Kafka(producer和consumer编程实践)

实践代码采用kafka-clients V0.10.0.0 编写

一、编写producer

第一步：使用./kafka-topics.sh 命令创建topic及partitions 分区数

./kafka-topics.sh --create--zookepper "172.16.49.173:2181" --topic "producer_test" --partitions 10 replication-factor 3

11[/code]第二步：实现

org.apache.kafka.clients.producer.Partitioner

分区接口，以实现自定义的消息分区

import java.util.List;
import java.util.Map;
import org.apache.kafka.clients.producer.Partitioner;
import org.apache.kafka.common.Cluster;
import org.apache.kafka.common.PartitionInfo;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MyPartition implements Partitioner {
private static Logger LOG = LoggerFactory.getLogger(MyPartition.class);
public MyPartition() {
// TODO Auto-generated constructor stub
}

@Override
public void configure(Map<String, ?> configs) {
// TODO Auto-generated method stub

}

@Override
public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
// TODO Auto-generated method stub
List<PartitionInfo> partitions = cluster.partitionsForTopic(topic);
int numPartitions = partitions.size();
int partitionNum = 0;
try {
partitionNum = Integer.parseInt((String) key);
} catch (Exception e) {
partitionNum = key.hashCode() ;
}
LOG.info("the message sendTo topic:"+ topic+" and the partitionNum:"+ partitionNum);
return Math.abs(partitionNum  % numPartitions);
}

@Override
public void close() {
// TODO Auto-generated method stub

}

}

1234567891011121314151617181920212223242526272829303132333435363738394041424312345678910111213141516171819202122232425262728293031323334353637383940414243[/code]第三步：编写 producer

import java.util.Properties;
import org.apache.kafka.clients.producer.Callback;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.ProducerRecord;
import org.apache.kafka.clients.producer.RecordMetadata;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class PartitionTest {
private static Logger LOG = LoggerFactory.getLogger(PartitionTest.class);

public static void main(String[] args) {
// TODO Auto-generated method stub
Properties props = new Properties();
props.put("bootstrap.servers", "172.16.49.173:9092;172.16.49.173:9093");

props.put("retries", 0);
// props.put("batch.size", 16384);
props.put("linger.ms", 1);
// props.put("buffer.memory", 33554432);
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("partitioner.class", "com.goodix.kafka.MyPartition");
KafkaProducer<String, String> producer = new KafkaProducer<String, String>(props);
ProducerRecord<String, String> record = new ProducerRecord<String, String>("producer_test", "2223132132",
"test23_60");
producer.send(record, new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception e) {
// TODO Auto-generated method stub
if (e != null)
LOG.error("the producer has a error:" + e.getMessage());
else {
LOG.info("The offset of the record we just sent is: " + metadata.offset());
LOG.info("The partition of the record we just sent is: " + metadata.partition());
}

}
});
try {
Thread.sleep(1000);
producer.close();
} catch (InterruptedException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}

}

}

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849501234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950[/code]备注： 要先用命令创建topic及partitions 分区数;否则在自定义的分区中如果有大于1的情况下，发送数据消息到kafka时会报

expired
due to timeout while requesting metadata from brokers

错误

二、使用Old Consumer High Level API编写consumer

第一步：编写具体处理消息的类

import java.io.UnsupportedEncodingException;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.message.MessageAndMetadata;

public class Consumerwork implements Runnable {
private static Logger LOG = LoggerFactory.getLogger(Consumerwork.class);
@SuppressWarnings("rawtypes")
private KafkaStream m_stream;
private int m_threadNumber;
@SuppressWarnings("rawtypes")
public Consumerwork(KafkaStream a_stream,int a_threadNumber) {
// TODO Auto-generated constructor stub
m_threadNumber = a_threadNumber;
m_stream = a_stream;
}

@SuppressWarnings("unchecked")
@Override
public void run() {
// TODO Auto-generated method stub
ConsumerIterator<byte[], byte[]> it = m_stream.iterator();
while (it.hasNext())
try {
MessageAndMetadata<byte[], byte[]> thisMetadata=it.next();
String jsonStr = new String(thisMetadata.message(),"utf-8") ;
LOG.info("Thread " + m_threadNumber + ": " +jsonStr);
LOG.info("partion"+thisMetadata.partition()+",offset:"+thisMetadata.offset());
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
} catch (UnsupportedEncodingException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}

1234567891011121314151617181920212223242526272829303132333435363738394041424312345678910111213141516171819202122232425262728293031323334353637383940414243[/code]第二步：编写启动Consumer主类

mport java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import java.util.Scanner;
import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
public class ConsumerGroup {
private final ConsumerConnector consumer;
private final String topic;
private ExecutorService executor;
private static Logger LOG = LoggerFactory.getLogger(ConsumerGroup.class);
public ConsumerGroup(String a_zookeeper, String a_groupId, String a_topic) {
consumer = kafka.consumer.Consumer.createJavaConsumerConnector(createConsumerConfig(a_zookeeper, a_groupId));
this.topic = a_topic;
}
public static void main(String[] args) {
Scanner sc = new Scanner(System.in);
System.out.println("请输入zookeeper集群地址(如zk1:2181,zk2:2181,zk3:2181)：");
String zooKeeper = sc.nextLine();
System.out.println("请输入指定的消费group名称：");
String groupId = sc.nextLine();
System.out.println("请输入指定的消费topic名称：");
String topic = sc.nextLine();
System.out.println("请输入指定的消费处理线程数：");
int threads = sc.nextInt();
LOG.info("Starting consumer kafka messages with zk:" + zooKeeper + " and the topic is " + topic);
ConsumerGroup example = new ConsumerGroup(zooKeeper, groupId, topic);
example.run(threads);

try {
Thread.sleep(1000);
} catch (InterruptedException ie) {

}
// example.shutdown();
}

private void shutdown() {
// TODO Auto-generated method stub
if (consumer != null)
consumer.shutdown();
if (executor != null)
executor.shutdown();
try {
if (!executor.awaitTermination(5000, TimeUnit.MILLISECONDS)) {
LOG.info("Timed out waiting for consumer threads to shut down, exiting uncleanly");
}
} catch (InterruptedException e) {
LOG.info("Interrupted during shutdown, exiting uncleanly");
}
}

private void run(int a_numThreads) {
// TODO Auto-generated method stub
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;
LOG.info("the streams size is "+streams.size());
for (final KafkaStream stream : streams) {
executor.submit(new com.goodix.kafka.oldconsumer.Consumerwork(stream, threadNumber));
//      consumer.commitOffsets();
threadNumber++;
}

}

private ConsumerConfig createConsumerConfig(String a_zookeeper, String a_groupId) {
// TODO Auto-generated method stub
Properties props = new Properties();
props.put("zookeeper.connect", a_zookeeper);
props.put("group.id", a_groupId);
props.put("zookeeper.session.timeout.ms", "60000");
props.put("zookeeper.sync.time.ms", "200");
props.put("auto.commit.interval.ms", "1000");
props.put("auto.offset.reset", "smallest");
//      props.put("rebalance.max.retries", "5");
//      props.put("rebalance.backoff.ms", "15000");
return new ConsumerConfig(props);
}

}

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485868788899091929394959697981234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677787980818283848586878889909192939495969798[/code]1. topicCountMap.put(topic, new Integer(a_numThreads)) 是告诉Kafka我有多少个线程来处理消息。(1). 这个线程数必须是小等于topic的partition分区数；可以通过

./kafka-topics.sh --describe --zookeeper "172.16.49.173:2181" --topic "producer_test"

命令来查看分区的情况(2). kafka会根据partition.assignment.strategy指定的分配策略来指定线程消费那些分区的消息；这里没有单独配置该项即是采用的默认值range策略（按照阶段平均分配）。比如分区有10个、线程数有3个，则线程 1消费0,1,2,3，线程2消费4,5,6,线程3消费7,8,9。另外一种是roundrobin(循环分配策略)，官方文档中写有使用该策略有两个前提条件的，所以一般不要去设定。(3). 经过测试：consumerMap.get(topic).size()，应该是获得的目前该topic有数据的分区数(4). stream即指的是来自一个或多个服务器上的一个或者多个partition的消息。每一个stream都对应一个单线程处理。因此，client能够设置满足自己需求的stream数目。总之，一个stream也许代表了多个服务器partion的消息的聚合，但是每一个 partition都只能到一个stream2. Executors.newFixedThreadPool(a_numThreads)是创建一个创建固定容量大小的缓冲池：每次提交一个任务就创建一个线程，直到线程达到线程池的最大大小。线程池的大小一旦达到最大值就会保持不变，如果某个线程因为执行异常而结束，那么线程池会补充一个新线程。3. props.put(“auto.offset.reset”, “smallest”) 是指定从最小没有被消费offset开始；如果没有指定该项则是默认的为largest，这样的话该consumer就得不到生产者先产生的消息。4. 要使用old consumer API需要引用kafka_2.11以及kafka-clients。

<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>0.10.0.0</version>
</dependency>
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.10.0.0</version>
</dependency>

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三、使用Old SimpleConsumerAPI编写consumer

这是一个更加底层和复杂的API

使用的场景

由于使用该API需要自己控制的项比较多，也比较复杂，官方给出了一些合适的适用场景，也可以理解成为这些场景是High Level Consumer API 不能够做到的

1. 针对一个消息读取多次
2. 在一个process中，仅仅处理一个topic中的一个partitions
3. 使用事务，确保每个消息只被处理一次

123123[/code]

需要处理的事情

1. 必须在程序中跟踪offset值
2. 必须找出指定Topic Partition中的lead broker
3. 必须处理broker的变动

123123[/code]

使用SimpleConsumer的步骤

首先，你必须知道读哪个topic的哪个partition
然后，找到负责该partition的broker leader，从而找到存有该partition副本的那个broker
再者，自己去写request并fetch数据
最终，还要注意需要识别和处理broker leader的改变

12341234[/code]

示例

package com.goodix.kafka.oldconsumer;
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;
import java.util.Scanner;

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class SimpleExample {
private static Logger LOG = LoggerFactory.getLogger(SimpleExample.class);
public static void main(String args[]) {
SimpleExample example = new SimpleExample();
Scanner sc = new Scanner(System.in);
System.out.println("请输入broker节点的ip地址(如172.16.49.173)");
String brokerIp = sc.nextLine();
List<String> seeds = new ArrayList<String>();
seeds.add(brokerIp);
System.out.println("请输入broker节点端口号(如9092)");
int port = Integer.parseInt( sc.nextLine());
System.out.println("请输入要订阅的topic名称(如test)");
String topic = sc.nextLine();
System.out.println("请输入要订阅要查找的分区(如0)");
int partition = Integer.parseInt( sc.nextLine());
System.out.println("请输入最大读取消息数量(如10000)");
long maxReads = Long.parseLong( sc.nextLine());

try {
example.run(maxReads, topic, partition, seeds, port);
} catch (Exception e) {
LOG.error("Oops:" + e);
e.printStackTrace();
}
}

private List<String> m_replicaBrokers = new ArrayList<String>();

public SimpleExample() {
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
//获取指定Topic partition的元数据
PartitionMetadata metadata = findLeader(a_seedBrokers, a_port, a_topic, a_partition);
if (metadata == null) {
LOG.error("Can't find metadata for Topic and Partition. Exiting");
return;
}
if (metadata.leader() == null) {
LOG.error("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);
LOG.error("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) {
LOG.error("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);
LOG.info("the messag's offset is :"+String.valueOf(messageAndOffset.offset()) + " and the value is :" + 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()) {
LOG.error("Error fetching data Offset Data the Broker. Reason: " + response.errorCode(topic, partition) );
return 0;
}
long[] offsets = response.offsets(topic, partition);
return offsets[0];
}
/**
* 找一个leader broker
* 遍历每个broker，取出该topic的metadata，然后再遍历其中的每个partition metadata，如果找到我们要找的partition就返回
* 根据返回的PartitionMetadata.leader().host()找到leader broker
* @param a_oldLeader
* @param a_topic
* @param a_partition
* @param a_port
* @return
* @throws Exception
*/
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) {
}
}
}
LOG.error("Unable to find new leader after Broker failure. Exiting");
throw new Exception("Unable to find new leader after Broker failure. Exiting");
}
/**
*
* @param a_seedBrokers
* @param a_port
* @param a_topic
* @param a_partition
* @return
*/
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) { //遍历每个broker
SimpleConsumer consumer = null;
try {
// 创建Simple Consumer，
consumer = new SimpleConsumer(seed, a_port, 100000, 64 * 1024, "leaderLookup");
List<String> topics = Collections.singletonList(a_topic);
TopicMetadataRequest req = new TopicMetadataRequest(topics);
//发送TopicMetadata Request请求
kafka.javaapi.TopicMetadataResponse resp = consumer.send(req);
//取到Topic的Metadata
List<TopicMetadata> metaData = resp.topicsMetadata();
//遍历每个partition的metadata
for (TopicMetadata item : metaData) {
for (PartitionMetadata part : item.partitionsMetadata()) {
// 判断是否是要找的partition
if (part.partitionId() == a_partition) {
returnMetaData = part;
//找到就返回
break loop;
}
}
}
} catch (Exception e) {
LOG.info("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.BrokerEndPoint replica : returnMetaData.replicas()) {
m_replicaBrokers.add(replica.host());
}
}
return returnMetaData;
}
}

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四、 使用NewConsumer API

(一)、自动提交offset偏移量

Properties props = new Properties();
//brokerServer(kafka)ip地址,不需要把所有集群中的地址都写上，可是一个或一部分
props.put("bootstrap.servers", "172.16.49.173:9092");
//设置consumer group name,必须设置
props.put("group.id", a_groupId);
//设置自动提交偏移量(offset),由auto.commit.interval.ms控制提交频率
props.put("enable.auto.commit", "true");
//偏移量(offset)提交频率
props.put("auto.commit.interval.ms", "1000");
//设置使用最开始的offset偏移量为该group.id的最早。如果不设置，则会是latest即该topic最新一个消息的offset
//如果采用latest，消费者只能得道其启动后，生产者生产的消息
props.put("auto.offset.reset", "earliest");
//设置心跳时间
props.put("session.timeout.ms", "30000");
//设置key以及value的解析（反序列）类
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);
//订阅topic
consumer.subscribe(Arrays.asList("topic_test"));
while (true) {
//每次取100条信息
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records)
System.out.printf("offset = %d, key = %s, value = %s", record.offset(), record.key(), record.value());
}

12345678910111213141516171819202122232425261234567891011121314151617181920212223242526[/code]需要注意的:group.id :必须设置auto.offset.reset：如果想获得消费者启动前生产者生产的消息，则必须设置为earliest；如果只需要获得消费者启动后生产者生产的消息，则不需要设置该项enable.auto.commit(默认值为true)：如果使用手动commit offset则需要设置为false，并再适当的地方调用

consumer.commitSync()

，否则每次启动消费折后都会从头开始消费信息(在auto.offset.reset=earliest的情况下);

（二）、 自己控制偏移量提交

很多时候，我们是希望在获得消息并经过一些逻辑处理后，才认为该消息已被消费，这可以通过自己控制偏移量提交来实现。示例1：批量提交偏移量

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Properties;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
* 手动批量提交偏移量
* @author lxh
*
*/
public class ManualOffsetConsumer {
private static Logger LOG = LoggerFactory.getLogger(ManualOffsetConsumer.class);
public ManualOffsetConsumer() {
// TODO Auto-generated constructor stub
}

public static void main(String[] args) {
// TODO Auto-generated method stub
Properties props = new Properties();
//props.put("bootstrap.servers", bootstrapServers);//"172.16.49.173:9092;172.16.49.173:9093");
//设置brokerServer(kafka)ip地址
props.put("bootstrap.servers", "172.16.49.173:9092");
//设置consumer group name
props.put("group.id","manual_g1");

props.put("enable.auto.commit", "false");

//设置使用最开始的offset偏移量为该group.id的最早。如果不设置，则会是latest即该topic最新一个消息的offset
//如果采用latest，消费者只能得道其启动后，生产者生产的消息
props.put("auto.offset.reset", "earliest");
//
props.put("session.timeout.ms", "30000");
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<String ,String>(props);
consumer.subscribe(Arrays.asList("producer_test"));
final int minBatchSize = 5;  //批量提交数量
List<ConsumerRecord<String, String>> buffer = new ArrayList<>();
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records) {
LOG.info("consumer message values is "+record.value()+" and the offset is "+ record.offset());
buffer.add(record);
}
if (buffer.size() >= minBatchSize) {
LOG.info("now commit offset");
consumer.commitSync();
buffer.clear();
}
}
}

}

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606112345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061[/code]示例2：消费完一个分区后手动提交偏移量

package com.goodix.kafka;

import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import java.util.Properties;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.clients.consumer.OffsetAndMetadata;
import org.apache.kafka.common.TopicPartition;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
* 消费完一个分区后手动提交偏移量
* @author lxh
*
*/
public class ManualCommitPartion {
private static Logger LOG = LoggerFactory.getLogger(ManualCommitPartion.class);
public ManualCommitPartion() {
// TODO Auto-generated constructor stub
}

public static void main(String[] args) {
// TODO Auto-generated method stub
Properties props = new Properties();
//props.put("bootstrap.servers", bootstrapServers);//"172.16.49.173:9092;172.16.49.173:9093");
//设置brokerServer(kafka)ip地址
props.put("bootstrap.servers", "172.16.49.173:9092");
//设置consumer group name
props.put("group.id","manual_g2");

props.put("enable.auto.commit", "false");

//设置使用最开始的offset偏移量为该group.id的最早。如果不设置，则会是latest即该topic最新一个消息的offset
//如果采用latest，消费者只能得道其启动后，生产者生产的消息
props.put("auto.offset.reset", "earliest");
//
props.put("session.timeout.ms", "30000");
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<String ,String>(props);
consumer.subscribe(Arrays.asList("producer_test"));
while (true) {
ConsumerRecords<String, String> records = consumer.poll(Long.MAX_VALUE);
for (TopicPartition partition : records.partitions()) {
List<ConsumerRecord<String, String>> partitionRecords = records.records(partition);
for (ConsumerRecord<String, String> record : partitionRecords) {
LOG.info("now consumer the message it's offset is :"+record.offset() + " and the value is :" + record.value());
}
long lastOffset = partitionRecords.get(partitionRecords.size() - 1).offset();
LOG.info("now commit the partition[ "+partition.partition()+"] offset");
consumer.commitSync(Collections.singletonMap(partition, new OffsetAndMetadata(lastOffset + 1)));
}
}
}

}

1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606112345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061[/code]

（三）、指定消费某个分区的消息

import java.util.Arrays;
import java.util.Properties;

import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
import org.apache.kafka.common.TopicPartition;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
* 消费指定分区的消息
* @author lxh
*
*/
public class ManualPartion {
private static Logger LOG = LoggerFactory.getLogger(ManualPartion.class);
public ManualPartion() {
// TODO Auto-generated constructor stub
}

public static void main(String[] args) {
Properties props = new Properties();
//设置brokerServer(kafka)ip地址
props.put("bootstrap.servers", "172.16.49.173:9092");
//设置consumer group name
props.put("group.id", "manual_g4");
//设置自动提交偏移量(offset),由auto.commit.interval.ms控制提交频率
props.put("enable.auto.commit", "true");
//偏移量(offset)提交频率
props.put("auto.commit.interval.ms", "1000");
//设置使用最开始的offset偏移量为该group.id的最早。如果不设置，则会是latest即该topic最新一个消息的offset
//如果采用latest，消费者只能得道其启动后，生产者生产的消息
props.put("auto.offset.reset", "earliest");
//
props.put("session.timeout.ms", "30000");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
TopicPartition partition0 = new TopicPartition("producer_test", 0);
TopicPartition partition1 = new TopicPartition("producer_test", 1);
KafkaConsumer<String ,String> consumer = new KafkaConsumer<String ,String>(props);
consumer.assign(Arrays.asList(partition0, partition1));
while (true) {
ConsumerRecords<String, String> records = consumer.poll(Long.MAX_VALUE);
for (ConsumerRecord<String, String> record : records)
System.out.printf("offset = %d, key = %s, value = %s  \r\n", record.offset(), record.key(), record.value());
try {
Thread.sleep(1000);
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}

}

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657[/code]

总结

使用newConsumer API 只需要引用kafka-clients即可newConsumer API 更加易懂、易用

<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.10.0.0</version>
</dependency>

1234512345[/code]

官方对于consumer与partition的建议

1. 如果consumer比partition多，是浪费，因为kafka的设计是在一个partition上是不允许并发的，所以consumer数不要大于partition数
2. 如果consumer比partition少，一个consumer会对应于多个partitions，这里主要合理分配consumer数和partition数，否则会导致partition里面的数据被取的不均匀。最好partiton数目是consumer数目的整数倍，所以partition数目很重要，比如取24，就很容易设定consumer数目
3. 如果consumer从多个partition读到数据，不保证数据间的顺序性，kafka只保证在一个partition上数据是有序的，但多个partition，根据你读的顺序会有不同
4. 增减consumer，broker，partition会导致rebalance，所以rebalance后consumer对应的partition会发生变化
5. High-level接口中获取不到数据的时候是会block的

有不明白的可以和我交流沟通讨论QQ:308385403