org.apache.kafka.clients.producer.KafkaProducer Maven / Gradle / Ivy
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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.kafka.clients.producer;
import org.apache.kafka.clients.ApiVersions;
import org.apache.kafka.clients.ClientUtils;
import org.apache.kafka.clients.CommonClientConfigs;
import org.apache.kafka.clients.KafkaClient;
import org.apache.kafka.clients.consumer.ConsumerGroupMetadata;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.OffsetAndMetadata;
import org.apache.kafka.clients.consumer.OffsetCommitCallback;
import org.apache.kafka.clients.producer.internals.BufferPool;
import org.apache.kafka.clients.producer.internals.BuiltInPartitioner;
import org.apache.kafka.clients.producer.internals.KafkaProducerMetrics;
import org.apache.kafka.clients.producer.internals.ProducerInterceptors;
import org.apache.kafka.clients.producer.internals.ProducerMetadata;
import org.apache.kafka.clients.producer.internals.ProducerMetrics;
import org.apache.kafka.clients.producer.internals.RecordAccumulator;
import org.apache.kafka.clients.producer.internals.Sender;
import org.apache.kafka.clients.producer.internals.TransactionManager;
import org.apache.kafka.clients.producer.internals.TransactionalRequestResult;
import org.apache.kafka.common.Cluster;
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.compress.Compression;
import org.apache.kafka.common.config.ConfigException;
import org.apache.kafka.common.errors.ApiException;
import org.apache.kafka.common.errors.AuthenticationException;
import org.apache.kafka.common.errors.AuthorizationException;
import org.apache.kafka.common.errors.InterruptException;
import org.apache.kafka.common.errors.InvalidTopicException;
import org.apache.kafka.common.errors.ProducerFencedException;
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.header.Header;
import org.apache.kafka.common.header.Headers;
import org.apache.kafka.common.header.internals.RecordHeaders;
import org.apache.kafka.common.internals.ClusterResourceListeners;
import org.apache.kafka.common.metrics.KafkaMetricsContext;
import org.apache.kafka.common.metrics.MetricConfig;
import org.apache.kafka.common.metrics.Metrics;
import org.apache.kafka.common.metrics.MetricsContext;
import org.apache.kafka.common.metrics.MetricsReporter;
import org.apache.kafka.common.metrics.Sensor;
import org.apache.kafka.common.record.AbstractRecords;
import org.apache.kafka.common.record.CompressionType;
import org.apache.kafka.common.record.RecordBatch;
import org.apache.kafka.common.requests.JoinGroupRequest;
import org.apache.kafka.common.serialization.Serializer;
import org.apache.kafka.common.telemetry.internals.ClientTelemetryReporter;
import org.apache.kafka.common.telemetry.internals.ClientTelemetryUtils;
import org.apache.kafka.common.utils.AppInfoParser;
import org.apache.kafka.common.utils.KafkaThread;
import org.apache.kafka.common.utils.LogContext;
import org.apache.kafka.common.utils.Time;
import org.apache.kafka.common.utils.Timer;
import org.apache.kafka.common.utils.Utils;
import org.slf4j.Logger;
import java.net.InetSocketAddress;
import java.time.Duration;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Properties;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.Future;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.atomic.AtomicReference;
/**
* 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("linger.ms", 1);
* 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 default setting "all"
* 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. The retries setting defaults to Integer.MAX_VALUE, and
* it's recommended to use delivery.timeout.ms to control retry behavior, instead of retries.
*
* 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. The threshold for time to block is determined by max.block.ms after which it throws
* a TimeoutException.
*
* 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 byte or string types.
*
* From Kafka 0.11, the KafkaProducer supports two additional modes: the idempotent producer and the transactional producer.
* The idempotent producer strengthens Kafka's delivery semantics from at least once to exactly once delivery. In particular
* producer retries will no longer introduce duplicates. The transactional producer allows an application to send messages
* to multiple partitions (and topics!) atomically.
*
*
* From Kafka 3.0, the enable.idempotence configuration defaults to true. When enabling idempotence,
* retries config will default to Integer.MAX_VALUE and the acks config will
* default to all. There are no API changes for the idempotent producer, so existing applications will
* not need to be modified to take advantage of this feature.
*
*
* To take advantage of the idempotent producer, it is imperative to avoid application level re-sends since these cannot
* be de-duplicated. As such, if an application enables idempotence, it is recommended to leave the retries
* config unset, as it will be defaulted to Integer.MAX_VALUE. Additionally, if a {@link #send(ProducerRecord)}
* returns an error even with infinite retries (for instance if the message expires in the buffer before being sent),
* then it is recommended to shut down the producer and check the contents of the last produced message to ensure that
* it is not duplicated. Finally, the producer can only guarantee idempotence for messages sent within a single session.
*
* To use the transactional producer and the attendant APIs, you must set the transactional.id
* configuration property. If the transactional.id is set, idempotence is automatically enabled along with
* the producer configs which idempotence depends on. Further, topics which are included in transactions should be configured
* for durability. In particular, the replication.factor should be at least 3, and the
* min.insync.replicas for these topics should be set to 2. Finally, in order for transactional guarantees
* to be realized from end-to-end, the consumers must be configured to read only committed messages as well.
*
*
* The purpose of the transactional.id is to enable transaction recovery across multiple sessions of a
* single producer instance. It would typically be derived from the shard identifier in a partitioned, stateful, application.
* As such, it should be unique to each producer instance running within a partitioned application.
*
* All the new transactional APIs are blocking and will throw exceptions on failure. The example
* below illustrates how the new APIs are meant to be used. It is similar to the example above, except that all
* 100 messages are part of a single transaction.
*
*
*
* {@code
* Properties props = new Properties();
* props.put("bootstrap.servers", "localhost:9092");
* props.put("transactional.id", "my-transactional-id");
* Producer producer = new KafkaProducer<>(props, new StringSerializer(), new StringSerializer());
*
* producer.initTransactions();
*
* try {
* producer.beginTransaction();
* for (int i = 0; i < 100; i++)
* producer.send(new ProducerRecord<>("my-topic", Integer.toString(i), Integer.toString(i)));
* producer.commitTransaction();
* } catch (ProducerFencedException | OutOfOrderSequenceException | AuthorizationException e) {
* // We can't recover from these exceptions, so our only option is to close the producer and exit.
* producer.close();
* } catch (KafkaException e) {
* // For all other exceptions, just abort the transaction and try again.
* producer.abortTransaction();
* }
* producer.close();
* }
*
*
* As is hinted at in the example, there can be only one open transaction per producer. All messages sent between the
* {@link #beginTransaction()} and {@link #commitTransaction()} calls will be part of a single transaction. When the
* transactional.id is specified, all messages sent by the producer must be part of a transaction.
*
*
* The transactional producer uses exceptions to communicate error states. In particular, it is not required
* to specify callbacks for producer.send() or to call .get() on the returned Future: a
* KafkaException would be thrown if any of the
* producer.send() or transactional calls hit an irrecoverable error during a transaction. See the {@link #send(ProducerRecord)}
* documentation for more details about detecting errors from a transactional send.
*
* By calling
* producer.abortTransaction() upon receiving a KafkaException we can ensure that any
* successful writes are marked as aborted, hence keeping the transactional guarantees.
*
*
* This client can communicate with brokers that are version 0.10.0 or newer. Older or newer brokers may not support
* certain client features. For instance, the transactional APIs need broker versions 0.11.0 or later. You will receive an
* UnsupportedVersionException when invoking an API that is not available in the running broker version.
*
*/
public class KafkaProducer implements Producer {
private final Logger log;
private static final String JMX_PREFIX = "kafka.producer";
public static final String NETWORK_THREAD_PREFIX = "kafka-producer-network-thread";
public static final String PRODUCER_METRIC_GROUP_NAME = "producer-metrics";
private final String clientId;
// Visible for testing
final Metrics metrics;
private final KafkaProducerMetrics producerMetrics;
private final Partitioner partitioner;
private final int maxRequestSize;
private final long totalMemorySize;
private final ProducerMetadata metadata;
private final RecordAccumulator accumulator;
private final Sender sender;
private final Thread ioThread;
private final Compression compression;
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 boolean partitionerIgnoreKeys;
private final ProducerInterceptors interceptors;
private final ApiVersions apiVersions;
private final TransactionManager transactionManager;
private final Optional clientTelemetryReporter;
/**
* 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).
*
* Note: after creating a {@code KafkaProducer} you must always {@link #close()} it to avoid resource leaks.
* @param configs The producer configs
*
*/
public KafkaProducer(final Map configs) {
this(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).
*
* Note: after creating a {@code KafkaProducer} you must always {@link #close()} it to avoid resource leaks.
* @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.appendSerializerToConfig(configs, keySerializer, valueSerializer)),
keySerializer, valueSerializer, null, null, null, Time.SYSTEM);
}
/**
* A producer is instantiated by providing a set of key-value pairs as configuration. Valid configuration strings
* are documented here.
*
* Note: after creating a {@code KafkaProducer} you must always {@link #close()} it to avoid resource leaks.
* @param properties The producer configs
*/
public KafkaProducer(Properties properties) {
this(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.
*
* Note: after creating a {@code KafkaProducer} you must always {@link #close()} it to avoid resource leaks.
* @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(Utils.propsToMap(properties), keySerializer, valueSerializer);
}
/**
* Check if partitioner is deprecated and log a warning if it is.
*/
@SuppressWarnings("deprecation")
private void warnIfPartitionerDeprecated() {
// Using DefaultPartitioner and UniformStickyPartitioner is deprecated, see KIP-794.
if (partitioner instanceof org.apache.kafka.clients.producer.internals.DefaultPartitioner) {
log.warn("DefaultPartitioner is deprecated. Please clear " + ProducerConfig.PARTITIONER_CLASS_CONFIG
+ " configuration setting to get the default partitioning behavior");
}
if (partitioner instanceof org.apache.kafka.clients.producer.UniformStickyPartitioner) {
log.warn("UniformStickyPartitioner is deprecated. Please clear " + ProducerConfig.PARTITIONER_CLASS_CONFIG
+ " configuration setting and set " + ProducerConfig.PARTITIONER_IGNORE_KEYS_CONFIG
+ " to 'true' to get the uniform sticky partitioning behavior");
}
}
// visible for testing
@SuppressWarnings({"unchecked", "this-escape"})
KafkaProducer(ProducerConfig config,
Serializer keySerializer,
Serializer valueSerializer,
ProducerMetadata metadata,
KafkaClient kafkaClient,
ProducerInterceptors interceptors,
Time time) {
try {
this.producerConfig = config;
this.time = time;
String transactionalId = config.getString(ProducerConfig.TRANSACTIONAL_ID_CONFIG);
this.clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
LogContext logContext;
if (transactionalId == null)
logContext = new LogContext(String.format("[Producer clientId=%s] ", clientId));
else
logContext = new LogContext(String.format("[Producer clientId=%s, transactionalId=%s] ", clientId, transactionalId));
log = logContext.logger(KafkaProducer.class);
log.trace("Starting the Kafka producer");
Map metricTags = Collections.singletonMap("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)
.recordLevel(Sensor.RecordingLevel.forName(config.getString(ProducerConfig.METRICS_RECORDING_LEVEL_CONFIG)))
.tags(metricTags);
List reporters = CommonClientConfigs.metricsReporters(clientId, config);
this.clientTelemetryReporter = CommonClientConfigs.telemetryReporter(clientId, config);
this.clientTelemetryReporter.ifPresent(reporters::add);
MetricsContext metricsContext = new KafkaMetricsContext(JMX_PREFIX,
config.originalsWithPrefix(CommonClientConfigs.METRICS_CONTEXT_PREFIX));
this.metrics = new Metrics(metricConfig, reporters, time, metricsContext);
this.producerMetrics = new KafkaProducerMetrics(metrics);
this.partitioner = config.getConfiguredInstance(
ProducerConfig.PARTITIONER_CLASS_CONFIG,
Partitioner.class,
Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId));
warnIfPartitionerDeprecated();
this.partitionerIgnoreKeys = config.getBoolean(ProducerConfig.PARTITIONER_IGNORE_KEYS_CONFIG);
long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
long retryBackoffMaxMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MAX_MS_CONFIG);
if (keySerializer == null) {
this.keySerializer = config.getConfiguredInstance(ProducerConfig.KEY_SERIALIZER_CLASS_CONFIG,
Serializer.class);
this.keySerializer.configure(config.originals(Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId)), 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(Collections.singletonMap(ProducerConfig.CLIENT_ID_CONFIG, clientId)), false);
} else {
config.ignore(ProducerConfig.VALUE_SERIALIZER_CLASS_CONFIG);
this.valueSerializer = valueSerializer;
}
List> interceptorList = ClientUtils.configuredInterceptors(config,
ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
ProducerInterceptor.class);
if (interceptors != null)
this.interceptors = interceptors;
else
this.interceptors = new ProducerInterceptors<>(interceptorList);
ClusterResourceListeners clusterResourceListeners = ClientUtils.configureClusterResourceListeners(
interceptorList,
reporters,
Arrays.asList(this.keySerializer, this.valueSerializer));
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
this.compression = configureCompression(config);
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
int deliveryTimeoutMs = configureDeliveryTimeout(config, log);
this.apiVersions = new ApiVersions();
this.transactionManager = configureTransactionState(config, logContext);
// There is no need to do work required for adaptive partitioning, if we use a custom partitioner.
boolean enableAdaptivePartitioning = partitioner == null &&
config.getBoolean(ProducerConfig.PARTITIONER_ADPATIVE_PARTITIONING_ENABLE_CONFIG);
RecordAccumulator.PartitionerConfig partitionerConfig = new RecordAccumulator.PartitionerConfig(
enableAdaptivePartitioning,
config.getLong(ProducerConfig.PARTITIONER_AVAILABILITY_TIMEOUT_MS_CONFIG)
);
// As per Kafka producer configuration documentation batch.size may be set to 0 to explicitly disable
// batching which in practice actually means using a batch size of 1.
int batchSize = Math.max(1, config.getInt(ProducerConfig.BATCH_SIZE_CONFIG));
this.accumulator = new RecordAccumulator(logContext,
batchSize,
compression,
lingerMs(config),
retryBackoffMs,
retryBackoffMaxMs,
deliveryTimeoutMs,
partitionerConfig,
metrics,
PRODUCER_METRIC_GROUP_NAME,
time,
apiVersions,
transactionManager,
new BufferPool(this.totalMemorySize, batchSize, metrics, time, PRODUCER_METRIC_GROUP_NAME));
List addresses = ClientUtils.parseAndValidateAddresses(config);
if (metadata != null) {
this.metadata = metadata;
} else {
this.metadata = new ProducerMetadata(retryBackoffMs,
retryBackoffMaxMs,
config.getLong(ProducerConfig.METADATA_MAX_AGE_CONFIG),
config.getLong(ProducerConfig.METADATA_MAX_IDLE_CONFIG),
logContext,
clusterResourceListeners,
Time.SYSTEM);
this.metadata.bootstrap(addresses);
}
this.errors = this.metrics.sensor("errors");
this.sender = newSender(logContext, kafkaClient, this.metadata);
String ioThreadName = NETWORK_THREAD_PREFIX + " | " + clientId;
this.ioThread = new KafkaThread(ioThreadName, this.sender, true);
this.ioThread.start();
config.logUnused();
AppInfoParser.registerAppInfo(JMX_PREFIX, clientId, metrics, time.milliseconds());
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(Duration.ofMillis(0), true);
// now propagate the exception
throw new KafkaException("Failed to construct kafka producer", t);
}
}
// visible for testing
KafkaProducer(ProducerConfig config,
LogContext logContext,
Metrics metrics,
Serializer keySerializer,
Serializer valueSerializer,
ProducerMetadata metadata,
RecordAccumulator accumulator,
TransactionManager transactionManager,
Sender sender,
ProducerInterceptors interceptors,
Partitioner partitioner,
Time time,
KafkaThread ioThread,
Optional clientTelemetryReporter) {
this.producerConfig = config;
this.time = time;
this.clientId = config.getString(ProducerConfig.CLIENT_ID_CONFIG);
this.log = logContext.logger(KafkaProducer.class);
this.metrics = metrics;
this.producerMetrics = new KafkaProducerMetrics(metrics);
this.partitioner = partitioner;
this.keySerializer = keySerializer;
this.valueSerializer = valueSerializer;
this.interceptors = interceptors;
this.maxRequestSize = config.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG);
this.totalMemorySize = config.getLong(ProducerConfig.BUFFER_MEMORY_CONFIG);
this.compression = configureCompression(config);
this.maxBlockTimeMs = config.getLong(ProducerConfig.MAX_BLOCK_MS_CONFIG);
this.partitionerIgnoreKeys = config.getBoolean(ProducerConfig.PARTITIONER_IGNORE_KEYS_CONFIG);
this.apiVersions = new ApiVersions();
this.transactionManager = transactionManager;
this.accumulator = accumulator;
this.errors = this.metrics.sensor("errors");
this.metadata = metadata;
this.sender = sender;
this.ioThread = ioThread;
this.clientTelemetryReporter = clientTelemetryReporter;
}
// visible for testing
Sender newSender(LogContext logContext, KafkaClient kafkaClient, ProducerMetadata metadata) {
int maxInflightRequests = producerConfig.getInt(ProducerConfig.MAX_IN_FLIGHT_REQUESTS_PER_CONNECTION);
int requestTimeoutMs = producerConfig.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
ProducerMetrics metricsRegistry = new ProducerMetrics(this.metrics);
Sensor throttleTimeSensor = Sender.throttleTimeSensor(metricsRegistry.senderMetrics);
KafkaClient client = kafkaClient != null ? kafkaClient : ClientUtils.createNetworkClient(producerConfig,
this.metrics,
"producer",
logContext,
apiVersions,
time,
maxInflightRequests,
metadata,
throttleTimeSensor,
clientTelemetryReporter.map(ClientTelemetryReporter::telemetrySender).orElse(null));
short acks = Short.parseShort(producerConfig.getString(ProducerConfig.ACKS_CONFIG));
return new Sender(logContext,
client,
metadata,
this.accumulator,
maxInflightRequests == 1,
producerConfig.getInt(ProducerConfig.MAX_REQUEST_SIZE_CONFIG),
acks,
producerConfig.getInt(ProducerConfig.RETRIES_CONFIG),
metricsRegistry.senderMetrics,
time,
requestTimeoutMs,
producerConfig.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG),
this.transactionManager,
apiVersions);
}
private static Compression configureCompression(ProducerConfig config) {
CompressionType type = CompressionType.forName(config.getString(ProducerConfig.COMPRESSION_TYPE_CONFIG));
switch (type) {
case GZIP: {
return Compression.gzip()
.level(config.getInt(ProducerConfig.COMPRESSION_GZIP_LEVEL_CONFIG))
.build();
}
case LZ4: {
return Compression.lz4()
.level(config.getInt(ProducerConfig.COMPRESSION_LZ4_LEVEL_CONFIG))
.build();
}
case ZSTD: {
return Compression.zstd()
.level(config.getInt(ProducerConfig.COMPRESSION_ZSTD_LEVEL_CONFIG))
.build();
}
default:
return Compression.of(type).build();
}
}
private static int lingerMs(ProducerConfig config) {
return (int) Math.min(config.getLong(ProducerConfig.LINGER_MS_CONFIG), Integer.MAX_VALUE);
}
private static int configureDeliveryTimeout(ProducerConfig config, Logger log) {
int deliveryTimeoutMs = config.getInt(ProducerConfig.DELIVERY_TIMEOUT_MS_CONFIG);
int lingerMs = lingerMs(config);
int requestTimeoutMs = config.getInt(ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
int lingerAndRequestTimeoutMs = (int) Math.min((long) lingerMs + requestTimeoutMs, Integer.MAX_VALUE);
if (deliveryTimeoutMs < lingerAndRequestTimeoutMs) {
if (config.originals().containsKey(ProducerConfig.DELIVERY_TIMEOUT_MS_CONFIG)) {
// throw an exception if the user explicitly set an inconsistent value
throw new ConfigException(ProducerConfig.DELIVERY_TIMEOUT_MS_CONFIG
+ " should be equal to or larger than " + ProducerConfig.LINGER_MS_CONFIG
+ " + " + ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG);
} else {
// override deliveryTimeoutMs default value to lingerMs + requestTimeoutMs for backward compatibility
deliveryTimeoutMs = lingerAndRequestTimeoutMs;
log.warn("{} should be equal to or larger than {} + {}. Setting it to {}.",
ProducerConfig.DELIVERY_TIMEOUT_MS_CONFIG, ProducerConfig.LINGER_MS_CONFIG,
ProducerConfig.REQUEST_TIMEOUT_MS_CONFIG, deliveryTimeoutMs);
}
}
return deliveryTimeoutMs;
}
private TransactionManager configureTransactionState(ProducerConfig config,
LogContext logContext) {
TransactionManager transactionManager = null;
if (config.getBoolean(ProducerConfig.ENABLE_IDEMPOTENCE_CONFIG)) {
final String transactionalId = config.getString(ProducerConfig.TRANSACTIONAL_ID_CONFIG);
final int transactionTimeoutMs = config.getInt(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG);
final long retryBackoffMs = config.getLong(ProducerConfig.RETRY_BACKOFF_MS_CONFIG);
transactionManager = new TransactionManager(
logContext,
transactionalId,
transactionTimeoutMs,
retryBackoffMs,
apiVersions
);
if (transactionManager.isTransactional())
log.info("Instantiated a transactional producer.");
else
log.info("Instantiated an idempotent producer.");
} else {
// ignore unretrieved configurations related to producer transaction
config.ignore(ProducerConfig.TRANSACTION_TIMEOUT_CONFIG);
}
return transactionManager;
}
/**
* Needs to be called before any other methods when the {@code transactional.id} is set in the configuration.
* This method does the following:
*
* - Ensures any transactions initiated by previous instances of the producer with the same
* {@code transactional.id} are completed. If the previous instance had failed with a transaction in
* progress, it will be aborted. If the last transaction had begun completion,
* but not yet finished, this method awaits its completion.
* - Gets the internal producer id and epoch, used in all future transactional
* messages issued by the producer.
*
* Note that this method will raise {@link TimeoutException} if the transactional state cannot
* be initialized before expiration of {@code max.block.ms}. Additionally, it will raise {@link InterruptException}
* if interrupted. It is safe to retry in either case, but once the transactional state has been successfully
* initialized, this method should no longer be used.
*
* @throws IllegalStateException if no {@code transactional.id} has been configured
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0)
* @throws org.apache.kafka.common.errors.AuthorizationException error indicating that the configured
* transactional.id is not authorized, or the idempotent producer id is unavailable. See the exception for
* more details. User may retry this function call after fixing the permission.
* @throws KafkaException if the producer has encountered a previous fatal error or for any other unexpected error
* @throws TimeoutException if the time taken for initialize the transaction has surpassed max.block.ms.
* @throws InterruptException if the thread is interrupted while blocked
*/
public void initTransactions() {
throwIfNoTransactionManager();
throwIfProducerClosed();
long now = time.nanoseconds();
TransactionalRequestResult result = transactionManager.initializeTransactions();
sender.wakeup();
result.await(maxBlockTimeMs, TimeUnit.MILLISECONDS);
producerMetrics.recordInit(time.nanoseconds() - now);
}
/**
* Should be called before the start of each new transaction. Note that prior to the first invocation
* of this method, you must invoke {@link #initTransactions()} exactly one time.
*
* @throws IllegalStateException if no {@code transactional.id} has been configured or if {@link #initTransactions()}
* has not yet been invoked
* @throws ProducerFencedException if another producer with the same transactional.id is active
* @throws org.apache.kafka.common.errors.InvalidProducerEpochException if the producer has attempted to produce with an old epoch
* to the partition leader. See the exception for more details
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0)
* @throws org.apache.kafka.common.errors.AuthorizationException fatal error indicating that the configured
* {@code transactional.id} is not authorized. See the exception for more details
* @throws KafkaException if the producer has encountered a previous fatal error or for any other unexpected error
*/
public void beginTransaction() throws ProducerFencedException {
throwIfNoTransactionManager();
throwIfProducerClosed();
long now = time.nanoseconds();
transactionManager.beginTransaction();
producerMetrics.recordBeginTxn(time.nanoseconds() - now);
}
/**
* Sends a list of specified offsets to the consumer group coordinator, and also marks
* those offsets as part of the current transaction. These offsets will be considered
* committed only if the transaction is committed successfully. The committed offset should
* be the next message your application will consume, i.e. lastProcessedMessageOffset + 1.
*
* This method should be used when you need to batch consumed and produced messages
* together, typically in a consume-transform-produce pattern. Thus, the specified
* {@code consumerGroupId} should be the same as config parameter {@code group.id} of the used
* {@link KafkaConsumer consumer}. Note, that the consumer should have {@code enable.auto.commit=false}
* and should also not commit offsets manually (via {@link KafkaConsumer#commitSync(Map) sync} or
* {@link KafkaConsumer#commitAsync(Map, OffsetCommitCallback) async} commits).
*
*
* This method is a blocking call that waits until the request has been received and acknowledged by the consumer group
* coordinator; but the offsets are not considered as committed until the transaction itself is successfully committed later (via
* the {@link #commitTransaction()} call).
*
* @throws IllegalStateException if no transactional.id has been configured, no transaction has been started
* @throws ProducerFencedException fatal error indicating another producer with the same transactional.id is active
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0)
* @throws org.apache.kafka.common.errors.UnsupportedForMessageFormatException fatal error indicating the message
* format used for the offsets topic on the broker does not support transactions
* @throws org.apache.kafka.common.errors.AuthorizationException fatal error indicating that the configured
* transactional.id is not authorized, or the consumer group id is not authorized.
* @throws org.apache.kafka.common.errors.InvalidProducerEpochException if the producer has attempted to produce with an old epoch
* to the partition leader. See the exception for more details
* @throws TimeoutException if the time taken for sending the offsets has surpassed max.block.ms.
* @throws KafkaException if the producer has encountered a previous fatal or abortable error, or for any
* other unexpected error
*
* @deprecated Since 3.0.0, please use {@link #sendOffsetsToTransaction(Map, ConsumerGroupMetadata)} instead.
*/
@Deprecated
public void sendOffsetsToTransaction(Map offsets,
String consumerGroupId) throws ProducerFencedException {
sendOffsetsToTransaction(offsets, new ConsumerGroupMetadata(consumerGroupId));
}
/**
* Sends a list of specified offsets to the consumer group coordinator, and also marks
* those offsets as part of the current transaction. These offsets will be considered
* committed only if the transaction is committed successfully. The committed offset should
* be the next message your application will consume, i.e. lastProcessedMessageOffset + 1.
*
* This method should be used when you need to batch consumed and produced messages
* together, typically in a consume-transform-produce pattern. Thus, the specified
* {@code groupMetadata} should be extracted from the used {@link KafkaConsumer consumer} via
* {@link KafkaConsumer#groupMetadata()} to leverage consumer group metadata. This will provide
* stronger fencing than just supplying the {@code consumerGroupId} and passing in {@code new ConsumerGroupMetadata(consumerGroupId)},
* however note that the full set of consumer group metadata returned by {@link KafkaConsumer#groupMetadata()}
* requires the brokers to be on version 2.5 or newer to understand.
*
*
* This method is a blocking call that waits until the request has been received and acknowledged by the consumer group
* coordinator; but the offsets are not considered as committed until the transaction itself is successfully committed later (via
* the {@link #commitTransaction()} call).
*
*
* Note, that the consumer should have {@code enable.auto.commit=false} and should
* also not commit offsets manually (via {@link KafkaConsumer#commitSync(Map) sync} or
* {@link KafkaConsumer#commitAsync(Map, OffsetCommitCallback) async} commits).
* This method will raise {@link TimeoutException} if the producer cannot send offsets before expiration of {@code max.block.ms}.
* Additionally, it will raise {@link InterruptException} if interrupted.
*
* @throws IllegalStateException if no transactional.id has been configured or no transaction has been started.
* @throws ProducerFencedException fatal error indicating another producer with the same transactional.id is active
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0) or
* the broker doesn't support the latest version of transactional API with all consumer group metadata
* (i.e. if its version is lower than 2.5.0).
* @throws org.apache.kafka.common.errors.UnsupportedForMessageFormatException fatal error indicating the message
* format used for the offsets topic on the broker does not support transactions
* @throws org.apache.kafka.common.errors.AuthorizationException fatal error indicating that the configured
* transactional.id is not authorized, or the consumer group id is not authorized.
* @throws org.apache.kafka.clients.consumer.CommitFailedException if the commit failed and cannot be retried
* (e.g. if the consumer has been kicked out of the group). Users should handle this by aborting the transaction.
* @throws org.apache.kafka.common.errors.FencedInstanceIdException if this producer instance gets fenced by broker due to a
* mis-configured consumer instance id within group metadata.
* @throws org.apache.kafka.common.errors.InvalidProducerEpochException if the producer has attempted to produce with an old epoch
* to the partition leader. See the exception for more details
* @throws KafkaException if the producer has encountered a previous fatal or abortable error, or for any
* other unexpected error
* @throws TimeoutException if the time taken for sending the offsets has surpassed max.block.ms.
* @throws InterruptException if the thread is interrupted while blocked
*/
public void sendOffsetsToTransaction(Map offsets,
ConsumerGroupMetadata groupMetadata) throws ProducerFencedException {
throwIfInvalidGroupMetadata(groupMetadata);
throwIfNoTransactionManager();
throwIfProducerClosed();
if (!offsets.isEmpty()) {
long start = time.nanoseconds();
TransactionalRequestResult result = transactionManager.sendOffsetsToTransaction(offsets, groupMetadata);
sender.wakeup();
result.await(maxBlockTimeMs, TimeUnit.MILLISECONDS);
producerMetrics.recordSendOffsets(time.nanoseconds() - start);
}
}
/**
* Commits the ongoing transaction. This method will flush any unsent records before actually committing the transaction.
*
* Further, if any of the {@link #send(ProducerRecord)} calls which were part of the transaction hit irrecoverable
* errors, this method will throw the last received exception immediately and the transaction will not be committed.
* So all {@link #send(ProducerRecord)} calls in a transaction must succeed in order for this method to succeed.
*
* If the transaction is committed successfully and this method returns without throwing an exception, it is guaranteed
* that all {@link Callback callbacks} for records in the transaction will have been invoked and completed.
* Note that exceptions thrown by callbacks are ignored; the producer proceeds to commit the transaction in any case.
*
* Note that this method will raise {@link TimeoutException} if the transaction cannot be committed before expiration
* of {@code max.block.ms}, but this does not mean the request did not actually reach the broker. In fact, it only indicates
* that we cannot get the acknowledgement response in time, so it's up to the application's logic
* to decide how to handle timeouts.
* Additionally, it will raise {@link InterruptException} if interrupted.
* It is safe to retry in either case, but it is not possible to attempt a different operation (such as abortTransaction)
* since the commit may already be in the progress of completing. If not retrying, the only option is to close the producer.
*
* @throws IllegalStateException if no transactional.id has been configured or no transaction has been started
* @throws ProducerFencedException fatal error indicating another producer with the same transactional.id is active
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0)
* @throws org.apache.kafka.common.errors.AuthorizationException fatal error indicating that the configured
* transactional.id is not authorized. See the exception for more details
* @throws org.apache.kafka.common.errors.InvalidProducerEpochException if the producer has attempted to produce with an old epoch
* to the partition leader. See the exception for more details
* @throws KafkaException if the producer has encountered a previous fatal or abortable error, or for any
* other unexpected error
* @throws TimeoutException if the time taken for committing the transaction has surpassed max.block.ms.
* @throws InterruptException if the thread is interrupted while blocked
*/
public void commitTransaction() throws ProducerFencedException {
throwIfNoTransactionManager();
throwIfProducerClosed();
long commitStart = time.nanoseconds();
TransactionalRequestResult result = transactionManager.beginCommit();
sender.wakeup();
result.await(maxBlockTimeMs, TimeUnit.MILLISECONDS);
producerMetrics.recordCommitTxn(time.nanoseconds() - commitStart);
}
/**
* Aborts the ongoing transaction. Any unflushed produce messages will be aborted when this call is made.
* This call will throw an exception immediately if any prior {@link #send(ProducerRecord)} calls failed with a
* {@link ProducerFencedException} or an instance of {@link org.apache.kafka.common.errors.AuthorizationException}.
*
* Note that this method will raise {@link TimeoutException} if the transaction cannot be aborted before expiration
* of {@code max.block.ms}, but this does not mean the request did not actually reach the broker. In fact, it only indicates
* that we cannot get the acknowledgement response in time, so it's up to the application's logic
* to decide how to handle timeouts. Additionally, it will raise {@link InterruptException} if interrupted.
* It is safe to retry in either case, but it is not possible to attempt a different operation (such as {@link #commitTransaction})
* since the abort may already be in the progress of completing. If not retrying, the only option is to close the producer.
*
* @throws IllegalStateException if no transactional.id has been configured or no transaction has been started
* @throws ProducerFencedException fatal error indicating another producer with the same transactional.id is active
* @throws org.apache.kafka.common.errors.InvalidProducerEpochException if the producer has attempted to produce with an old epoch
* to the partition leader. See the exception for more details
* @throws org.apache.kafka.common.errors.UnsupportedVersionException fatal error indicating the broker
* does not support transactions (i.e. if its version is lower than 0.11.0.0)
* @throws org.apache.kafka.common.errors.AuthorizationException fatal error indicating that the configured
* transactional.id is not authorized. See the exception for more details
* @throws KafkaException if the producer has encountered a previous fatal error or for any other unexpected error
* @throws TimeoutException if the time taken for aborting the transaction has surpassed max.block.ms.
* @throws InterruptException if the thread is interrupted while blocked
*/
public void abortTransaction() throws ProducerFencedException {
throwIfNoTransactionManager();
throwIfProducerClosed();
log.info("Aborting incomplete transaction");
long abortStart = time.nanoseconds();
TransactionalRequestResult result = transactionManager.beginAbort();
sender.wakeup();
result.await(maxBlockTimeMs, TimeUnit.MILLISECONDS);
producerMetrics.recordAbortTxn(time.nanoseconds() - abortStart);
}
/**
* 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 (except for rare cases described below)
* 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.
* Can block for the following cases: 1) For the first record being sent to
* the cluster by this client for the given topic. In this case it will block for up to {@code max.block.ms} milliseconds if
* Kafka cluster is unreachable; 2) Allocating a buffer if buffer pool doesn't have any free buffers.
*
* 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 the producer is configured with acks = 0, the {@link RecordMetadata}
* will have offset = -1 because the producer does not wait for the acknowledgement from the broker.
* 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();
* } else {
* 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);
* }
*
*
* When used as part of a transaction, it is not necessary to define a callback or check the result of the future
* in order to detect errors from send. If any of the send calls failed with an irrecoverable error,
* the final {@link #commitTransaction()} call will fail and throw the exception from the last failed send. When
* this happens, your application should call {@link #abortTransaction()} to reset the state and continue to send
* data.
*
*
* Some transactional send errors cannot be resolved with a call to {@link #abortTransaction()}. In particular,
* if a transactional send finishes with a {@link ProducerFencedException}, a {@link org.apache.kafka.common.errors.OutOfOrderSequenceException},
* a {@link org.apache.kafka.common.errors.UnsupportedVersionException}, or an
* {@link org.apache.kafka.common.errors.AuthorizationException}, then the only option left is to call {@link #close()}.
* Fatal errors cause the producer to enter a defunct state in which future API calls will continue to raise
* the same underlying error wrapped in a new {@link KafkaException}.
*
*
* It is a similar picture when idempotence is enabled, but no transactional.id has been configured.
* In this case, {@link org.apache.kafka.common.errors.UnsupportedVersionException} and
* {@link org.apache.kafka.common.errors.AuthorizationException} are considered fatal errors. However,
* {@link ProducerFencedException} does not need to be handled. Additionally, it is possible to continue
* sending after receiving an {@link org.apache.kafka.common.errors.OutOfOrderSequenceException}, but doing so
* can result in out of order delivery of pending messages. To ensure proper ordering, you should close the
* producer and create a new instance.
*
*
* If the message format of the destination topic is not upgraded to 0.11.0.0, idempotent and transactional
* produce requests will fail with an {@link org.apache.kafka.common.errors.UnsupportedForMessageFormatException}
* error. If this is encountered during a transaction, it is possible to abort and continue. But note that future
* sends to the same topic will continue receiving the same exception until the topic is upgraded.
*
*
* 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 AuthenticationException if authentication fails. See the exception for more details
* @throws AuthorizationException fatal error indicating that the producer is not allowed to write
* @throws IllegalStateException if a transactional.id has been configured and no transaction has been started, or
* when send is invoked after producer has been closed.
* @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 TimeoutException If the record could not be appended to the send buffer due to memory unavailable
* or missing metadata within {@code max.block.ms}.
* @throws KafkaException If a Kafka related error occurs that does not belong to the public API exceptions.
*/
@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.onSend(record);
return doSend(interceptedRecord, callback);
}
// Verify that this producer instance has not been closed. This method throws IllegalStateException if the producer
// has already been closed.
private void throwIfProducerClosed() {
if (sender == null || !sender.isRunning())
throw new IllegalStateException("Cannot perform operation after producer has been closed");
}
/**
* Call deprecated {@link Partitioner#onNewBatch}
*/
@SuppressWarnings("deprecation")
private void onNewBatch(String topic, Cluster cluster, int prevPartition) {
assert partitioner != null;
partitioner.onNewBatch(topic, cluster, prevPartition);
}
/**
* Implementation of asynchronously send a record to a topic.
*/
private Future doSend(ProducerRecord record, Callback callback) {
// Append callback takes care of the following:
// - call interceptors and user callback on completion
// - remember partition that is calculated in RecordAccumulator.append
AppendCallbacks appendCallbacks = new AppendCallbacks(callback, this.interceptors, record);
try {
throwIfProducerClosed();
// first make sure the metadata for the topic is available
long nowMs = time.milliseconds();
ClusterAndWaitTime clusterAndWaitTime;
try {
clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), nowMs, maxBlockTimeMs);
} catch (KafkaException e) {
if (metadata.isClosed())
throw new KafkaException("Producer closed while send in progress", e);
throw e;
}
nowMs += clusterAndWaitTime.waitedOnMetadataMs;
long remainingWaitMs = Math.max(0, maxBlockTimeMs - clusterAndWaitTime.waitedOnMetadataMs);
Cluster cluster = clusterAndWaitTime.cluster;
byte[] serializedKey;
try {
serializedKey = keySerializer.serialize(record.topic(), record.headers(), 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", cce);
}
byte[] serializedValue;
try {
serializedValue = valueSerializer.serialize(record.topic(), record.headers(), 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", cce);
}
// Try to calculate partition, but note that after this call it can be RecordMetadata.UNKNOWN_PARTITION,
// which means that the RecordAccumulator would pick a partition using built-in logic (which may
// take into account broker load, the amount of data produced to each partition, etc.).
int partition = partition(record, serializedKey, serializedValue, cluster);
setReadOnly(record.headers());
Header[] headers = record.headers().toArray();
int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
compression.type(), serializedKey, serializedValue, headers);
ensureValidRecordSize(serializedSize);
long timestamp = record.timestamp() == null ? nowMs : record.timestamp();
// A custom partitioner may take advantage on the onNewBatch callback.
boolean abortOnNewBatch = partitioner != null;
// Append the record to the accumulator. Note, that the actual partition may be
// calculated there and can be accessed via appendCallbacks.topicPartition.
RecordAccumulator.RecordAppendResult result = accumulator.append(record.topic(), partition, timestamp, serializedKey,
serializedValue, headers, appendCallbacks, remainingWaitMs, abortOnNewBatch, nowMs, cluster);
assert appendCallbacks.getPartition() != RecordMetadata.UNKNOWN_PARTITION;
if (result.abortForNewBatch) {
int prevPartition = partition;
onNewBatch(record.topic(), cluster, prevPartition);
partition = partition(record, serializedKey, serializedValue, cluster);
if (log.isTraceEnabled()) {
log.trace("Retrying append due to new batch creation for topic {} partition {}. The old partition was {}", record.topic(), partition, prevPartition);
}
result = accumulator.append(record.topic(), partition, timestamp, serializedKey,
serializedValue, headers, appendCallbacks, remainingWaitMs, false, nowMs, cluster);
}
// Add the partition to the transaction (if in progress) after it has been successfully
// appended to the accumulator. We cannot do it before because the partition may be
// unknown or the initially selected partition may be changed when the batch is closed
// (as indicated by `abortForNewBatch`). Note that the `Sender` will refuse to dequeue
// batches from the accumulator until they have been added to the transaction.
if (transactionManager != null) {
transactionManager.maybeAddPartition(appendCallbacks.topicPartition());
}
if (result.batchIsFull || result.newBatchCreated) {
log.trace("Waking up the sender since topic {} partition {} is either full or getting a new batch", record.topic(), appendCallbacks.getPartition());
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) {
TopicPartition tp = appendCallbacks.topicPartition();
RecordMetadata nullMetadata = new RecordMetadata(tp, -1, -1, RecordBatch.NO_TIMESTAMP, -1, -1);
callback.onCompletion(nullMetadata, e);
}
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
if (transactionManager != null) {
transactionManager.maybeTransitionToErrorState(e);
}
return new FutureFailure(e);
} catch (InterruptedException e) {
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw new InterruptException(e);
} catch (KafkaException e) {
this.errors.record();
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw e;
} catch (Exception e) {
// we notify interceptor about all exceptions, since onSend is called before anything else in this method
this.interceptors.onSendError(record, appendCallbacks.topicPartition(), e);
throw e;
}
}
private void setReadOnly(Headers headers) {
if (headers instanceof RecordHeaders) {
((RecordHeaders) headers).setReadOnly();
}
}
/**
* Wait for cluster metadata including partitions for the given topic to be available.
* @param topic The topic we want metadata for
* @param partition A specific partition expected to exist in metadata, or null if there's no preference
* @param nowMs The current time in ms
* @param maxWaitMs The maximum time in ms for waiting on the metadata
* @return The cluster containing topic metadata and the amount of time we waited in ms
* @throws TimeoutException if metadata could not be refreshed within {@code max.block.ms}
* @throws KafkaException for all Kafka-related exceptions, including the case where this method is called after producer close
*/
private ClusterAndWaitTime waitOnMetadata(String topic, Integer partition, long nowMs, long maxWaitMs) throws InterruptedException {
Cluster cluster = metadata.fetch();
if (cluster.invalidTopics().contains(topic))
throw new InvalidTopicException(topic);
// add topic to metadata topic list if it is not there already and reset expiry
metadata.add(topic, nowMs);
Integer partitionsCount = cluster.partitionCountForTopic(topic);
// Return cached metadata if we have it, and if the record's partition is either undefined
// or within the known partition range
if (partitionsCount != null && (partition == null || partition < partitionsCount))
return new ClusterAndWaitTime(cluster, 0);
long remainingWaitMs = maxWaitMs;
long elapsed = 0;
// Issue metadata requests until we have metadata for the topic and the requested partition,
// or until maxWaitTimeMs is exceeded. This is necessary in case the metadata
// is stale and the number of partitions for this topic has increased in the meantime.
long nowNanos = time.nanoseconds();
do {
if (partition != null) {
log.trace("Requesting metadata update for partition {} of topic {}.", partition, topic);
} else {
log.trace("Requesting metadata update for topic {}.", topic);
}
metadata.add(topic, nowMs + elapsed);
int version = metadata.requestUpdateForTopic(topic);
sender.wakeup();
try {
metadata.awaitUpdate(version, remainingWaitMs);
} catch (TimeoutException ex) {
// Rethrow with original maxWaitMs to prevent logging exception with remainingWaitMs
throw new TimeoutException(
String.format("Topic %s not present in metadata after %d ms.",
topic, maxWaitMs));
}
cluster = metadata.fetch();
elapsed = time.milliseconds() - nowMs;
if (elapsed >= maxWaitMs) {
throw new TimeoutException(partitionsCount == null ?
String.format("Topic %s not present in metadata after %d ms.",
topic, maxWaitMs) :
String.format("Partition %d of topic %s with partition count %d is not present in metadata after %d ms.",
partition, topic, partitionsCount, maxWaitMs));
}
metadata.maybeThrowExceptionForTopic(topic);
remainingWaitMs = maxWaitMs - elapsed;
partitionsCount = cluster.partitionCountForTopic(topic);
} while (partitionsCount == null || (partition != null && partition >= partitionsCount));
producerMetrics.recordMetadataWait(time.nanoseconds() - nowNanos);
return new ClusterAndWaitTime(cluster, elapsed);
}
/**
* Validate that the record size isn't too large
*/
private void ensureValidRecordSize(int size) {
if (size > maxRequestSize)
throw new RecordTooLargeException("The message is " + size +
" bytes when serialized which is larger than " + maxRequestSize + ", which is the value of the " +
ProducerConfig.MAX_REQUEST_SIZE_CONFIG + " configuration.");
if (size > 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.commitSync();
* }
*
*
* 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.
*
*
* Applications don't need to call this method for transactional producers, since the {@link #commitTransaction()} will
* flush all buffered records before performing the commit. This ensures that all the {@link #send(ProducerRecord)}
* calls made since the previous {@link #beginTransaction()} are completed before the commit.
*
*
* @throws InterruptException If the thread is interrupted while blocked
*/
@Override
public void flush() {
log.trace("Flushing accumulated records in producer.");
long start = time.nanoseconds();
this.accumulator.beginFlush();
this.sender.wakeup();
try {
this.accumulator.awaitFlushCompletion();
} catch (InterruptedException e) {
throw new InterruptException("Flush interrupted.", e);
} finally {
producerMetrics.recordFlush(time.nanoseconds() - start);
}
}
/**
* Get the partition metadata for the given topic. This can be used for custom partitioning.
* @throws AuthenticationException if authentication fails. See the exception for more details
* @throws AuthorizationException if not authorized to the specified topic. See the exception for more details
* @throws InterruptException if the thread is interrupted while blocked
* @throws TimeoutException if metadata could not be refreshed within {@code max.block.ms}
* @throws KafkaException for all Kafka-related exceptions, including the case where this method is called after producer close
*/
@Override
public List partitionsFor(String topic) {
Objects.requireNonNull(topic, "topic cannot be null");
try {
return waitOnMetadata(topic, null, time.milliseconds(), maxBlockTimeMs).cluster.partitionsForTopic(topic);
} catch (InterruptedException e) {
throw new InterruptException(e);
}
}
/**
* Get the full set of internal metrics maintained by the producer.
*/
@Override
public Map metrics() {
return Collections.unmodifiableMap(this.metrics.metrics());
}
/**
* Determines the client's unique client instance ID used for telemetry. This ID is unique to
* this specific client instance and will not change after it is initially generated.
* The ID is useful for correlating client operations with telemetry sent to the broker and
* to its eventual monitoring destinations.
*
* If telemetry is enabled, this will first require a connection to the cluster to generate
* the unique client instance ID. This method waits up to {@code timeout} for the producer
* client to complete the request.
*
* Client telemetry is controlled by the {@link ProducerConfig#ENABLE_METRICS_PUSH_CONFIG}
* configuration option.
*
* @param timeout The maximum time to wait for producer 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
* producer 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) {
if (!clientTelemetryReporter.isPresent()) {
throw new IllegalStateException("Telemetry is not enabled. Set config `" + ProducerConfig.ENABLE_METRICS_PUSH_CONFIG + "` to `true`.");
}
return ClientTelemetryUtils.fetchClientInstanceId(clientTelemetryReporter.get(), timeout);
}
/**
* 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.
* @throws KafkaException If an unexpected error occurs while trying to close the client, this error should be treated
* as fatal and indicate the client is no longer usable.
*/
@Override
public void close() {
close(Duration.ofMillis(Long.MAX_VALUE));
}
/**
* 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. It will also abort the ongoing transaction if it's not
* already completing.
*
* If invoked from within a {@link Callback} this method will not block and will be equivalent to
* close(Duration.ofMillis(0)). 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.
* @throws InterruptException If the thread is interrupted while blocked.
* @throws KafkaException If an unexpected error occurs while trying to close the client, this error should be treated
* as fatal and indicate the client is no longer usable.
* @throws IllegalArgumentException If the timeout is negative.
*
*/
@Override
public void close(Duration timeout) {
close(timeout, false);
}
private void close(Duration timeout, boolean swallowException) {
long timeoutMs = timeout.toMillis();
if (timeoutMs < 0)
throw new IllegalArgumentException("The timeout cannot be negative.");
log.info("Closing the Kafka producer with timeoutMillis = {} ms.", timeoutMs);
// this will keep track of the first encountered exception
AtomicReference firstException = new AtomicReference<>();
boolean invokedFromCallback = Thread.currentThread() == this.ioThread;
if (timeoutMs > 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.",
timeoutMs);
} else {
// Try to close gracefully.
final Timer closeTimer = time.timer(timeout);
if (this.sender != null) {
this.sender.initiateClose();
closeTimer.update();
}
if (this.ioThread != null) {
try {
this.ioThread.join(closeTimer.remainingMs());
} catch (InterruptedException t) {
firstException.compareAndSet(null, new InterruptException(t));
log.error("Interrupted while joining ioThread", t);
} finally {
closeTimer.update();
}
}
clientTelemetryReporter.ifPresent(reporter -> reporter.initiateClose(closeTimer.remainingMs()));
}
}
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.", timeoutMs);
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, new InterruptException(e));
}
}
}
Utils.closeQuietly(interceptors, "producer interceptors", firstException);
Utils.closeQuietly(producerMetrics, "producer metrics wrapper", firstException);
Utils.closeQuietly(metrics, "producer metrics", firstException);
Utils.closeQuietly(keySerializer, "producer keySerializer", firstException);
Utils.closeQuietly(valueSerializer, "producer valueSerializer", firstException);
Utils.closeQuietly(partitioner, "producer partitioner", firstException);
clientTelemetryReporter.ifPresent(reporter -> Utils.closeQuietly(reporter, "producer telemetry reporter", firstException));
AppInfoParser.unregisterAppInfo(JMX_PREFIX, clientId, metrics);
Throwable exception = firstException.get();
if (exception != null && !swallowException) {
if (exception instanceof InterruptException) {
throw (InterruptException) exception;
}
throw new KafkaException("Failed to close kafka producer", exception);
}
log.debug("Kafka producer has been closed");
}
/**
* computes partition for given record.
* if the record has partition returns the value otherwise
* if custom partitioner is specified, call it to compute partition
* otherwise try to calculate partition based on key.
* If there is no key or key should be ignored return
* RecordMetadata.UNKNOWN_PARTITION to indicate any partition
* can be used (the partition is then calculated by built-in
* partitioning logic).
*/
private int partition(ProducerRecord record, byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
if (record.partition() != null)
return record.partition();
if (partitioner != null) {
int customPartition = partitioner.partition(
record.topic(), record.key(), serializedKey, record.value(), serializedValue, cluster);
if (customPartition < 0) {
throw new IllegalArgumentException(String.format(
"The partitioner generated an invalid partition number: %d. Partition number should always be non-negative.", customPartition));
}
return customPartition;
}
if (serializedKey != null && !partitionerIgnoreKeys) {
// hash the keyBytes to choose a partition
return BuiltInPartitioner.partitionForKey(serializedKey, cluster.partitionsForTopic(record.topic()).size());
} else {
return RecordMetadata.UNKNOWN_PARTITION;
}
}
private void throwIfInvalidGroupMetadata(ConsumerGroupMetadata groupMetadata) {
if (groupMetadata == null) {
throw new IllegalArgumentException("Consumer group metadata could not be null");
} else if (groupMetadata.generationId() > 0
&& JoinGroupRequest.UNKNOWN_MEMBER_ID.equals(groupMetadata.memberId())) {
throw new IllegalArgumentException("Passed in group metadata " + groupMetadata + " has generationId > 0 but member.id ");
}
}
private void throwIfNoTransactionManager() {
if (transactionManager == null)
throw new IllegalStateException("Cannot use transactional methods without enabling transactions " +
"by setting the " + ProducerConfig.TRANSACTIONAL_ID_CONFIG + " configuration property");
}
// Visible for testing
String getClientId() {
return clientId;
}
private static class ClusterAndWaitTime {
final Cluster cluster;
final long waitedOnMetadataMs;
ClusterAndWaitTime(Cluster cluster, long waitedOnMetadataMs) {
this.cluster = cluster;
this.waitedOnMetadataMs = waitedOnMetadataMs;
}
}
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;
}
}
/**
* Callbacks that are called by the RecordAccumulator append functions:
* - user callback
* - interceptor callbacks
* - partition callback
*/
private class AppendCallbacks implements RecordAccumulator.AppendCallbacks {
private final Callback userCallback;
private final ProducerInterceptors interceptors;
private final String topic;
private final Integer recordPartition;
private final String recordLogString;
private volatile int partition = RecordMetadata.UNKNOWN_PARTITION;
private volatile TopicPartition topicPartition;
private AppendCallbacks(Callback userCallback, ProducerInterceptors interceptors, ProducerRecord record) {
this.userCallback = userCallback;
this.interceptors = interceptors;
// Extract record info as we don't want to keep a reference to the record during
// whole lifetime of the batch.
// We don't want to have an NPE here, because the interceptors would not be notified (see .doSend).
topic = record != null ? record.topic() : null;
recordPartition = record != null ? record.partition() : null;
recordLogString = log.isTraceEnabled() && record != null ? record.toString() : "";
}
@Override
public void onCompletion(RecordMetadata metadata, Exception exception) {
if (metadata == null) {
metadata = new RecordMetadata(topicPartition(), -1, -1, RecordBatch.NO_TIMESTAMP, -1, -1);
}
this.interceptors.onAcknowledgement(metadata, exception);
if (this.userCallback != null)
this.userCallback.onCompletion(metadata, exception);
}
@Override
public void setPartition(int partition) {
assert partition != RecordMetadata.UNKNOWN_PARTITION;
this.partition = partition;
if (log.isTraceEnabled()) {
// Log the message here, because we don't know the partition before that.
log.trace("Attempting to append record {} with callback {} to topic {} partition {}", recordLogString, userCallback, topic, partition);
}
}
public int getPartition() {
return partition;
}
public TopicPartition topicPartition() {
if (topicPartition == null && topic != null) {
if (partition != RecordMetadata.UNKNOWN_PARTITION)
topicPartition = new TopicPartition(topic, partition);
else if (recordPartition != null)
topicPartition = new TopicPartition(topic, recordPartition);
else
topicPartition = new TopicPartition(topic, RecordMetadata.UNKNOWN_PARTITION);
}
return topicPartition;
}
}
}