datahub于kafka区别

 kafka的客户端也支持其他语言，这里主要介绍python和java的实现，这两门语言比较主流和热门

图中有四个分区，每个图形对应一个consumer，任意一对一即可

获取topic的分区数，每个分区创建一个进程消费分区中的数据。

每个进程的实例中，先要创建连接kafka的实例，然后指定连接到哪个topic（主图），哪个分区

之后要设置kafka的偏移量，kafka中每条消息都有偏移量，如果消费者突然宕机了，则可以从上个偏移量继续消费

提交偏移量的工作客户端都会默认操作，因此提交偏移量可选

后续会根据伪代码描述编写程序

GroupA和GourpB都能拿到当前topic的全部数据，组消费可以复制消费，即kafka会复制消息分别发送给组A和组B

流数N指代每个Gourp中有都少个consumer，上图中A有2个流，B有4个流

每个consumer实力也需要创建连接kafka的实例，设置连接到哪个topic和分区

也可以设置偏移量，与分区消费一样

按组消费可以选择从头消费还是从最新消费

PT代表topic T下的所有分区，CG代表Group中有多少个consumer实例

排序分区parition，排序consumer

对于前面的例子GourpA，就是PT=4，CG=2，所以N等于2

 

分区模式中，所有生产者也默认至少发送一次消息，但是可以自定义发送一次接受一次，或者只发送一次不管是否接收

kafka版本与服务器一致即可

 

pom文件如下

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
  <modelVersion>4.0.0</modelVersion>

  <groupId>com.jike.kafkatest</groupId>
  <artifactId>JikeKafka</artifactId>
  <version>1.0</version>
  <packaging>jar</packaging>

  <name>JikeKafka</name>
  <url>http://maven.apache.org</url>

  <properties>
    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
  </properties>

  <dependencies>
    <dependency>
      <groupId>junit</groupId>
      <artifactId>junit</artifactId>
      <version>3.8.1</version>
      <scope>test</scope>
    </dependency>
    <dependency>
      <groupId>org.apache.kafka</groupId>
      <artifactId>kafka_2.9.2</artifactId>
      <version>0.8.1.1</version>
      <exclusions>
       <exclusion>
        <artifactId>jmxri</artifactId>
        <groupId>com.sun.jmx</groupId>
       </exclusion>
       <exclusion>
        <artifactId>jms</artifactId>
        <groupId>javax.jms</groupId>
       </exclusion>
       <exclusion>
        <artifactId>jmxtools</artifactId>
        <groupId>com.sun.jdmk</groupId>
       </exclusion>
      </exclusions>
    </dependency>
    <dependency>
        <groupId>org.apache.avro</groupId>
        <artifactId>avro</artifactId>
        <version>1.7.3</version>
    </dependency>
    <dependency>
        <groupId>org.apache.avro</groupId>
        <artifactId>avro-ipc</artifactId>
        <version>1.7.3</version>
    </dependency>
  </dependencies>
  <build>
    <sourceDirectory>src/main/java</sourceDirectory>
    <testSourceDirectory>src/test/java</testSourceDirectory>
    <plugins>
      <!--
        Bind the maven-assembly-plugin to the package phase
        this will create a jar file without the storm dependencies
        suitable for deployment to a cluster.
       -->
      <plugin>
        <artifactId>maven-assembly-plugin</artifactId>
        <configuration>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
          <archive>
            <manifest>
              <mainClass></mainClass>
            </manifest>
          </archive>
        </configuration>
        <executions>
          <execution>
            <id>make-assembly</id>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
    </plugin>  
    </plugins>
  </build> 
</project>

分组模式下Java代码如下：

package kafka.consumer.group;

import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
 
public class ConsumerTest implements Runnable {
    private KafkaStream m_stream;
    private int m_threadNumber;
 
    public ConsumerTest(KafkaStream a_stream, int a_threadNumber) {
        m_threadNumber = a_threadNumber;
        m_stream = a_stream;
    }
 
    public void run() {
        ConsumerIterator<byte[], byte[]> it = m_stream.iterator();
        while (it.hasNext()){
            System.out.println("Thread " + m_threadNumber + ": " + new String(it.next().message()));
            
        }
        System.out.println("Shutting down Thread: " + m_threadNumber);
    }
}

package kafka.consumer.group;

import kafka.consumer.ConsumerConfig;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
 
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;

public class GroupConsumerTest extends Thread {
    private final ConsumerConnector consumer;
    private final String topic;
    private  ExecutorService executor;
    
    public GroupConsumerTest(String a_zookeeper, String a_groupId, String a_topic){
        consumer = kafka.consumer.Consumer.createJavaConsumerConnector(
                createConsumerConfig(a_zookeeper, a_groupId));
        this.topic = a_topic;
    }
    
    public void shutdown() {
        if (consumer != null) consumer.shutdown();
        if (executor != null) executor.shutdown();
        try {
            if (!executor.awaitTermination(Long.MAX_VALUE, TimeUnit.MILLISECONDS)) {
                System.out.println("Timed out waiting for consumer threads to shut down, exiting uncleanly");
            }
        } catch (InterruptedException e) {
            System.out.println("Interrupted during shutdown, exiting uncleanly");
        }
   }
 
    public void run(int a_numThreads) {
        Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
        topicCountMap.put(topic, new Integer(a_numThreads));
        Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer.createMessageStreams(topicCountMap);
        List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);
 
        // now launch all the threads
        //
        executor = Executors.newFixedThreadPool(a_numThreads);
 
        // now create an object to consume the messages
        //
        int threadNumber = 0;
        for (final KafkaStream stream : streams) {
            executor.submit(new ConsumerTest(stream, threadNumber));
            threadNumber++;
        }
    }
    private static ConsumerConfig createConsumerConfig(String a_zookeeper, String a_groupId) {
        Properties props = new Properties();
        props.put("zookeeper.connect", a_zookeeper);
        props.put("group.id", a_groupId);
        props.put("zookeeper.session.timeout.ms", "40000");
        props.put("zookeeper.sync.time.ms", "2000");
        props.put("auto.commit.interval.ms", "1000");
 
        return new ConsumerConfig(props);
    }
    
    public static void main(String[] args) {
        if(args.length < 1){
            System.out.println("Please assign partition number.");
        }
        
        String zooKeeper = "10.206.216.13:12181,10.206.212.14:12181,10.206.209.25:12181";
        String groupId = "jikegrouptest";
        String topic = "jiketest";
        int threads = Integer.parseInt(args[0]);
 
        GroupConsumerTest example = new GroupConsumerTest(zooKeeper, groupId, topic);
        example.run(threads);
 
        try {
            Thread.sleep(Long.MAX_VALUE);
        } catch (InterruptedException ie) {
 
        }
        example.shutdown();
    }
}

分区模式下Java代码如下：

package kafka.consumer.partition;

import kafka.api.FetchRequest;
import kafka.api.FetchRequestBuilder;
import kafka.api.PartitionOffsetRequestInfo;
import kafka.common.ErrorMapping;
import kafka.common.TopicAndPartition;
import kafka.javaapi.*;
import kafka.javaapi.consumer.SimpleConsumer;
import kafka.message.MessageAndOffset;
 
import java.nio.ByteBuffer;
import java.util.ArrayList;
import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;

public class PartitionConsumerTest {
    public static void main(String args[]) {
        PartitionConsumerTest example = new PartitionConsumerTest();
        long maxReads = Long.MAX_VALUE;
        String topic = "jiketest";
        if(args.length < 1){
            System.out.println("Please assign partition number.");
        }
        
        List<String> seeds = new ArrayList<String>();
        String hosts="10.206.216.13,10.206.212.14,10.206.209.25";
        String[] hostArr = hosts.split(",");
        for(int index = 0;index < hostArr.length;index++){
            seeds.add(hostArr[index].trim());
        }
        
        int port = 19092;
         
        int partLen = Integer.parseInt(args[0]);
        for(int index=0;index < partLen;index++){
            try {
                example.run(maxReads, topic, index/*partition*/, seeds, port);
            } catch (Exception e) {
                System.out.println("Oops:" + e);
                 e.printStackTrace();
            }
        }
    }
    
    private List<String> m_replicaBrokers = new ArrayList<String>();
     
        public PartitionConsumerTest() {
            m_replicaBrokers = new ArrayList<String>();
        }
     
        public void run(long a_maxReads, String a_topic, int a_partition, List<String> a_seedBrokers, int a_port) throws Exception {
            // find the meta data about the topic and partition we are interested in
            //
            PartitionMetadata metadata = findLeader(a_seedBrokers, a_port, a_topic, a_partition);
            if (metadata == null) {
                System.out.println("Can't find metadata for Topic and Partition. Exiting");
                return;
            }
            if (metadata.leader() == null) {
                System.out.println("Can't find Leader for Topic and Partition. Exiting");
                return;
            }
            String leadBroker = metadata.leader().host();
            String clientName = "Client_" + a_topic + "_" + a_partition;
     
            SimpleConsumer consumer = new SimpleConsumer(leadBroker, a_port, 100000, 64 * 1024, clientName);
            long readOffset = getLastOffset(consumer,a_topic, a_partition, kafka.api.OffsetRequest.EarliestTime(), clientName);
     
            int numErrors = 0;
            while (a_maxReads > 0) {
                if (consumer == null) {
                    consumer = new SimpleConsumer(leadBroker, a_port, 100000, 64 * 1024, clientName);
                }
                FetchRequest req = new FetchRequestBuilder()
                        .clientId(clientName)
                        .addFetch(a_topic, a_partition, readOffset, 100000) // Note: this fetchSize of 100000 might need to be increased if large batches are written to Kafka
                        .build();
                FetchResponse fetchResponse = consumer.fetch(req);
     
                if (fetchResponse.hasError()) {
                    numErrors++;
                    // Something went wrong!
                    short code = fetchResponse.errorCode(a_topic, a_partition);
                    System.out.println("Error fetching data from the Broker:" + leadBroker + " Reason: " + code);
                    if (numErrors > 5) break;
                    if (code == ErrorMapping.OffsetOutOfRangeCode())  {
                        // We asked for an invalid offset. For simple case ask for the last element to reset
                        readOffset = getLastOffset(consumer,a_topic, a_partition, kafka.api.OffsetRequest.LatestTime(), clientName);
                        continue;
                    }
                    consumer.close();
                    consumer = null;
                    leadBroker = findNewLeader(leadBroker, a_topic, a_partition, a_port);
                    continue;
                }
                numErrors = 0;
     
                long numRead = 0;
                for (MessageAndOffset messageAndOffset : fetchResponse.messageSet(a_topic, a_partition)) {
                    long currentOffset = messageAndOffset.offset();
                    if (currentOffset < readOffset) {
                        System.out.println("Found an old offset: " + currentOffset + " Expecting: " + readOffset);
                        continue;
                    }
                    readOffset = messageAndOffset.nextOffset();
                    ByteBuffer payload = messageAndOffset.message().payload();
     
                    byte[] bytes = new byte[payload.limit()];
                    payload.get(bytes);
                    System.out.println(String.valueOf(messageAndOffset.offset()) + ": " + new String(bytes, "UTF-8"));
                    numRead++;
                    a_maxReads--;
                }
     
                if (numRead == 0) {
                    try {
                        Thread.sleep(1000);
                    } catch (InterruptedException ie) {
                    }
                }
            }
            if (consumer != null) consumer.close();
        }
     
        public static long getLastOffset(SimpleConsumer consumer, String topic, int partition,
                                         long whichTime, String clientName) {
            TopicAndPartition topicAndPartition = new TopicAndPartition(topic, partition);
            Map<TopicAndPartition, PartitionOffsetRequestInfo> requestInfo = new HashMap<TopicAndPartition, PartitionOffsetRequestInfo>();
            requestInfo.put(topicAndPartition, new PartitionOffsetRequestInfo(whichTime, 1));
            kafka.javaapi.OffsetRequest request = new kafka.javaapi.OffsetRequest(
                    requestInfo, kafka.api.OffsetRequest.CurrentVersion(), clientName);
            OffsetResponse response = consumer.getOffsetsBefore(request);
     
            if (response.hasError()) {
                System.out.println("Error fetching data Offset Data the Broker. Reason: " + response.errorCode(topic, partition) );
                return 0;
            }
            long[] offsets = response.offsets(topic, partition);
            return offsets[0];
        }
     
        private String findNewLeader(String a_oldLeader, String a_topic, int a_partition, int a_port) throws Exception {
            for (int i = 0; i < 3; i++) {
                boolean goToSleep = false;
                PartitionMetadata metadata = findLeader(m_replicaBrokers, a_port, a_topic, a_partition);
                if (metadata == null) {
                    goToSleep = true;
                } else if (metadata.leader() == null) {
                    goToSleep = true;
                } else if (a_oldLeader.equalsIgnoreCase(metadata.leader().host()) && i == 0) {
                    // first time through if the leader hasn't changed give ZooKeeper a second to recover
                    // second time, assume the broker did recover before failover, or it was a non-Broker issue
                    //
                    goToSleep = true;
                } else {
                    return metadata.leader().host();
                }
                if (goToSleep) {
                    try {
                        Thread.sleep(1000);
                    } catch (InterruptedException ie) {
                    }
                }
            }
            System.out.println("Unable to find new leader after Broker failure. Exiting");
            throw new Exception("Unable to find new leader after Broker failure. Exiting");
        }
     
        private PartitionMetadata findLeader(List<String> a_seedBrokers, int a_port, String a_topic, int a_partition) {
            PartitionMetadata returnMetaData = null;
            loop:
            for (String seed : a_seedBrokers) {
                SimpleConsumer consumer = null;
                try {
                    consumer = new SimpleConsumer(seed, a_port, 100000, 64 * 1024, "leaderLookup");
                    List<String> topics = Collections.singletonList(a_topic);
                    TopicMetadataRequest req = new TopicMetadataRequest(topics);
                    kafka.javaapi.TopicMetadataResponse resp = consumer.send(req);
     
                    List<TopicMetadata> metaData = resp.topicsMetadata();
                    for (TopicMetadata item : metaData) {
                        for (PartitionMetadata part : item.partitionsMetadata()) {
                            if (part.partitionId() == a_partition) {
                                returnMetaData = part;
                                break loop;
                            }
                        }
                    }
                } catch (Exception e) {
                    System.out.println("Error communicating with Broker [" + seed + "] to find Leader for [" + a_topic
                            + ", " + a_partition + "] Reason: " + e);
                } finally {
                    if (consumer != null) consumer.close();
                }
            }
            if (returnMetaData != null) {
                m_replicaBrokers.clear();
                for (kafka.cluster.Broker replica : returnMetaData.replicas()) {
                    m_replicaBrokers.add(replica.host());
                }
            }
            return returnMetaData;
        }
}

参数调优

 接下来实现生产者，能够像kafka中传递消息

生产者发送消息后会不断确认kafka集群是否收到，如果没收到就会重发，如果达到最大次数就会结束生产

异步生产的时候，消息会事先缓存在客户端，可以设置最大消息缓存数或者累计缓存时间，如果达到设置的标准，就会打包发送给kafka服务器

两种模型伪代码描述非常相似，所以用一个就能表示

先创建链接实例，之后配置负载均衡

在设置生产者参数的时候就会定义是同步还是异步

 

同步模型由于需要同步，所以丢失率基本为0

异步模型中，每个分区每秒可以发送50万条消息

接下来实现Java客户端程序编写：

pom文件与上述pom文件一样

同步模型代码如下：

package kafka.producer.sync;
import java.util.*;

import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;

public class SyncProduce {
    public static void main(String[] args) {
        long events = Long.MAX_VALUE;
        Random rnd = new Random();
 
        Properties props = new Properties();
        props.put("metadata.broker.list", "10.206.216.13:19092,10.206.212.14:19092,10.206.209.25:19092");
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        //kafka.serializer.DefaultEncoder
        props.put("partitioner.class", "kafka.producer.partiton.SimplePartitioner");
        //kafka.producer.DefaultPartitioner: based on the hash of the key
        props.put("request.required.acks", "1");
        //0;  绝不等确认  1:   leader的一个副本收到这条消息，并发回确认 -1：   leader的所有副本都收到这条消息，并发回确认
 
        ProducerConfig config = new ProducerConfig(props);
 
        Producer<String, String> producer = new Producer<String, String>(config);
 
        for (long nEvents = 0; nEvents < events; nEvents++) { 
               long runtime = new Date().getTime();  
               String ip = "192.168.2." + rnd.nextInt(255); 
               String msg = runtime + ",www.example.com," + ip; 
               //eventKey必须有（即使自己的分区算法不会用到这个key，也不能设为null或者""）,否者自己的分区算法根本得不到调用
               KeyedMessage<String, String> data = new KeyedMessage<String, String>("jiketest", ip, msg);
                                                               //             eventTopic, eventKey, eventBody
               producer.send(data);
               try {
                   Thread.sleep(1000);
               } catch (InterruptedException ie) {
               }
        }
        producer.close();
    }
}

异步模型代码如下：

package kafka.producer.async;

import java.util.*;

import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;


public class ASyncProduce {
    public static void main(String[] args) {
        long events = Long.MAX_VALUE;
        Random rnd = new Random();
 
        Properties props = new Properties();
        props.put("metadata.broker.list", "10.206.216.13:19092,10.206.212.14:19092,10.206.209.25:19092");
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        //kafka.serializer.DefaultEncoder
        props.put("partitioner.class", "kafka.producer.partiton.SimplePartitioner");
        //kafka.producer.DefaultPartitioner: based on the hash of the key
        //props.put("request.required.acks", "1");
        props.put("producer.type", "async");
        //props.put("producer.type", "1");
        // 1: async 2: sync
 
        ProducerConfig config = new ProducerConfig(props);
 
        Producer<String, String> producer = new Producer<String, String>(config);
 
        for (long nEvents = 0; nEvents < events; nEvents++) { 
               long runtime = new Date().getTime();  
               String ip = "192.168.2." + rnd.nextInt(255); 
               String msg = runtime + ",www.example.com," + ip; 
               KeyedMessage<String, String> data = new KeyedMessage<String, String>("jiketest", ip, msg);
               producer.send(data);
               try {
                   Thread.sleep(1000);
               } catch (InterruptedException ie) {
               }
        }
        producer.close();
    }
}

分区算法：

package kafka.producer.partiton;

import kafka.producer.Partitioner;
import kafka.utils.VerifiableProperties;
 
public class SimplePartitioner implements Partitioner {
    public SimplePartitioner (VerifiableProperties props) {
 
    }
 
    public int partition(Object key, int a_numPartitions) {
        int partition = 0;
        String stringKey = (String) key;
        int offset = stringKey.lastIndexOf('.');
        if (offset > 0) {
           partition = Integer.parseInt( stringKey.substring(offset+1)) % a_numPartitions;
        }
       return partition;
  }
 
}