kafka负载均衡相关资料收集（二）

【转】关于kafka producer 分区策略的思考

from：http://blog.csdn.net/ouyang111222/article/details/51086037

今天跑了一个简单的kafka produce程序，如下所示

public class kafkaProducer  extends Thread{

    private String topic;  

    public kafkaProducer(String topic){  
        super();  
        this.topic = topic;  
    }  


    @Override  
    public void run() {  
        Producer producer = createProducer();  
        int i=0;  
        while(true){
            i++;
            String  string = "hello"+i;
            producer.send(new KeyedMessage<Integer, String>(topic,string));  
            if(i==100){
                break;
            }
            try {  
                TimeUnit.SECONDS.sleep(1);  
            } catch (InterruptedException e) {  
                e.printStackTrace();  
            }  
        }  
    }  

    private Producer createProducer() {  
        Properties properties = new Properties();  
        properties.put("serializer.class", StringEncoder.class.getName());  
        properties.put("metadata.broker.list", "ip1:9092,ip2:9092,ip3:9092");
        return new Producer<Integer, String>(new ProducerConfig(properties));  
     }  


    public static void main(String[] args) {  
        new kafkaProducer("user11").start();  
    } 
} 

发现其只向topic：user11中的某一个partiton中写数据。一下子感觉不对啊，kafka不是号称可以实现producer的消息均发吗？后来查了一下相关的参数：partitioner.class

1 partitioner.class

# 分区的策略
# 默认为kafka.producer.DefaultPartitioner，取模
partitioner.class = kafka.producer.DefaultPartitioner

在上面的程序中，我在producer中没有定义分区策略，也就是说程序采用默认的kafka.producer.DefaultPartitioner，来看看源码中是怎么定义的：

class DefaultPartitioner(props: VerifiableProperties = null) extends Partitioner {
  private val random = new java.util.Random

  def partition(key: Any, numPartitions: Int): Int = {
    Utils.abs(key.hashCode) % numPartitions
  }
}

其核心思想就是对每个消息的key的hash值对partition数取模得到。再来看看我的程序中有这么一段：

producer.send(new KeyedMessage<Integer, String>(topic,string))

来看看keyMessage:

case class KeyedMessage[K, V](val topic: String, val key: K, val partKey: Any, val message: V) {
  if(topic == null)
    throw new IllegalArgumentException("Topic cannot be null.")

  def this(topic: String, message: V) = this(topic, null.asInstanceOf[K], null, message)

  def this(topic: String, key: K, message: V) = this(topic, key, key, message)

  def partitionKey = {
    if(partKey != null)
      partKey
    else if(hasKey)
      key
    else
      null  
  }

  def hasKey = key != null
}

由于上面生产者代码中没有传入key，所以程序调用：

def this(topic: String, message: V) = this(topic, null.asInstanceOf[K], null, message)

但是如果key为null时会发送到哪个分区？我在实验的时候发现，每次运行生产者线程好像发送的分区都不太相同。具体的解释可以参考博文：http://colobu.com/2015/01/22/which-kafka-partition-will-keyedMessages-be-sent-to-if-key-is-null/

好的问题发现了该怎么解决呢？只需要在生产者线程中对每条消息指定key，如下：

producer.send(new KeyedMessage<String, String>(topic,String.valueOf(i),string)); 

看看效果： 

2 自定义partitioner.class

如下所示为自定义的分区函数，分区函数实现了Partitioner接口

public class PersonalPartition implements Partitioner{

    public PersonalPartition(VerifiableProperties properties){

    }

    public int partition(Object arg0, int arg1) {
        if(arg0==null){
            return 0;
        }
        else{
            return 1;
        }   
    }

}

然后修改配置即可：

 properties.put("partitioner.class", "com.xx.kafka.PersonalPartition"); 

3 向指定的partition写入数据

当然，也可以向topic中指定的partition中写数据，如下代码为向”user11”中partition 1中写入数据：

public class kafkaProducer  extends Thread{

    private String topic;  

    public kafkaProducer(String topic){  
        super();  
        this.topic = topic;  
    }  

    @Override  
    public void run() {  
        KafkaProducer producer = createProducer();  
        int i=0;  
        while(true){
            i++;
            String  string = "hello"+i;
            producer.send(new ProducerRecord(topic,1,null,string.getBytes()));
            if(i==10000){
                break;
            }
            try {  
                TimeUnit.SECONDS.sleep(1);  
            } catch (InterruptedException e) {  
                e.printStackTrace();  
            }  
        }  
    }  

    private KafkaProducer createProducer() {  
        Properties properties = new Properties();  
        properties.put("serializer.class", StringEncoder.class.getName());  
        properties.put("metadata.broker.list", "ip1:9092,ip2:9092,ip3:9092");
        return new KafkaProducer(properties);  
     } 
 } 

其结果如下：