Log4j1、Logback 以及 Log4j2 性能测试对
- 2019 年 10 月 29 日
- 筆記
来源：https://dwz.cn/woS9vbcQ
- 环境
- 准备
- 测试
- 结果
环境
jdk：1.7.0_79 cpu：[email protected] 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/
