Flume-ng+Kafka+storm

xuyangus · 发表于 2017-5-21 14:12:59

　　出处：http://blog.csdn.net/zxcvg/article/details/18600335/ 谢谢
吐个槽：在word文档中写好的包括图片在csdn粘贴过来后图片必须重新上传不爽啊！！
Flume-ng+Kafka+storm的学习笔记

Flume-ng
Flume是一个分布式、可靠、和高可用的海量日志采集、聚合和传输的系统。

Flume的文档可以看http://flume.apache.org/FlumeUserGuide.html 官方的英文文档介绍的比较全面。
不过这里写写自己的见解

这个是flume的架构图
从上图可以看到几个名词：
Agent: 一个Agent包含Source、Channel、Sink和其他的组件。Flume就是一个或多个Agent构成的。
      Source：数据源。简单的说就是agent获取数据的入口。
       Channel：管道。数据流通和存储的通道。一个source必须至少和一个channel关联。
       Sink：用来接收channel传输的数据并将之传送到指定的地方。传送成功后数据从channel中删除。

Flume具有高可扩展性可随意组合：

注意 source是接收源 sink是发送源

上图是一个source将数据发给3个channel 其中的sink2将数据发给JMS ，sink3将数据发给另一个source。
总的来说flume的扩展性非常高根据需要可随意组合。
现在在说说一个概念叫Event：
Event是flume的数据传输的基本单元。Flume本质上是将数据作为一个event从源头传到结尾。是由可选的Headers和载有数据的一个byte array构成。
代码结构：

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/**
* Basic representation of a data object inFlume.
* Provides access to data as it flows throughthe system.
*/
public interface Event{
/**
* Returns a map of name-valuepairs describing the data stored in the body.
*/
public Map<String, String> getHeaders();
/**
* Set the event headers
* @param headersMap of headers to replace the current headers.
*/
public void setHeaders(Map<String, String> headers);
/**
* Returns the raw byte array of the datacontained in this event.
*/
public byte[] getBody();
/**
* Sets the raw byte array of the datacontained in this event.
* @param body Thedata.
*/
public void setBody(byte[] body);
}

　　

这个是网上找的flume channel ，source，sink的汇总
链接是http://abloz.com/2013/02/26/flume-channel-source-sink-summary.html

Component	Type	Description	Implementation Class
Channel	memory	In-memory, fast, non-durable event transport	MemoryChannel
Channel	file	A channel for reading, writing, mapping, and manipulating a file	FileChannel
Channel	jdbc	JDBC-based, durable event transport (Derby-based)	JDBCChannel
Channel	recoverablememory	A durable channel implementation that uses the local file system for its storage	RecoverableMemoryChannel
Channel	org.apache.flume.channel.PseudoTxnMemoryChannel	Mainly for testing purposes. Not meant for production use.	PseudoTxnMemoryChannel
Channel	(custom type as FQCN)	Your own Channel impl.	(custom FQCN)
Source	avro	Avro Netty RPC event source	AvroSource
Source	exec	Execute a long-lived Unix process and read from stdout	ExecSource
Source	netcat	Netcat style TCP event source	NetcatSource
Source	seq	Monotonically incrementing sequence generator event source	SequenceGeneratorSource
Source	org.apache.flume.source.StressSource	Mainly for testing purposes. Not meant for production use. Serves as a continuous source of events where each event has the same payload. The payload consists of some number of bytes (specified bysize property, defaults to 500) where each byte has the signed value Byte.MAX_VALUE (0x7F, or 127).	org.apache.flume.source.StressSource
Source	syslogtcp	SyslogTcpSource
Source	syslogudp	SyslogUDPSource
Source	org.apache.flume.source.avroLegacy.AvroLegacySource	AvroLegacySource
Source	org.apache.flume.source.thriftLegacy.ThriftLegacySource	ThriftLegacySource
Source	org.apache.flume.source.scribe.ScribeSource	ScribeSource
Source	(custom type as FQCN)	Your own Source impl.	(custom FQCN)
Sink	hdfs	Writes all events received to HDFS (with support for rolling, bucketing, HDFS-200 append, and more)	HDFSEventSink
Sink	org.apache.flume.sink.hbase.HBaseSink	A simple sink that reads events from a channel and writes them to HBase.	org.apache.flume.sink.hbase.HBaseSink
Sink	org.apache.flume.sink.hbase.AsyncHBaseSink	org.apache.flume.sink.hbase.AsyncHBaseSink
Sink	logger	Log events at INFO level via configured logging subsystem (log4j by default)	LoggerSink
Sink	avro	Sink that invokes a pre-defined Avro protocol method for all events it receives (when paired with an avro source, forms tiered collection)	AvroSink
Sink	file_roll	RollingFileSink
Sink	irc	IRCSink
Sink	null	/dev/null for Flume – blackhole all events received	NullSink
Sink	(custom type as FQCN)	Your own Sink impl.	(custom FQCN)
ChannelSelector	replicating	ReplicatingChannelSelector
ChannelSelector	multiplexing	MultiplexingChannelSelector
ChannelSelector	(custom type)	Your own ChannelSelector impl.	(custom FQCN)
SinkProcessor	default	DefaultSinkProcessor
SinkProcessor	failover	FailoverSinkProcessor
SinkProcessor	load_balance	Provides the ability to load-balance flow over multiple sinks.	LoadBalancingSinkProcessor
SinkProcessor	(custom type as FQCN)	Your own SinkProcessor impl.	(custom FQCN)
Interceptor$Builder	host	HostInterceptor$Builder
Interceptor$Builder	timestamp	TimestampInterceptor	TimestampInterceptor$Builder
Interceptor$Builder	static	StaticInterceptor$Builder
Interceptor$Builder	regex_filter	RegexFilteringInterceptor$Builder
Interceptor$Builder	(custom type as FQCN)	Your own Interceptor$Builder impl.	(custom FQCN)
EventSerializer$Builder	text	BodyTextEventSerializer$Builder
EventSerializer$Builder	avro_event	FlumeEventAvroEventSerializer$Builder
EventSerializer	org.apache.flume.sink.hbase.SimpleHbaseEventSerializer	SimpleHbaseEventSerializer
EventSerializer	org.apache.flume.sink.hbase.SimpleAsyncHbaseEventSerializer	SimpleAsyncHbaseEventSerializer
EventSerializer	org.apache.flume.sink.hbase.RegexHbaseEventSerializer	RegexHbaseEventSerializer
HbaseEventSerializer	Custom implementation of serializer for HBaseSink. (custom type as FQCN)	Your own HbaseEventSerializer impl.	(custom FQCN)
AsyncHbaseEventSerializer	Custom implementation of serializer for AsyncHbase sink. (custom type as FQCN)	Your own AsyncHbaseEventSerializer impl.	(custom FQCN)
EventSerializer$Builder	Custom implementation of serializer for all sinks except for HBaseSink and AsyncHBaseSink. (custom type as FQCN)	Your own EventSerializer$Builder impl.

下面介绍下kafka以及kafka和flume的整合
Kafka：
       从这个链接抄了些内容下来http://dongxicheng.org/search-engine/kafka/
Kafka是Linkedin于2010年12月份开源的消息系统，它主要用于处理活跃的流式数据。活跃的流式数据在web网站应用中非常常见，这些数据包括网站的pv、用户访问了什么内容，搜索了什么内容等。这些数据通常以日志的形式记录下来，然后每隔一段时间进行一次统计处理。
传统的日志分析系统提供了一种离线处理日志信息的可扩展方案，但若要进行实时处理，通常会有较大延迟。而现有的消（队列）系统能够很好的处理实时或者近似实时的应用，但未处理的数据通常不会写到磁盘上，这对于Hadoop之类（一小时或者一天只处理一部分数据）的离线应用而言，可能存在问题。Kafka正是为了解决以上问题而设计的，它能够很好地离线和在线应用。
2、设计目标
（1）数据在磁盘上存取代价为O(1)。一般数据在磁盘上是使用BTree存储的，存取代价为O（lgn）。
（2）高吞吐率。即使在普通的节点上每秒钟也能处理成百上千的message。
（3）显式分布式，即所有的producer、broker和consumer都会有多个，均为分布式的。
（4）支持数据并行加载到Hadoop中。
3、 KafKa部署结构

kafka是显式分布式架构，producer、broker（Kafka）和consumer都可以有多个。Kafka的作用类似于缓存，即活跃的数据和离线处理系统之间的缓存。几个基本概念：
（1）message（消息）是通信的基本单位，每个producer可以向一个topic（主题）发布一些消息。如果consumer订阅了这个主题，那么新发布的消息就会广播给这些consumer。
（2）Kafka是显式分布式的，多个producer、consumer和broker可以运行在一个大的集群上，作为一个逻辑整体对外提供服务。对于consumer，多个consumer可以组成一个group，这个message只能传输给某个group中的某一个consumer.

  数据从producer推送到broker，接着consumer在从broker上拉取数据。Zookeeper是一个分布式服务框架用来解决分布式应用中的数据管理问题等。
在kafka中有几个重要概念producer生产者 consumer 消费者 topic 主题。
我们来实际开发一个简单的生产者消费者的例子。
生产者：

[java] view plaincopyprint?

public classProducerTest {
public static void main(String[] args) {
Properties props = newProperties();
props.setProperty("metadata.broker.list","xx.xx.xx.xx:9092");
props.setProperty("serializer.class","kafka.serializer.StringEncoder");
props.put("request.required.acks","1");
ProducerConfigconfig = new ProducerConfig(props);
Producer<String, String> producer = newProducer<String, String>(config);
KeyedMessage<String, String> data = newKeyedMessage<String, String>("kafka","test-kafka");
try {
producer.send(data);
} catch (Exception e) {
e.printStackTrace();
}
producer.close();
}
}

上面的代码中的xx.xx.xx.xx是kafka server的地址.
上面代码的意思就是向主题 kafka中同步（不配置的话默认是同步发射）发送了一个信息是test-kafka.
下面来看看消费者：

[java] view plaincopyprint?

public classConsumerTest extends Thread {
private finalConsumerConnector consumer;
private final String topic;
public static voidmain(String[] args) {
ConsumerTest consumerThread = newConsumerTest("kafka");
consumerThread.start();
}
publicConsumerTest(String topic) {
consumer =kafka.consumer.Consumer
.createJavaConsumerConnector(createConsumerConfig());
this.topic =topic;
}
private staticConsumerConfig createConsumerConfig() {
Properties props = newProperties();
props.put("zookeeper.connect","xx.xx.xx.xx:2181");
props.put("group.id", "0");
props.put("zookeeper.session.timeout.ms","10000");
// props.put("zookeeper.sync.time.ms", "200");
// props.put("auto.commit.interval.ms", "1000");
return newConsumerConfig(props);
}
public void run(){
Map<String,Integer> topickMap = new HashMap<String, Integer>();
topickMap.put(topic, 1);
Map<String, List<KafkaStream<byte[],byte[]>>> streamMap =consumer.createMessageStreams(topickMap);
KafkaStream<byte[],byte[]>stream = streamMap.get(topic).get(0);
ConsumerIterator<byte[],byte[]> it =stream.iterator();
System.out.println("--------------------------");
while(it.hasNext()){
//
System.out.println("(consumer)--> " +new String(it.next().message()));
}
}
}

上面的代码就是负责接收生产者发送过来的消息测试的时候先开启消费者然后再运行生产者即可看到效果。
接下来我们将flume 和kafka进行整合：
在flume的source数据源接收到数据后通过管道到达sink，我们需要写一个kafkaSink 来将sink从channel接收的数据作为kafka的生产者将数据发送给消费者。
具体代码：

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public class KafkaSink extends AbstractSinkimplementsConfigurable {
private static final Log logger = LogFactory.getLog(KafkaSink.class);
private Stringtopic;
private Producer<String, String>producer;
@Override
public Status process()throwsEventDeliveryException {
Channel channel =getChannel();
Transaction tx =channel.getTransaction();
try {
tx.begin();
Event e = channel.take();
if(e ==null) {
tx.rollback();
return Status.BACKOFF;
}
KeyedMessage<String,String> data = new KeyedMessage<String, String>(topic,newString(e.getBody()));
producer.send(data);
logger.info("Message: {}"+new String( e.getBody()));
tx.commit();
return Status.READY;
} catch(Exceptione) {
logger.error("KafkaSinkException:{}",e);
tx.rollback();
return Status.BACKOFF;
} finally {
tx.close();
}
}
@Override
public void configure(Context context) {
topic = "kafka";
Properties props = newProperties();
props.setProperty("metadata.broker.list","xx.xx.xx.xx:9092");
props.setProperty("serializer.class","kafka.serializer.StringEncoder");
// props.setProperty("producer.type", "async");
// props.setProperty("batch.num.messages", "1");
props.put("request.required.acks","1");
ProducerConfigconfig = new ProducerConfig(props);
producer = newProducer<String, String>(config);
}
}

将此文件打成jar包传到flume的lib下面如果你也用的是maven的话需要用到assembly 将依赖的jar包一起打包进去。
在flume的配置是如下：

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agent1.sources = source1
agent1.sinks = sink1
agent1.channels =channel1
# Describe/configuresource1
agent1.sources.source1.type= avro
agent1.sources.source1.bind= localhost
agent1.sources.source1.port= 44444
# Describe sink1
agent1.sinks.sink1.type= xx.xx.xx.KafkaSink(这是类的路径地址)
# Use a channel whichbuffers events in memory
agent1.channels.channel1.type= memory
agent1.channels.channel1.capacity= 1000
agent1.channels.channel1.transactionCapactiy= 100
# Bind the source andsink to the channel
agent1.sources.source1.channels= channel1
agent1.sinks.sink1.channel= channel1

　　

测试的话是avro的方式传送数据的可以这样测试
bin/flume-ng avro-client--conf conf -H localhost -p 44444 -F/data/flumetmp/a
/data/flumetmp/a 这个为文件的地址.
测试的时候在本地一定要把上面写的消费者程序打开以便接收数据测试是否成功。
接下来我们介绍下storm然后将kafka的消费者和storm进行整合：
Storm：
Storm是一个分布式的实时消息处理系统。
Storm各个组件之间的关系：

Storm集群主要由一个主节点和一群工作节点（worker node）组成，通过 Zookeeper进行协调。
主节点：主节点通常运行一个后台程序 —— Nimbus，用于响应分布在集群中的节点，分配任务和监测故障。
工作节点： Supervisor,负责接受nimbus分配的任务，启动和停止属于自己管理的worker进程。Nimbus和Supervisor之间的协调由zookeeper完成。
Worker：处理逻辑的进程，在其中运行着多个Task，每个task 是一组spout/blots的组合。

Topology:是storm的实时应用程序，从启动开始一直运行，只要有tuple过来就会触发执行。拓扑：storm的消息流动很像一个拓扑结构。
2. stream是storm的核心概念，一个stream是一个持续的tuple序列，这些tuple被以分布式并行的方式创建和处理。
3. spouts是一个stream的源头，spouts负责从外部系统读取数据，并组装成tuple发射出去，tuple被发射后就开始再topology中传播。
4. bolt是storm中处理数据的核心，storm中所有的数据处理都是在bolt中完成的
这里就简单介绍一些概念具体的可以看些详细的教程。

我们接下来开始整合storm和kafka。
从上面的介绍得知storm的spout是负责从外部读取数据的所以我们需要开发一个KafkaSpout 来作为kafka的消费者和storm的数据接收源。可以看看这个https://github.com/HolmesNL/kafka-spout。我在下面只写一个简单的可供测试。
具体代码：

[java] view plaincopyprint?

public class KafkaSpout implements IRichSpout {
private static final Log logger = LogFactory.getLog(KafkaSpout.class);
/**
*
*/
private static final long serialVersionUID = -5569857211173547938L;
SpoutOutputCollector collector;
private ConsumerConnectorconsumer;
private Stringtopic;
public KafkaSpout(String topic) {
this.topic = topic;
}
@Override
public void open(Map conf, TopologyContext context,
SpoutOutputCollector collector) {
this.collector = collector;
}
private static ConsumerConfig createConsumerConfig() {
Properties props = newProperties();
props.put("zookeeper.connect","xx.xx.xx.xx:2181");
props.put("group.id","0");
props.put("zookeeper.session.timeout.ms","10000");
//props.put("zookeeper.sync.time.ms", "200");
//props.put("auto.commit.interval.ms", "1000");
return new ConsumerConfig(props);
}
@Override
public void close() {
// TODOAuto-generated method stub
}
@Override
public void activate() {
this.consumer = Consumer.createJavaConsumerConnector(createConsumerConfig());
Map<String, Integer> topickMap = newHashMap<String, Integer>();
topickMap.put(topic,new Integer(1));
Map<String, List<KafkaStream<byte[],byte[]>>>streamMap =consumer.createMessageStreams(topickMap);
KafkaStream<byte[],byte[]>stream = streamMap.get(topic).get(0);
ConsumerIterator<byte[],byte[]> it =stream.iterator();
while (it.hasNext()) {
String value = newString(it.next().message());
System.out.println("(consumer)-->" + value);
collector.emit(new Values(value), value);
}
}
@Override
public void deactivate() {
// TODOAuto-generated method stub
}
private boolean isComplete;
@Override
public void nextTuple() {
}
@Override
public void ack(Object msgId) {
// TODOAuto-generated method stub
}
@Override
public void fail(Object msgId) {
// TODOAuto-generated method stub
}
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("KafkaSpout"));
}
@Override
public Map<String, Object> getComponentConfiguration() {
// TODOAuto-generated method stub
return null;
}
}
public class FileBlots implementsIRichBolt{
OutputCollector collector;
public void prepare(Map stormConf, TopologyContext context,
OutputCollector collector) {
this.collector = collector;
}
public void execute(Tuple input) {
String line = input.getString(0);
for(String str : line.split("\\s+")){
List a = newArrayList();
a.add(input);
this.collector.emit(a,newValues(str));
}
this.collector.ack(input);
}
public void cleanup() {
}
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("words"));
}
public Map<String, Object> getComponentConfiguration() {
// TODOAuto-generated method stub
return null;
}
}
public class WordsCounterBlots implementsIRichBolt{
OutputCollector collector;
Map<String, Integer> counter;
public void prepare(Map stormConf, TopologyContext context,
OutputCollector collector) {
this.collector = collector;
this.counter =new HashMap<String, Integer>();
}
public void execute(Tuple input) {
String word = input.getString(0);
Integer integer = this.counter.get(word);
if(integer !=null){
integer +=1;
this.counter.put(word, integer);
}else{
this.counter.put(word, 1);
}
System.out.println("execute");
Jedis jedis = JedisUtils.getJedis();
jedis.incrBy(word, 1);
System.out.println("=============================================");
this.collector.ack(input);
}
public void cleanup() {
for(Entry<String, Integer> entry :this.counter.entrySet()){
System.out.println("------:"+entry.getKey()+"=="+entry.getValue());
}
}
public void declareOutputFields(OutputFieldsDeclarer declarer) {
}
public Map<String, Object> getComponentConfiguration() {
// TODOAuto-generated method stub
return null;
}
}

　　

Topology测试：

[java] view plaincopyprint?

public class KafkaTopology {
public static void main(String[] args) {
try {
JedisUtils.initialPool("xx.xx.xx.xx", 6379);
} catch (Exception e) {
e.printStackTrace();
}
TopologyBuilder builder = newTopologyBuilder(); builder.setSpout("kafka",new KafkaSpout("kafka"));
builder.setBolt("file-blots",new FileBlots()).shuffleGrouping("kafka");
builder.setBolt("words-counter",new WordsCounterBlots(),2).fieldsGrouping("file-blots",new Fields("words"));
Config config = new Config();
config.setDebug(true);
LocalCluster local = newLocalCluster();
local.submitTopology("counter", config, builder.createTopology());
}
}

　　

至此flume + kafka+storm的整合就写完了。注意这个是初始学习阶段做的测试不可正式用于线上环境，在写本文之时已经离测试过去了一段时间所以可能会有些错误请见谅。

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