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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
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 *    http://www.apache.org/licenses/LICENSE-2.0
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package org.apache.kafka.clients.consumer;

import org.apache.kafka.clients.KafkaClient;
import org.apache.kafka.clients.consumer.internals.ConsumerDelegate;
import org.apache.kafka.clients.consumer.internals.ConsumerDelegateCreator;
import org.apache.kafka.clients.consumer.internals.ConsumerMetadata;
import org.apache.kafka.clients.consumer.internals.KafkaConsumerMetrics;
import org.apache.kafka.clients.consumer.internals.SubscriptionState;
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.Uuid;
import org.apache.kafka.common.errors.InterruptException;
import org.apache.kafka.common.metrics.Metrics;
import org.apache.kafka.common.serialization.Deserializer;
import org.apache.kafka.common.utils.LogContext;
import org.apache.kafka.common.utils.Time;
import org.apache.kafka.common.utils.Timer;

import java.time.Duration;
import java.util.Collection;
import java.util.ConcurrentModificationException;
import java.util.List;
import java.util.Map;
import java.util.OptionalLong;
import java.util.Properties;
import java.util.Set;
import java.util.regex.Pattern;

import static org.apache.kafka.common.utils.Utils.propsToMap;

/**
 * A client that consumes records from a Kafka cluster.
 * 

* 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 consumer groups. *

* 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 Multi-threaded Processing for more details. * *

Cross-Version Compatibility

* 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. *

* *

Offsets and Consumer Position

* 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: *

* 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)}. *

* 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}). *

* This distinction gives the consumer control over when a record is considered consumed. It is discussed in further * detail below. * *

Consumer Groups and Topic Subscriptions

* * Kafka uses the concept of consumer groups 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. *

* 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. *

* 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 rebalancing the group and is discussed in more * detail below. 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. *

* 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). *

* 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. *

* 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 Storing Offsets Outside Kafka for more details. *

* It is also possible for the consumer to manually assign 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. * *

Detecting Consumer Failures

* * 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. *

* 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. *

* The consumer provides two configuration settings to control the behavior of the poll loop: *

    *
  1. max.poll.interval.ms: 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.
  2. *
  3. max.poll.records: 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.
  4. *
*

* 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. * *

Usage Examples

* The consumer APIs offer flexibility to cover a variety of consumption use cases. Here are some examples to * demonstrate how to use them. * *

Automatic Offset Committing

* This example demonstrates a simple usage of Kafka's consumer api that relies on automatic offset committing. *

*

 *     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());
 *     }
 * 
* * 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). *

* Setting {@code enable.auto.commit} means that offsets are committed automatically with a frequency controlled by * the config {@code auto.commit.interval.ms}. *

* In this example the consumer is subscribing to the topics foo and bar as part of a group of consumers * called test as configured with {@code group.id}. *

* 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. * *

Manual Offset Control

* * 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. *

*

 *     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();
 *         }
 *     }
 * 
* * 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. *

* 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. *

* 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." *

* 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. *

*

 *     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();
 *     }
 * 
* * Note: The committed offset should always be the offset of the next message that your application will read. * Thus, when calling {@link #commitSync(Map) commitSync(offsets)} you should add one to the offset of the last message processed. * *

Manual Partition Assignment

* * 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: *

*

    *
  • 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. *
  • 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. *
*

* 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. * *

 *     String topic = "foo";
 *     TopicPartition partition0 = new TopicPartition(topic, 0);
 *     TopicPartition partition1 = new TopicPartition(topic, 1);
 *     consumer.assign(Arrays.asList(partition0, partition1));
 * 
* * 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. *

* 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}). * *

Storing Offsets Outside Kafka

* * 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. *

* Here are a couple of examples of this type of usage: *

    *
  • 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. *
  • 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. *
*

* Each record comes with its own offset, so to manage your own offset you just need to do the following: * *

    *
  • Configure enable.auto.commit=false *
  • Use the offset provided with each {@link ConsumerRecord} to save your position. *
  • On restart restore the position of the consumer using {@link #seek(TopicPartition, long)}. *
* *

* 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)}. *

* Another common use for {@link ConsumerRebalanceListener} is to flush any caches the application maintains for * partitions that are moved elsewhere. * *

Controlling The Consumer's Position

* * 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. *

* There are several instances where manually controlling the consumer's position can be useful. *

* 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. *

* 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). *

* 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). * *

Consumption Flow Control

* * 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. * *

* 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 bootstrapping 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. * *

* 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. * *

Reading Transactional Messages

* *

* 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. * *

* In read_committed 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 read_committed mode. Instead, the end offset of a partition for a read_committed * 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).

* *

* 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. * *

* 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. * *

Multi-threaded Processing

* * The Kafka consumer is NOT thread-safe. It is the responsibility of the user to ensure that multi-threaded access * is properly synchronized. Un-synchronized access will result in {@link ConcurrentModificationException}. * *

* 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: * *

 * 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();
 *     }
 * }
 * 
* * Then in a separate thread, the consumer can be shutdown by setting the closed flag and waking up the consumer. * *

*

 *     closed.set(true);
 *     consumer.wakeup();
 * 
* *

* 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. * *

* We have intentionally avoided implementing a particular threading model for processing. This leaves several * options for implementing multi-threaded processing of records. * *

1. One Consumer Per Thread

* * A simple option is to give each thread its own consumer instance. Here are the pros and cons of this approach: *
    *
  • PRO: It is the easiest to implement *
  • PRO: It is often the fastest as no inter-thread co-ordination is needed *
  • PRO: 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). *
  • CON: 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. *
  • CON: 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. *
  • CON: The number of total threads across all processes will be limited by the total number of partitions. *
* *

2. Decouple Consumption and Processing

* * 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: *
    *
  • PRO: 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. *
  • CON: 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. *
  • CON: Manually committing the position becomes harder as it requires that all threads co-ordinate to ensure * that processing is complete for that partition. *
* * 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 implements Consumer { private final static ConsumerDelegateCreator CREATOR = new ConsumerDelegateCreator(); private final ConsumerDelegate delegate; /** * A consumer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings * are documented here. 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). *

* Valid configuration strings are documented at {@link ConsumerConfig}. *

* Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks. * * @param configs The consumer configs */ public KafkaConsumer(Map configs) { this(configs, null, null); } /** * A consumer is instantiated by providing a {@link java.util.Properties} object as configuration. *

* Valid configuration strings are documented at {@link ConsumerConfig}. *

* 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}. *

* Valid configuration strings are documented at {@link ConsumerConfig}. *

* 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 keyDeserializer, Deserializer valueDeserializer) { this(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}. *

* Valid configuration strings are documented at {@link ConsumerConfig}. *

* 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 configs, Deserializer keyDeserializer, Deserializer valueDeserializer) { this(new ConsumerConfig(ConsumerConfig.appendDeserializerToConfig(configs, keyDeserializer, valueDeserializer)), keyDeserializer, valueDeserializer); } KafkaConsumer(ConsumerConfig config, Deserializer keyDeserializer, Deserializer valueDeserializer) { delegate = CREATOR.create(config, keyDeserializer, valueDeserializer); } KafkaConsumer(LogContext logContext, Time time, ConsumerConfig config, Deserializer keyDeserializer, Deserializer valueDeserializer, KafkaClient client, SubscriptionState subscriptions, ConsumerMetadata metadata, List assignors) { delegate = CREATOR.create( logContext, time, config, keyDeserializer, valueDeserializer, client, subscriptions, metadata, assignors ); } /** * 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 assignment() { return delegate.assignment(); } /** * 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 subscription() { return delegate.subscription(); } /** * Subscribe to the given list of topics to get dynamically * assigned partitions. Topic subscriptions are not incremental. This list will replace the current * assignment (if there is one). 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()}. * *

* 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: *

    *
  • Number of partitions change for any of the subscribed topics *
  • A subscribed topic is created or deleted *
  • An existing member of the consumer group is shutdown or fails *
  • A new member is added to the consumer group *
*

* 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 topics, ConsumerRebalanceListener listener) { delegate.subscribe(topics, listener); } /** * Subscribe to the given list of topics to get dynamically assigned partitions. * Topic subscriptions are not incremental. This list will replace the current * assignment (if there is one). 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()}. * *

* 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 topics) { delegate.subscribe(topics); } /** * 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. *

* 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) { delegate.subscribe(pattern, listener); } /** * 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. *

* 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) { delegate.subscribe(pattern); } /** * 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() { delegate.unsubscribe(); } /** * 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). *

* If the given list of topic partitions is empty, it is treated the same as {@link #unsubscribe()}. *

* 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)}. *

* 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 partitions) { delegate.assign(partitions); } /** * 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. *

* 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 KIP-266 for more information. */ @Deprecated @Override public ConsumerRecords poll(final long timeoutMs) { return delegate.poll(timeoutMs); } /** * 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. *

* 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 * *

* 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 poll(final Duration timeout) { return delegate.poll(timeout); } /** * Commit offsets returned on the last {@link #poll(Duration) poll()} for all the subscribed list of topics and * partitions. *

* 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. *

* 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). *

* 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 group.id 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() { delegate.commitSync(); } /** * Commit offsets returned on the last {@link #poll(Duration) poll()} for all the subscribed list of topics and * partitions. *

* 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. *

* 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. *

* 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 group.id 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) { delegate.commitSync(timeout); } /** * Commit the specified offsets for the specified list of topics and partitions. *

* 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. *

* 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). *

* 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 group.id 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 offsets) { delegate.commitSync(offsets); } /** * Commit the specified offsets for the specified list of topics and partitions. *

* 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. *

* 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. *

* 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 group.id 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 offsets, final Duration timeout) { delegate.commitSync(offsets, timeout); } /** * 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() { delegate.commitAsync(); } /** * Commit offsets returned on the last {@link #poll(Duration) poll()} for the subscribed list of topics and partitions. *

* 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. *

* This is an asynchronous call and will not block. Any errors encountered are either passed to the callback * (if provided) or discarded. *

* 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) { delegate.commitAsync(callback); } /** * Commit the specified offsets for the specified list of topics and partitions to Kafka. *

* 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. *

* This is an asynchronous call and will not block. Any errors encountered are either passed to the callback * (if provided) or discarded. *

* 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 offsets, OffsetCommitCallback callback) { delegate.commitAsync(offsets, callback); } /** * 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 *

* The next Consumer Record which will be retrieved when poll() is invoked will have the offset specified, given that * a record with that offset exists (i.e. it is a valid offset). *

* {@link #seekToBeginning(Collection)} will go to the first offset in the topic. * seek(0) is equivalent to seekToBeginning for a TopicPartition with beginning offset 0, * assuming that there is a record at offset 0 still available. * {@link #seekToEnd(Collection)} is equivalent to seeking to the last offset of the partition, but behavior depends on * {@code isolation.level}, so see {@link #seekToEnd(Collection)} documentation for more details. *

* Seeking to the offset smaller than the log start offset or larger than the log end offset * means an invalid offset is reached. * Invalid offset behaviour is controlled by the {@code auto.offset.reset} property. * If this is set to "earliest", the next poll will return records from the starting offset. * If it is set to "latest", it will seek to the last offset (similar to seekToEnd()). * If it is set to "none", an {@code OffsetOutOfRangeException} will be thrown. *

* Note that, the seek offset won't change to the in-flight fetch request, it will take effect in next fetch request. * So, the consumer might wait for {@code fetch.max.wait.ms} before starting to fetch the records from desired offset. * * @param partition the TopicPartition on which the seek will be performed. * @param offset the next offset returned by poll(). * @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) { delegate.seek(partition, offset); } /** * 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) { delegate.seek(partition, offsetAndMetadata); } /** * 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 partitions) { delegate.seekToBeginning(partitions); } /** * 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. *

* 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 partitions) { delegate.seekToEnd(partitions); } /** * Get the offset of the next record 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. *

* 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 delegate.position(partition); } /** * Get the offset of the next record 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. *

* 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) { return delegate.position(partition, timeout); } /** * 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. *

* 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 delegate.committed(partition); } /** * 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. *

* 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 delegate.committed(partition, timeout); } /** * 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. *

* If any of the partitions requested do not exist, an exception would be thrown. *

* 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 committed(final Set partitions) { return delegate.committed(partitions); } /** * 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. *

* If any of the partitions requested do not exist, an exception would be thrown. *

* 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 committed(final Set partitions, final Duration timeout) { return delegate.committed(partitions, timeout); } /** * Determines the client's unique client instance ID used for telemetry. This ID is unique to * this specific client instance and will not change after it is initially generated. * The ID is useful for correlating client operations with telemetry sent to the broker and * to its eventual monitoring destinations. *

* If telemetry is enabled, this will first require a connection to the cluster to generate * the unique client instance ID. This method waits up to {@code timeout} for the consumer * client to complete the request. *

* Client telemetry is controlled by the {@link ConsumerConfig#ENABLE_METRICS_PUSH_CONFIG} * configuration option. * * @param timeout The maximum time to wait for consumer client to determine its client instance ID. * The value must be non-negative. Specifying a timeout of zero means do not * wait for the initial request to complete if it hasn't already. * @throws InterruptException If the thread is interrupted while blocked. * @throws KafkaException If an unexpected error occurs while trying to determine the client * instance ID, though this error does not necessarily imply the * consumer client is otherwise unusable. * @throws IllegalArgumentException If the {@code timeout} is negative. * @throws IllegalStateException If telemetry is not enabled ie, config `{@code enable.metrics.push}` * is set to `{@code false}`. * @return The client's assigned instance id used for metrics collection. */ @Override public Uuid clientInstanceId(Duration timeout) { return delegate.clientInstanceId(timeout); } /** * Get the metrics kept by the consumer */ @Override public Map metrics() { return delegate.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 partitionsFor(String topic) { return delegate.partitionsFor(topic); } /** * 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 partitionsFor(String topic, Duration timeout) { return delegate.partitionsFor(topic, timeout); } /** * 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> listTopics() { return delegate.listTopics(); } /** * 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> listTopics(Duration timeout) { return delegate.listTopics(timeout); } /** * 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 partitions) { delegate.pause(partitions); } /** * 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 partitions) { delegate.resume(partitions); } /** * 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 paused() { return delegate.paused(); } /** * 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 offsetsForTimes(Map timestampsToSearch) { return delegate.offsetsForTimes(timestampsToSearch); } /** * 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 offsetsForTimes(Map timestampsToSearch, Duration timeout) { return delegate.offsetsForTimes(timestampsToSearch, timeout); } /** * Get the first offset for the given partitions. *

* 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 beginningOffsets(Collection partitions) { return delegate.beginningOffsets(partitions); } /** * Get the first offset for the given partitions. *

* 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 beginningOffsets(Collection partitions, Duration timeout) { return delegate.beginningOffsets(partitions, timeout); } /** * 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. * *

* 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 endOffsets(Collection partitions) { return delegate.endOffsets(partitions); } /** * 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. * *

* 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 endOffsets(Collection partitions, Duration timeout) { return delegate.endOffsets(partitions, timeout); } /** * 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. * *

* This method uses locally cached metadata. If the log end offset is not known yet, it triggers a request to fetch * the log end offset, but returns immediately. * * @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) { return delegate.currentLag(topicPartition); } /** * 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() { return delegate.groupMetadata(); } /** * 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. *

* 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) { delegate.enforceRebalance(reason); } /** * @see #enforceRebalance(String) */ @Override public void enforceRebalance() { delegate.enforceRebalance(); } /** * 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() { delegate.close(); } /** * 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) { delegate.close(timeout); } /** * 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() { delegate.wakeup(); } // Functions below are for testing only String clientId() { return delegate.clientId(); } Metrics metricsRegistry() { return delegate.metricsRegistry(); } KafkaConsumerMetrics kafkaConsumerMetrics() { return delegate.kafkaConsumerMetrics(); } boolean updateAssignmentMetadataIfNeeded(final Timer timer) { return delegate.updateAssignmentMetadataIfNeeded(timer); } }





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