没有向ZK写offset数据

两天公司要学习kafka,结合之前的storm,做了一个简单的集成,之前也参考了网上的例子一些例子,发现或多或少都有一些问题。所以自己做了一个。

 

    这个是网上其他人遇到的问题,给摘录一下,防止以后自己和大家出现:

这几天工作需要使用storm+kafka,基本场景是应用出现错误,发送日志到kafka的某个topic,storm订阅该topic,然后进行后续处理。场景非常简单,但是在学习过程中,遇到一个奇怪的异常情况:使用KafkaSpout读取topic数据时,没有向ZK写offset数据,致使每次都从头开始读取。纠结了两天,终于碰巧找到原因:应该使用BaseBasicBolt作为bolt的父类,而不是BaseRichBolt

通过本文记录一下这种情况,后文中根据上述场景提供几个简单的例子。基础理论查看storm笔记:storm基本概念,或查看Storm 简介

基本订阅

基本场景:订阅kafka的某个topic,然后在读取的消息前加上自定义的字符串,然后写回到kafka另外一个topic。

从Kafka读取数据的Spout使用storm.kafka.KafkaSpout,向Kafka写数据的Bolt使用storm.kafka.bolt.KafkaBolt。中间进行进行数据处理的Bolt定义为TopicMsgBolt。闲言少叙,奉上代码:


public class TopicMsgTopology {
    public static void main(String[] args) throws Exception {
        // 配置Zookeeper地址
        BrokerHosts brokerHosts = new ZkHosts("zk1:2181,zk2:2281,zk3:2381");
        // 配置Kafka订阅的Topic,以及zookeeper中数据节点目录和名字
        SpoutConfig spoutConfig = new SpoutConfig(brokerHosts, "msgTopic1", "/topology/root", "topicMsgTopology");
        // 配置KafkaBolt中的kafka.broker.properties
        Config conf = new Config();
        Properties props = new Properties();
        // 配置Kafka broker地址
        props.put("metadata.broker.list", "dev2_55.wfj-search:9092");
        // serializer.class为消息的序列化类
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        conf.put("kafka.broker.properties", props);
        // 配置KafkaBolt生成的topic
        conf.put("topic", "msgTopic2");
        spoutConfig.scheme = new SchemeAsMultiScheme(new MessageScheme());
        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout("msgKafkaSpout", new KafkaSpout(spoutConfig));
        builder.setBolt("msgSentenceBolt", new TopicMsgBolt()).shuffleGrouping("msgKafkaSpout");
        builder.setBolt("msgKafkaBolt", new KafkaBolt<String, Integer>()).shuffleGrouping("msgSentenceBolt");
        if (args.length == 0) {
            String topologyName = "kafkaTopicTopology";
            LocalCluster cluster = new LocalCluster();
            cluster.submitTopology(topologyName, conf, builder.createTopology());
            Utils.sleep(100000);
            cluster.killTopology(topologyName);
            cluster.shutdown();
        } else {
            conf.setNumWorkers(1);
            StormSubmitter.submitTopology(args[0], conf, builder.createTopology());
        }
    }
}

storm.kafka.ZkHosts构造方法的参数是zookeeper标准配置地址的形式(ZooKeeper环境搭建可以查看ZooKeeper安装部署),zk1、zk2、zk3在本地配置了host,因为服务器使用的伪分布式模式,因此几个端口号不是默认的2181。

storm.kafka.SpoutConfig构造方法第一个参数为上述的storm.kafka.ZkHosts对象,第二个为待订阅的topic名称,第三个参数zkRoot为写读取topic时的偏移量offset数据的节点(zk node),第四个参数为该节点上的次级节点名(有个地方说这个是spout的id)。

backtype.storm.Config对象是配置storm的topology(拓扑)所需要的基础配置。

backtype.storm.spout.SchemeAsMultiScheme的构造方法输入的参数是订阅kafka数据的处理参数,这里的MessageScheme是自定义的,代码如下:

public class MessageScheme implements Scheme {
    private static final Logger logger = LoggerFactory.getLogger(MessageScheme.class);

    @Override
    public List<Object> deserialize(byte[] ser) {
        try {
            String msg = new String(ser, "UTF-8");
            logger.info("get one message is {}", msg);
            return new Values(msg);
        } catch (UnsupportedEncodingException ignored) {
            return null;
        }
    }

    @Override
    public Fields getOutputFields() {
        return new Fields("msg");
    }
}

MessageScheme类中getOutputFields方法是KafkaSpout向后发送tuple(storm传输数据的最小结构)的名字,需要与接收数据的Bolt中统一(在这个例子中可以不统一,因为后面直接取第0条数据,但是在wordCount的那个例子中就需要统一了)。

TopicMsgBolt类是从storm.kafka.KafkaSpout接收数据的Bolt,对接收到的数据进行处理,然后向后传输给storm.kafka.bolt.KafkaBolt。代码如下:

public class TopicMsgBolt extends BaseBasicBolt {
    private static final Logger logger = LoggerFactory.getLogger(TopicMsgBolt.class);

    @Override
    public void execute(Tuple input, BasicOutputCollector collector) {
        String word = (String) input.getValue(0);
        String out = "Message got is '" + word + "'!";
        logger.info("out={}", out);
        collector.emit(new Values(out));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        declarer.declare(new Fields("message"));
    }
}

此处需要特别注意的是,要使用backtype.storm.topology.base.BaseBasicBolt对象作为父类,否则不会在zk记录偏移量offset数据。

需要编写的代码已完成,接下来就是在搭建好的storm、kafka中进行测试:

# 创建topic
./bin/kafka-topics.sh --create --zookeeper zk1:2181,zk2:2281,zk3:2381 --replication-factor 1 --partitions 1 --topic msgTopic1
./bin/kafka-topics.sh --create --zookeeper zk1:2181,zk2:2281,zk3:2381 --replication-factor 1 --partitions 1 --topic msgTopic2

接下来需要分别对msgTopic1、msgTopic2启动producer(生产者)与consumer(消费者):

# 对msgTopic1启动producer,用于发送数据
./bin/kafka-console-producer.sh --broker-list dev2_55.wfj-search:9092 --topic msgTopic1
# 对msgTopic2启动consumer,用于查看发送数据的处理结果
./bin/kafka-console-consumer.sh --zookeeper zk1:2181,zk2:2281,zk3:2381 --topic msgTopic2 --from-beginning

然后将打好的jar包上传到storm的nimbus(可以使用远程上传或先上传jar包到nimbus节点所在服务器,然后本地执行):

# ./bin/storm jar topology TopicMsgTopology.jar cn.howardliu.demo.storm.kafka.topicMsg.TopicMsgTopology TopicMsgTopology

待对应的worker启动好之后,就可以在msgTopic1的producer对应终端输入数据,然后在msgTopic2的consumer对应终端查看输出结果了。

有几点需要注意的:

  1. 必须先创建msgTopic1、msgTopic2两个topic;
  2. 定义的bolt必须使用BaseBasicBolt作为父类,不能够使用BaseRichBolt,否则无法记录偏移量;
  3. zookeeper最好使用至少三个节点的分布式模式或伪分布式模式,否则会出现一些异常情况;
  4. 在整个storm下,spout、bolt的id必须唯一,否则会出现异常。
  5. TopicMsgBolt类作为storm.kafka.bolt.KafkaBolt前的最后一个Bolt,需要将输出数据名称定义为message,否则KafkaBolt无法接收数据。

wordCount

简单的输入输出做完了,来点复杂点儿的场景:从某个topic定于消息,然后根据空格分词,统计单词数量,然后将当前输入的单词数量推送到另一个topic。

首先规划需要用到的类:

  1. 从KafkaSpout接收数据并进行处理的backtype.storm.spout.Scheme子类;
  2. 数据切分bolt:SplitSentenceBolt
  3. 计数bolt:WordCountBolt
  4. 报表bolt:ReportBolt
  5. topology定义:WordCountTopology
  6. 最后再加一个原样显示订阅数据的bolt:SentenceBolt

backtype.storm.spout.Scheme子类可以使用上面已经定义过的MessageScheme,此处不再赘述。

SplitSentenceBolt是对输入数据进行分割,简单的使用String类的split方法,然后将每个单词命名为“word”,向后传输,代码如下:

public class SplitSentenceBolt extends BaseBasicBolt {
    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("word"));
    }

    @Override
    public void execute(Tuple input, BasicOutputCollector collector) {
        String sentence = input.getStringByField("msg");
        String[] words = sentence.split(" ");
        Arrays.asList(words).forEach(word -> collector.emit(new Values(word)));
    }
}

SentenceBolt是从KafkaSpout接收数据,然后直接输出。在拓扑图上就是从输入分叉,一个进入SplitSentenceBolt,一个进入SentenceBolt。这种结构可以应用在Lambda架构中,代码如下:

public class SentenceBolt extends BaseBasicBolt {
    private static final Logger logger = LoggerFactory.getLogger(SentenceBolt.class);

    @Override
    public void execute(Tuple tuple, BasicOutputCollector basicOutputCollector) {
        String msg = tuple.getStringByField("msg");
        logger.info("get one message is {}", msg);
        basicOutputCollector.emit(new Values(msg));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("sentence"));
    }
}

WordCountBolt是对接收到的单词进行汇总统一,然后将单词“word”及其对应数量“count”向后传输,代码如下:

public class WordCountBolt extends BaseBasicBolt {
    private Map<String, Long> counts = null;

    @Override
    public void prepare(Map stormConf, TopologyContext context) {
        this.counts = new ConcurrentHashMap<>();
        super.prepare(stormConf, context);
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("word", "count"));
    }

    @Override
    public void execute(Tuple input, BasicOutputCollector collector) {
        String word = input.getStringByField("word");
        Long count = this.counts.get(word);
        if (count == null) {
            count = 0L;
        }
        count++;
        this.counts.put(word, count);
        collector.emit(new Values(word, count));
    }
}

ReportBolt是对接收到的单词及数量进行整理,拼成json格式,然后继续向后传输,代码如下:

public class ReportBolt extends BaseBasicBolt {
    @Override
    public void execute(Tuple input, BasicOutputCollector collector) {
        String word = input.getStringByField("word");
        Long count = input.getLongByField("count");
        String reportMessage = "{'word': '" + word + "', 'count': '" + count + "'}";
        collector.emit(new Values(reportMessage));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("message"));
    }
}

最后是定义topology(拓扑)WordCountTopology,代码如下:

public class WordCountTopology {
    private static final String KAFKA_SPOUT_ID = "kafkaSpout";
    private static final String SENTENCE_BOLT_ID = "sentenceBolt";
    private static final String SPLIT_BOLT_ID = "sentenceSplitBolt";
    private static final String WORD_COUNT_BOLT_ID = "sentenceWordCountBolt";
    private static final String REPORT_BOLT_ID = "reportBolt";
    private static final String KAFKA_BOLT_ID = "kafkabolt";
    private static final String CONSUME_TOPIC = "sentenceTopic";
    private static final String PRODUCT_TOPIC = "wordCountTopic";
    private static final String ZK_ROOT = "/topology/root";
    private static final String ZK_ID = "wordCount";
    private static final String DEFAULT_TOPOLOGY_NAME = "sentenceWordCountKafka";

    public static void main(String[] args) throws Exception {
        // 配置Zookeeper地址
        BrokerHosts brokerHosts = new ZkHosts("zk1:2181,zk2:2281,zk3:2381");
        // 配置Kafka订阅的Topic,以及zookeeper中数据节点目录和名字
        SpoutConfig spoutConfig = new SpoutConfig(brokerHosts, CONSUME_TOPIC, ZK_ROOT, ZK_ID);
        spoutConfig.scheme = new SchemeAsMultiScheme(new MessageScheme());

        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout(KAFKA_SPOUT_ID, new KafkaSpout(spoutConfig));
        builder.setBolt(SENTENCE_BOLT_ID, new SentenceBolt()).shuffleGrouping(KAFKA_SPOUT_ID);
        builder.setBolt(SPLIT_BOLT_ID, new SplitSentenceBolt()).shuffleGrouping(KAFKA_SPOUT_ID);
        builder.setBolt(WORD_COUNT_BOLT_ID, new WordCountBolt()).fieldsGrouping(SPLIT_BOLT_ID, new Fields("word"));
        builder.setBolt(REPORT_BOLT_ID, new ReportBolt()).shuffleGrouping(WORD_COUNT_BOLT_ID);
        builder.setBolt(KAFKA_BOLT_ID, new KafkaBolt<String, Long>()).shuffleGrouping(REPORT_BOLT_ID);

        Config config = new Config();
        Map<String, String> map = new HashMap<>();
        map.put("metadata.broker.list", "dev2_55.wfj-search:9092");// 配置Kafka broker地址
        map.put("serializer.class", "kafka.serializer.StringEncoder");// serializer.class为消息的序列化类
        config.put("kafka.broker.properties", map);// 配置KafkaBolt中的kafka.broker.properties
        config.put("topic", PRODUCT_TOPIC);// 配置KafkaBolt生成的topic

        if (args.length == 0) {
            LocalCluster cluster = new LocalCluster();
            cluster.submitTopology(DEFAULT_TOPOLOGY_NAME, config, builder.createTopology());
            Utils.sleep(100000);
            cluster.killTopology(DEFAULT_TOPOLOGY_NAME);
            cluster.shutdown();
        } else {
            config.setNumWorkers(1);
            StormSubmitter.submitTopology(args[0], config, builder.createTopology());
        }
    }
}

除了上面提过应该注意的地方,此处还需要注意,storm.kafka.SpoutConfig定义的zkRoot与id应该与第一个例子中不同(至少保证id不同,否则两个topology将使用一个节点记录偏移量)。

 


基本场景是应用出现错误,发送日志到kafka的某个topic,storm订阅该topic,然后进行后续处理。场景非常简单,但是在学习过程中,遇到一个奇怪的异常情况:使用KafkaSpout读取topic数据时,没有向ZK写offset数据,致使每次都从头开始读取。纠结了两天,终于碰巧找到原因:应该使用BaseRichBolt

        

基本订阅 :

基本场景:订阅kafka的某个topic,然后在读取的消息前加上自定义的字符串,然后写回到kafka另外一个topic。  从Kafka读取数据的Spout使用storm.kafka.KafkaSpout,向Kafka写数据的Bolt使用storm.kafka.bolt.KafkaBolt。中间进行进行数据处理的Bolt定义为TopicMsgBolt。
  

 

 


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import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.StormSubmitter;
import backtype.storm.spout.SchemeAsMultiScheme;
import backtype.storm.topology.IBasicBolt;
import backtype.storm.topology.TopologyBuilder;
import backtype.storm.utils.Utils;
import storm.kafka.BrokerHosts;
import storm.kafka.KafkaSpout;
import storm.kafka.SpoutConfig;
import storm.kafka.ZkHosts;
import storm.kafka.bolt.KafkaBolt;
 
import java.util.Properties;
 
public class TopicMsgTopology {
    public static void main(String[] args) throws Exception {
        // 配置Zookeeper地址
        BrokerHosts brokerHosts = new ZkHosts("localhost:2181");
        // 配置Kafka订阅的Topic,以及zookeeper中数据节点目录和名字
        SpoutConfig spoutConfig = new SpoutConfig(brokerHosts, "msgTopic1""/topology/root1""topicMsgTopology");
        // 配置KafkaBolt中的kafka.broker.properties
        Config conf = new Config();
        Properties props = new Properties();
        // 配置Kafka broker地址
        props.put("metadata.broker.list""localhost:9092");
        // serializer.class为消息的序列化类
        props.put("serializer.class""kafka.serializer.StringEncoder");
        conf.put("kafka.broker.properties", props);
        // 配置KafkaBolt生成的topic
        conf.put("topic""msgTopic2");
        spoutConfig.scheme = new SchemeAsMultiScheme(new MessageScheme());
        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout("msgKafkaSpout"new KafkaSpout(spoutConfig));
        builder.setBolt("msgSentenceBolt", (IBasicBolt) new TopicMsgBolt()).shuffleGrouping("msgKafkaSpout");
        builder.setBolt("msgKafkaBolt"new KafkaBolt<String, Integer>()).shuffleGrouping("msgSentenceBolt");
        if (args.length == 0) {
            String topologyName = "kafkaTopicTopology";
            LocalCluster cluster = new LocalCluster();
            cluster.submitTopology(topologyName, conf, builder.createTopology());
            Utils.sleep(100000);
            cluster.killTopology(topologyName);
            cluster.shutdown();
        else {
            conf.setNumWorkers(1);
            StormSubmitter.submitTopology(args[0], conf, builder.createTopology());
        }
    }
}


posted @ 2017-02-23 10:51  一片黑  阅读(290)  评论(0编辑  收藏  举报