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  • log4j输出日志到flume

    现需要通过log4j将日志输出到flume,通过flume将日志写到文件或hdfs中

    配置flume-config文件

    • 将日志下沉至文件
    a1.sources = r1
    a1.sinks = k1
    a1.channels = c1
    
    # 如果需要本地机器连接服务器flume,需要配置为0.0.0.0
    a1.sources.r1.type = avro
    a1.sources.r1.bind = 0.0.0.0
    a1.sources.r1.port = 44444
    
    # 将日志下沉至文件
    a1.sinks.k1.type = file_roll
    a1.sinks.k1.sink.directory = /home/hadoop/tmp
    a1.sinks.k1.sink.rollInterval=86400
    a1.sinks.k1.sink.batchSize=100
    a1.sinks.k1.sink.serializer=text
    a1.sinks.k1.sink.serializer.appendNewline = false
    
    # Use a channel which buffers events in memory
    a1.channels.c1.type = memory
    a1.channels.c1.capacity = 1000
    a1.channels.c1.transactionCapacity = 1000
    
    # Bind the source and sink to the channel
    a1.sources.r1.channels = c1
    a1.sinks.k1.channel = c1
    
    
    • 将日志下沉至HDFS
    agent1.sources = source1
    agent1.sinks = sink1
    agent1.channels = channel1
    
    # 如果需要本地机器连接服务器flume,需要配置为0.0.0.0
    agent1.sources.source1.type = avro
    agent1.sources.source1.bind = 0.0.0.0
    agent1.sources.source1.port = 44444
    
    # Describe sink1
    agent1.sinks.sink1.type = hdfs
    agent1.sinks.sink1.hdfs.path =hdfs://master:9000/log/flume-collection/%y-%m-%d/%H-%M
    agent1.sinks.sink1.hdfs.filePrefix = test_log
    agent1.sinks.sink1.hdfs.maxOpenFiles = 5000
    agent1.sinks.sink1.hdfs.batchSize= 100
    agent1.sinks.sink1.hdfs.fileType = DataStream
    agent1.sinks.sink1.hdfs.writeFormat =Text
    agent1.sinks.sink1.hdfs.rollSize = 102400
    agent1.sinks.sink1.hdfs.rollCount = 1000000
    agent1.sinks.sink1.hdfs.rollInterval = 60
    agent1.sinks.sink1.hdfs.round = true
    agent1.sinks.sink1.hdfs.roundValue = 10
    agent1.sinks.sink1.hdfs.roundUnit = minute
    agent1.sinks.sink1.hdfs.useLocalTimeStamp = true
    
    # Use a channel which buffers events in memory
    agent1.channels.channel1.type = memory
    agent1.channels.channel1.keep-alive = 120
    agent1.channels.channel1.capacity = 500000
    agent1.channels.channel1.transactionCapacity = 600
    
    # Bind the source and sink to the channel
    agent1.sources.source1.channels = channel1
    agent1.sinks.sink1.channel = channel1
    
    

    maven配置pom.xml文件

     <dependency>
        <groupId>org.slf4j</groupId>
        <artifactId>slf4j-log4j12</artifactId>
        <version>1.7.25</version>
    </dependency>
    
    <dependency>
        <groupId>org.apache.flume.flume-ng-clients</groupId>
        <artifactId>flume-ng-log4jappender</artifactId>
        <version>1.7.0</version>
    </dependency>
    

    配置log4j.properties

    log4j.category.com.xxx=INFO,console,flume
    
    log4j.appender.console=org.apache.log4j.ConsoleAppender
    log4j.appender.console.target=System.out
    log4j.appender.console.layout=org.apache.log4j.PatternLayout
    log4j.appender.console.layout.ConversionPattern="%d{yyyy-MM-dd HH:mm:ss} %p [%c:%L] - %m%n
    
    log4j.appender.flume = org.apache.flume.clients.log4jappender.Log4jAppender
    log4j.appender.flume.Threshold=INFO
    log4j.appender.flume.Hostname = [flume-ip]
    log4j.appender.flume.Port = [flume-port]
    log4j.appender.flume.UnsafeMode = true
    log4j.appender.flume.layout=org.apache.log4j.PatternLayout
    log4j.appender.flume.layout.ConversionPattern=%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n
    

    编写测试类

    public class ProduceLog {
    	static Logger logger = Logger.getLogger(ProduceLog.class);
    
    	public static void main(String[] args) throws InterruptedException {
    
    		while (true) {
    			// 每隔两秒log输出一下当前系统时间戳
    			logger.info(String.valueOf(new Date().getTime()));
    			Thread.sleep(2000);
    			try {
    				throw new Exception("exception msg");
    			}
    			catch (Exception e) {
    				logger.error("error:" + e.getMessage());
    			}
    		}
    	}
    }
    

    问题总结

    • flume和项目不在同一台机器上,需要远程写入到服务器
    agent1.sources.source1.bind = 0.0.0.0
    
    • log4j:ERROR Flume append() failed.
    如果使用log4j,需要将日志写入flume,在maven配置依赖jar包的时候一定要添加:flume-ng-log4jappender
    
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  • 原文地址:https://www.cnblogs.com/0xcafedaddy/p/6856641.html
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