这里看一下kafka Java API里分区的策略，然后自定义一个分区器。Kafka版本：2.11
1.默认分区：
在Kafka生产者的send函数里会调用分区函数：
Producer<String, String> procuder = new KafkaProducer<String,String>(props);
//ProducerRecord<String, String> record = new ProducerRecord<>("ly","","");
for (int i = 0;i < 30;i++) {
String value = "value_" + i;
ProducerRecord<String, String> msg = new ProducerRecord<String, String>(topic, value);
procuder.send(msg);
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}这是我们一般调用生产者send消息的用法。接着看一下send函数：
@Override
public Future<RecordMetadata> send(ProducerRecord<K, V> record) {
return send(record, null);
} @Override
public Future<RecordMetadata> send(ProducerRecord<K, V> record, Callback callback) {
// intercept the record, which can be potentially modified; this method does not throw exceptions
ProducerRecord<K, V> interceptedRecord = this.interceptors.onSend(record);
return doSend(interceptedRecord, callback);
}看下doSend函数：
private Future<RecordMetadata> doSend(ProducerRecord<K, V> record, Callback callback) {
TopicPartition tp = null;
try {
// first make sure the metadata for the topic is available
ClusterAndWaitTime clusterAndWaitTime = waitOnMetadata(record.topic(), record.partition(), maxBlockTimeMs);
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);
}
int partition = partition(record, serializedKey, serializedValue, cluster);
tp = new TopicPartition(record.topic(), partition);
setReadOnly(record.headers());
Header[] headers = record.headers().toArray();
int serializedSize = AbstractRecords.estimateSizeInBytesUpperBound(apiVersions.maxUsableProduceMagic(),
compressionType, serializedKey, serializedValue, headers);
ensureValidRecordSize(serializedSize);
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 = new InterceptorCallback<>(callback, this.interceptors, tp);
if (transactionManager != null && transactionManager.isTransactional())
transactionManager.maybeAddPartitionToTransaction(tp);
RecordAccumulator.RecordAppendResult result = accumulator.append(tp, timestamp, serializedKey,
serializedValue, headers, 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();
this.interceptors.onSendError(record, tp, e);
return new FutureFailure(e);
} catch (InterruptedException e) {
this.errors.record();
this.interceptors.onSendError(record, tp, e);
throw new InterruptException(e);
} catch (BufferExhaustedException e) {
this.errors.record();
this.metrics.sensor("buffer-exhausted-records").record();
this.interceptors.onSendError(record, tp, e);
throw e;
} catch (KafkaException e) {
this.errors.record();
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
this.interceptors.onSendError(record, tp, e);
throw e;
}
}这里会先计算出key和value的序列化值，然后调用：
int partition = partition(record, serializedKey, serializedValue, cluster); /**
* 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<K, V> record, byte[] serializedKey, byte[] serializedValue, Cluster cluster) {
Integer partition = record.partition();
return partition != null ?
partition :
partitioner.partition(
record.topic(), record.key(), serializedKey, record.value(), serializedValue, cluster);
}record的构造函数提供了指定分区的版本：
/**
* Creates a record to be sent to a specified topic and partition
*
* @param topic The topic the record will be appended to
* @param partition The partition to which the record should be sent
* @param key The key that will be included in the record
* @param value The record contents
*/
public ProducerRecord(String topic, Integer partition, K key, V value) {
this(topic, partition, null, key, value, null);
}私有partition函数会优先取record中指定的分区，如果不存在，再使用分区策略。
分区接口：
public interface Partitioner extends Configurable, Closeable {
/**
* Compute the partition for the given record.
*
* @param topic The topic name
* @param key The key to partition on (or null if no key)
* @param keyBytes The serialized key to partition on( or null if no key)
* @param value The value to partition on or null
* @param valueBytes The serialized value to partition on or null
* @param cluster The current cluster metadata
*/
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster);
/**
* This is called when partitioner is closed.
*/
public void close();
}该接口传递了key和value的原值以及序列化之后的值，这都是我们可以用于分区的标准。有一个default的实现类：
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package org.apache.kafka.clients.producer.internals;
import java.util.List;
import java.util.Map;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.concurrent.ThreadLocalRandom;
import java.util.concurrent.atomic.AtomicInteger;
import org.apache.kafka.clients.producer.Partitioner;
import org.apache.kafka.common.Cluster;
import org.apache.kafka.common.PartitionInfo;
import org.apache.kafka.common.utils.Utils;
/**
* The default partitioning strategy:
* <ul>
* <li>If a partition is specified in the record, use it
* <li>If no partition is specified but a key is present choose a partition based on a hash of the key
* <li>If no partition or key is present choose a partition in a round-robin fashion
*/
public class DefaultPartitioner implements Partitioner {
private final ConcurrentMap<String, AtomicInteger> topicCounterMap = new ConcurrentHashMap<>();
public void configure(Map<String, ?> configs) {}
/**
* Compute the partition for the given record.
*
* @param topic The topic name
* @param key The key to partition on (or null if no key)
* @param keyBytes serialized key to partition on (or null if no key)
* @param value The value to partition on or null
* @param valueBytes serialized value to partition on or null
* @param cluster The current cluster metadata
*/
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
int numPartitions = partitions.size();
if (keyBytes == null) {
int nextValue = nextValue(topic);
List<PartitionInfo> availablePartitions = cluster.availablePartitionsForTopic(topic);
if (availablePartitions.size() > 0) {
int part = Utils.toPositive(nextValue) % availablePartitions.size();
return availablePartitions.get(part).partition();
} else {
// no partitions are available, give a non-available partition
return Utils.toPositive(nextValue) % numPartitions;
}
} else {
// hash the keyBytes to choose a partition
return Utils.toPositive(Utils.murmur2(keyBytes)) % numPartitions;
}
}
private int nextValue(String topic) {
AtomicInteger counter = topicCounterMap.get(topic);
if (null == counter) {
counter = new AtomicInteger(ThreadLocalRandom.current().nextInt());
AtomicInteger currentCounter = topicCounterMap.putIfAbsent(topic, counter);
if (currentCounter != null) {
counter = currentCounter;
}
}
return counter.getAndIncrement();
}
public void close() {}
}
这里使用的是key的序列化以后的值。如果指定了key，那么就为该topic初始化一个值，然后每一次累加1，与size取摸后作为分区索引。如果没有指定key，那么机会随机生成一个数，与size取摸后做索引。这里的size会自动计算出合法的分区值，这与broker配置的分区数目有关。假设broker一共配置了n个分区，那么分区的索引范围为：[0,n)。其他的分区索引是不合法的。如果使用了不合法的分区所引会报错。并且kafka的broker一旦配置了分区数目且启动一次，那么分区数目则无法通过修改broker配置文件并重启来改变，必须在zk中删除原有的数据，比如zk 的data文件夹，再重启才可以修改分区。
2.自定义分区器：
我们只需要实现partition接口即可。同时在生产者的配置文件中设置。
public class DPartitioner implements Partitioner {
@Override
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
if("p0".equals(key))
return 0;
else
return 1;
}
@Override
public void close() {
}
@Override
public void configure(Map<String, ?> configs) {
}
}这里很简单，根据key的原值来做分区，如果key是p0，则返回分区0。否则返回分区1。
生产者：
package com.liyao.kafka_T;
import java.util.Properties;
import java.util.concurrent.TimeUnit;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
public class Pros {
public static String topic = "parTopic";
public static void main(String[] args) {
Properties props = new Properties();
props.put("bootstrap.servers", "127.0.0.1:9096,127.0.0.1:9097");
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("partitioner.class", "com.liyao.kafka_T.DPartitioner");
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
//生产者发送消息
Producer<String, String> procuder = new KafkaProducer<String,String>(props);
//ProducerRecord<String, String> record = new ProducerRecord<>("ly","","");
for (int i = 0;i < 30;i++) {
String value = "value_" + i;
ProducerRecord<String, String> msg = new ProducerRecord<String, String>(topic, "p0", value);
procuder.send(msg);
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
for (int i = 0;i < 3;i++) {
String value = "value_xxx_" + i;
ProducerRecord<String, String> msg = new ProducerRecord<String, String>(topic, "xxxx", value);
procuder.send(msg);
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
}消费者：
package com.liyao.kafka_T;
import kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import java.util.*;
public class Cons {
public void f1(){
Properties props = new Properties();
props.put("bootstrap.servers", "127.0.0.1:9096");
props.put("group.id", "g6");
props.put("zookeeper.session.timeout.ms", "400");
props.put("zookeeper.sync.time.ms", "200");
props.put("auto.commit.interval.ms", "1000");
props.put("auto.offset.reset", "earliest");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
consumer.subscribe(Arrays.asList(Pros.topic));
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records)
System.out.printf("partition = %d, offset = %d, key = %s, value = %s%n", record.partition(), record.offset(), record.key(), record.value());
}
}
public static void main(String args[]){
new Cons().f1();
}
}
结果：
可以看到分区生效了。