org.apache.kafka.clients.producer.KafkaProducer Maven / Gradle / Ivy
/**
* Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE
* file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file
* to You under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the
* License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
* an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
* specific language governing permissions and limitations under the License.
*/
package org.apache.kafka.clients.producer;
import java.net.InetSocketAddress;
import java.util.Collections;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicInteger;
import java.util.concurrent.atomic.AtomicReference;
import org.apache.kafka.clients.ClientUtils;
import org.apache.kafka.clients.Metadata;
import org.apache.kafka.clients.NetworkClient;
import org.apache.kafka.clients.producer.internals.RecordAccumulator;
import org.apache.kafka.clients.producer.internals.Sender;
import org.apache.kafka.clients.producer.internals.ProducerInterceptors;
import org.apache.kafka.common.Cluster;
import org.apache.kafka.common.KafkaException;
import org.apache.kafka.common.Metric;
import org.apache.kafka.common.PartitionInfo;
import org.apache.kafka.common.TopicPartition;
import org.apache.kafka.common.config.ConfigException;
import org.apache.kafka.common.errors.ApiException;
import org.apache.kafka.common.errors.InterruptException;
import org.apache.kafka.common.errors.RecordTooLargeException;
import org.apache.kafka.common.errors.SerializationException;
import org.apache.kafka.common.errors.TimeoutException;
import org.apache.kafka.common.errors.TopicAuthorizationException;
import org.apache.kafka.common.metrics.JmxReporter;
import org.apache.kafka.common.metrics.MetricConfig;
import org.apache.kafka.common.MetricName;
import org.apache.kafka.common.metrics.Metrics;
import org.apache.kafka.common.metrics.MetricsReporter;
import org.apache.kafka.common.metrics.Sensor;
import org.apache.kafka.common.network.Selector;
import org.apache.kafka.common.network.ChannelBuilder;
import org.apache.kafka.common.record.CompressionType;
import org.apache.kafka.common.record.Record;
import org.apache.kafka.common.record.Records;
import org.apache.kafka.common.serialization.Serializer;
import org.apache.kafka.common.utils.AppInfoParser;
import org.apache.kafka.common.utils.KafkaThread;
import org.apache.kafka.common.utils.SystemTime;
import org.apache.kafka.common.utils.Time;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
* A Kafka client that publishes records to the Kafka cluster.
*
* The producer is thread safe and sharing a single producer instance across threads will generally be faster than
* having multiple instances.
*
* Here is a simple example of using the producer to send records with strings containing sequential numbers as the key/value
* pairs.
*
* {@code
* Properties props = new Properties();
* props.put("bootstrap.servers", "localhost:9092");
* props.put("acks", "all");
* props.put("retries", 0);
* props.put("batch.size", 16384);
* props.put("linger.ms", 1);
* props.put("buffer.memory", 33554432);
* props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
* props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
*
* Producer producer = new KafkaProducer<>(props);
* for(int i = 0; i < 100; i++)
* producer.send(new ProducerRecord("my-topic", Integer.toString(i), Integer.toString(i)));
*
* producer.close();
* }
*
* The producer consists of a pool of buffer space that holds records that haven't yet been transmitted to the server
* as well as a background I/O thread that is responsible for turning these records into requests and transmitting them
* to the cluster. Failure to close the producer after use will leak these resources.
*
* The {@link #send(ProducerRecord) send()} method is asynchronous. When called it adds the record to a buffer of pending record sends
* and immediately returns. This allows the producer to batch together individual records for efficiency.
*
* The acks config controls the criteria under which requests are considered complete. The "all" setting
* we have specified will result in blocking on the full commit of the record, the slowest but most durable setting.
*
* If the request fails, the producer can automatically retry, though since we have specified retries
* as 0 it won't. Enabling retries also opens up the possibility of duplicates (see the documentation on
* message delivery semantics for details).
*
* The producer maintains buffers of unsent records for each partition. These buffers are of a size specified by
* the batch.size config. Making this larger can result in more batching, but requires more memory (since we will
* generally have one of these buffers for each active partition).
*
* By default a buffer is available to send immediately even if there is additional unused space in the buffer. However if you
* want to reduce the number of requests you can set linger.ms to something greater than 0. This will
* instruct the producer to wait up to that number of milliseconds before sending a request in hope that more records will
* arrive to fill up the same batch. This is analogous to Nagle's algorithm in TCP. For example, in the code snippet above,
* likely all 100 records would be sent in a single request since we set our linger time to 1 millisecond. However this setting
* would add 1 millisecond of latency to our request waiting for more records to arrive if we didn't fill up the buffer. Note that
* records that arrive close together in time will generally batch together even with linger.ms=0 so under heavy load
* batching will occur regardless of the linger configuration; however setting this to something larger than 0 can lead to fewer, more
* efficient requests when not under maximal load at the cost of a small amount of latency.
*
* The buffer.memory controls the total amount of memory available to the producer for buffering. If records
* are sent faster than they can be transmitted to the server then this buffer space will be exhausted. When the buffer space is
* exhausted additional send calls will block. For uses where you want to avoid any blocking you can set block.on.buffer.full=false which
* will cause the send call to result in an exception.
*
* The key.serializer and value.serializer instruct how to turn the key and value objects the user provides with
* their ProducerRecord into bytes. You can use the included {@link org.apache.kafka.common.serialization.ByteArraySerializer} or
* {@link org.apache.kafka.common.serialization.StringSerializer} for simple string or byte types.
*/
public class KafkaProducer implements Producer {
private static final Logger log = LoggerFactory.getLogger(KafkaProducer.class);
private static final AtomicInteger PRODUCER_CLIENT_ID_SEQUENCE = new AtomicInteger(1);
private static final String JMX_PREFIX = "kafka.producer";
private String clientId;
private final Partitioner partitioner;
private final int maxRequestSize;
private final long totalMemorySize;
private final Metadata metadata;
private final RecordAccumulator accumulator;
private final Sender sender;
private final Metrics metrics;
private final Thread ioThread;
private final CompressionType compressionType;
private final Sensor errors;
private final Time time;
private final Serializer keySerializer;
private final Serializer valueSerializer;
private final ProducerConfig producerConfig;
private final long maxBlockTimeMs;
private final int requestTimeoutMs;
private final ProducerInterceptors interceptors;
/**
* A producer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings
* are documented here. Values can be
* either strings or Objects of the appropriate type (for example a numeric configuration would accept either the
* string "42" or the integer 42).
* @param configs The producer configs
*
*/
public KafkaProducer(Map configs) {
this(new ProducerConfig(configs), null, null);
}
/**
* A producer is instantiated by providing a set of key-value pairs as configuration, a key and a value {@link Serializer}.
* Valid configuration strings are documented here.
* Values can be either strings or Objects of the appropriate type (for example a numeric configuration would accept
* either the string "42" or the integer 42).
* @param configs The producer configs
* @param keySerializer The serializer for key that implements {@link Serializer}. The configure() method won't be
* called in the producer when the serializer is passed in directly.
* @param valueSerializer The serializer for value that implements {@link Serializer}. The configure() method won't
* be called in the producer when the serializer is passed in directly.
*/
public KafkaProducer(Map configs, Serializer keySerializer, Serializer valueSerializer) {
this(new ProducerConfig(ProducerConfig.addSerializerToConfig(configs, keySerializer, valueSerializer)),
keySerializer, valueSerializer);
}
/**
* A producer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings
* are documented here.
* @param properties The producer configs
*/
public KafkaProducer(Properties properties) {
this(new ProducerConfig(properties), null, null);
}
/**
* A producer is instantiated by providing a set of key-value pairs as configuration, a key and a value {@link Serializer}.
* Valid configuration strings are documented here.
* @param properties The producer configs
* @param keySerializer The serializer for key that implements {@link Serializer}. The configure() method won't be
* called in the producer when the serializer is passed in directly.
* @param valueSerializer The serializer for value that implements {@link Serializer}. The configure() method won't
* be called in the producer when the serializer is passed in directly.
*/
public KafkaProducer(Properties properties, Serializer keySerializer, Serializer valueSerializer) {
this(new ProducerConfig(ProducerConfig.addSerializerToConfig(properties, keySerializer, valueSerializer)),
keySerializer, valueSerializer);
}
@SuppressWarnings({"unchecked", "deprecation"})
private KafkaProducer(ProducerConfig config, Serializer keySerializer, Serializer valueSerializer) {
try {
log.trace("Starting the Kafka producer");
Map userProvidedConfigs = config.originals();
this.producerConfig = config;
this.time = new SystemTime();
clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
if (clientId.length() <= 0)
clientId = "producer-" + PRODUCER_CLIENT_ID_SEQUENCE.getAndIncrement();
Map metricTags = new LinkedHashMap();
metricTags.put("client-id", clientId);
MetricConfig metricConfig = new MetricConfig().samples(config.getInt(ProducerConfig.METRICS_NUM_SAMPLES_CONFIG))
.timeWindow(config.getLong(ProducerConfig.METRICS_SAMPLE_WINDOW_MS_CONFIG), TimeUnit.MILLISECONDS)
.tags(metricTags);
List reporters = config.getConfiguredInstances(ProducerConfig.METRIC_REPORTER_CLASSES_CONFIG,
MetricsReporter.class);
reporters.add(new JmxReporter(JMX_PREFIX));
this.metrics = new Metrics(metricConfig, reporters, time);
this.partitioner = config.getConfiguredInstance(ProducerConfig.PARTITIONER_CLASS_CONFIG, Partitioner.class);
long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
this.metadata = new Metadata(retryBackoffMs, config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG));
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
this.compressionType = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));
/* check for user defined settings.
* If the BLOCK_ON_BUFFER_FULL is set to true,we do not honor METADATA_FETCH_TIMEOUT_CONFIG.
* This should be removed with release 0.9 when the deprecated configs are removed.
*/
if (userProvidedConfigs.containsKey(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG)) {
log.warn(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
boolean blockOnBufferFull = config.getBoolean(ProducerConfig.BLOCK_ON_BUFFER_FULL_CONFIG);
if (blockOnBufferFull) {
this.maxBlockTimeMs = Long.MAX_VALUE;
} else if (userProvidedConfigs.containsKey(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.maxBlockTimeMs = config.getLong(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG);
} else {
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
}
} else if (userProvidedConfigs.containsKey(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG + " config is deprecated and will be removed soon. " +
"Please use " + ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.maxBlockTimeMs = config.getLong(ProducerConfig.METADATA_FETCH_TIMEOUT_CONFIG);
} else {
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
}
/* check for user defined settings.
* If the TIME_OUT config is set use that for request timeout.
* This should be removed with release 0.9
*/
if (userProvidedConfigs.containsKey(ProducerConfig.TIMEOUT_CONFIG)) {
log.warn(ProducerConfig.TIMEOUT_CONFIG + " config is deprecated and will be removed soon. Please use " +
ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
this.requestTimeoutMs = config.getInt(ProducerConfig.TIMEOUT_CONFIG);
} else {
this.requestTimeoutMs = config.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
}
this.accumulator = new RecordAccumulator(config.getInt(ProducerConfig.BATCH_SIZE_CONFIG),
this.totalMemorySize,
this.compressionType,
config.getLong(ProducerConfig.LINGER_MS_CONFIG),
retryBackoffMs,
metrics,
time);
List addresses = ClientUtils.parseAndValidateAddresses(config.getList(ProducerConfig.BOOTSTRAP_SERVERS_CONFIG));
this.metadata.update(Cluster.bootstrap(addresses), time.milliseconds());
ChannelBuilder channelBuilder = ClientUtils.createChannelBuilder(config.values());
NetworkClient client = new NetworkClient(
new Selector(config.getLong(ProducerConfig.CONNECTIONS_MAX_IDLE_MS_CONFIG), this.metrics, time, "producer", channelBuilder),
this.metadata,
clientId,
config.getInt(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION),
config.getLong(ProducerConfig.RECONNECT_BACKOFF_MS_CONFIG),
config.getInt(ProducerConfig.SEND_BUFFER_CONFIG),
config.getInt(ProducerConfig.RECEIVE_BUFFER_CONFIG),
this.requestTimeoutMs, time);
this.sender = new Sender(client,
this.metadata,
this.accumulator,
config.getInt(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION) == 1,
config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
(short) parseAcks(config.getString(ProducerConfig.ACKS_CONFIG)),
config.getInt(ProducerConfig.RETRIES_CONFIG),
this.metrics,
new SystemTime(),
clientId,
this.requestTimeoutMs);
String ioThreadName = "kafka-producer-network-thread" + (clientId.length() > 0 ? " | " + clientId : "");
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
this.errors = this.metrics.sensor("errors");
if (keySerializer == null) {
this.keySerializer = config.getConfiguredInstance(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.keySerializer.configure(config.originals(), true);
} else {
config.ignore(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG);
this.keySerializer = keySerializer;
}
if (valueSerializer == null) {
this.valueSerializer = config.getConfiguredInstance(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.valueSerializer.configure(config.originals(), false);
} else {
config.ignore(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG);
this.valueSerializer = valueSerializer;
}
// load interceptors and make sure they get clientId
userProvidedConfigs.put(ProducerConfig.CLIENT_ID_CONFIG, clientId);
List> interceptorList = (List) (new ProducerConfig(userProvidedConfigs)).getConfiguredInstances(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
ProducerInterceptor.class);
this.interceptors = interceptorList.isEmpty() ? null : new ProducerInterceptors<>(interceptorList);
config.logUnused();
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId);
log.debug("Kafka producer started");
} catch (Throwable t) {
// call close methods if internal objects are already constructed
// this is to prevent resource leak. see KAFKA-2121
close(0, TimeUnit.MILLISECONDS, true);
// now propagate the exception
throw new KafkaException("Failed to construct kafka producer", t);
}
}
private static int parseAcks(String acksString) {
try {
return acksString.trim().equalsIgnoreCase("all") ? -1 : Integer.parseInt(acksString.trim());
} catch (NumberFormatException e) {
throw new ConfigException("Invalid configuration value for 'acks': " + acksString);
}
}
/**
* Asynchronously send a record to a topic. Equivalent to send(record, null).
* See {@link #send(ProducerRecord, Callback)} for details.
*/
@Override
public Future send(ProducerRecord record) {
return send(record, null);
}
/**
* Asynchronously send a record to a topic and invoke the provided callback when the send has been acknowledged.
*
* The send is asynchronous and this method will return immediately once the record has been stored in the buffer of
* records waiting to be sent. This allows sending many records in parallel without blocking to wait for the
* response after each one.
*
* The result of the send is a {@link RecordMetadata} specifying the partition the record was sent to, the offset
* it was assigned and the timestamp of the record. If
* {@link org.apache.kafka.common.record.TimestampType#CREATE_TIME CreateTime} is used by the topic, the timestamp
* will be the user provided timestamp or the record send time if the user did not specify a timestamp for the
* record. If {@link org.apache.kafka.common.record.TimestampType#LOG_APPEND_TIME LogAppendTime} is used for the
* topic, the timestamp will be the Kafka broker local time when the message is appended.
*
* Since the send call is asynchronous it returns a {@link java.util.concurrent.Future Future} for the
* {@link RecordMetadata} that will be assigned to this record. Invoking {@link java.util.concurrent.Future#get()
* get()} on this future will block until the associated request completes and then return the metadata for the record
* or throw any exception that occurred while sending the record.
*
* If you want to simulate a simple blocking call you can call the get() method immediately:
*
*
* {@code
* byte[] key = "key".getBytes();
* byte[] value = "value".getBytes();
* ProducerRecord record = new ProducerRecord("my-topic", key, value)
* producer.send(record).get();
* }
*
* Fully non-blocking usage can make use of the {@link Callback} parameter to provide a callback that
* will be invoked when the request is complete.
*
*
* {@code
* ProducerRecord record = new ProducerRecord("the-topic", key, value);
* producer.send(myRecord,
* new Callback() {
* public void onCompletion(RecordMetadata metadata, Exception e) {
* if(e != null)
* e.printStackTrace();
* System.out.println("The offset of the record we just sent is: " + metadata.offset());
* }
* });
* }
*
*
* Callbacks for records being sent to the same partition are guaranteed to execute in order. That is, in the
* following example callback1 is guaranteed to execute before callback2:
*
*
* {@code
* producer.send(new ProducerRecord(topic, partition, key1, value1), callback1);
* producer.send(new ProducerRecord(topic, partition, key2, value2), callback2);
* }
*
*
* Note that callbacks will generally execute in the I/O thread of the producer and so should be reasonably fast or
* they will delay the sending of messages from other threads. If you want to execute blocking or computationally
* expensive callbacks it is recommended to use your own {@link java.util.concurrent.Executor} in the callback body
* to parallelize processing.
*
* @param record The record to send
* @param callback A user-supplied callback to execute when the record has been acknowledged by the server (null
* indicates no callback)
*
* @throws InterruptException If the thread is interrupted while blocked
* @throws SerializationException If the key or value are not valid objects given the configured serializers
* @throws BufferExhaustedException If block.on.buffer.full=false and the buffer is full.
*
*/
@Override
public Future send(ProducerRecord record, Callback callback) {
// intercept the record, which can be potentially modified; this method does not throw exceptions
ProducerRecord interceptedRecord = this.interceptors == null ? record : this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
}
/**
* Implementation of asynchronously send a record to a topic. Equivalent to send(record, null).
* See {@link #send(ProducerRecord, Callback)} for details.
*/
private Future doSend(ProducerRecord record, Callback callback) {
TopicPartition tp = null;
try {
// first make sure the metadata for the topic is available
long waitedOnMetadataMs = waitOnMetadata(record.topic(), this.maxBlockTimeMs);
long remainingWaitMs = Math.max(0, this.maxBlockTimeMs - waitedOnMetadataMs);
byte[] serializedKey;
try {
serializedKey = keySerializer.serialize(record.topic(), record.key());
} catch (ClassCastException cce) {
throw new SerializationException("Can't convert key of class " + record.key().getClass().getName() +
" to class " + producerConfig.getClass(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG).getName() +
" specified in key.serializer");
}
byte[] serializedValue;
try {
serializedValue = valueSerializer.serialize(record.topic(), record.value());
} catch (ClassCastException cce) {
throw new SerializationException("Can't convert value of class " + record.value().getClass().getName() +
" to class " + producerConfig.getClass(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG).getName() +
" specified in value.serializer");
}
int partition = partition(record, serializedKey, serializedValue, metadata.fetch());
int serializedSize = Records.LOG_OVERHEAD + Record.recordSize(serializedKey, serializedValue);
ensureValidRecordSize(serializedSize);
tp = new TopicPartition(record.topic(), partition);
long timestamp = record.timestamp() == null ? time.milliseconds() : record.timestamp();
log.trace("Sending record {} with callback {} to topic {} partition {}", record, callback, record.topic(), partition);
// producer callback will make sure to call both 'callback' and interceptor callback
Callback interceptCallback = this.interceptors == null ? callback : new InterceptorCallback<>(callback, this.interceptors, tp);
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey, serializedValue, interceptCallback, remainingWaitMs);
if (result.batchIsFull || result.newBatchCreated) {
log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), partition);
this.sender.wakeup();
}
return result.future;
// handling exceptions and record the errors;
// for API exceptions return them in the future,
// for other exceptions throw directly
} catch (ApiException e) {
log.debug("Exception occurred during message send:", e);
if (callback != null)
callback.onCompletion(null, e);
this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
return new FutureFailure(e);
} catch (InterruptedException e) {
this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw new InterruptException(e);
} catch (BufferExhaustedException e) {
this.errors.record();
this.metrics.sensor("buffer-exhausted-records").record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (KafkaException e) {
this.errors.record();
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (Exception e) {
// we notify interceptor about all exceptions, since onSend is called before anything else in this method
if (this.interceptors != null)
this.interceptors.onSendError(record, tp, e);
throw e;
}
}
/**
* Wait for cluster metadata including partitions for the given topic to be available.
* @param topic The topic we want metadata for
* @param maxWaitMs The maximum time in ms for waiting on the metadata
* @return The amount of time we waited in ms
*/
private long waitOnMetadata(String topic, long maxWaitMs) throws InterruptedException {
// add topic to metadata topic list if it is not there already.
if (!this.metadata.containsTopic(topic))
this.metadata.add(topic);
if (metadata.fetch().partitionsForTopic(topic) != null)
return 0;
long begin = time.milliseconds();
long remainingWaitMs = maxWaitMs;
while (metadata.fetch().partitionsForTopic(topic) == null) {
log.trace("Requesting metadata update for topic {}.", topic);
int version = metadata.requestUpdate();
sender.wakeup();
metadata.awaitUpdate(version, remainingWaitMs);
long elapsed = time.milliseconds() - begin;
if (elapsed >= maxWaitMs)
throw new TimeoutException("Failed to update metadata after " + maxWaitMs + " ms.");
if (metadata.fetch().unauthorizedTopics().contains(topic))
throw new TopicAuthorizationException(topic);
remainingWaitMs = maxWaitMs - elapsed;
}
return time.milliseconds() - begin;
}
/**
* Validate that the record size isn't too large
*/
private void ensureValidRecordSize(int size) {
if (size > this.maxRequestSize)
throw new RecordTooLargeException("The message is " + size +
" bytes when serialized which is larger than the maximum request size you have configured with the " +
ProducerConfig.MAX_REQUEST_SIZE_CONFIG +
" configuration.");
if (size > this.totalMemorySize)
throw new RecordTooLargeException("The message is " + size +
" bytes when serialized which is larger than the total memory buffer you have configured with the " +
ProducerConfig.BUFFER_MEMORY_CONFIG +
" configuration.");
}
/**
* Invoking this method makes all buffered records immediately available to send (even if linger.ms is
* greater than 0) and blocks on the completion of the requests associated with these records. The post-condition
* of flush() is that any previously sent record will have completed (e.g. Future.isDone() == true).
* A request is considered completed when it is successfully acknowledged
* according to the acks configuration you have specified or else it results in an error.
*
* Other threads can continue sending records while one thread is blocked waiting for a flush call to complete,
* however no guarantee is made about the completion of records sent after the flush call begins.
*
* This method can be useful when consuming from some input system and producing into Kafka. The flush() call
* gives a convenient way to ensure all previously sent messages have actually completed.
*
* This example shows how to consume from one Kafka topic and produce to another Kafka topic:
*
* {@code
* for(ConsumerRecord record: consumer.poll(100))
* producer.send(new ProducerRecord("my-topic", record.key(), record.value());
* producer.flush();
* consumer.commit();
* }
*
*
* Note that the above example may drop records if the produce request fails. If we want to ensure that this does not occur
* we need to set retries=<large_number> in our config.
*
* @throws InterruptException If the thread is interrupted while blocked
*/
@Override
public void flush() {
log.trace("Flushing accumulated records in producer.");
this.accumulator.beginFlush();
this.sender.wakeup();
try {
this.accumulator.awaitFlushCompletion();
} catch (InterruptedException e) {
throw new InterruptException("Flush interrupted.", e);
}
}
/**
* Get the partition metadata for the give topic. This can be used for custom partitioning.
* @throws InterruptException If the thread is interrupted while blocked
*/
@Override
public List partitionsFor(String topic) {
try {
waitOnMetadata(topic, this.maxBlockTimeMs);
} catch (InterruptedException e) {
throw new InterruptException(e);
}
return this.metadata.fetch().partitionsForTopic(topic);
}
/**
* Get the full set of internal metrics maintained by the producer.
*/
@Override
public Map metrics() {
return Collections.unmodifiableMap(this.metrics.metrics());
}
/**
* Close this producer. This method blocks until all previously sent requests complete.
* This method is equivalent to close(Long.MAX_VALUE, TimeUnit.MILLISECONDS).
*
* If close() is called from {@link Callback}, a warning message will be logged and close(0, TimeUnit.MILLISECONDS)
* will be called instead. We do this because the sender thread would otherwise try to join itself and
* block forever.
*
*
* @throws InterruptException If the thread is interrupted while blocked
*/
@Override
public void close() {
close(Long.MAX_VALUE, TimeUnit.MILLISECONDS);
}
/**
* This method waits up to timeout for the producer to complete the sending of all incomplete requests.
*
* If the producer is unable to complete all requests before the timeout expires, this method will fail
* any unsent and unacknowledged records immediately.
*
* If invoked from within a {@link Callback} this method will not block and will be equivalent to
* close(0, TimeUnit.MILLISECONDS). This is done since no further sending will happen while
* blocking the I/O thread of the producer.
*
* @param timeout The maximum time to wait for producer to complete any pending requests. The value should be
* non-negative. Specifying a timeout of zero means do not wait for pending send requests to complete.
* @param timeUnit The time unit for the timeout
* @throws InterruptException If the thread is interrupted while blocked
* @throws IllegalArgumentException If the timeout is negative.
*/
@Override
public void close(long timeout, TimeUnit timeUnit) {
close(timeout, timeUnit, false);
}
private void close(long timeout, TimeUnit timeUnit, boolean swallowException) {
if (timeout < 0)
throw new IllegalArgumentException("The timeout cannot be negative.");
log.info("Closing the Kafka producer with timeoutMillis = {} ms.", timeUnit.toMillis(timeout));
// this will keep track of the first encountered exception
AtomicReference firstException = new AtomicReference();
boolean invokedFromCallback = Thread.currentThread() == this.ioThread;
if (timeout > 0) {
if (invokedFromCallback) {
log.warn("Overriding close timeout {} ms to 0 ms in order to prevent useless blocking due to self-join. " +
"This means you have incorrectly invoked close with a non-zero timeout from the producer call-back.", timeout);
} else {
// Try to close gracefully.
if (this.sender != null)
this.sender.initiateClose();
if (this.ioThread != null) {
try {
this.ioThread.join(timeUnit.toMillis(timeout));
} catch (InterruptedException t) {
firstException.compareAndSet(null, t);
log.error("Interrupted while joining ioThread", t);
}
}
}
}
if (this.sender != null && this.ioThread != null && this.ioThread.isAlive()) {
log.info("Proceeding to force close the producer since pending requests could not be completed " +
"within timeout {} ms.", timeout);
this.sender.forceClose();
// Only join the sender thread when not calling from callback.
if (!invokedFromCallback) {
try {
this.ioThread.join();
} catch (InterruptedException e) {
firstException.compareAndSet(null, e);
}
}
}
ClientUtils.closeQuietly(interceptors, "producer interceptors", firstException);
ClientUtils.closeQuietly(metrics, "producer metrics", firstException);
ClientUtils.closeQuietly(keySerializer, "producer keySerializer", firstException);
ClientUtils.closeQuietly(valueSerializer, "producer valueSerializer", firstException);
AppInfoParser.unregisterAppInfo(JMX_PREFIX, clientId);
log.debug("The Kafka producer has closed.");
if (firstException.get() != null && !swallowException)
throw new KafkaException("Failed to close kafka producer", firstException.get());
}
/**
* computes partition for given record.
* if the record has partition returns the value otherwise
* calls configured partitioner class to compute the partition.
*/
private int partition(ProducerRecord record, byte[] serializedKey , byte[] serializedValue, Cluster cluster) {
Integer partition = record.partition();
if (partition != null) {
List partitions = cluster.partitionsForTopic(record.topic());
int lastPartition = partitions.size() - 1;
// they have given us a partition, use it
if (partition < 0 || partition > lastPartition) {
throw new IllegalArgumentException(String.format("Invalid partition given with record: %d is not in the range [0...%d].", partition, lastPartition));
}
return partition;
}
return this.partitioner.partition(record.topic(), record.key(), serializedKey, record.value(), serializedValue,
cluster);
}
private static class FutureFailure implements Future {
private final ExecutionException exception;
public FutureFailure(Exception exception) {
this.exception = new ExecutionException(exception);
}
@Override
public boolean cancel(boolean interrupt) {
return false;
}
@Override
public RecordMetadata get() throws ExecutionException {
throw this.exception;
}
@Override
public RecordMetadata get(long timeout, TimeUnit unit) throws ExecutionException {
throw this.exception;
}
@Override
public boolean isCancelled() {
return false;
}
@Override
public boolean isDone() {
return true;
}
}
/**
* A callback called when producer request is complete. It in turn calls user-supplied callback (if given) and
* notifies producer interceptors about the request completion.
*/
private static class InterceptorCallback implements Callback {
private final Callback userCallback;
private final ProducerInterceptors interceptors;
private final TopicPartition tp;
public InterceptorCallback(Callback userCallback, ProducerInterceptors interceptors,
TopicPartition tp) {
this.userCallback = userCallback;
this.interceptors = interceptors;
this.tp = tp;
}
public void onCompletion(RecordMetadata metadata, Exception exception) {
if (this.interceptors != null) {
if (metadata == null) {
this.interceptors.onAcknowledgement(new RecordMetadata(tp, -1, -1, Record.NO_TIMESTAMP, -1, -1, -1),
exception);
} else {
this.interceptors.onAcknowledgement(metadata, exception);
}
}
if (this.userCallback != null)
this.userCallback.onCompletion(metadata, exception);
}
}
}