1. 自定義source
http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.15.1/FlumeDeveloperGuide.html#source
一個簡單的自定義source
package com.wxx.bigdata.hadoop.mapreduce.flume;
import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.EventDeliveryException;
import org.apache.flume.PollableSource;
import org.apache.flume.conf.Configurable;
import org.apache.flume.event.SimpleEvent;
import org.apache.flume.source.AbstractSource;
/**
* 自定義flume的資料源
*/
public class CustomerSource extends AbstractSource implements Configurable, PollableSource {
private String prefix;
private String suffix;
/**
* 處理event
* @return
* @throws EventDeliveryException
*/
@Override
public Status process() throws EventDeliveryException {
Status status = null;
try {
for(int i = 1; i <= 100; i++){
SimpleEvent simpleEvent = new SimpleEvent();
simpleEvent.setBody((prefix + i + suffix).getBytes());
// 不需要關注事務,processEvent()方法中,由channel去處理
getChannelProcessor().processEvent(simpleEvent);
}
status = Status.READY;
} catch (Exception e) {
e.printStackTrace();
status = Status.BACKOFF;
} finally {
}
try {
Thread.sleep(5000);
} catch (InterruptedException e) {
e.printStackTrace();
}
//傳回狀态的結果
return status;
}
@Override
public long getBackOffSleepIncrement() {
return 0;
}
@Override
public long getMaxBackOffSleepInterval() {
return 0;
}
/**
* 擷取agent傳入的資訊
* @param context
*/
@Override
public void configure(Context context) {
this.prefix = context.getString("prefix", "ruozedata");
this.suffix = context.getString("suffix");
}
}
打jar包上傳到/home/hadoop/app/apache-flume-1.6.0-cdh5.15.1-bin/lib
啟動腳本
flume-ng agent \
--name a1 \
--conf-file /home/hadoop/script/flume/customer/customer_source.conf \
--conf $FLUME_HOME/conf \
-Dflume.root.logger=INFO,console
結果如下圖
mysql自定義Source請參考: https://github.com/keedio/flume-ng-sql-source
2. 自定義Sink
官網位址如下
http://archive.cloudera.com/cdh5/cdh/5/flume-ng-1.6.0-cdh5.15.1/FlumeDeveloperGuide.html#sink
代碼如下
package com.wxx.bigdata.hadoop.mapreduce.flume;
import org.apache.flume.*;
import org.apache.flume.conf.Configurable;
import org.apache.flume.sink.AbstractSink;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
/**
*
*/
public class CustomerSink extends AbstractSink implements Configurable {
private static final Logger logger = LoggerFactory.getLogger(CustomerSink.class);
private String prefix;
private String suffix;
@Override
public Status process() throws EventDeliveryException {
Status status = null;
Channel channel = getChannel();
Transaction transaction = channel.getTransaction();
transaction.begin();
try {
Event event ;
while (true){
event = channel.take();
if(event != null){
break;
}
}
String body = new String(event.getBody());
logger.info(prefix + body + suffix);
transaction.commit();
status = Status.READY;
}catch (Exception e){
transaction.rollback();
status = Status.BACKOFF;
}finally {
transaction.close();
}
return status;
}
@Override
public void configure(Context context) {
this.prefix = context.getString("prefix", "customer");
this.suffix = context.getString("suffix");
}
}
将檔案打成jar包上傳到$FLUME_HOME/lib
Flmue配置檔案為
#定義agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#定義source
a1.sources.r1.type = netcat
a1.sources.r1.bind = hadoop000
a1.sources.r1.port = 44444
#定義channnel
a1.channels.c1.type = memory
#定義sink
a1.sinks.k1.type = com.wxx.bigdata.hadoop.mapreduce.flume.CustomerSink
a1.sinks.k1.prefix = TEST
a1.sinks.k1.suffix = 2019
#定義配置關系
a1.sinks.k1.channel = c1
a1.sources.r1.channels = c1
啟動腳本
flume-ng agent \
--name a1 \
--conf-file /home/hadoop/script/flume/customer/customer_sink.conf \
--conf $FLUME_HOME/conf \
-Dflume.root.logger=INFO,console
3. 自定義攔截器
a1 的資料經過攔截器處理後,如果body中包含"PEK",則發送到a2,否則發送到a3
代碼如下
package com.wxx.bigdata.hadoop.mapreduce.flume;
import org.apache.flume.Context;
import org.apache.flume.Event;
import org.apache.flume.interceptor.Interceptor;
import org.apache.flume.interceptor.InterceptorBuilderFactory;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
public class CustomerInterceptor implements Interceptor {
private List<Event> events;
@Override
public void initialize() {
events = new ArrayList<>();
}
@Override
public Event intercept(Event event) {
Map<String, String> headers = event.getHeaders();
String body = new String(event.getBody());
if(body.contains("PEK")){
headers.put("type","PEK");
}else{
headers.put("type","OTHER");
}
return event;
}
@Override
public List<Event> intercept(List<Event> list) {
events.clear();
for(Event event : list){
events.add(intercept(event));
}
return events;
}
@Override
public void close() {
}
public static class Builder implements Interceptor.Builder{
@Override
public Interceptor build() {
return new CustomerInterceptor();
}
@Override
public void configure(Context context) {
}
}
}
三個agnet的配置檔案如下
#定義agent
a1.sources = r1
a1.sinks = k1 k2
a1.channels = c1 c2
#定義source
a1.sources.r1.type = netcat
a1.sources.r1.bind = hadoop000
a1.sources.r1.port = 44444
#interceptors
a1.sources.r1.interceptors = i1
a1.sources.r1.interceptors.i1.type = com.wxx.bigdata.hadoop.mapreduce.flume.CustomerInterceptor$Builder
#定義selector
a1.sources.r1.selector.type = multiplexing
a1.sources.r1.selector.header = type
a1.sources.r1.selector.mapping.PEK = c1
a1.sources.r1.selector.mapping.OTHER = c2
#定義channnel
a1.channels.c1.type = memory
a1.channels.c2.type = memory
#定義sink
a1.sinks.k1.type = avro
a1.sinks.k1.hostname = hadoop000
a1.sinks.k1.port = 44445
a1.sinks.k2.type = avro
a1.sinks.k2.hostname = hadoop000
a1.sinks.k2.port = 44446
#定義配置關系
a1.sources.r1.channels = c1 c2
a1.sinks.k1.channel = c1
a1.sinks.k2.channel = c2
#定義agent
a2.sources = r1
a2.sinks = k1
a2.channels = c1
#定義source
a2.sources.r1.type = avro
a2.sources.r1.bind = hadoop000
a2.sources.r1.port = 44445
#定義channnel
a2.channels.c1.type = memory
#定義sink
a2.sinks.k1.type = logger
#定義配置關系
a2.sinks.k1.channel = c1
a2.sources.r1.channels = c1
#定義agent
a1.sources = r1
a1.sinks = k1
a1.channels = c1
#定義source
a1.sources.r1.type = avro
a1.sources.r1.bind = hadoop000
a1.sources.r1.port = 44446
#定義channnel
a1.channels.c1.type = memory
#定義sink
a1.sinks.k1.type = logger
#定義配置關系
a1.sinks.k1.channel = c1
a1.sources.r1.channels = c1
啟動腳本如下
flume-ng agent \
--name a3 \
--conf-file /home/hadoop/script/flume/customer/customer_interceptor03.conf \
--conf $FLUME_HOME/conf \
-Dflume.root.logger=INFO,console
flume-ng agent \
--name a2 \
--conf-file /home/hadoop/script/flume/customer/customer_interceptor02.conf \
--conf $FLUME_HOME/conf \
-Dflume.root.logger=INFO,console
flume-ng agent \
--name a1 \
--conf-file /home/hadoop/script/flume/customer/customer_interceptor01.conf \
--conf $FLUME_HOME/conf \
-Dflume.root.logger=INFO,console
在一個terminal中輸入
a2下
a3下