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KafkaConsumer.java
2453 lines (2329 loc) · 141 KB
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KafkaConsumer.java
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.kafka.clients.consumer;
import org.apache.kafka.clients.ApiVersions;
import org.apache.kafka.clients.ClientDnsLookup;
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.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.InvalidConfigurationException;
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.JmxReporter;
import org.apache.kafka.common.metrics.MetricConfig;
import org.apache.kafka.common.metrics.Metrics;
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.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;
import static org.apache.kafka.clients.consumer.internals.PartitionAssignorAdapter.getAssignorInstances;
/**
* 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;
* }
*
* 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 AtomicInteger CONSUMER_CLIENT_ID_SEQUENCE = new AtomicInteger(1);
private static final String JMX_PREFIX = "kafka.consumer";
static final long DEFAULT_CLOSE_TIMEOUT_MS = 30 * 1000;
// Visible for testing
final Metrics metrics;
final KafkaConsumerMetrics kafkaConsumerMetrics;
private Logger log;
private final String clientId;
private 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 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 cachedSubscriptionHashAllFetchPositions;
/**
* 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 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.addDeserializerToConfig(configs, keyDeserializer, valueDeserializer)),
keyDeserializer,
valueDeserializer);
}
/**
* 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(new ConsumerConfig(ConsumerConfig.addDeserializerToConfig(properties, keyDeserializer, valueDeserializer)),
keyDeserializer, valueDeserializer);
}
@SuppressWarnings("unchecked")
private KafkaConsumer(ConsumerConfig config, Deserializer<K> keyDeserializer, Deserializer<V> valueDeserializer) {
try {
GroupRebalanceConfig groupRebalanceConfig = new GroupRebalanceConfig(config,
GroupRebalanceConfig.ProtocolType.CONSUMER);
this.groupId = groupRebalanceConfig.groupId;
this.clientId = buildClientId(config.getString(CommonClientConfigs.CLIENT_ID_CONFIG), groupRebalanceConfig);
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 + "] ");
} else {
logContext = new LogContext("[Consumer clientId=" + clientId + ", groupId=" + groupId + "] ");
}
this.log = logContext.logger(getClass());
boolean enableAutoCommit = config.getBoolean(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG);
if (groupId == null) { // overwrite in case of default group id where the config is not explicitly provided
if (!config.originals().containsKey(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG))
enableAutoCommit = false;
else if (enableAutoCommit)
throw new InvalidConfigurationException(ConsumerConfig.ENABLE_AUTO_COMMIT_CONFIG + " cannot be set to true when default group id (null) is used.");
} else if (groupId.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);
// load interceptors and make sure they get clientId
Map<String, Object> userProvidedConfigs = config.originals();
userProvidedConfigs.put(ConsumerConfig.CLIENT_ID_CONFIG, clientId);
List<ConsumerInterceptor<K, V>> interceptorList = (List) (new ConsumerConfig(userProvidedConfigs, false)).getConfiguredInstances(ConsumerConfig.INTERCEPTOR_CLASSES_CONFIG,
ConsumerInterceptor.class);
this.interceptors = new ConsumerInterceptors<>(interceptorList);
if (keyDeserializer == null) {
this.keyDeserializer = config.getConfiguredInstance(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, Deserializer.class);
this.keyDeserializer.configure(config.originals(), 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(), 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);
IsolationLevel 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),
ClientDnsLookup.forConfig(config.getString(ConsumerConfig.CLIENT_DNS_LOOKUP_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 = getAssignorInstances(config.getList(ConsumerConfig.PARTITION_ASSIGNMENT_STRATEGY_CONFIG), config.originals());
// no coordinator will be constructed for the default (null) group id
this.coordinator = groupId == null ? null :
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);
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.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 = groupId;
this.kafkaConsumerMetrics = new KafkaConsumerMetrics(metrics, "consumer");
}
private static String buildClientId(String configuredClientId, GroupRebalanceConfig rebalanceConfig) {
if (!configuredClientId.isEmpty())
return configuredClientId;
if (rebalanceConfig.groupId != null && !rebalanceConfig.groupId.isEmpty())
return "consumer-" + rebalanceConfig.groupId + "-" + rebalanceConfig.groupInstanceId.orElseGet(() ->
CONSUMER_CLIENT_ID_SEQUENCE.getAndIncrement() + "");
return "consumer-" + CONSUMER_CLIENT_ID_SEQUENCE.getAndIncrement();
}
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 = config.getConfiguredInstances(ConsumerConfig.METRIC_REPORTER_CLASSES_CONFIG,
MetricsReporter.class, Collections.singletonMap(ConsumerConfig.CLIENT_ID_CONFIG, clientId));
reporters.add(new JmxReporter(JMX_PREFIX));
return new Metrics(metricConfig, reporters, time);
}
/**
* 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 (topic == null || topic.trim().isEmpty())
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