当前位置:首页 > 开发 > 开源软件 > 正文

【Kafka十三】Kafka Simple Consumer

发表于: 2015-04-13   作者:bit1129   来源:转载   浏览:
摘要: 代码中关于Host和Port是割裂开的,这会导致单机环境下的伪分布式Kafka集群环境下,这个例子没法运行。 实际情况是需要将host和port绑定到一起,   package kafka.examples.lowlevel; import kafka.api.FetchRequest; import kafka.api.FetchRequestBuilder; impo

代码中关于Host和Port是割裂开的,这会导致单机环境下的伪分布式Kafka集群环境下,这个例子没法运行。

实际情况是需要将host和port绑定到一起,

 

package kafka.examples.lowlevel;

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.*;

public class KafkaLowLevelConsumer {
    //参数说明:
    /*
    Maximum number of messages to read (so we don’t loop forever)
    Topic to read from
    Partition to read from
    One broker to use for Metadata lookup
    Port the brokers listen on
     */
    public static void main(String args[]) {
        KafkaLowLevelConsumer consumer = new KafkaLowLevelConsumer();

        //读取的消息数
        long maxReads = Long.parseLong(args[0]);

        //读取的topic名称
        String topic = args[1];

        //读取的partition,从0开始的
        int partition = Integer.parseInt(args[2]);


        List<String> seeds = new ArrayList<String>();
        seeds.add(args[3]);

        //seed broker的监听端口,每个Topic和Partition的信息是存放于zk目录:/brokers/topics/learn.topic.p8.r2
        int port = Integer.parseInt(args[4]);
        try {
            consumer.run(maxReads, topic, partition, seeds, port);
        } catch (Exception e) {
            System.out.println("Oops:" + e);
            e.printStackTrace();
        }
    }

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

    public KafkaLowLevelConsumer() {
        replicaBrokers = new ArrayList<String>();
    }

    public void run(long maxReads, String topic, int partition, List<String> seedBrokers, int port) throws Exception {

        //获取指定topic和partition的元信息,PartitionMetadata的leader和replicas方法返回leader和replicas brokers
        PartitionMetadata metadata = findLeader(seedBrokers, port, topic, partition);
        if (metadata == null) {
            System.out.println("Can't find metadata for Topic and Partition. Exiting");
            return;
        }

        //获取lead partition所在的broker
        if (metadata.leader() == null) {
            System.out.println("Can't find Leader for Topic and Partition. Exiting");
            return;
        }

        //获取leader broker的host信息,不包括端口信息
        String leadBroker = metadata.leader().host();
        String clientName = "Client_" + topic + "_" + partition;

        //构造SimpleConsumer,为什么port和leaderBroker不一致?
        //这里的leadBroker, port是配对的,应该是metadata.leader().port()
        SimpleConsumer consumer = new SimpleConsumer(leadBroker, port, 100000, 64 * 1024, clientName);


        //获取读取的offset
        long readOffset = getLastOffset(consumer, topic, partition, kafka.api.OffsetRequest.EarliestTime(), clientName);

        int numErrors = 0;
        while (maxReads > 0) {
            if (consumer == null) {
                consumer = new SimpleConsumer(leadBroker, port, 100000, 64 * 1024, clientName);
            }
            FetchRequest req = new FetchRequestBuilder()
                    .clientId(clientName)
                    .addFetch(topic, 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);

            //Since the SimpleConsumer doesn't handle lead Broker failures, you have to write a bit of code to handle it.
            if (fetchResponse.hasError()) {
                numErrors++;
                // Something went wrong!
                short code = fetchResponse.errorCode(topic, 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, topic, partition, kafka.api.OffsetRequest.LatestTime(), clientName);
                    continue;
                }
                consumer.close();
                consumer = null;
                leadBroker = findNewLeader(leadBroker, topic, partition, port);
                continue;
            }
            numErrors = 0;

            long numRead = 0;
            for (MessageAndOffset messageAndOffset : fetchResponse.messageSet(topic, 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++;
                maxReads--;
            }
            //This method uses the findLeader() logic we defined earlier to find the new leader,
            // except here we only try to connect to one of the replicas for the topic/partition.
            // This way if we can’t reach any of the Brokers with the data we are interested in we give up and exit hard.
            //Since it may take a short time for ZooKeeper to detect the leader loss and assign a new leader, we sleep if we don’t get an answer.
            // In reality ZooKeeper often does the failover very quickly so you never sleep
            if (numRead == 0) {
                try {
                    Thread.sleep(1000);
                } catch (InterruptedException ie) {
                }
            }
        }
        if (consumer != null) consumer.close();
    }

    // Finding Starting Offset for Reads
    // Now define where to start reading data. Kafka includes two constants to help,
    // kafka.api.OffsetRequest.EarliestTime() finds the beginning of the data in the logs and starts streaming from there,
    // kafka.api.OffsetRequest.LatestTime() will only stream new messages.
    // Don’t assume that offset 0 is the beginning offset, since messages age out of the log over time.
    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);
        //Get a list of valid offsets (up to maxSize) before the given time.
        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];
    }

    //Since the SimpleConsumer doesn't handle lead Broker failures, you have to write a bit of code to handle it.
    //Here, once the fetch returns an error, we log the reason, close the consumer then try to figure out who the new leader is.
    private String findNewLeader(String oldLeader, String topic, int partition, int port) throws Exception {
        for (int i = 0; i < 3; i++) {
            boolean goToSleep;
            PartitionMetadata metadata = findLeader(replicaBrokers, port, topic, partition);
            if (metadata == null) {
                goToSleep = true;
            } else if (metadata.leader() == null) {
                goToSleep = true;
            } else if (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");
    }

    ///根据指定的Broker,查找指定topic和partition的Lead Partition
    //Finding the Lead Broker for a Topic and Partition
    //The easiest way to do this is to pass in a set of known Brokers to your logic,
    // either via a properties file or the command line.
    // These don’t have to be all the Brokers in the cluster,
    // rather just a set where you can start looking for a live Broker to query for Leader information.
    //seedBrokers是使用的replicaBrokers列表
    //调用PartitionMetadata的leader和replicas方法可以得到该Partition对应的Leader和Replicas Broker信息
    private PartitionMetadata findLeader(List<String> seedBrokers, int port, String topic, int partition) {

        PartitionMetadata returnMetaData = null;
        loop:
        for (String seed : seedBrokers) {
            SimpleConsumer consumer = null;
            try {
                /**
                 class SimpleConsumer(val host: String,
                 val port: Int,
                 val soTimeout: Int,
                 val bufferSize: Int,
                 val clientId: String)
                 */
                consumer = new SimpleConsumer(seed, port, 100000, 64 * 1024, "leaderLookup");
                List<String> topics = Collections.singletonList(topic);
                TopicMetadataRequest req = new TopicMetadataRequest(topics);
                kafka.javaapi.TopicMetadataResponse resp = consumer.send(req);

                //The call to topicsMetadata() asks the Broker you are connected to for all the details about the topic we are interested in
                List<TopicMetadata> metaData = resp.topicsMetadata();
                for (TopicMetadata item : metaData) {
                    //The loop on partitionsMetadata iterates through all the partitions until we find the one we want. Once we find it, we can break out of all the loops.
                    for (PartitionMetadata part : item.partitionsMetadata()) {
                        if (part.partitionId() == partition) {
                            returnMetaData = part;
                            break loop;
                        }
                    }
                }
            } catch (Exception e) {
                System.out.println("Error communicating with Broker [" + seed + "] to find Leader for [" + topic
                        + ", " + partition + "] Reason: " + e);
            } finally {
                if (consumer != null) consumer.close();
            }
        }
        if (returnMetaData != null) {
            replicaBrokers.clear();

            ///将replicaBrokers进行缓存
            for (kafka.cluster.Broker replica : returnMetaData.replicas()) {
                replicaBrokers.add(replica.host());
            }
        }
        return returnMetaData;
    }
}

 参考:https://cwiki.apache.org/confluence/display/KAFKA/0.8.0+SimpleConsumer+Example

 

 

【Kafka十三】Kafka Simple Consumer

  • 0

    开心

    开心

  • 0

    板砖

    板砖

  • 0

    感动

    感动

  • 0

    有用

    有用

  • 0

    疑问

    疑问

  • 0

    难过

    难过

  • 0

    无聊

    无聊

  • 0

    震惊

    震惊

编辑推荐
consumer就是接收producer发布的消息进行处理的应用。 上图描述了consumer消费消息的high-level层工
最近和实验室哥们儿在搞storm kafka的东西。 两者始终不能连接起来。 突然想到以前自己写consumer的
High Level Consumer 很多时候,客户程序只是希望从Kafka读取数据,不太关心消息offset的处理。同时
High Level Consumer 很多时候,客户程序只是希望从Kafka读取数据,不太关心消息offset的处理。同时
High Level Consumer 很多时候,客户程序只是希望从Kafka读取数据,不太关心消息offset的处理。同时
High Level Consumer 很多时候,客户程序只是希望从Kafka读取数据,不太关心消息offset的处理。同时
转载自:http://my.oschina.net/ielts0909/blog?catalog=263107&p=2 最近一直忙着各种设计和文档,
8 kafka
kafka设计原理介绍 背景介绍 Kafka简介   Kafka是一种分布式的,基于发布/订阅的消息系统。主要设
9 kafka
topics主题 producers 生产者(发布消息到主题) consumers 消费者(订阅主题并处理消息) broker
10 kafka
【原创声明】本文属作者原创,已授权InfoQ中文站首发,转载请务必在文章开头标明出自“Jason's Blog
版权所有 IT知识库 CopyRight © 2009-2015 IT知识库 IT610.com , All Rights Reserved. 京ICP备09083238号