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摘要: 原创出处 https://my.oschina.net/OutOfMemory/blog/789267 「ksfzhaohui」欢迎转载,保留摘要,谢谢!


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环境

jdk:1.7.0_79 cpu:i5-4570@3.20GHz 4核 eclipse:3.7 操作系统:win7

准备

1.log4j:1.7.21

<dependency>
<groupId>org.slf4j</groupId>
<artifactId>slf4j-log4j12</artifactId>
<version>1.7.21</version>
</dependency>

log4j.xml

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE log4j:configuration SYSTEM "log4j.dtd">
<log4j:configuration xmlns:log4j='http://jakarta.apache.org/log4j/'>

<appender name="myConsole" class="org.apache.log4j.ConsoleAppender">
<layout class="org.apache.log4j.PatternLayout">
<param name="ConversionPattern" value="[%d{dd HH:mm:ss,SSS\} %-5p] [%t] %c{2\} - %m%n" />
</layout>

<!--过滤器设置输出的级别 -->
<filter class="org.apache.log4j.varia.LevelRangeFilter">
<param name="levelMin" value="debug" />
<param name="levelMax" value="warn" />
<param name="AcceptOnMatch" value="true" />
</filter>
</appender>

<appender name="myFile" class="org.apache.log4j.DailyRollingFileAppender">
<param name="File" value="log4jTest.log" />
<param name="Append" value="true" />
<param name="DatePattern" value="'.'yyyy-MM-dd'.log'" />
<layout class="org.apache.log4j.PatternLayout">
<param name="ConversionPattern" value="[%t] - %m%n" />
</layout>
</appender>

<appender name="async_file" class="org.apache.log4j.AsyncAppender">
<param name="BufferSize" value="32" />
<appender-ref ref="myFile" />
</appender>

<logger name="org.logTest" additivity="false">
<level value="info" />
<appender-ref ref="async_file" /> <!-- 同步:FILE 异步:async_file -->
</logger>

</log4j:configuration>

2.logback:1.1.7

<dependency>
<groupId>ch.qos.logback</groupId>
<artifactId>logback-classic</artifactId>
<version>1.1.7</version>
</dependency>

logback.xml

<configuration>
<appender name="STDOUT" class="ch.qos.logback.core.ConsoleAppender">
<!-- encoder 默认配置为PatternLayoutEncoder -->
<encoder>
<pattern>%d{HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n
</pattern>
</encoder>
</appender>

<appender name="FILE" class="ch.qos.logback.core.FileAppender">
<file>testFile.log</file>
<append>true</append>
<encoder>
<pattern>[%t] - %m%n
</pattern>
</encoder>
</appender>

<!-- 异步输出 -->
<appender name="ASYNC" class="ch.qos.logback.classic.AsyncAppender">
<discardingThreshold>0</discardingThreshold>
<appender-ref ref="FILE" />
</appender>

<logger name="org.logTest" level="INFO"
additivity="false">
<appender-ref ref="ASYNC" /> <!-- 同步:FILE 异步:ASYNC-->
</logger>

<root level="ERROR">
<appender-ref ref="STDOUT" />
</root>
</configuration>

3.log4j2:2.6.2

<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-core</artifactId>
<version>2.6.2</version>
</dependency>
<dependency>
<groupId>org.apache.logging.log4j</groupId>
<artifactId>log4j-slf4j-impl</artifactId>
<version>2.6.2</version>
</dependency>
<dependency>
<groupId>com.lmax</groupId>
<artifactId>disruptor</artifactId>
<version>3.3.4</version>
</dependency>

log4j2.xml

<?xml version="1.0" encoding="UTF-8"?>
<!--设置log4j2的自身log级别为warn -->
<configuration status="warn">

<appenders>
<console name="Console" target="SYSTEM_OUT">
<PatternLayout pattern="[%d{HH:mm:ss:SSS}] [%p] - %l - %m%n" />
</console>

<RollingFile name="RollingFileInfo" fileName="info.log"
filePattern="${sys:user.home}/logs/hpaasvc/$${date:yyyy-MM}/info-%d{yyyy-MM-dd}-%i.log">
<Filters>
<ThresholdFilter level="INFO" />
<ThresholdFilter level="WARN" onMatch="DENY"
onMismatch="NEUTRAL" />
</Filters>
<PatternLayout pattern="[%t] - %m%n" />
<Policies>
<TimeBasedTriggeringPolicy />
<SizeBasedTriggeringPolicy size="100 MB" />
</Policies>
</RollingFile>

<RandomAccessFile name="RandomAccessFile" fileName="asyncWithLocation.log"
immediateFlush="false" append="true">
<PatternLayout>
<Pattern>[%t] - %m%n</Pattern>
</PatternLayout>
</RandomAccessFile>

</appenders>

<loggers>
<!-- <AsyncLogger name="asynLogger" level="trace"
includeLocation="true">
<AppenderRef ref="RandomAccessFile" />
</AsyncLogger> -->
<Root level="info" includeLocation="true">
<AppenderRef ref="RollingFileInfo" />
</Root>
</loggers>

</configuration>

测试

准备50条线程同时记录1000000条数据,然后统计时间,详细代码如下:

import java.util.concurrent.CountDownLatch;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class App {
private static Logger log = LoggerFactory.getLogger(App.class);

public static void main(String[] args) throws InterruptedException {
int messageSize = 1000000;
int threadSize = 50;
final int everySize = messageSize / threadSize;

final CountDownLatch cdl = new CountDownLatch(threadSize);
long startTime = System.currentTimeMillis();
for (int ts = 0; ts < threadSize; ts++) {
new Thread(new Runnable() {

@Override
public void run() {
for (int es = 0; es < everySize; es++) {
log.info("======info");
}
cdl.countDown();
}
}).start();
}

cdl.await();
long endTime = System.currentTimeMillis();
System.out.println("log4j1:messageSize = " + messageSize
+ ",threadSize = " + threadSize + ",costTime = "
+ (endTime - startTime) + "ms");
}
}

log4j1和logback的同步和异步分别修改为对应的appender就行了 log4j2的异步方式提供了2中模式: 1.全局开启 设置Log4jContextSelector系统属性为: org.apache.logging.log4j.core.async.AsyncLoggerContextSelector

System.setProperty("Log4jContextSelector", "org.apache.logging.log4j.core.async.AsyncLoggerContextSelector");

2.混合同步异步模式 不需要设置Log4jContextSelector,但是需要使用AsyncLogger标签

更多详细参考官方文档:http://logging.apache.org/log4j/2.x/manual/async.html#AllAsync

结果

分别测试完以后统计成表格如下:

img

log4j2的异步模式表现了绝对的性能优势,优势主要得益于Disruptor框架的使用

LMAX Disruptor technology. Asynchronous Loggers internally use the Disruptor, a lock-free inter-thread communication library, instead of queues, resulting in higher throughput and lower latency.

一个无锁的线程间通信库代替了原来的队列

更多Disruptor :

http://developer.51cto.com/art/201306/399370.htm http://ifeve.com/disruptor/

文章目录
  1. 1. 环境
  2. 2. 准备
  3. 3. 测试
  4. 4. 结果