();
try {
return Collections.unmodifiableSet(new HashSet<>(this.subscriptions.subscription()));
} finally {
release();
}
}
/**
* Subscribe to the given list of topics to get dynamically
* assigned partitions. <b>Topic subscriptions are not incremental. This list will replace the current
* assignment (if there is one).</b> Note that it is not possible to combine topic subscription with group management
* with manual partition assignment through {@link #assign(Collection)}.
*
* If the given list of topics is empty, it is treated the same as {@link #unsubscribe()}.
*
* <p>
* As part of group management, the consumer will keep track of the list of consumers that belong to a particular
* group and will trigger a rebalance operation if any one of the following events are triggered:
* <ul>
* <li>Number of partitions change for any of the subscribed topics
* <li>A subscribed topic is created or deleted
* <li>An existing member of the consumer group is shutdown or fails
* <li>A new member is added to the consumer group
* </ul>
* <p>
* When any of these events are triggered, the provided listener will be invoked first to indicate that
* the consumer's assignment has been revoked, and then again when the new assignment has been received.
* Note that rebalances will only occur during an active call to {@link #poll(Duration)}, so callbacks will
* also only be invoked during that time.
*
* The provided listener will immediately override any listener set in a previous call to subscribe.
* It is guaranteed, however, that the partitions revoked/assigned through this interface are from topics
* subscribed in this call. See {@link ConsumerRebalanceListener} for more details.
*
* @param topics The list of topics to subscribe to
* @param listener Non-null listener instance to get notifications on partition assignment/revocation for the
* subscribed topics
* @throws IllegalArgumentException If topics is null or contains null or empty elements, or if listener is null
* @throws IllegalStateException If {@code subscribe()} is called previously with pattern, or assign is called
* previously (without a subsequent call to {@link #unsubscribe()}), or if not
* configured at-least one partition assignment strategy
*/
@Override
public void subscribe(Collection<String> topics, ConsumerRebalanceListener listener) {
acquireAndEnsureOpen();
try {
maybeThrowInvalidGroupIdException();
if (topics == null)
throw new IllegalArgumentException("Topic collection to subscribe to cannot be null");
if (topics.isEmpty()) {
// treat subscribing to empty topic list as the same as unsubscribing
this.unsubscribe();
} else {
for (String topic : topics) {
if (Utils.isBlank(topic))
throw new IllegalArgumentException("Topic collection to subscribe to cannot contain null or empty topic");
}
throwIfNoAssignorsConfigured();
fetcher.clearBufferedDataForUnassignedTopics(topics);
log.info("Subscribed to topic(s): {}", Utils.join(topics, ", "));
if (this.subscriptions.subscribe(new HashSet<>(topics), listener))
metadata.requestUpdateForNewTopics();
}
} finally {
release();
}
}
/**
* Subscribe to the given list of topics to get dynamically assigned partitions.
* <b>Topic subscriptions are not incremental. This list will replace the current
* assignment (if there is one).</b> It is not possible to combine topic subscription with group management
* with manual partition assignment through {@link #assign(Collection)}.
*
* If the given list of topics is empty, it is treated the same as {@link #unsubscribe()}.
*
* <p>
* This is a short-hand for {@link #subscribe(Collection, ConsumerRebalanceListener)}, which
* uses a no-op listener. If you need the ability to seek to particular offsets, you should prefer
* {@link #subscribe(Collection, ConsumerRebalanceListener)}, since group rebalances will cause partition offsets
* to be reset. You should also provide your own listener if you are doing your own offset
* management since the listener gives you an opportunity to commit offsets before a rebalance finishes.
*
* @param topics The list of topics to subscribe to
* @throws IllegalArgumentException If topics is null or contains null or empty elements
* @throws IllegalStateException If {@code subscribe()} is called previously with pattern, or assign is called
* previously (without a subsequent call to {@link #unsubscribe()}), or if not
* configured at-least one partition assignment strategy
*/
@Override
public void subscribe(Collection<String> topics) {
subscribe(topics, new NoOpConsumerRebalanceListener());
}
/**
* Subscribe to all topics matching specified pattern to get dynamically assigned partitions.
* The pattern matching will be done periodically against all topics existing at the time of check.
* This can be controlled through the {@code metadata.max.age.ms} configuration: by lowering
* the max metadata age, the consumer will refresh metadata more often and check for matching topics.
* <p>
* See {@link #subscribe(Collection, ConsumerRebalanceListener)} for details on the
* use of the {@link ConsumerRebalanceListener}. Generally rebalances are triggered when there
* is a change to the topics matching the provided pattern and when consumer group membership changes.
* Group rebalances only take place during an active call to {@link #poll(Duration)}.
*
* @param pattern Pattern to subscribe to
* @param listener Non-null listener instance to get notifications on partition assignment/revocation for the
* subscribed topics
* @throws IllegalArgumentException If pattern or listener is null
* @throws IllegalStateException If {@code subscribe()} is called previously with topics, or assign is called
* previously (without a subsequent call to {@link #unsubscribe()}), or if not
* configured at-least one partition assignment strategy
*/
@Override
public void subscribe(Pattern pattern, ConsumerRebalanceListener listener) {
maybeThrowInvalidGroupIdException();
if (pattern == null || pattern.toString().equals(""))
throw new IllegalArgumentException("Topic pattern to subscribe to cannot be " + (pattern == null ?
"null" : "empty"));
acquireAndEnsureOpen();
try {
throwIfNoAssignorsConfigured();
log.info("Subscribed to pattern: '{}'", pattern);
this.subscriptions.subscribe(pattern, listener);
this.coordinator.updatePatternSubscription(metadata.fetch());
this.metadata.requestUpdateForNewTopics();
} finally {
release();
}
}
/**
* Subscribe to all topics matching specified pattern to get dynamically assigned partitions.
* The pattern matching will be done periodically against topics existing at the time of check.
* <p>
* This is a short-hand for {@link #subscribe(Pattern, ConsumerRebalanceListener)}, which
* uses a no-op listener. If you need the ability to seek to particular offsets, you should prefer
* {@link #subscribe(Pattern, ConsumerRebalanceListener)}, since group rebalances will cause partition offsets
* to be reset. You should also provide your own listener if you are doing your own offset
* management since the listener gives you an opportunity to commit offsets before a rebalance finishes.
*
* @param pattern Pattern to subscribe to
* @throws IllegalArgumentException If pattern is null
* @throws IllegalStateException If {@code subscribe()} is called previously with topics, or assign is called
* previously (without a subsequent call to {@link #unsubscribe()}), or if not
* configured at-least one partition assignment strategy
*/
@Override
public void subscribe(Pattern pattern) {
subscribe(pattern, new NoOpConsumerRebalanceListener());
}
/**
* Unsubscribe from topics currently subscribed with {@link #subscribe(Collection)} or {@link #subscribe(Pattern)}.
* This also clears any partitions directly assigned through {@link #assign(Collection)}.
*
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. rebalance callback errors)
*/
public void unsubscribe() {
acquireAndEnsureOpen();
try {
fetcher.clearBufferedDataForUnassignedPartitions(Collections.emptySet());
if (this.coordinator != null) {
this.coordinator.onLeavePrepare();
this.coordinator.maybeLeaveGroup("the consumer unsubscribed from all topics");
}
this.subscriptions.unsubscribe();
log.info("Unsubscribed all topics or patterns and assigned partitions");
} finally {
release();
}
}
/**
* Manually assign a list of partitions to this consumer. This interface does not allow for incremental assignment
* and will replace the previous assignment (if there is one).
* <p>
* If the given list of topic partitions is empty, it is treated the same as {@link #unsubscribe()}.
* <p>
* Manual topic assignment through this method does not use the consumer's group management
* functionality. As such, there will be no rebalance operation triggered when group membership or cluster and topic
* metadata change. Note that it is not possible to use both manual partition assignment with {@link #assign(Collection)}
* and group assignment with {@link #subscribe(Collection, ConsumerRebalanceListener)}.
* <p>
* If auto-commit is enabled, an async commit (based on the old assignment) will be triggered before the new
* assignment replaces the old one.
*
* @param partitions The list of partitions to assign this consumer
* @throws IllegalArgumentException If partitions is null or contains null or empty topics
* @throws IllegalStateException If {@code subscribe()} is called previously with topics or pattern
* (without a subsequent call to {@link #unsubscribe()})
*/
@Override
public void assign(Collection<TopicPartition> partitions) {
acquireAndEnsureOpen();
try {
if (partitions == null) {
throw new IllegalArgumentException("Topic partition collection to assign to cannot be null");
} else if (partitions.isEmpty()) {
this.unsubscribe();
} else {
for (TopicPartition tp : partitions) {
String topic = (tp != null) ? tp.topic() : null;
if (Utils.isBlank(topic))
throw new IllegalArgumentException("Topic partitions to assign to cannot have null or empty topic");
}
fetcher.clearBufferedDataForUnassignedPartitions(partitions);
// make sure the offsets of topic partitions the consumer is unsubscribing from
// are committed since there will be no following rebalance
if (coordinator != null)
this.coordinator.maybeAutoCommitOffsetsAsync(time.milliseconds());
log.info("Assigned to partition(s): {}", Utils.join(partitions, ", "));
if (this.subscriptions.assignFromUser(new HashSet<>(partitions)))
metadata.requestUpdateForNewTopics();
}
} finally {
release();
}
}
/**
* Fetch data for the topics or partitions specified using one of the subscribe/assign APIs. It is an error to not have
* subscribed to any topics or partitions before polling for data.
* <p>
* On each poll, consumer will try to use the last consumed offset as the starting offset and fetch sequentially. The last
* consumed offset can be manually set through {@link #seek(TopicPartition, long)} or automatically set as the last committed
* offset for the subscribed list of partitions
*
*
* @param timeoutMs The time, in milliseconds, spent waiting in poll if data is not available in the buffer.
* If 0, returns immediately with any records that are available currently in the buffer, else returns empty.
* Must not be negative.
* @return map of topic to records since the last fetch for the subscribed list of topics and partitions
*
* @throws org.apache.kafka.clients.consumer.InvalidOffsetException if the offset for a partition or set of
* partitions is undefined or out of range and no offset reset policy has been configured
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if caller lacks Read access to any of the subscribed
* topics or to the configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. invalid groupId or
* session timeout, errors deserializing key/value pairs, or any new error cases in future versions)
* @throws java.lang.IllegalArgumentException if the timeout value is negative
* @throws java.lang.IllegalStateException if the consumer is not subscribed to any topics or manually assigned any
* partitions to consume from
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*
* @deprecated Since 2.0. Use {@link #poll(Duration)}, which does not block beyond the timeout awaiting partition
* assignment. See <a href="https://cwiki.apache.org/confluence/x/5kiHB">KIP-266</a> for more information.
*/
@Deprecated
@Override
public ConsumerRecords<K, V> poll(final long timeoutMs) {
return poll(time.timer(timeoutMs), false);
}
/**
* Fetch data for the topics or partitions specified using one of the subscribe/assign APIs. It is an error to not have
* subscribed to any topics or partitions before polling for data.
* <p>
* On each poll, consumer will try to use the last consumed offset as the starting offset and fetch sequentially. The last
* consumed offset can be manually set through {@link #seek(TopicPartition, long)} or automatically set as the last committed
* offset for the subscribed list of partitions
*
* <p>
* This method returns immediately if there are records available or if the position advances past control records
* or aborted transactions when isolation.level=read_committed.
* Otherwise, it will await the passed timeout. If the timeout expires, an empty record set will be returned.
* Note that this method may block beyond the timeout in order to execute custom
* {@link ConsumerRebalanceListener} callbacks.
*
*
* @param timeout The maximum time to block (must not be greater than {@link Long#MAX_VALUE} milliseconds)
*
* @return map of topic to records since the last fetch for the subscribed list of topics and partitions
*
* @throws org.apache.kafka.clients.consumer.InvalidOffsetException if the offset for a partition or set of
* partitions is undefined or out of range and no offset reset policy has been configured
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if caller lacks Read access to any of the subscribed
* topics or to the configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. invalid groupId or
* session timeout, errors deserializing key/value pairs, your rebalance callback thrown exceptions,
* or any new error cases in future versions)
* @throws java.lang.IllegalArgumentException if the timeout value is negative
* @throws java.lang.IllegalStateException if the consumer is not subscribed to any topics or manually assigned any
* partitions to consume from
* @throws java.lang.ArithmeticException if the timeout is greater than {@link Long#MAX_VALUE} milliseconds.
* @throws org.apache.kafka.common.errors.InvalidTopicException if the current subscription contains any invalid
* topic (per {@link org.apache.kafka.common.internals.Topic#validate(String)})
* @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
* when the broker doesn't support this feature
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public ConsumerRecords<K, V> poll(final Duration timeout) {
return poll(time.timer(timeout), true);
}
/**
* @throws KafkaException if the rebalance callback throws exception
*/
private ConsumerRecords<K, V> poll(final Timer timer, final boolean includeMetadataInTimeout) {
acquireAndEnsureOpen();
try {
this.kafkaConsumerMetrics.recordPollStart(timer.currentTimeMs());
if (this.subscriptions.hasNoSubscriptionOrUserAssignment()) {
throw new IllegalStateException("Consumer is not subscribed to any topics or assigned any partitions");
}
do {
client.maybeTriggerWakeup();
if (includeMetadataInTimeout) {
// try to update assignment metadata BUT do not need to block on the timer for join group
updateAssignmentMetadataIfNeeded(timer, false);
} else {
while (!updateAssignmentMetadataIfNeeded(time.timer(Long.MAX_VALUE), true)) {
log.warn("Still waiting for metadata");
}
}
final Fetch<K, V> fetch = pollForFetches(timer);
if (!fetch.isEmpty()) {
// before returning the fetched records, we can send off the next round of fetches
// and avoid block waiting for their responses to enable pipelining while the user
// is handling the fetched records.
//
// NOTE: since the consumed position has already been updated, we must not allow
// wakeups or any other errors to be triggered prior to returning the fetched records.
if (fetcher.sendFetches() > 0 || client.hasPendingRequests()) {
client.transmitSends();
}
if (fetch.records().isEmpty()) {
log.trace("Returning empty records from `poll()` "
+ "since the consumer's position has advanced for at least one topic partition");
}
return this.interceptors.onConsume(new ConsumerRecords<>(fetch.records()));
}
} while (timer.notExpired());
return ConsumerRecords.empty();
} finally {
release();
this.kafkaConsumerMetrics.recordPollEnd(timer.currentTimeMs());
}
}
boolean updateAssignmentMetadataIfNeeded(final Timer timer, final boolean waitForJoinGroup) {
if (coordinator != null && !coordinator.poll(timer, waitForJoinGroup)) {
return false;
}
return updateFetchPositions(timer);
}
/**
* @throws KafkaException if the rebalance callback throws exception
*/
private Fetch<K, V> pollForFetches(Timer timer) {
long pollTimeout = coordinator == null ? timer.remainingMs() :
Math.min(coordinator.timeToNextPoll(timer.currentTimeMs()), timer.remainingMs());
// if data is available already, return it immediately
final Fetch<K, V> fetch = fetcher.collectFetch();
if (!fetch.isEmpty()) {
return fetch;
}
// send any new fetches (won't resend pending fetches)
fetcher.sendFetches();
// We do not want to be stuck blocking in poll if we are missing some positions
// since the offset lookup may be backing off after a failure
// NOTE: the use of cachedSubscriptionHasAllFetchPositions means we MUST call
// updateAssignmentMetadataIfNeeded before this method.
if (!cachedSubscriptionHasAllFetchPositions && pollTimeout > retryBackoffMs) {
pollTimeout = retryBackoffMs;
}
log.trace("Polling for fetches with timeout {}", pollTimeout);
Timer pollTimer = time.timer(pollTimeout);
client.poll(pollTimer, () -> {
// since a fetch might be completed by the background thread, we need this poll condition
// to ensure that we do not block unnecessarily in poll()
return !fetcher.hasAvailableFetches();
});
timer.update(pollTimer.currentTimeMs());
return fetcher.collectFetch();
}
/**
* Commit offsets returned on the last {@link #poll(Duration) poll()} for all the subscribed list of topics and
* partitions.
* <p>
* This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
* every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used.
* <p>
* This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
* (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
* <p>
* Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
* (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
*
* @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
* This fatal error can only occur if you are using automatic group management with {@link #subscribe(Collection)},
* or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
* when you are trying to commit to partitions that are no longer assigned to this consumer because the
* consumer is for example no longer part of the group this exception would be thrown.
* @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
* so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
* complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
* NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
* and also for those partitions that are still assigned their fetch positions may have changed too
* if more records are returned from the {@link #poll(Duration)} call.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
* is too large or if the topic does not exist).
* @throws org.apache.kafka.common.errors.TimeoutException if the timeout specified by {@code default.api.timeout.ms} expires
* before successful completion of the offset commit
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitSync() {
commitSync(Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Commit offsets returned on the last {@link #poll(Duration) poll()} for all the subscribed list of topics and
* partitions.
* <p>
* This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
* every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used.
* <p>
* This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the passed timeout expires.
* <p>
* Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
* (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
*
* @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
* This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
* or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
* when you are trying to commit to partitions that are no longer assigned to this consumer because the
* consumer is for example no longer part of the group this exception would be thrown.
* @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
* so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
* complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
* NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
* and also for those partitions that are still assigned their fetch positions may have changed too
* if more records are returned from the {@link #poll(Duration)} call.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
* is too large or if the topic does not exist).
* @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
* of the offset commit
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitSync(Duration timeout) {
commitSync(subscriptions.allConsumed(), timeout);
}
/**
* Commit the specified offsets for the specified list of topics and partitions.
* <p>
* This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
* rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used. The committed offset should be the next message your application will consume,
* i.e. lastProcessedMessageOffset + 1. If automatic group management with {@link #subscribe(Collection)} is used,
* then the committed offsets must belong to the currently auto-assigned partitions.
* <p>
* This is a synchronous commit and will block until either the commit succeeds or an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
* (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
* <p>
* Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
* (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
*
* @param offsets A map of offsets by partition with associated metadata
* @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
* This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
* or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
* when you are trying to commit to partitions that are no longer assigned to this consumer because the
* consumer is for example no longer part of the group this exception would be thrown.
* @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
* so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
* complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
* NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
* and also for those partitions that are still assigned their fetch positions may have changed too
* if more records are returned from the {@link #poll(Duration)} call, so when you retry committing
* you should consider updating the passed in {@code offset} parameter.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws java.lang.IllegalArgumentException if the committed offset is negative
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
* is too large or if the topic does not exist).
* @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
* of the offset commit
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitSync(final Map<TopicPartition, OffsetAndMetadata> offsets) {
commitSync(offsets, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Commit the specified offsets for the specified list of topics and partitions.
* <p>
* This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
* rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used. The committed offset should be the next message your application will consume,
* i.e. lastProcessedMessageOffset + 1. If automatic group management with {@link #subscribe(Collection)} is used,
* then the committed offsets must belong to the currently auto-assigned partitions.
* <p>
* This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout expires.
* <p>
* Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
* (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
*
* @param offsets A map of offsets by partition with associated metadata
* @param timeout The maximum amount of time to await completion of the offset commit
* @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
* This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
* or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
* when you are trying to commit to partitions that are no longer assigned to this consumer because the
* consumer is for example no longer part of the group this exception would be thrown.
* @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
* so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
* complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
* NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
* and also for those partitions that are still assigned their fetch positions may have changed too
* if more records are returned from the {@link #poll(Duration)} call, so when you retry committing
* you should consider updating the passed in {@code offset} parameter.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws java.lang.IllegalArgumentException if the committed offset is negative
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
* is too large or if the topic does not exist).
* @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
* of the offset commit
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitSync(final Map<TopicPartition, OffsetAndMetadata> offsets, final Duration timeout) {
acquireAndEnsureOpen();
long commitStart = time.nanoseconds();
try {
maybeThrowInvalidGroupIdException();
offsets.forEach(this::updateLastSeenEpochIfNewer);
if (!coordinator.commitOffsetsSync(new HashMap<>(offsets), time.timer(timeout))) {
throw new TimeoutException("Timeout of " + timeout.toMillis() + "ms expired before successfully " +
"committing offsets " + offsets);
}
} finally {
kafkaConsumerMetrics.recordCommitSync(time.nanoseconds() - commitStart);
release();
}
}
/**
* Commit offsets returned on the last {@link #poll(Duration)} for all the subscribed list of topics and partition.
* Same as {@link #commitAsync(OffsetCommitCallback) commitAsync(null)}
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitAsync() {
commitAsync(null);
}
/**
* Commit offsets returned on the last {@link #poll(Duration) poll()} for the subscribed list of topics and partitions.
* <p>
* This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
* every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used.
* <p>
* This is an asynchronous call and will not block. Any errors encountered are either passed to the callback
* (if provided) or discarded.
* <p>
* Offsets committed through multiple calls to this API are guaranteed to be sent in the same order as
* the invocations. Corresponding commit callbacks are also invoked in the same order. Additionally note that
* offsets committed through this API are guaranteed to complete before a subsequent call to {@link #commitSync()}
* (and variants) returns.
*
* @param callback Callback to invoke when the commit completes
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitAsync(OffsetCommitCallback callback) {
commitAsync(subscriptions.allConsumed(), callback);
}
/**
* Commit the specified offsets for the specified list of topics and partitions to Kafka.
* <p>
* This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
* rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
* should not be used. The committed offset should be the next message your application will consume,
* i.e. lastProcessedMessageOffset + 1. If automatic group management with {@link #subscribe(Collection)} is used,
* then the committed offsets must belong to the currently auto-assigned partitions.
* <p>
* This is an asynchronous call and will not block. Any errors encountered are either passed to the callback
* (if provided) or discarded.
* <p>
* Offsets committed through multiple calls to this API are guaranteed to be sent in the same order as
* the invocations. Corresponding commit callbacks are also invoked in the same order. Additionally note that
* offsets committed through this API are guaranteed to complete before a subsequent call to {@link #commitSync()}
* (and variants) returns.
*
* @param offsets A map of offsets by partition with associate metadata. This map will be copied internally, so it
* is safe to mutate the map after returning.
* @param callback Callback to invoke when the commit completes
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
*/
@Override
public void commitAsync(final Map<TopicPartition, OffsetAndMetadata> offsets, OffsetCommitCallback callback) {
acquireAndEnsureOpen();
try {
maybeThrowInvalidGroupIdException();
log.debug("Committing offsets: {}", offsets);
offsets.forEach(this::updateLastSeenEpochIfNewer);
coordinator.commitOffsetsAsync(new HashMap<>(offsets), callback);
} finally {
release();
}
}
/**
* Overrides the fetch offsets that the consumer will use on the next {@link #poll(Duration) poll(timeout)}. If this API
* is invoked for the same partition more than once, the latest offset will be used on the next poll(). Note that
* you may lose data if this API is arbitrarily used in the middle of consumption, to reset the fetch offsets
*
* @throws IllegalArgumentException if the provided offset is negative
* @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
*/
@Override
public void seek(TopicPartition partition, long offset) {
if (offset < 0)
throw new IllegalArgumentException("seek offset must not be a negative number");
acquireAndEnsureOpen();
try {
log.info("Seeking to offset {} for partition {}", offset, partition);
SubscriptionState.FetchPosition newPosition = new SubscriptionState.FetchPosition(
offset,
Optional.empty(), // This will ensure we skip validation
this.metadata.currentLeader(partition));
this.subscriptions.seekUnvalidated(partition, newPosition);
} finally {
release();
}
}
/**
* Overrides the fetch offsets that the consumer will use on the next {@link #poll(Duration) poll(timeout)}. If this API
* is invoked for the same partition more than once, the latest offset will be used on the next poll(). Note that
* you may lose data if this API is arbitrarily used in the middle of consumption, to reset the fetch offsets. This
* method allows for setting the leaderEpoch along with the desired offset.
*
* @throws IllegalArgumentException if the provided offset is negative
* @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
*/
@Override
public void seek(TopicPartition partition, OffsetAndMetadata offsetAndMetadata) {
long offset = offsetAndMetadata.offset();
if (offset < 0) {
throw new IllegalArgumentException("seek offset must not be a negative number");
}
acquireAndEnsureOpen();
try {
if (offsetAndMetadata.leaderEpoch().isPresent()) {
log.info("Seeking to offset {} for partition {} with epoch {}",
offset, partition, offsetAndMetadata.leaderEpoch().get());
} else {
log.info("Seeking to offset {} for partition {}", offset, partition);
}
Metadata.LeaderAndEpoch currentLeaderAndEpoch = this.metadata.currentLeader(partition);
SubscriptionState.FetchPosition newPosition = new SubscriptionState.FetchPosition(
offsetAndMetadata.offset(),
offsetAndMetadata.leaderEpoch(),
currentLeaderAndEpoch);
this.updateLastSeenEpochIfNewer(partition, offsetAndMetadata);
this.subscriptions.seekUnvalidated(partition, newPosition);
} finally {
release();
}
}
/**
* Seek to the first offset for each of the given partitions. This function evaluates lazily, seeking to the
* first offset in all partitions only when {@link #poll(Duration)} or {@link #position(TopicPartition)} are called.
* If no partitions are provided, seek to the first offset for all of the currently assigned partitions.
*
* @throws IllegalArgumentException if {@code partitions} is {@code null}
* @throws IllegalStateException if any of the provided partitions are not currently assigned to this consumer
*/
@Override
public void seekToBeginning(Collection<TopicPartition> partitions) {
if (partitions == null)
throw new IllegalArgumentException("Partitions collection cannot be null");
acquireAndEnsureOpen();
try {
Collection<TopicPartition> parts = partitions.size() == 0 ? this.subscriptions.assignedPartitions() : partitions;
subscriptions.requestOffsetReset(parts, OffsetResetStrategy.EARLIEST);
} finally {
release();
}
}
/**
* Seek to the last offset for each of the given partitions. This function evaluates lazily, seeking to the
* final offset in all partitions only when {@link #poll(Duration)} or {@link #position(TopicPartition)} are called.
* If no partitions are provided, seek to the final offset for all of the currently assigned partitions.
* <p>
* If {@code isolation.level=read_committed}, the end offset will be the Last Stable Offset, i.e., the offset
* of the first message with an open transaction.
*
* @throws IllegalArgumentException if {@code partitions} is {@code null}
* @throws IllegalStateException if any of the provided partitions are not currently assigned to this consumer
*/
@Override
public void seekToEnd(Collection<TopicPartition> partitions) {
if (partitions == null)
throw new IllegalArgumentException("Partitions collection cannot be null");
acquireAndEnsureOpen();
try {
Collection<TopicPartition> parts = partitions.size() == 0 ? this.subscriptions.assignedPartitions() : partitions;
subscriptions.requestOffsetReset(parts, OffsetResetStrategy.LATEST);
} finally {
release();
}
}
/**
* Get the offset of the <i>next record</i> that will be fetched (if a record with that offset exists).
* This method may issue a remote call to the server if there is no current position for the given partition.
* <p>
* This call will block until either the position could be determined or an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
* (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
*
* @param partition The partition to get the position for
* @return The current position of the consumer (that is, the offset of the next record to be fetched)
* @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
* @throws org.apache.kafka.clients.consumer.InvalidOffsetException if no offset is currently defined for
* the partition
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
* when the broker doesn't support this feature
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the position cannot be determined before the
* timeout specified by {@code default.api.timeout.ms} expires
*/
@Override
public long position(TopicPartition partition) {
return position(partition, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get the offset of the <i>next record</i> that will be fetched (if a record with that offset exists).
* This method may issue a remote call to the server if there is no current position
* for the given partition.
* <p>
* This call will block until the position can be determined, an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout expires.
*
* @param partition The partition to get the position for
* @param timeout The maximum amount of time to await determination of the current position
* @return The current position of the consumer (that is, the offset of the next record to be fetched)
* @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
* @throws org.apache.kafka.clients.consumer.InvalidOffsetException if no offset is currently defined for
* the partition
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.TimeoutException if the position cannot be determined before the
* passed timeout expires
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
*/
@Override
public long position(TopicPartition partition, final Duration timeout) {
acquireAndEnsureOpen();
try {
if (!this.subscriptions.isAssigned(partition))
throw new IllegalStateException("You can only check the position for partitions assigned to this consumer.");
Timer timer = time.timer(timeout);
do {
SubscriptionState.FetchPosition position = this.subscriptions.validPosition(partition);
if (position != null)
return position.offset;
updateFetchPositions(timer);
client.poll(timer);
} while (timer.notExpired());
throw new TimeoutException("Timeout of " + timeout.toMillis() + "ms expired before the position " +
"for partition " + partition + " could be determined");
} finally {
release();
}
}
/**
* Get the last committed offset for the given partition (whether the commit happened by this process or
* another). This offset will be used as the position for the consumer in the event of a failure.
* <p>
* This call will do a remote call to get the latest committed offset from the server, and will block until the
* committed offset is gotten successfully, an unrecoverable error is encountered (in which case it is thrown to
* the caller), or the timeout specified by {@code default.api.timeout.ms} expires (in which case a
* {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
*
* @param partition The partition to check
* @return The last committed offset and metadata or null if there was no prior commit
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
* the timeout specified by {@code default.api.timeout.ms} expires.
*
* @deprecated since 2.4 Use {@link #committed(Set)} instead
*/
@Deprecated
@Override
public OffsetAndMetadata committed(TopicPartition partition) {
return committed(partition, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get the last committed offset for the given partition (whether the commit happened by this process or
* another). This offset will be used as the position for the consumer in the event of a failure.
* <p>
* This call will block until the position can be determined, an unrecoverable error is
* encountered (in which case it is thrown to the caller), or the timeout expires.
*
* @param partition The partition to check
* @param timeout The maximum amount of time to await the current committed offset
* @return The last committed offset and metadata or null if there was no prior commit
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
* expiration of the timeout
*
* @deprecated since 2.4 Use {@link #committed(Set, Duration)} instead
*/
@Deprecated
@Override
public OffsetAndMetadata committed(TopicPartition partition, final Duration timeout) {
return committed(Collections.singleton(partition), timeout).get(partition);
}
/**
* Get the last committed offsets for the given partitions (whether the commit happened by this process or
* another). The returned offsets will be used as the position for the consumer in the event of a failure.
* <p>
* If any of the partitions requested do not exist, an exception would be thrown.
* <p>
* This call will do a remote call to get the latest committed offsets from the server, and will block until the
* committed offsets are gotten successfully, an unrecoverable error is encountered (in which case it is thrown to
* the caller), or the timeout specified by {@code default.api.timeout.ms} expires (in which case a
* {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
*
* @param partitions The partitions to check
* @return The latest committed offsets for the given partitions; {@code null} will be returned for the
* partition if there is no such message.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
* when the broker doesn't support this feature
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
* the timeout specified by {@code default.api.timeout.ms} expires.
*/
@Override
public Map<TopicPartition, OffsetAndMetadata> committed(final Set<TopicPartition> partitions) {
return committed(partitions, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get the last committed offsets for the given partitions (whether the commit happened by this process or
* another). The returned offsets will be used as the position for the consumer in the event of a failure.
* <p>
* If any of the partitions requested do not exist, an exception would be thrown.
* <p>
* This call will block to do a remote call to get the latest committed offsets from the server.
*
* @param partitions The partitions to check
* @param timeout The maximum amount of time to await the latest committed offsets
* @return The latest committed offsets for the given partitions; {@code null} will be returned for the
* partition if there is no such message.
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
* configured groupId. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
* expiration of the timeout
*/
@Override
public Map<TopicPartition, OffsetAndMetadata> committed(final Set<TopicPartition> partitions, final Duration timeout) {
acquireAndEnsureOpen();
long start = time.nanoseconds();
try {
maybeThrowInvalidGroupIdException();
final Map<TopicPartition, OffsetAndMetadata> offsets;
offsets = coordinator.fetchCommittedOffsets(partitions, time.timer(timeout));
if (offsets == null) {
throw new TimeoutException("Timeout of " + timeout.toMillis() + "ms expired before the last " +
"committed offset for partitions " + partitions + " could be determined. Try tuning default.api.timeout.ms " +
"larger to relax the threshold.");
} else {
offsets.forEach(this::updateLastSeenEpochIfNewer);
return offsets;
}
} finally {
kafkaConsumerMetrics.recordCommitted(time.nanoseconds() - start);
release();
}
}
/**
* Get the metrics kept by the consumer
*/
@Override
public Map<MetricName, ? extends Metric> metrics() {
return Collections.unmodifiableMap(this.metrics.metrics());
}
/**
* Get metadata about the partitions for a given topic. This method will issue a remote call to the server if it
* does not already have any metadata about the given topic.
*
* @param topic The topic to get partition metadata for
*
* @return The list of partitions, which will be empty when the given topic is not found
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the specified topic. See the exception for more details
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* the amount of time allocated by {@code default.api.timeout.ms} expires.
*/
@Override
public List<PartitionInfo> partitionsFor(String topic) {
return partitionsFor(topic, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get metadata about the partitions for a given topic. This method will issue a remote call to the server if it
* does not already have any metadata about the given topic.
*
* @param topic The topic to get partition metadata for
* @param timeout The maximum of time to await topic metadata
*
* @return The list of partitions, which will be empty when the given topic is not found
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the specified topic. See
* the exception for more details
* @throws org.apache.kafka.common.errors.TimeoutException if topic metadata cannot be fetched before expiration
* of the passed timeout
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
*/
@Override
public List<PartitionInfo> partitionsFor(String topic, Duration timeout) {
acquireAndEnsureOpen();
try {
Cluster cluster = this.metadata.fetch();
List<PartitionInfo> parts = cluster.partitionsForTopic(topic);
if (!parts.isEmpty())
return parts;
Timer timer = time.timer(timeout);
Map<String, List<PartitionInfo>> topicMetadata = fetcher.getTopicMetadata(
new MetadataRequest.Builder(Collections.singletonList(topic), metadata.allowAutoTopicCreation()), timer);
return topicMetadata.getOrDefault(topic, Collections.emptyList());
} finally {
release();
}
}
/**
* Get metadata about partitions for all topics that the user is authorized to view. This method will issue a
* remote call to the server.
* @return The map of topics and its partitions
*
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* the amount of time allocated by {@code default.api.timeout.ms} expires.
*/
@Override
public Map<String, List<PartitionInfo>> listTopics() {
return listTopics(Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get metadata about partitions for all topics that the user is authorized to view. This method will issue a
* remote call to the server.
*
* @param timeout The maximum time this operation will block to fetch topic metadata
*
* @return The map of topics and its partitions
* @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
* function is called
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
* this function is called
* @throws org.apache.kafka.common.errors.TimeoutException if the topic metadata could not be fetched before
* expiration of the passed timeout
* @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
*/
@Override
public Map<String, List<PartitionInfo>> listTopics(Duration timeout) {
acquireAndEnsureOpen();
try {
return fetcher.getAllTopicMetadata(time.timer(timeout));
} finally {
release();
}
}
/**
* Suspend fetching from the requested partitions. Future calls to {@link #poll(Duration)} will not return
* any records from these partitions until they have been resumed using {@link #resume(Collection)}.
* Note that this method does not affect partition subscription. In particular, it does not cause a group
* rebalance when automatic assignment is used.
*
* Note: Rebalance will not preserve the pause/resume state.
* @param partitions The partitions which should be paused
* @throws IllegalStateException if any of the provided partitions are not currently assigned to this consumer
*/
@Override
public void pause(Collection<TopicPartition> partitions) {
acquireAndEnsureOpen();
try {
log.debug("Pausing partitions {}", partitions);
for (TopicPartition partition: partitions) {
subscriptions.pause(partition);
}
} finally {
release();
}
}
/**
* Resume specified partitions which have been paused with {@link #pause(Collection)}. New calls to
* {@link #poll(Duration)} will return records from these partitions if there are any to be fetched.
* If the partitions were not previously paused, this method is a no-op.
* @param partitions The partitions which should be resumed
* @throws IllegalStateException if any of the provided partitions are not currently assigned to this consumer
*/
@Override
public void resume(Collection<TopicPartition> partitions) {
acquireAndEnsureOpen();
try {
log.debug("Resuming partitions {}", partitions);
for (TopicPartition partition: partitions) {
subscriptions.resume(partition);
}
} finally {
release();
}
}
/**
* Get the set of partitions that were previously paused by a call to {@link #pause(Collection)}.
*
* @return The set of paused partitions
*/
@Override
public Set<TopicPartition> paused() {
acquireAndEnsureOpen();
try {
return Collections.unmodifiableSet(subscriptions.pausedPartitions());
} finally {
release();
}
}
/**
* Look up the offsets for the given partitions by timestamp. The returned offset for each partition is the
* earliest offset whose timestamp is greater than or equal to the given timestamp in the corresponding partition.
*
* This is a blocking call. The consumer does not have to be assigned the partitions.
* If the message format version in a partition is before 0.10.0, i.e. the messages do not have timestamps, null
* will be returned for that partition.
*
* @param timestampsToSearch the mapping from partition to the timestamp to look up.
*
* @return a mapping from partition to the timestamp and offset of the first message with timestamp greater
* than or equal to the target timestamp. {@code null} will be returned for the partition if there is no
* such message.
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws IllegalArgumentException if the target timestamp is negative
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* the amount of time allocated by {@code default.api.timeout.ms} expires.
* @throws org.apache.kafka.common.errors.UnsupportedVersionException if the broker does not support looking up
* the offsets by timestamp
*/
@Override
public Map<TopicPartition, OffsetAndTimestamp> offsetsForTimes(Map<TopicPartition, Long> timestampsToSearch) {
return offsetsForTimes(timestampsToSearch, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Look up the offsets for the given partitions by timestamp. The returned offset for each partition is the
* earliest offset whose timestamp is greater than or equal to the given timestamp in the corresponding partition.
*
* This is a blocking call. The consumer does not have to be assigned the partitions.
* If the message format version in a partition is before 0.10.0, i.e. the messages do not have timestamps, null
* will be returned for that partition.
*
* @param timestampsToSearch the mapping from partition to the timestamp to look up.
* @param timeout The maximum amount of time to await retrieval of the offsets
*
* @return a mapping from partition to the timestamp and offset of the first message with timestamp greater
* than or equal to the target timestamp. {@code null} will be returned for the partition if there is no
* such message.
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws IllegalArgumentException if the target timestamp is negative
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* expiration of the passed timeout
* @throws org.apache.kafka.common.errors.UnsupportedVersionException if the broker does not support looking up
* the offsets by timestamp
*/
@Override
public Map<TopicPartition, OffsetAndTimestamp> offsetsForTimes(Map<TopicPartition, Long> timestampsToSearch, Duration timeout) {
acquireAndEnsureOpen();
try {
for (Map.Entry<TopicPartition, Long> entry : timestampsToSearch.entrySet()) {
// we explicitly exclude the earliest and latest offset here so the timestamp in the returned
// OffsetAndTimestamp is always positive.
if (entry.getValue() < 0)
throw new IllegalArgumentException("The target time for partition " + entry.getKey() + " is " +
entry.getValue() + ". The target time cannot be negative.");
}
return fetcher.offsetsForTimes(timestampsToSearch, time.timer(timeout));
} finally {
release();
}
}
/**
* Get the first offset for the given partitions.
* <p>
* This method does not change the current consumer position of the partitions.
*
* @see #seekToBeginning(Collection)
*
* @param partitions the partitions to get the earliest offsets.
* @return The earliest available offsets for the given partitions
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* expiration of the configured {@code default.api.timeout.ms}
*/
@Override
public Map<TopicPartition, Long> beginningOffsets(Collection<TopicPartition> partitions) {
return beginningOffsets(partitions, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get the first offset for the given partitions.
* <p>
* This method does not change the current consumer position of the partitions.
*
* @see #seekToBeginning(Collection)
*
* @param partitions the partitions to get the earliest offsets
* @param timeout The maximum amount of time to await retrieval of the beginning offsets
*
* @return The earliest available offsets for the given partitions
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* expiration of the passed timeout
*/
@Override
public Map<TopicPartition, Long> beginningOffsets(Collection<TopicPartition> partitions, Duration timeout) {
acquireAndEnsureOpen();
try {
return fetcher.beginningOffsets(partitions, time.timer(timeout));
} finally {
release();
}
}
/**
* Get the end offsets for the given partitions. In the default {@code read_uncommitted} isolation level, the end
* offset is the high watermark (that is, the offset of the last successfully replicated message plus one). For
* {@code read_committed} consumers, the end offset is the last stable offset (LSO), which is the minimum of
* the high watermark and the smallest offset of any open transaction. Finally, if the partition has never been
* written to, the end offset is 0.
*
* <p>
* This method does not change the current consumer position of the partitions.
*
* @see #seekToEnd(Collection)
*
* @param partitions the partitions to get the end offsets.
* @return The end offsets for the given partitions.
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
* the amount of time allocated by {@code default.api.timeout.ms} expires
*/
@Override
public Map<TopicPartition, Long> endOffsets(Collection<TopicPartition> partitions) {
return endOffsets(partitions, Duration.ofMillis(defaultApiTimeoutMs));
}
/**
* Get the end offsets for the given partitions. In the default {@code read_uncommitted} isolation level, the end
* offset is the high watermark (that is, the offset of the last successfully replicated message plus one). For
* {@code read_committed} consumers, the end offset is the last stable offset (LSO), which is the minimum of
* the high watermark and the smallest offset of any open transaction. Finally, if the partition has never been
* written to, the end offset is 0.
*
* <p>
* This method does not change the current consumer position of the partitions.
*
* @see #seekToEnd(Collection)
*
* @param partitions the partitions to get the end offsets.
* @param timeout The maximum amount of time to await retrieval of the end offsets
*
* @return The end offsets for the given partitions.
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
* @throws org.apache.kafka.common.errors.TimeoutException if the offsets could not be fetched before
* expiration of the passed timeout
*/
@Override
public Map<TopicPartition, Long> endOffsets(Collection<TopicPartition> partitions, Duration timeout) {
acquireAndEnsureOpen();
try {
return fetcher.endOffsets(partitions, time.timer(timeout));
} finally {
release();
}
}
/**
* Get the consumer's current lag on the partition. Returns an "empty" {@link OptionalLong} if the lag is not known,
* for example if there is no position yet, or if the end offset is not known yet.
*
* <p>
* This method uses locally cached metadata and never makes a remote call.
*
* @param topicPartition The partition to get the lag for.
*
* @return This {@code Consumer} instance's current lag for the given partition.
*
* @throws IllegalStateException if the {@code topicPartition} is not assigned
**/
@Override
public OptionalLong currentLag(TopicPartition topicPartition) {
acquireAndEnsureOpen();
try {
final Long lag = subscriptions.partitionLag(topicPartition, isolationLevel);
// if the log end offset is not known and hence cannot return lag and there is
// no in-flight list offset requested yet,
// issue a list offset request for that partition so that next time
// we may get the answer; we do not need to wait for the return value
// since we would not try to poll the network client synchronously
if (lag == null) {
if (subscriptions.partitionEndOffset(topicPartition, isolationLevel) == null &&
!subscriptions.partitionEndOffsetRequested(topicPartition)) {
log.info("Requesting the log end offset for {} in order to compute lag", topicPartition);
subscriptions.requestPartitionEndOffset(topicPartition);
fetcher.endOffsets(Collections.singleton(topicPartition), time.timer(0L));
}
return OptionalLong.empty();
}
return OptionalLong.of(lag);
} finally {
release();
}
}
/**
* Return the current group metadata associated with this consumer.
*
* @return consumer group metadata
* @throws org.apache.kafka.common.errors.InvalidGroupIdException if consumer does not have a group
*/
@Override
public ConsumerGroupMetadata groupMetadata() {
acquireAndEnsureOpen();
try {
maybeThrowInvalidGroupIdException();
return coordinator.groupMetadata();
} finally {
release();
}
}
/**
* Alert the consumer to trigger a new rebalance by rejoining the group. This is a nonblocking call that forces
* the consumer to trigger a new rebalance on the next {@link #poll(Duration)} call. Note that this API does not
* itself initiate the rebalance, so you must still call {@link #poll(Duration)}. If a rebalance is already in
* progress this call will be a no-op. If you wish to force an additional rebalance you must complete the current
* one by calling poll before retrying this API.
* <p>
* You do not need to call this during normal processing, as the consumer group will manage itself
* automatically and rebalance when necessary. However there may be situations where the application wishes to
* trigger a rebalance that would otherwise not occur. For example, if some condition external and invisible to
* the Consumer and its group changes in a way that would affect the userdata encoded in the
* {@link org.apache.kafka.clients.consumer.ConsumerPartitionAssignor.Subscription Subscription}, the Consumer
* will not be notified and no rebalance will occur. This API can be used to force the group to rebalance so that
* the assignor can perform a partition reassignment based on the latest userdata. If your assignor does not use
* this userdata, or you do not use a custom
* {@link org.apache.kafka.clients.consumer.ConsumerPartitionAssignor ConsumerPartitionAssignor}, you should not
* use this API.
*
* @param reason The reason why the new rebalance is needed.
*
* @throws java.lang.IllegalStateException if the consumer does not use group subscription
*/
@Override
public void enforceRebalance(final String reason) {
acquireAndEnsureOpen();
try {
if (coordinator == null) {
throw new IllegalStateException("Tried to force a rebalance but consumer does not have a group.");
}
coordinator.requestRejoin(reason == null || reason.isEmpty() ? DEFAULT_REASON : reason);
} finally {
release();
}
}
/**
* @see #enforceRebalance(String)
*/
@Override
public void enforceRebalance() {
enforceRebalance(null);
}
/**
* Close the consumer, waiting for up to the default timeout of 30 seconds for any needed cleanup.
* If auto-commit is enabled, this will commit the current offsets if possible within the default
* timeout. See {@link #close(Duration)} for details. Note that {@link #wakeup()}
* cannot be used to interrupt close.
*
* @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted
* before or while this function is called
* @throws org.apache.kafka.common.KafkaException for any other error during close
*/
@Override
public void close() {
close(Duration.ofMillis(DEFAULT_CLOSE_TIMEOUT_MS));
}
/**
* Tries to close the consumer cleanly within the specified timeout. This method waits up to
* {@code timeout} for the consumer to complete pending commits and leave the group.
* If auto-commit is enabled, this will commit the current offsets if possible within the
* timeout. If the consumer is unable to complete offset commits and gracefully leave the group
* before the timeout expires, the consumer is force closed. Note that {@link #wakeup()} cannot be
* used to interrupt close.
*
* @param timeout The maximum time to wait for consumer to close gracefully. The value must be
* non-negative. Specifying a timeout of zero means do not wait for pending requests to complete.
*
* @throws IllegalArgumentException If the {@code timeout} is negative.
* @throws InterruptException If the thread is interrupted before or while this function is called
* @throws org.apache.kafka.common.KafkaException for any other error during close
*/
@Override
public void close(Duration timeout) {
if (timeout.toMillis() < 0)
throw new IllegalArgumentException("The timeout cannot be negative.");
acquire();
try {
if (!closed) {
// need to close before setting the flag since the close function
// itself may trigger rebalance callback that needs the consumer to be open still
close(timeout.toMillis(), false);
}
} finally {
closed = true;
release();
}
}
/**
* Wakeup the consumer. This method is thread-safe and is useful in particular to abort a long poll.
* The thread which is blocking in an operation will throw {@link org.apache.kafka.common.errors.WakeupException}.
* If no thread is blocking in a method which can throw {@link org.apache.kafka.common.errors.WakeupException}, the next call to such a method will raise it instead.
*/
@Override
public void wakeup() {
this.client.wakeup();
}
private ClusterResourceListeners configureClusterResourceListeners(Deserializer<K> keyDeserializer, Deserializer<V> valueDeserializer, List<?>... candidateLists) {
ClusterResourceListeners clusterResourceListeners = new ClusterResourceListeners();
for (List<?> candidateList: candidateLists)
clusterResourceListeners.maybeAddAll(candidateList);
clusterResourceListeners.maybeAdd(keyDeserializer);
clusterResourceListeners.maybeAdd(valueDeserializer);
return clusterResourceListeners;
}
private void close(long timeoutMs, boolean swallowException) {
log.trace("Closing the Kafka consumer");
AtomicReference<Throwable> firstException = new AtomicReference<>();
try {
if (coordinator != null)
coordinator.close(time.timer(Math.min(timeoutMs, requestTimeoutMs)));
} catch (Throwable t) {
firstException.compareAndSet(null, t);
log.error("Failed to close coordinator", t);
}
Utils.closeQuietly(fetcher, "fetcher", firstException);
Utils.closeQuietly(interceptors, "consumer interceptors", firstException);
Utils.closeQuietly(kafkaConsumerMetrics, "kafka consumer metrics", firstException);
Utils.closeQuietly(metrics, "consumer metrics", firstException);
Utils.closeQuietly(client, "consumer network client", firstException);
Utils.closeQuietly(keyDeserializer, "consumer key deserializer", firstException);
Utils.closeQuietly(valueDeserializer, "consumer value deserializer", firstException);
AppInfoParser.unregisterAppInfo(JMX_PREFIX, clientId, metrics);
log.debug("Kafka consumer has been closed");
Throwable exception = firstException.get();
if (exception != null && !swallowException) {
if (exception instanceof InterruptException) {
throw (InterruptException) exception;
}
throw new KafkaException("Failed to close kafka consumer", exception);
}
}
/**
* Set the fetch position to the committed position (if there is one)
* or reset it using the offset reset policy the user has configured.
*
* @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
* @throws NoOffsetForPartitionException If no offset is stored for a given partition and no offset reset policy is
* defined
* @return true iff the operation completed without timing out
*/
private boolean updateFetchPositions(final Timer timer) {
// If any partitions have been truncated due to a leader change, we need to validate the offsets
fetcher.validateOffsetsIfNeeded();
cachedSubscriptionHasAllFetchPositions = subscriptions.hasAllFetchPositions();
if (cachedSubscriptionHasAllFetchPositions) return true;
// If there are any partitions which do not have a valid position and are not
// awaiting reset, then we need to fetch committed offsets. We will only do a
// coordinator lookup if there are partitions which have missing positions, so
// a consumer with manually assigned partitions can avoid a coordinator dependence
// by always ensuring that assigned partitions have an initial position.
if (coordinator != null && !coordinator.refreshCommittedOffsetsIfNeeded(timer)) return false;
// If there are partitions still needing a position and a reset policy is defined,
// request reset using the default policy. If no reset strategy is defined and there
// are partitions with a missing position, then we will raise an exception.
subscriptions.resetInitializingPositions();
// Finally send an asynchronous request to lookup and update the positions of any
// partitions which are awaiting reset.
fetcher.resetOffsetsIfNeeded();
return true;
}
/**
* Acquire the light lock and ensure that the consumer hasn't been closed.
* @throws IllegalStateException If the consumer has been closed
*/
private void acquireAndEnsureOpen() {
acquire();
if (this.closed) {
release();
throw new IllegalStateException("This consumer has already been closed.");
}
}
/**
* Acquire the light lock protecting this consumer from multi-threaded access. Instead of blocking
* when the lock is not available, however, we just throw an exception (since multi-threaded usage is not
* supported).
* @throws ConcurrentModificationException if another thread already has the lock
*/
private void acquire() {
long threadId = Thread.currentThread().getId();
if (threadId != currentThread.get() && !currentThread.compareAndSet(NO_CURRENT_THREAD, threadId))
throw new ConcurrentModificationException("KafkaConsumer is not safe for multi-threaded access");
refcount.incrementAndGet();
}
/**
* Release the light lock protecting the consumer from multi-threaded access.
*/
private void release() {
if (refcount.decrementAndGet() == 0)
currentThread.set(NO_CURRENT_THREAD);
}
private void throwIfNoAssignorsConfigured() {
if (assignors.isEmpty())
throw new IllegalStateException("Must configure at least one partition assigner class name to " +
ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG + " configuration property");
}
private void maybeThrowInvalidGroupIdException() {
if (!groupId.isPresent())
throw new InvalidGroupIdException("To use the group management or offset commit APIs, you must " +
"provide a valid " + ConsumerConfig.GROUP_ID_CONFIG + " in the consumer configuration.");
}
private void updateLastSeenEpochIfNewer(TopicPartition topicPartition, OffsetAndMetadata offsetAndMetadata) {
if (offsetAndMetadata != null)
offsetAndMetadata.leaderEpoch().ifPresent(epoch -> metadata.updateLastSeenEpochIfNewer(topicPartition, epoch));
}
// Functions below are for testing only
String getClientId() {
return clientId;
}
boolean updateAssignmentMetadataIfNeeded(final Timer timer) {
return updateAssignmentMetadataIfNeeded(timer, true);
}
}
相关信息
相关文章
kafka CommitFailedException 源码
kafka ConsumerGroupMetadata 源码
kafka ConsumerPartitionAssignor 源码
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