Kafka消费数据的角色分为普通消费者和高级消费者，其介绍如下：
17.1 普通消费者
特点：1）一个消息读取多次
2）在一个处理过程中只消费某个broker上的partition的部分消息
3）必须在程序中跟踪offset值
4）必须找出指定TopicPartition中的lead broker
5）必须处理broker的变动
客户端编程必须按照以下步骤：
1）从所有活跃的broker中找出哪个是指定TopicPartition中的leader broker
2）构造请求
3）发送请求查询数据
4）处理leader broker变更
客户端代码如下：
public class KafkaSimpleConsumer {
private List<String> m_replicaBrokers = new ArrayList<String>();
public KafkaSimpleConsumer() {
m_replicaBrokers = new ArrayList<String>();
}
public static void main(String args[]) {
KafkaSimpleConsumer example = new KafkaSimpleConsumer();
// 最大读取消息数量
long maxReads = Long.parseLong("3");
// 要订阅的topic
String topic = "mytopic";
// 要查找的分区
int partition = Integer.parseInt("0");
// broker节点的ip
List<String> seeds = new ArrayList<String>();
seeds.add("192.168.4.30");
seeds.add("192.168.4.31");
seeds.add("192.168.4.32");
// 端口
int port = Integer.parseInt("9092");
try {
example.run(maxReads, topic, partition, seeds, port);
} catch (Exception e) {
System.out.println("Oops:" + e);
e.printStackTrace();
}
}
public void run(long a_maxReads, String a_topic, int a_partition, List<String> a_seedBrokers, int a_port) throws Exception {
// 获取指定Topic partition的元数据
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;
}
//找到leader broker
String leadBroker = metadata.leader().host();
String clientName = "Client_" + a_topic + "_" + a_partition;
//链接leader broker
SimpleConsumer consumer = new SimpleConsumer(leadBroker, a_port, 100000, 64 * 1024, clientName);
//获取topic的最新偏移量
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);
}
//本质上就是发送FetchRequest请求
FetchRequest req = new FetchRequestBuilder().clientId(clientName).addFetch(a_topic, a_partition, readOffset, 100000).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;
//处理topic的partition的leader发生变更的情况
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) {//过滤旧的数据
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);
//打印消息
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];
}
/**
* @param a_oldLeader
* @param a_topic
* @param a_partition
* @param a_port
* @return String
* @throws Exception
*找一个leader broker，其实就是发送TopicMetadataRequest请求
*/
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;
}
}
17.2 高级消费者
特点：
1）消费过的数据无法再次消费，如果想要再次消费数据，要么换另一个group
2）为了记录每次消费的位置，必须提交TopicAndPartition的offset，offset提交支持两种方式：
①提交至ZK (频繁操作zk是效率比较低的)
②提交至kafka内部
3）客户端通过stream获取数据，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stream。
4）consumer和partition的关系：
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
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
客户端编程必须按照以下步骤：
1）设计topic和stream的关系，即K为topic，V为stream的个数N
2）开启N个消费组线程消费这N个stream
客户端代码如下：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 kafka.consumer.Consumer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.consumer.ConsumerIterator;
/**
* 详细可以参考：https://cwiki.apache.org/confluence/display/KAFKA/Consumer+Group+Example
*
* @author Fung
*/
public class KafkaHighConsumer {
private final ConsumerConnector consumer;
private final String topic;
private ExecutorService executor;
public KafkaHighConsumer(String a_zookeeper, String a_groupId, String a_topic) {
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();
}
public void run(int numThreads) {
Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
//设计topic和stream的关系，即K为topic，V为stream的个数N
topicCountMap.put(topic, new Integer(numThreads));
//获取numThreads个stream
Map<String, List<KafkaStream<byte[], byte[]>>> consumerMap = consumer
.createMessageStreams(topicCountMap);
List<KafkaStream<byte[], byte[]>> streams = consumerMap.get(topic);
executor = Executors.newFixedThreadPool(numThreads);
int threadNumber = 0;
//开启N个消费组线程消费这N个stream
for (final KafkaStream stream : streams) {
executor.submit(new ConsumerMsgTask(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", "400");
props.put("zookeeper.sync.time.ms", "200");
props.put("auto.commit.interval.ms", "1000");
return new ConsumerConfig(props);
}
public static void main(String[] arg) {
String[] args = {"172.168.63.221:2188", "group-1", "page_visits", "12"};
String zooKeeper = args[0];
String groupId = args[1];
String topic = args[2];
int threads = Integer.parseInt(args[3]);
KafkaHighConsumer demo = new KafkaHighConsumer(zooKeeper, groupId, topic);
demo.run(threads);
try {
Thread.sleep(10000);
} catch (InterruptedException ie) {
}
demo.shutdown();
}
public class ConsumerMsgTask implements Runnable {
private KafkaStream m_stream;
private int m_threadNumber;
public ConsumerMsgTask(KafkaStream stream, int threadNumber) {
m_threadNumber = threadNumber;
m_stream = stream;
}
public void run() {// KafkaStream的本质就是一个网络迭代器
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);
}
}
/**
* Created by Administrator on 2016/4/11.
*/
public static class KafkaProducer {
}
}
其具体的消费逻辑如下：
