/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements. See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License. You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
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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.SubscriptionState;
import org.apache.kafka.clients.consumer.internals.metrics.KafkaConsumerMetrics;
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.errors.InvalidRegularExpression;
import org.apache.kafka.common.metrics.KafkaMetric;
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.
 * <p>
 * This client transparently handles the failure of Kafka brokers, and transparently adapts as topic partitions
 * it fetches migrate within the cluster. This client also interacts with the broker to allow groups of
 * consumers to load balance consumption using <a href="#consumergroups">consumer groups</a>.
 * <p>
 * The consumer maintains TCP connections to the necessary brokers to fetch data.
 * Failure to close the consumer after use will leak these connections.
 * The consumer is not thread-safe. See <a href="#multithreaded">Multi-threaded Processing</a> for more details.
 *
 * <h3>Cross-Version Compatibility</h3>
 * This client can communicate with brokers that are version 0.10.0 or newer. Older or newer brokers may not support
 * certain features. For example, 0.10.0 brokers do not support offsetsForTimes, because this feature was added
 * in version 0.10.1. You will receive an {@link org.apache.kafka.common.errors.UnsupportedVersionException}
 * when invoking an API that is not available on the running broker version.
 * <p>
 *
 * <h3>Offsets and Consumer Position</h3>
 * Kafka maintains a numerical offset for each record in a partition. This offset acts as a unique identifier of
 * a record within that partition, and also denotes the position of the consumer in the partition. For example, a consumer
 * which is at position 5 has consumed records with offsets 0 through 4 and will next receive the record with offset 5.
 * Note that offsets are not guaranteed to be consecutive (such as compacted topic or when records have been produced
 * using transactions). For example, if the consumer did read a record with offset 4, but 5 is not an offset
 * with a record, its position might advance to 6 (or higher) directly. Similarly, if the consumer's position is 5,
 * but there is no record with offset 5, the consumer will return the record with the next higher offset.
 * There are actually two notions of position relevant to the user of the consumer:
 * <p>
 * The {@link #position(TopicPartition) position} of the consumer gives the offset of the next record that will be given
 * out. It will be one larger than the highest offset the consumer has seen in that partition. It automatically advances
 * every time the consumer receives messages in a call to {@link #poll(Duration)}.
 * <p>
 * The {@link #commitSync() committed position} is the last offset that has been stored securely. Should the
 * process fail and restart, this is the offset that the consumer will recover to. The consumer can either automatically commit
 * offsets periodically; or it can choose to control this committed position manually by calling one of the commit APIs
 * (e.g. {@link #commitSync() commitSync} and {@link #commitAsync(OffsetCommitCallback) commitAsync}).
 * <p>
 * This distinction gives the consumer control over when a record is considered consumed. It is discussed in further
 * detail below.
 *
 * <h3><a name="consumergroups">Consumer Groups and Topic Subscriptions</a></h3>
 *
 * Kafka uses the concept of <i>consumer groups</i> to allow a pool of processes to divide the work of consuming and
 * processing records. These processes can either be running on the same machine or they can be
 * distributed over many machines to provide scalability and fault tolerance for processing. All consumer instances
 * sharing the same {@code group.id} will be part of the same consumer group.
 * <p>
 * Each consumer in a group can dynamically set the list of topics it wants to subscribe to through one of the
 * {@link #subscribe(Collection, ConsumerRebalanceListener) subscribe} APIs. Kafka will deliver each message in the
 * subscribed topics to one process in each consumer group. This is achieved by balancing the partitions between all
 * members in the consumer group so that each partition is assigned to exactly one consumer in the group. So if there
 * is a topic with four partitions, and a consumer group with two processes, each process would consume from two partitions.
 * <p>
 * Membership in a consumer group is maintained dynamically: if a process fails, the partitions assigned to it will
 * be reassigned to other consumers in the same group. Similarly, if a new consumer joins the group, partitions will be moved
 * from existing consumers to the new one. This is known as <i>rebalancing</i> the group and is discussed in more
 * detail <a href="#failuredetection">below</a>. Group rebalancing is also used when new partitions are added
 * to one of the subscribed topics or when a new topic matching a {@link #subscribe(Pattern, ConsumerRebalanceListener) subscribed regex}
 * is created. The group will automatically detect the new partitions through periodic metadata refreshes and
 * assign them to members of the group.
 * <p>
 * Conceptually you can think of a consumer group as being a single logical subscriber that happens to be made up of
 * multiple processes. As a multi-subscriber system, Kafka naturally supports having any number of consumer groups for a
 * given topic without duplicating data (additional consumers are actually quite cheap).
 * <p>
 * This is a slight generalization of the functionality that is common in messaging systems. To get semantics similar to
 * a queue in a traditional messaging system all processes would be part of a single consumer group and hence record
 * delivery would be balanced over the group like with a queue. Unlike a traditional messaging system, though, you can
 * have multiple such groups. To get semantics similar to pub-sub in a traditional messaging system each process would
 * have its own consumer group, so each process would subscribe to all the records published to the topic.
 * <p>
 * In addition, when group reassignment happens automatically, consumers can be notified through a {@link ConsumerRebalanceListener},
 * which allows them to finish necessary application-level logic such as state cleanup, manual offset
 * commits, etc. See <a href="#rebalancecallback">Storing Offsets Outside Kafka</a> for more details.
 * <p>
 * It is also possible for the consumer to <a href="#manualassignment">manually assign</a> specific partitions
 * (similar to the older "simple" consumer) using {@link #assign(Collection)}. In this case, dynamic partition
 * assignment and consumer group coordination will be disabled.
 *
 * <h3><a name="failuredetection">Detecting Consumer Failures</a></h3>
 *
 * After subscribing to a set of topics, the consumer will automatically join the group when {@link #poll(Duration)} is
 * invoked. The poll API is designed to ensure consumer liveness. As long as you continue to call poll, the consumer
 * will stay in the group and continue to receive messages from the partitions it was assigned. Underneath the covers,
 * the consumer sends periodic heartbeats to the server. If the consumer crashes or is unable to send heartbeats for
 * a duration of {@code session.timeout.ms}, then the consumer will be considered dead and its partitions will
 * be reassigned.
 * <p>
 * It is also possible that the consumer could encounter a "livelock" situation where it is continuing
 * to send heartbeats, but no progress is being made. To prevent the consumer from holding onto its partitions
 * indefinitely in this case, we provide a liveness detection mechanism using the {@code max.poll.interval.ms}
 * setting. Basically if you don't call poll at least as frequently as the configured max interval,
 * then the client will proactively leave the group so that another consumer can take over its partitions. When this happens,
 * you may see an offset commit failure (as indicated by a {@link CommitFailedException} thrown from a call to {@link #commitSync()}).
 * This is a safety mechanism which guarantees that only active members of the group are able to commit offsets.
 * So to stay in the group, you must continue to call poll.
 * <p>
 * The consumer provides two configuration settings to control the behavior of the poll loop:
 * <ol>
 *     <li><code>max.poll.interval.ms</code>: By increasing the interval between expected polls, you can give
 *     the consumer more time to handle a batch of records returned from {@link #poll(Duration)}. The drawback
 *     is that increasing this value may delay a group rebalance since the consumer will only join the rebalance
 *     inside the call to poll. You can use this setting to bound the time to finish a rebalance, but
 *     you risk slower progress if the consumer cannot actually call {@link #poll(Duration) poll} often enough.</li>
 *     <li><code>max.poll.records</code>: Use this setting to limit the total records returned from a single
 *     call to poll. This can make it easier to predict the maximum that must be handled within each poll
 *     interval. By tuning this value, you may be able to reduce the poll interval, which will reduce the
 *     impact of group rebalancing.</li>
 * </ol>
 * <p>
 * For use cases where message processing time varies unpredictably, neither of these options may be sufficient.
 * The recommended way to handle these cases is to move message processing to another thread, which allows
 * the consumer to continue calling {@link #poll(Duration) poll} while the processor is still working.
 * Some care must be taken to ensure that committed offsets do not get ahead of the actual position.
 * Typically, you must disable automatic commits and manually commit processed offsets for records only after the
 * thread has finished handling them (depending on the delivery semantics you need).
 * Note also that you will need to {@link #pause(Collection) pause} the partition so that no new records are received
 * from poll until after thread has finished handling those previously returned.
 *
 * <h3>Usage Examples</h3>
 * The consumer APIs offer flexibility to cover a variety of consumption use cases. Here are some examples to
 * demonstrate how to use them.
 *
 * <h4>Automatic Offset Committing</h4>
 * This example demonstrates a simple usage of Kafka's consumer api that relies on automatic offset committing.
 * <p>
 * <pre>
 *     Properties props = new Properties();
 *     props.setProperty(&quot;bootstrap.servers&quot;, &quot;localhost:9092&quot;);
 *     props.setProperty(&quot;group.id&quot;, &quot;test&quot;);
 *     props.setProperty(&quot;enable.auto.commit&quot;, &quot;true&quot;);
 *     props.setProperty(&quot;auto.commit.interval.ms&quot;, &quot;1000&quot;);
 *     props.setProperty(&quot;key.deserializer&quot;, &quot;org.apache.kafka.common.serialization.StringDeserializer&quot;);
 *     props.setProperty(&quot;value.deserializer&quot;, &quot;org.apache.kafka.common.serialization.StringDeserializer&quot;);
 *     KafkaConsumer&lt;String, String&gt; consumer = new KafkaConsumer&lt;&gt;(props);
 *     consumer.subscribe(Arrays.asList(&quot;foo&quot;, &quot;bar&quot;));
 *     while (true) {
 *         ConsumerRecords&lt;String, String&gt; records = consumer.poll(Duration.ofMillis(100));
 *         for (ConsumerRecord&lt;String, String&gt; record : records)
 *             System.out.printf(&quot;offset = %d, key = %s, value = %s%n&quot;, record.offset(), record.key(), record.value());
 *     }
 * </pre>
 *
 * The connection to the cluster is bootstrapped by specifying a list of one or more brokers to contact using the
 * configuration {@code bootstrap.servers}. This list is just used to discover the rest of the brokers in the
 * cluster and need not be an exhaustive list of servers in the cluster (though you may want to specify more than one in
 * case there are servers down when the client is connecting).
 * <p>
 * Setting {@code enable.auto.commit} means that offsets are committed automatically with a frequency controlled by
 * the config {@code auto.commit.interval.ms}.
 * <p>
 * In this example the consumer is subscribing to the topics <i>foo</i> and <i>bar</i> as part of a group of consumers
 * called <i>test</i> as configured with {@code group.id}.
 * <p>
 * The deserializer settings specify how to turn bytes into objects. For example, by specifying string deserializers, we
 * are saying that our record's key and value will just be simple strings.
 *
 * <h4>Manual Offset Control</h4>
 *
 * Instead of relying on the consumer to periodically commit consumed offsets, users can also control when records
 * should be considered as consumed and hence commit their offsets. This is useful when the consumption of the messages
 * is coupled with some processing logic and hence a message should not be considered as consumed until it is completed processing.

 * <p>
 * <pre>
 *     Properties props = new Properties();
 *     props.setProperty(&quot;bootstrap.servers&quot;, &quot;localhost:9092&quot;);
 *     props.setProperty(&quot;group.id&quot;, &quot;test&quot;);
 *     props.setProperty(&quot;enable.auto.commit&quot;, &quot;false&quot;);
 *     props.setProperty(&quot;key.deserializer&quot;, &quot;org.apache.kafka.common.serialization.StringDeserializer&quot;);
 *     props.setProperty(&quot;value.deserializer&quot;, &quot;org.apache.kafka.common.serialization.StringDeserializer&quot;);
 *     KafkaConsumer&lt;String, String&gt; consumer = new KafkaConsumer&lt;&gt;(props);
 *     consumer.subscribe(Arrays.asList(&quot;foo&quot;, &quot;bar&quot;));
 *     final int minBatchSize = 200;
 *     List&lt;ConsumerRecord&lt;String, String&gt;&gt; buffer = new ArrayList&lt;&gt;();
 *     while (true) {
 *         ConsumerRecords&lt;String, String&gt; records = consumer.poll(Duration.ofMillis(100));
 *         for (ConsumerRecord&lt;String, String&gt; record : records) {
 *             buffer.add(record);
 *         }
 *         if (buffer.size() &gt;= minBatchSize) {
 *             insertIntoDb(buffer);
 *             consumer.commitSync();
 *             buffer.clear();
 *         }
 *     }
 * </pre>
 *
 * In this example we will consume a batch of records and batch them up in memory. When we have enough records
 * batched, we will insert them into a database. If we allowed offsets to auto commit as in the previous example, records
 * would be considered consumed after they were returned to the user in {@link #poll(Duration) poll}. It would then be
 * possible
 * for our process to fail after batching the records, but before they had been inserted into the database.
 * <p>
 * To avoid this, we will manually commit the offsets only after the corresponding records have been inserted into the
 * database. This gives us exact control of when a record is considered consumed. This raises the opposite possibility:
 * the process could fail in the interval after the insert into the database but before the commit (even though this
 * would likely just be a few milliseconds, it is a possibility). In this case the process that took over consumption
 * would consume from last committed offset and would repeat the insert of the last batch of data. Used in this way
 * Kafka provides what is often called "at-least-once" delivery guarantees, as each record will likely be delivered one
 * time but in failure cases could be duplicated.
 * <p>
 * <b>Note: Using automatic offset commits can also give you "at-least-once" delivery, but the requirement is that
 * you must consume all data returned from each call to {@link #poll(Duration)} before any subsequent calls, or before
 * {@link #close() closing} the consumer. If you fail to do either of these, it is possible for the committed offset
 * to get ahead of the consumed position, which results in missing records. The advantage of using manual offset
 * control is that you have direct control over when a record is considered "consumed."</b>
 * <p>
 * The above example uses {@link #commitSync() commitSync} to mark all received records as committed. In some cases
 * you may wish to have even finer control over which records have been committed by specifying an offset explicitly.
 * In the example below we commit offset after we finish handling the records in each partition.
 * <p>
 * <pre>
 *     try {
 *         while(running) {
 *             ConsumerRecords&lt;String, String&gt; records = consumer.poll(Duration.ofMillis(Long.MAX_VALUE));
 *             for (TopicPartition partition : records.partitions()) {
 *                 List&lt;ConsumerRecord&lt;String, String&gt;&gt; partitionRecords = records.records(partition);
 *                 for (ConsumerRecord&lt;String, String&gt; record : partitionRecords) {
 *                     System.out.println(record.offset() + &quot;: &quot; + record.value());
 *                 }
 *                 consumer.commitSync(Collections.singletonMap(partition, records.nextOffsets().get(partition)));
 *             }
 *         }
 *     } finally {
 *       consumer.close();
 *     }
 * </pre>
 *
 * <b>Note: The committed offset should always be the offset of the next message that your application will read.</b>
 * Thus, when calling {@link #commitSync(Map) commitSync(offsets)} you should use {@code nextRecordToBeProcessed.offset()}
 * or if {@link ConsumerRecords} is exhausted already {@link ConsumerRecords#nextOffsets()} instead.
 * You should also add the leader epoch as commit metadata, which can be obtained from
 * {@link ConsumerRecord#leaderEpoch()} or {@link ConsumerRecords#nextOffsets()}.
 *
 * <h4><a name="manualassignment">Manual Partition Assignment</a></h4>
 *
 * In the previous examples, we subscribed to the topics we were interested in and let Kafka dynamically assign a
 * fair share of the partitions for those topics based on the active consumers in the group. However, in
 * some cases you may need finer control over the specific partitions that are assigned. For example:
 * <p>
 * <ul>
 * <li>If the process is maintaining some kind of local state associated with that partition (like a
 * local on-disk key-value store), then it should only get records for the partition it is maintaining on disk.
 * <li>If the process itself is highly available and will be restarted if it fails (perhaps using a
 * cluster management framework like YARN, Mesos, or AWS facilities, or as part of a stream processing framework). In
 * this case there is no need for Kafka to detect the failure and reassign the partition since the consuming process
 * will be restarted on another machine.
 * </ul>
 * <p>
 * To use this mode, instead of subscribing to the topic using {@link #subscribe(Collection) subscribe}, you just call
 * {@link #assign(Collection)} with the full list of partitions that you want to consume.
 *
 * <pre>
 *     String topic = &quot;foo&quot;;
 *     TopicPartition partition0 = new TopicPartition(topic, 0);
 *     TopicPartition partition1 = new TopicPartition(topic, 1);
 *     consumer.assign(Arrays.asList(partition0, partition1));
 * </pre>
 *
 * Once assigned, you can call {@link #poll(Duration) poll} in a loop, just as in the preceding examples to consume
 * records. The group that the consumer specifies is still used for committing offsets, but now the set of partitions
 * will only change with another call to {@link #assign(Collection) assign}. Manual partition assignment does
 * not use group coordination, so consumer failures will not cause assigned partitions to be rebalanced. Each consumer
 * acts independently even if it shares a groupId with another consumer. To avoid offset commit conflicts, you should
 * usually ensure that the groupId is unique for each consumer instance.
 * <p>
 * Note that it isn't possible to mix manual partition assignment (i.e. using {@link #assign(Collection) assign})
 * with dynamic partition assignment through topic subscription (i.e. using {@link #subscribe(Collection) subscribe}).
 *
 * <h4><a name="rebalancecallback">Storing Offsets Outside Kafka</h4>
 *
 * The consumer application need not use Kafka's built-in offset storage, it can store offsets in a store of its own
 * choosing. The primary use case for this is allowing the application to store both the offset and the results of the
 * consumption in the same system in a way that both the results and offsets are stored atomically. This is not always
 * possible, but when it is it will make the consumption fully atomic and give "exactly once" semantics that are
 * stronger than the default "at-least once" semantics you get with Kafka's offset commit functionality.
 * <p>
 * Here are a couple of examples of this type of usage:
 * <ul>
 * <li>If the results of the consumption are being stored in a relational database, storing the offset in the database
 * as well can allow committing both the results and offset in a single transaction. Thus either the transaction will
 * succeed and the offset will be updated based on what was consumed or the result will not be stored and the offset
 * won't be updated.
 * <li>If the results are being stored in a local store it may be possible to store the offset there as well. For
 * example a search index could be built by subscribing to a particular partition and storing both the offset and the
 * indexed data together. If this is done in a way that is atomic, it is often possible to have it be the case that even
 * if a crash occurs that causes unsync'd data to be lost, whatever is left has the corresponding offset stored as well.
 * This means that in this case the indexing process that comes back having lost recent updates just resumes indexing
 * from what it has ensuring that no updates are lost.
 * </ul>
 * <p>
 * Each record comes with its own offset, so to manage your own offset you just need to do the following:
 *
 * <ul>
 * <li>Configure <code>enable.auto.commit=false</code>
 * <li>Use the offset provided with each {@link ConsumerRecord} to save your position.
 * <li>On restart restore the position of the consumer using {@link #seek(TopicPartition, long)}.
 * </ul>
 *
 * <p>
 * This type of usage is simplest when the partition assignment is also done manually (this would be likely in the
 * search index use case described above). If the partition assignment is done automatically special care is
 * needed to handle the case where partition assignments change. This can be done by providing a
 * {@link ConsumerRebalanceListener} instance in the call to {@link #subscribe(Collection, ConsumerRebalanceListener)}
 * and {@link #subscribe(Pattern, ConsumerRebalanceListener)}.
 * For example, when partitions are taken from a consumer the consumer will want to commit its offset for those partitions by
 * implementing {@link ConsumerRebalanceListener#onPartitionsRevoked(Collection)}. When partitions are assigned to a
 * consumer, the consumer will want to look up the offset for those new partitions and correctly initialize the consumer
 * to that position by implementing {@link ConsumerRebalanceListener#onPartitionsAssigned(Collection)}.
 * <p>
 * Another common use for {@link ConsumerRebalanceListener} is to flush any caches the application maintains for
 * partitions that are moved elsewhere.
 *
 * <h4>Controlling The Consumer's Position</h4>
 *
 * In most use cases the consumer will simply consume records from beginning to end, periodically committing its
 * position (either automatically or manually). However Kafka allows the consumer to manually control its position,
 * moving forward or backwards in a partition at will. This means a consumer can re-consume older records, or skip to
 * the most recent records without actually consuming the intermediate records.
 * <p>
 * There are several instances where manually controlling the consumer's position can be useful.
 * <p>
 * One case is for time-sensitive record processing it may make sense for a consumer that falls far enough behind to not
 * attempt to catch up processing all records, but rather just skip to the most recent records.
 * <p>
 * Another use case is for a system that maintains local state as described in the previous section. In such a system
 * the consumer will want to initialize its position on start-up to whatever is contained in the local store. Likewise
 * if the local state is destroyed (say because the disk is lost) the state may be recreated on a new machine by
 * re-consuming all the data and recreating the state (assuming that Kafka is retaining sufficient history).
 * <p>
 * Kafka allows specifying the position using {@link #seek(TopicPartition, long)} to specify the new position. Special
 * methods for seeking to the earliest and latest offset the server maintains are also available (
 * {@link #seekToBeginning(Collection)} and {@link #seekToEnd(Collection)} respectively).
 *
 * <h4>Consumption Flow Control</h4>
 *
 * If a consumer is assigned multiple partitions to fetch data from, it will try to consume from all of them at the same time,
 * effectively giving these partitions the same priority for consumption. However in some cases consumers may want to
 * first focus on fetching from some subset of the assigned partitions at full speed, and only start fetching other partitions
 * when these partitions have few or no data to consume.
 *
 * <p>
 * One of such cases is stream processing, where processor fetches from two topics and performs the join on these two streams.
 * When one of the topics is long lagging behind the other, the processor would like to pause fetching from the ahead topic
 * in order to get the lagging stream to catch up. Another example is 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.
 *
 * <p>
 * Kafka supports dynamic controlling of consumption flows by using {@link #pause(Collection)} and {@link #resume(Collection)}
 * to pause the consumption on the specified assigned partitions and resume the consumption
 * on the specified paused partitions respectively in the future {@link #poll(Duration)} calls.
 *
 * <h3>Reading Transactional Messages</h3>
 *
 * <p>
 * Transactions were introduced in Kafka 0.11.0 wherein applications can write to multiple topics and partitions atomically.
 * In order for this to work, consumers reading from these partitions should be configured to only read committed data.
 * This can be achieved by setting the {@code isolation.level=read_committed} in the consumer's configuration.
 *
 * <p>
 * In <code>read_committed</code> mode, the consumer will read only those transactional messages which have been
 * successfully committed. It will continue to read non-transactional messages as before. There is no client-side
 * buffering in <code>read_committed</code> mode. Instead, the end offset of a partition for a <code>read_committed</code>
 * consumer would be the offset of the first message in the partition belonging to an open transaction. This offset
 * is known as the 'Last Stable Offset'(LSO).</p>
 *
 * <p>
 * A {@code read_committed} consumer will only read up to the LSO and filter out any transactional
 * messages which have been aborted. The LSO also affects the behavior of {@link #seekToEnd(Collection)} and
 * {@link #endOffsets(Collection)} for {@code read_committed} consumers, details of which are in each method's documentation.
 * Finally, the fetch lag metrics are also adjusted to be relative to the LSO for {@code read_committed} consumers.
 *
 * <p>
 * Partitions with transactional messages will include commit or abort markers which indicate the result of a transaction.
 * There markers are not returned to applications, yet have an offset in the log. As a result, applications reading from
 * topics with transactional messages will see gaps in the consumed offsets. These missing messages would be the transaction
 * markers, and they are filtered out for consumers in both isolation levels. Additionally, applications using
 * {@code read_committed} consumers may also see gaps due to aborted transactions, since those messages would not
 * be returned by the consumer and yet would have valid offsets.
 *
 * <h3><a name="multithreaded">Multi-threaded Processing</a></h3>
 *
 * The Kafka consumer is NOT thread-safe. It is the responsibility of the user to ensure that multi-threaded access
 * is properly synchronized. Un-synchronized access will result in {@link ConcurrentModificationException}.
 *
 * <p>
 * The only exception to this rule is {@link #wakeup()}, which can safely be used from an external thread to
 * interrupt an active operation. In this case, a {@link org.apache.kafka.common.errors.WakeupException} will be
 * thrown from the thread blocking on the operation. This can be used to shutdown the consumer from another thread.
 * The following snippet shows the typical pattern:
 *
 * <pre>
 * public class KafkaConsumerRunner implements Runnable {
 *     private final AtomicBoolean closed = new AtomicBoolean(false);
 *     private final KafkaConsumer consumer;
 *
 *     public KafkaConsumerRunner(KafkaConsumer consumer) {
 *       this.consumer = consumer;
 *     }
 *
 *     {@literal}@Override
 *     public void run() {
 *         try {
 *             consumer.subscribe(Arrays.asList("topic"));
 *             while (!closed.get()) {
 *                 ConsumerRecords records = consumer.poll(Duration.ofMillis(10000));
 *                 // Handle new records
 *             }
 *         } catch (WakeupException e) {
 *             // Ignore exception if closing
 *             if (!closed.get()) throw e;
 *         } finally {
 *             consumer.close();
 *         }
 *     }
 *
 *     // Shutdown hook which can be called from a separate thread
 *     public void shutdown() {
 *         closed.set(true);
 *         consumer.wakeup();
 *     }
 * }
 * </pre>
 *
 * Then in a separate thread, the consumer can be shutdown by setting the closed flag and waking up the consumer.
 *
 * <p>
 * <pre>
 *     closed.set(true);
 *     consumer.wakeup();
 * </pre>
 *
 * <p>
 * Note that while it is possible to use thread interrupts instead of {@link #wakeup()} to abort a blocking operation
 * (in which case, {@link InterruptException} will be raised), we discourage their use since they may cause a clean
 * shutdown of the consumer to be aborted. Interrupts are mainly supported for those cases where using {@link #wakeup()}
 * is impossible, e.g. when a consumer thread is managed by code that is unaware of the Kafka client.
 *
 * <p>
 * We have intentionally avoided implementing a particular threading model for processing. This leaves several
 * options for implementing multi-threaded processing of records.
 *
 * <h4>1. One Consumer Per Thread</h4>
 *
 * A simple option is to give each thread its own consumer instance. Here are the pros and cons of this approach:
 * <ul>
 * <li><b>PRO</b>: It is the easiest to implement
 * <li><b>PRO</b>: It is often the fastest as no inter-thread co-ordination is needed
 * <li><b>PRO</b>: It makes in-order processing on a per-partition basis very easy to implement (each thread just
 * processes messages in the order it receives them).
 * <li><b>CON</b>: More consumers means more TCP connections to the cluster (one per thread). In general Kafka handles
 * connections very efficiently so this is generally a small cost.
 * <li><b>CON</b>: Multiple consumers means more requests being sent to the server and slightly less batching of data
 * which can cause some drop in I/O throughput.
 * <li><b>CON</b>: The number of total threads across all processes will be limited by the total number of partitions.
 * </ul>
 *
 * <h4>2. Decouple Consumption and Processing</h4>
 *
 * Another alternative is to have one or more consumer threads that do all data consumption and hands off
 * {@link ConsumerRecords} instances to a blocking queue consumed by a pool of processor threads that actually handle
 * the record processing.
 *
 * This option likewise has pros and cons:
 * <ul>
 * <li><b>PRO</b>: This option allows independently scaling the number of consumers and processors. This makes it
 * possible to have a single consumer that feeds many processor threads, avoiding any limitation on partitions.
 * <li><b>CON</b>: Guaranteeing order across the processors requires particular care as the threads will execute
 * independently an earlier chunk of data may actually be processed after a later chunk of data just due to the luck of
 * thread execution timing. For processing that has no ordering requirements this is not a problem.
 * <li><b>CON</b>: Manually committing the position becomes harder as it requires that all threads co-ordinate to ensure
 * that processing is complete for that partition.
 * </ul>
 *
 * There are many possible variations on this approach. For example each processor thread can have its own queue, and
 * the consumer threads can hash into these queues using the TopicPartition to ensure in-order consumption and simplify
 * commit.
 */
public class KafkaConsumer<K, V> implements Consumer<K, V> {

    private static final ConsumerDelegateCreator CREATOR = new ConsumerDelegateCreator();

    private final ConsumerDelegate<K, V> delegate;

    /**
     * A consumer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings
     * are documented <a href="http://kafka.apache.org/documentation.html#consumerconfigs" >here</a>. Values can be
     * either strings or objects of the appropriate type (for example a numeric configuration would accept either the
     * string "42" or the integer 42).
     * <p>
     * Valid configuration strings are documented at {@link ConsumerConfig}.
     * <p>
     * Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
     *
     * @param configs The consumer configs
     */
    public KafkaConsumer(Map<String, Object> configs) {
        this(configs, null, null);
    }

    /**
     * A consumer is instantiated by providing a {@link java.util.Properties} object as configuration.
     * <p>
     * Valid configuration strings are documented at {@link ConsumerConfig}.
     * <p>
     * Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
     *
     * @param properties The consumer configuration properties
     */
    public KafkaConsumer(Properties properties) {
        this(properties, null, null);
    }

    /**
     * A consumer is instantiated by providing a {@link java.util.Properties} object as configuration, and a
     * key and a value {@link Deserializer}.
     * <p>
     * Valid configuration strings are documented at {@link ConsumerConfig}.
     * <p>
     * Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
     *
     * @param properties The consumer configuration properties
     * @param keyDeserializer The deserializer for key that implements {@link Deserializer}. The configure() method
     *            won't be called in the consumer when the deserializer is passed in directly.
     * @param valueDeserializer The deserializer for value that implements {@link Deserializer}. The configure() method
     *            won't be called in the consumer when the deserializer is passed in directly.
     */
    public KafkaConsumer(Properties properties,
                         Deserializer<K> keyDeserializer,
                         Deserializer<V> valueDeserializer) {
        this(propsToMap(properties), keyDeserializer, valueDeserializer);
    }

    /**
     * A consumer is instantiated by providing a set of key-value pairs as configuration, and a key and a value {@link Deserializer}.
     * <p>
     * Valid configuration strings are documented at {@link ConsumerConfig}.
     * <p>
     * Note: after creating a {@code KafkaConsumer} you must always {@link #close()} it to avoid resource leaks.
     *
     * @param configs The consumer configs
     * @param keyDeserializer The deserializer for key that implements {@link Deserializer}. The configure() method
     *            won't be called in the consumer when the deserializer is passed in directly.
     * @param valueDeserializer The deserializer for value that implements {@link Deserializer}. The configure() method
     *            won't be called in the consumer when the deserializer is passed in directly.
     */
    public KafkaConsumer(Map<String, Object> configs,
                         Deserializer<K> keyDeserializer,
                         Deserializer<V> valueDeserializer) {
        this(new ConsumerConfig(ConsumerConfig.appendDeserializerToConfig(configs, keyDeserializer, valueDeserializer)),
                keyDeserializer, valueDeserializer);
    }

    KafkaConsumer(ConsumerConfig config, Deserializer<K> keyDeserializer, Deserializer<V> valueDeserializer) {
        delegate = CREATOR.create(config, keyDeserializer, valueDeserializer);
    }

    KafkaConsumer(LogContext logContext,
                  Time time,
                  ConsumerConfig config,
                  Deserializer<K> keyDeserializer,
                  Deserializer<V> valueDeserializer,
                  KafkaClient client,
                  SubscriptionState subscriptions,
                  ConsumerMetadata metadata,
                  List<ConsumerPartitionAssignor> 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<TopicPartition> 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<String> subscription() {
        return delegate.subscription();
    }

    /**
     * Subscribe to the given list of topics to get dynamically
     * assigned partitions. <b>Topic subscriptions are not incremental. This list will replace the current
     * assignment (if there is one).</b> Note that it is not possible to combine topic subscription with group management
     * with manual partition assignment through {@link #assign(Collection)}.
     *
     * If the given list of topics is empty, it is treated the same as {@link #unsubscribe()}.
     *
     * <p>
     * As part of group management, the consumer will keep track of the list of consumers that belong to a particular
     * group and will trigger a rebalance operation if any one of the following events are triggered:
     * <ul>
     * <li>Number of partitions change for any of the subscribed topics
     * <li>A subscribed topic is created or deleted
     * <li>An existing member of the consumer group is shutdown or fails
     * <li>A new member is added to the consumer group
     * </ul>
     * <p>
     * When any of these events are triggered, the provided listener will be invoked first to indicate that
     * the consumer's assignment has been revoked, and then again when the new assignment has been received.
     * Note that rebalances will only occur during an active call to {@link #poll(Duration)}, so callbacks will
     * also only be invoked during that time.
     *
     * The provided listener will immediately override any listener set in a previous call to subscribe.
     * It is guaranteed, however, that the partitions revoked/assigned through this interface are from topics
     * subscribed in this call. See {@link ConsumerRebalanceListener} for more details.
     *
     * @param topics The list of topics to subscribe to
     * @param listener Non-null listener instance to get notifications on partition assignment/revocation for the
     *                 subscribed topics
     * @throws IllegalArgumentException If topics is null or contains null or empty elements, or if listener is null
     * @throws IllegalStateException If {@code subscribe()} is called previously with pattern, or assign is called
     *                               previously (without a subsequent call to {@link #unsubscribe()}), or if not
     *                               configured at-least one partition assignment strategy
     */
    @Override
    public void subscribe(Collection<String> topics, ConsumerRebalanceListener listener) {
        delegate.subscribe(topics, listener);
    }

    /**
     * Subscribe to the given list of topics to get dynamically assigned partitions.
     * <b>Topic subscriptions are not incremental. This list will replace the current
     * assignment (if there is one).</b> It is not possible to combine topic subscription with group management
     * with manual partition assignment through {@link #assign(Collection)}.
     *
     * If the given list of topics is empty, it is treated the same as {@link #unsubscribe()}.
     *
     * <p>
     * This is a short-hand for {@link #subscribe(Collection, ConsumerRebalanceListener)}, which
     * uses a no-op listener. If you need the ability to seek to particular offsets, you should prefer
     * {@link #subscribe(Collection, ConsumerRebalanceListener)}, since group rebalances will cause partition offsets
     * to be reset. You should also provide your own listener if you are doing your own offset
     * management since the listener gives you an opportunity to commit offsets before a rebalance finishes.
     *
     * @param topics The list of topics to subscribe to
     * @throws IllegalArgumentException If topics is null or contains null or empty elements
     * @throws IllegalStateException If {@code subscribe()} is called previously with pattern, or assign is called
     *                               previously (without a subsequent call to {@link #unsubscribe()}), or if not
     *                               configured at-least one partition assignment strategy
     */
    @Override
    public void subscribe(Collection<String> topics) {
        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.
     * <p>
     * See {@link #subscribe(Collection, ConsumerRebalanceListener)} for details on the
     * use of the {@link ConsumerRebalanceListener}. Generally rebalances are triggered when there
     * is a change to the topics matching the provided pattern and when consumer group membership changes.
     * Group rebalances only take place during an active call to {@link #poll(Duration)}.
     *
     * @param pattern Pattern to subscribe to
     * @param listener Non-null listener instance to get notifications on partition assignment/revocation for the
     *                 subscribed topics
     * @throws IllegalArgumentException If pattern or listener is null
     * @throws IllegalStateException If {@code subscribe()} is called previously with topics, or assign is called
     *                               previously (without a subsequent call to {@link #unsubscribe()}), or if not
     *                               configured at-least one partition assignment strategy
     */
    @Override
    public void subscribe(Pattern pattern, ConsumerRebalanceListener listener) {
        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.
     * <p>
     * This is a short-hand for {@link #subscribe(Pattern, ConsumerRebalanceListener)}, which
     * uses a no-op listener. If you need the ability to seek to particular offsets, you should prefer
     * {@link #subscribe(Pattern, ConsumerRebalanceListener)}, since group rebalances will cause partition offsets
     * to be reset. You should also provide your own listener if you are doing your own offset
     * management since the listener gives you an opportunity to commit offsets before a rebalance finishes.
     *
     * @param pattern Pattern to subscribe to
     * @throws IllegalArgumentException If pattern is null
     * @throws IllegalStateException If {@code subscribe()} is called previously with topics, or assign is called
     *                               previously (without a subsequent call to {@link #unsubscribe()}), or if not
     *                               configured at-least one partition assignment strategy
     */
    @Override
    public void subscribe(Pattern pattern) {
        delegate.subscribe(pattern);
    }

    /**
     * Subscribe to all topics matching the specified pattern, to get dynamically assigned partitions.
     * The pattern matching will be done periodically against all topics. This is only supported under the
     * CONSUMER group protocol (see {@link ConsumerConfig#GROUP_PROTOCOL_CONFIG}).
     * <p>
     * If the provided pattern is not compatible with Google RE2/J, an {@link InvalidRegularExpression} will be
     * eventually thrown on a call to {@link #poll(Duration)} following this call to subscribe.
     * <p>
     * See {@link #subscribe(Collection, ConsumerRebalanceListener)} for details on the
     * use of the {@link ConsumerRebalanceListener}. Generally, rebalances are triggered when there
     * is a change to the topics matching the provided pattern and when consumer group membership changes.
     * Group rebalances only take place during an active call to {@link #poll(Duration)}.
     *
     * @param pattern  Pattern to subscribe to, that must be compatible with Google RE2/J.
     * @param listener Non-null listener instance to get notifications on partition assignment/revocation for the
     *                 subscribed topics.
     * @throws IllegalArgumentException If pattern is null or empty, or if the 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()}).
     */
    @Override
    public void subscribe(SubscriptionPattern pattern, ConsumerRebalanceListener listener) {
        delegate.subscribe(pattern, listener);
    }

    /**
     * Subscribe to all topics matching the specified pattern, to get dynamically assigned partitions.
     * The pattern matching will be done periodically against topics. This is only supported under the
     * CONSUMER group protocol (see {@link ConsumerConfig#GROUP_PROTOCOL_CONFIG})
     * <p>
     * If the provided pattern is not compatible with Google RE2/J, an {@link InvalidRegularExpression} will be
     * eventually thrown on a call to {@link #poll(Duration)} following this call to subscribe.
     * <p>
     * This is a short-hand for {@link #subscribe(Pattern, ConsumerRebalanceListener)}, which
     * uses a no-op listener. If you need the ability to seek to particular offsets, you should prefer
     * {@link #subscribe(Pattern, ConsumerRebalanceListener)}, since group rebalances will cause partition offsets
     * to be reset. You should also provide your own listener if you are doing your own offset
     * management since the listener gives you an opportunity to commit offsets before a rebalance finishes.
     *
     * @param pattern Pattern to subscribe to, that must be compatible with Google RE2/J.
     * @throws IllegalArgumentException If pattern is null or empty.
     * @throws IllegalStateException    If {@code subscribe()} is called previously with topics, or assign is called
     *                                  previously (without a subsequent call to {@link #unsubscribe()}).
     */
    @Override
    public void subscribe(SubscriptionPattern 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).
     * <p>
     * If the given list of topic partitions is empty, it is treated the same as {@link #unsubscribe()}.
     * <p>
     * Manual topic assignment through this method does not use the consumer's group management
     * functionality. As such, there will be no rebalance operation triggered when group membership or cluster and topic
     * metadata change. Note that it is not possible to use both manual partition assignment with {@link #assign(Collection)}
     * and group assignment with {@link #subscribe(Collection, ConsumerRebalanceListener)}.
     * <p>
     * If auto-commit is enabled, an async commit (based on the old assignment) will be triggered before the new
     * assignment replaces the old one.
     *
     * @param partitions The list of partitions to assign this consumer
     * @throws IllegalArgumentException If partitions is null or contains null or empty topics
     * @throws IllegalStateException If {@code subscribe()} is called previously with topics or pattern
     *                               (without a subsequent call to {@link #unsubscribe()})
     */
    @Override
    public void assign(Collection<TopicPartition> partitions) {
        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.
     * <p>
     * On each poll, consumer will try to use the last consumed offset as the starting offset and fetch sequentially. The last
     * consumed offset can be manually set through {@link #seek(TopicPartition, long)} or automatically set as the last committed
     * offset for the subscribed list of partitions
     *
     * <p>
     * This method returns immediately if there are records available or if the position advances past control records
     * or aborted transactions when isolation.level=read_committed.
     * Otherwise, it will await the passed timeout. If the timeout expires, an empty record set will be returned.
     * Note that this method may block beyond the timeout in order to execute custom
     * {@link ConsumerRebalanceListener} callbacks.
     *
     *
     * @param timeout The maximum time to block (must not be greater than {@link Long#MAX_VALUE} milliseconds)
     *
     * @return map of topic to records since the last fetch for the subscribed list of topics and partitions
     *
     * @throws org.apache.kafka.clients.consumer.InvalidOffsetException if the offset for a partition or set of
     *             partitions is undefined or out of range and no offset reset policy has been configured
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if caller lacks Read access to any of the subscribed
     *             topics or to the configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. invalid groupId or
     *             session timeout, errors deserializing key/value pairs, your rebalance callback thrown exceptions,
     *             or any new error cases in future versions)
     * @throws java.lang.IllegalArgumentException if the timeout value is negative
     * @throws java.lang.IllegalStateException if the consumer is not subscribed to any topics or manually assigned any
     *             partitions to consume from
     * @throws java.lang.ArithmeticException if the timeout is greater than {@link Long#MAX_VALUE} milliseconds.
     * @throws org.apache.kafka.common.errors.InvalidTopicException if the current subscription contains any invalid
     *             topic (per {@link org.apache.kafka.common.internals.Topic#validate(String)})
     * @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
     *             when the broker doesn't support this feature. Also, if the consumer attempts to subscribe to a
     *             SubscriptionPattern via {@link #subscribe(SubscriptionPattern)} or
     *             {@link #subscribe(SubscriptionPattern, ConsumerRebalanceListener)} and the broker doesn't
     *             support this feature.
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer instance gets fenced by broker.
     */
    @Override
    public ConsumerRecords<K, V> poll(final Duration timeout) {
        return delegate.poll(timeout);
    }

    /**
     * Commit offsets returned on the last {@link #poll(Duration) poll()} for all the subscribed list of topics and
     * partitions.
     * <p>
     * This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
     * every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used.
     * <p>
     * This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
     * (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
     * <p>
     * Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
     * (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
     *
     * @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
     *             This fatal error can only occur if you are using automatic group management with {@link #subscribe(Collection)},
     *             or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
     *             when you are trying to commit to partitions that are no longer assigned to this consumer because the
     *             consumer is for example no longer part of the group this exception would be thrown.
     * @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
     *            so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
     *            complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
     *            NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
     *            and also for those partitions that are still assigned their fetch positions may have changed too
     *            if more records are returned from the {@link #poll(Duration)} call.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
     *             is too large or if the topic does not exist).
     * @throws org.apache.kafka.common.errors.TimeoutException if the timeout specified by {@code default.api.timeout.ms} expires
     *            before successful completion of the offset commit
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *            and this 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.
     * <p>
     * This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
     * every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used.
     * <p>
     * This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the passed timeout expires.
     * <p>
     * Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
     * (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
     *
     * @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
     *             This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
     *             or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
     *             when you are trying to commit to partitions that are no longer assigned to this consumer because the
     *             consumer is for example no longer part of the group this exception would be thrown.
     * @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
     *            so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
     *            complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
     *            NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
     *            and also for those partitions that are still assigned their fetch positions may have changed too
     *            if more records are returned from the {@link #poll(Duration)} call.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
     *             is too large or if the topic does not exist).
     * @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
     *            of the offset commit
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *            and this 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.
     * <p>
     * This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
     * rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used. The committed offset should be the next message your application will consume,
     * i.e. {@code nextRecordToBeProcessed.offset()} (or {@link ConsumerRecords#nextOffsets()}).
     * You should also add the leader epoch as commit metadata, which can be obtained from
     * {@link ConsumerRecord#leaderEpoch()} or {@link ConsumerRecords#nextOffsets()}.
     * If automatic group management with {@link #subscribe(Collection)} is used,
     * then the committed offsets must belong to the currently auto-assigned partitions.
     * <p>
     * This is a synchronous commit and will block until either the commit succeeds or an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
     * (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
     * <p>
     * Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
     * (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
     *
     * @param offsets A map of offsets by partition with associated metadata
     * @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
     *             This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
     *             or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
     *             when you are trying to commit to partitions that are no longer assigned to this consumer because the
     *             consumer is for example no longer part of the group this exception would be thrown.
     * @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
     *            so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
     *            complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
     *            NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
     *            and also for those partitions that are still assigned their fetch positions may have changed too
     *            if more records are returned from the {@link #poll(Duration)} call, so when you retry committing
     *            you should consider updating the passed in {@code offset} parameter.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws java.lang.IllegalArgumentException if the committed offset is negative
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
     *             is too large or if the topic does not exist).
     * @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
     *            of the offset commit
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *            and this instance gets fenced by broker.
     */
    @Override
    public void commitSync(final Map<TopicPartition, OffsetAndMetadata> offsets) {
        delegate.commitSync(offsets);
    }

    /**
     * Commit the specified offsets for the specified list of topics and partitions.
     * <p>
     * This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
     * rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used. The committed offset should be the next message your application will consume,
     * i.e. {@code nextRecordToBeProcessed.offset()} (or {@link ConsumerRecords#nextOffsets()}).
     * You should also add the leader epoch as commit metadata, which can be obtained from
     * {@link ConsumerRecord#leaderEpoch()} or {@link ConsumerRecords#nextOffsets()}.
     * If automatic group management with {@link #subscribe(Collection)} is used,
     * then the committed offsets must belong to the currently auto-assigned partitions.
     * <p>
     * This is a synchronous commit and will block until either the commit succeeds, an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the timeout expires.
     * <p>
     * Note that asynchronous offset commits sent previously with the {@link #commitAsync(OffsetCommitCallback)}
     * (or similar) are guaranteed to have their callbacks invoked prior to completion of this method.
     *
     * @param offsets A map of offsets by partition with associated metadata
     * @param timeout The maximum amount of time to await completion of the offset commit
     * @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried.
     *             This can only occur if you are using automatic group management with {@link #subscribe(Collection)},
     *             or if there is an active group with the same <code>group.id</code> which is using group management. In such cases,
     *             when you are trying to commit to partitions that are no longer assigned to this consumer because the
     *             consumer is for example no longer part of the group this exception would be thrown.
     * @throws org.apache.kafka.common.errors.RebalanceInProgressException if the consumer instance is in the middle of a rebalance
     *            so it is not yet determined which partitions would be assigned to the consumer. In such cases you can first
     *            complete the rebalance by calling {@link #poll(Duration)} and commit can be reconsidered afterwards.
     *            NOTE when you reconsider committing after the rebalance, the assigned partitions may have changed,
     *            and also for those partitions that are still assigned their fetch positions may have changed too
     *            if more records are returned from the {@link #poll(Duration)} call, so when you retry committing
     *            you should consider updating the passed in {@code offset} parameter.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws java.lang.IllegalArgumentException if the committed offset is negative
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors (e.g. if offset metadata
     *             is too large or if the topic does not exist).
     * @throws org.apache.kafka.common.errors.TimeoutException if the timeout expires before successful completion
     *            of the offset commit
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *            and this instance gets fenced by broker.
     */
    @Override
    public void commitSync(final Map<TopicPartition, OffsetAndMetadata> 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 is using the classic group protocol
     *            and this 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.
     * <p>
     * This commits offsets only to Kafka. The offsets committed using this API will be used on the first fetch after
     * every rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used.
     * <p>
     * This is an asynchronous call and will not block. Any errors encountered are either passed to the callback
     * (if provided) or discarded.
     * <p>
     * Offsets committed through multiple calls to this API are guaranteed to be sent in the same order as
     * the invocations. Corresponding commit callbacks are also invoked in the same order. Additionally note that
     * offsets committed through this API are guaranteed to complete before a subsequent call to {@link #commitSync()}
     * (and variants) returns.
     *
     * @param callback Callback to invoke when the commit completes
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *             and this 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.
     * <p>
     * This commits offsets to Kafka. The offsets committed using this API will be used on the first fetch after every
     * rebalance and also on startup. As such, if you need to store offsets in anything other than Kafka, this API
     * should not be used. The committed offset should be the next message your application will consume,
     * i.e. {@code nextRecordToBeProcessed.offset()} (or {@link ConsumerRecords#nextOffsets()}).
     * You should also add the leader epoch as commit metadata, which can be obtained from
     * {@link ConsumerRecord#leaderEpoch()} or {@link ConsumerRecords#nextOffsets()}.
     * If automatic group management with {@link #subscribe(Collection)} is used,
     * then the committed offsets must belong to the currently auto-assigned partitions.
     * <p>
     * This is an asynchronous call and will not block. Any errors encountered are either passed to the callback
     * (if provided) or discarded.
     * <p>
     * Offsets committed through multiple calls to this API are guaranteed to be sent in the same order as
     * the invocations. Corresponding commit callbacks are also invoked in the same order. Additionally note that
     * offsets committed through this API are guaranteed to complete before a subsequent call to {@link #commitSync()}
     * (and variants) returns.
     *
     * @param offsets A map of offsets by partition with associate metadata. This map will be copied internally, so it
     *                is safe to mutate the map after returning.
     * @param callback Callback to invoke when the commit completes
     * @throws org.apache.kafka.common.errors.FencedInstanceIdException if this consumer is using the classic group protocol
     *             and this instance gets fenced by broker.
     */
    @Override
    public void commitAsync(final Map<TopicPartition, OffsetAndMetadata> 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
     * <p>
     * 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).
     * <p>
     * {@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.
     * <p>
     * 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.
     * <p>
     * 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<TopicPartition> 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.
     * <p>
     * If {@code isolation.level=read_committed}, the end offset will be the Last Stable Offset, i.e., the offset
     * of the first message with an open transaction.
     *
     * @throws IllegalArgumentException if {@code partitions} is {@code null}
     * @throws IllegalStateException if any of the provided partitions are not currently assigned to this consumer
     */
    @Override
    public void seekToEnd(Collection<TopicPartition> partitions) {
        delegate.seekToEnd(partitions);
    }

    /**
     * Get the offset of the <i>next record</i> that will be fetched (if a record with that offset exists).
     * This method may issue a remote call to the server if there is no current position for the given partition.
     * <p>
     * This call will block until either the position could be determined or an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the timeout specified by {@code default.api.timeout.ms} expires
     * (in which case a {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
     *
     * @param partition The partition to get the position for
     * @return The current position of the consumer (that is, the offset of the next record to be fetched)
     * @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
     * @throws org.apache.kafka.clients.consumer.InvalidOffsetException if no offset is currently defined for
     *             the partition
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
     *             when the broker doesn't support this feature
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
     * @throws org.apache.kafka.common.errors.TimeoutException if the position cannot be determined before the
     *             timeout specified by {@code default.api.timeout.ms} expires
     */
    @Override
    public long position(TopicPartition partition) {
        return delegate.position(partition);
    }

    /**
     * Get the offset of the <i>next record</i> that will be fetched (if a record with that offset exists).
     * This method may issue a remote call to the server if there is no current position
     * for the given partition.
     * <p>
     * This call will block until the position can be determined, an unrecoverable error is
     * encountered (in which case it is thrown to the caller), or the timeout expires.
     *
     * @param partition The partition to get the position for
     * @param timeout The maximum amount of time to await determination of the current position
     * @return The current position of the consumer (that is, the offset of the next record to be fetched)
     * @throws IllegalStateException if the provided TopicPartition is not assigned to this consumer
     * @throws org.apache.kafka.clients.consumer.InvalidOffsetException if no offset is currently defined for
     *             the partition
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.TimeoutException if the position cannot be determined before the
     *             passed timeout expires
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
     */
    @Override
    public long position(TopicPartition partition, final Duration timeout) {
        return delegate.position(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.
     * <p>
     * If any of the partitions requested do not exist, an exception would be thrown.
     * <p>
     * This call will do a remote call to get the latest committed offsets from the server, and will block until the
     * committed offsets are gotten successfully, an unrecoverable error is encountered (in which case it is thrown to
     * the caller), or the timeout specified by {@code default.api.timeout.ms} expires (in which case a
     * {@link org.apache.kafka.common.errors.TimeoutException} is thrown to the caller).
     *
     * @param partitions The partitions to check
     * @return The latest committed offsets for the given partitions; {@code null} will be returned for the
     *         partition if there is no such message.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.errors.UnsupportedVersionException if the consumer attempts to fetch stable offsets
     *             when the broker doesn't support this feature
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
     * @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
     *             the timeout specified by {@code default.api.timeout.ms} expires.
     */
    @Override
    public Map<TopicPartition, OffsetAndMetadata> committed(final Set<TopicPartition> partitions) {
        return 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.
     * <p>
     * If any of the partitions requested do not exist, an exception would be thrown.
     * <p>
     * This call will block to do a remote call to get the latest committed offsets from the server.
     *
     * @param partitions The partitions to check
     * @param timeout  The maximum amount of time to await the latest committed offsets
     * @return The latest committed offsets for the given partitions; {@code null} will be returned for the
     *         partition if there is no such message.
     * @throws org.apache.kafka.common.errors.WakeupException if {@link #wakeup()} is called before or while this
     *             function is called
     * @throws org.apache.kafka.common.errors.InterruptException if the calling thread is interrupted before or while
     *             this function is called
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic or to the
     *             configured groupId. See the exception for more details
     * @throws org.apache.kafka.common.KafkaException for any other unrecoverable errors
     * @throws org.apache.kafka.common.errors.TimeoutException if the committed offset cannot be found before
     *             expiration of the timeout
     */
    @Override
    public Map<TopicPartition, OffsetAndMetadata> committed(final Set<TopicPartition> partitions, final Duration timeout) {
        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.
     * <p>
     * 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.
     * <p>
     * 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<MetricName, ? extends Metric> 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<PartitionInfo> 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<PartitionInfo> 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<String, List<PartitionInfo>> 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<String, List<PartitionInfo>> 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<TopicPartition> 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<TopicPartition> partitions) {
        delegate.resume(partitions);
    }

    /**
     * Add the provided application metric for subscription.
     * This metric will be added to this client's metrics
     * that are available for subscription and sent as
     * telemetry data to the broker.
     * The provided metric must map to an OTLP metric data point
     * type in the OpenTelemetry v1 metrics protobuf message types.
     * Specifically, the metric should be one of the following:
     * <ul>
     *  <li>
     *     `Sum`: Monotonic total count meter (Counter). Suitable for metrics like total number of X, e.g., total bytes sent.
     *  </li>
     *  <li>
     *     `Gauge`: Non-monotonic current value meter (UpDownCounter). Suitable for metrics like current value of Y, e.g., current queue count.
     *  </li>
     * </ul>
     * Metrics not matching these types are silently ignored.
     * Executing this method for a previously registered metric is a benign operation and results in updating that metrics entry.
     *
     * @param metric The application metric to register
     */
    @Override
    public void registerMetricForSubscription(KafkaMetric metric) {
        delegate.registerMetricForSubscription(metric);
    }

    /**
     * Remove the provided application metric for subscription.
     * This metric is removed from this client's metrics
     * and will not be available for subscription any longer.
     * Executing this method with a metric that has not been registered is a
     * benign operation and does not result in any action taken (no-op).
     *
     * @param metric The application metric to remove
     */
    @Override
    public void unregisterMetricFromSubscription(KafkaMetric metric) {
        delegate.unregisterMetricFromSubscription(metric);
    }

    /**
     * Get the set of partitions that were previously paused by a call to {@link #pause(Collection)}.
     *
     * @return The set of paused partitions
     */
    @Override
    public Set<TopicPartition> paused() {
        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<TopicPartition, OffsetAndTimestamp> offsetsForTimes(Map<TopicPartition, Long> 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<TopicPartition, OffsetAndTimestamp> offsetsForTimes(Map<TopicPartition, Long> timestampsToSearch, Duration timeout) {
        return delegate.offsetsForTimes(timestampsToSearch, timeout);
    }

    /**
     * Get the first offset for the given partitions.
     * <p>
     * This method does not change the current consumer position of the partitions.
     *
     * @see #seekToBeginning(Collection)
     *
     * @param partitions the partitions to get the earliest offsets.
     * @return The earliest available offsets for the given partitions
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
     * @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
     *         expiration of the configured {@code default.api.timeout.ms}
     */
    @Override
    public Map<TopicPartition, Long> beginningOffsets(Collection<TopicPartition> partitions) {
        return delegate.beginningOffsets(partitions);
    }

    /**
     * Get the first offset for the given partitions.
     * <p>
     * This method does not change the current consumer position of the partitions.
     *
     * @see #seekToBeginning(Collection)
     *
     * @param partitions the partitions to get the earliest offsets
     * @param timeout The maximum amount of time to await retrieval of the beginning offsets
     *
     * @return The earliest available offsets for the given partitions
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
     * @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
     *         expiration of the passed timeout
     */
    @Override
    public Map<TopicPartition, Long> beginningOffsets(Collection<TopicPartition> partitions, Duration timeout) {
        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.
     *
     * <p>
     * This method does not change the current consumer position of the partitions.
     *
     * @see #seekToEnd(Collection)
     *
     * @param partitions the partitions to get the end offsets.
     * @return The end offsets for the given partitions.
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
     * @throws org.apache.kafka.common.errors.TimeoutException if the offset metadata could not be fetched before
     *         the amount of time allocated by {@code default.api.timeout.ms} expires
     */
    @Override
    public Map<TopicPartition, Long> endOffsets(Collection<TopicPartition> partitions) {
        return 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.
     *
     * <p>
     * This method does not change the current consumer position of the partitions.
     *
     * @see #seekToEnd(Collection)
     *
     * @param partitions the partitions to get the end offsets.
     * @param timeout The maximum amount of time to await retrieval of the end offsets
     *
     * @return The end offsets for the given partitions.
     * @throws org.apache.kafka.common.errors.AuthenticationException if authentication fails. See the exception for more details
     * @throws org.apache.kafka.common.errors.AuthorizationException if not authorized to the topic(s). See the exception for more details
     * @throws org.apache.kafka.common.errors.TimeoutException if the offsets could not be fetched before
     *         expiration of the passed timeout
     */
    @Override
    public Map<TopicPartition, Long> endOffsets(Collection<TopicPartition> partitions, Duration timeout) {
        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.
     *
     * <p>
     * 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.
     * <p>
     * You do not need to call this during normal processing, as the consumer group will manage itself
     * automatically and rebalance when necessary. However there may be situations where the application wishes to
     * trigger a rebalance that would otherwise not occur. For example, if some condition external and invisible to
     * the Consumer and its group changes in a way that would affect the userdata encoded in the
     * {@link org.apache.kafka.clients.consumer.ConsumerPartitionAssignor.Subscription Subscription}, the Consumer
     * will not be notified and no rebalance will occur. This API can be used to force the group to rebalance so that
     * the assignor can perform a partition reassignment based on the latest userdata. If your assignor does not use
     * this userdata, or you do not use a custom
     * {@link org.apache.kafka.clients.consumer.ConsumerPartitionAssignor ConsumerPartitionAssignor}, you should not
     * use this API.
     *
     * @param reason The reason why the new rebalance is needed.
     *
     * @throws java.lang.IllegalStateException if the consumer does not use group subscription
     */
    @Override
    public void enforceRebalance(final String reason) {
        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.
     * <p>
     * The actual maximum wait time is bounded by the {@link ConsumerConfig#REQUEST_TIMEOUT_MS_CONFIG} setting, which
     * only applies to operations performed with the broker (coordinator-related requests and
     * fetch sessions). Even if a larger timeout is specified, the consumer will not wait longer than
     * {@link ConsumerConfig#REQUEST_TIMEOUT_MS_CONFIG} for these requests to complete during the close operation.
     * Note that the execution time of callbacks (such as {@link OffsetCommitCallback} and
     * {@link ConsumerRebalanceListener}) does not consume time from the close timeout.
     *
     * @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);
    }
}
