实践代码采用kafka-clients V0.10.0.0 编写
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
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 }}
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(); } }}
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
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(); } }}
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); }}
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>
这是一个更加底层和复杂的API
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partitions3. 使用事务,确保每个消息只被处理一次
1. 必须在程序中跟踪offset值2. 必须找出指定Topic Partition中的lead broker3. 必须处理broker的变动
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
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; }}
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topicconsumer.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()); }
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);
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(); } } }}
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))); } } }}
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(); } } }}
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>
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
参考文章:
Java线程池使用说明
Java并发编程:线程池的使用
如何确定Kafka的分区数、key和consumer线程数s
Kafka Consumer接口
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