如何提高springboot服务吞吐量

背景

生产环境偶尔会有一些慢请求致使系统性能降低，吞吐量降低，下面介绍几种优化建议。

方案

一、undertow替换tomcat

电子商务类型网站大多都是短请求，通常响应时间都在100ms，这时能够将web容器从tomcat替换为undertow，下面介绍下步骤： 一、增长pom配置

<dependency>
            <groupid>org.springframework.boot</groupid>
            <artifactid>spring-boot-starter-web</artifactid>
            <exclusions>
                <exclusion>
                    <groupid>org.springframework.boot</groupid>
                    <artifactid>spring-boot-starter-tomcat</artifactid>
                </exclusion>
            </exclusions>
        </dependency>
<dependency>
            <groupid>org.springframework.boot</groupid>
            <artifactid>spring-boot-starter-undertow</artifactid>
        </dependency>

二、增长相关配置

server:
  undertow:
    direct-buffers: true
    io-threads: 4
    worker-threads: 160

从新启动能够在控制台看到容器已经切换为undertow了

二、缓存

将部分热点数据或者静态数据放到本地缓存或者redis中，若是有须要能够定时更新缓存数据

三、异步

在代码过程当中咱们不少代码都不须要等返回结果，也就是部分代码是能够并行执行，这个时候可使用异步，最简单的方案是使用springboot提供的@Async注解，固然也能够经过线程池来实现，下面简单介绍下异步步骤。 一、pom依赖 通常springboot引入web相关依赖就行

<dependency>
            <groupid>org.springframework.boot</groupid>
            <artifactid>spring-boot-starter-web</artifactid>
        </dependency>

二、在启动类中增长@EnableAsync注解

@EnableAsync
@SpringBootApplication
public class AppApplication
{
    public static void main(String[] args)
    {
        SpringApplication.run(AppApplication.class, args);
    }
}

三、须要时在指定方法中增长@Async注解，若是是须要等待返回值，则demo以下

@Async
    public Future<string> doReturn(int i){
        try {
            // 这个方法须要调用500毫秒
            Thread.sleep(500);
        } catch (InterruptedException e) {
            e.printStackTrace();
        }
        // 消息汇总
        return new AsyncResult&lt;&gt;("异步调用");
    }

四、若是有线程变量或者logback中的mdc，能够增长传递

import org.slf4j.MDC;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.task.TaskDecorator;
import org.springframework.scheduling.annotation.AsyncConfigurerSupport;
import org.springframework.scheduling.annotation.EnableAsync;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;

import java.util.Map;
import java.util.concurrent.Executor;

/**
 * @Description:
 */
@EnableAsync
@Configuration
public class AsyncConfig extends AsyncConfigurerSupport {
    @Override
    public Executor getAsyncExecutor() {
        ThreadPoolTaskExecutor executor = new ThreadPoolTaskExecutor();
        executor.setTaskDecorator(new MdcTaskDecorator());
        executor.initialize();
        return executor;
    }
}

class MdcTaskDecorator implements TaskDecorator {

    @Override
    public Runnable decorate(Runnable runnable) {
        Map<string, string> contextMap = MDC.getCopyOfContextMap();
        return () -&gt; {
            try {
                MDC.setContextMap(contextMap);
                runnable.run();
            } finally {
                MDC.clear();
            }
        };
    }
}

五、有时候异步须要增长阻塞

import lombok.extern.slf4j.Slf4j;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.scheduling.concurrent.ThreadPoolTaskExecutor;

import java.util.concurrent.Executor;
import java.util.concurrent.ThreadPoolExecutor;

@Configuration
@Slf4j
public class TaskExecutorConfig {

    @Bean("localDbThreadPoolTaskExecutor")
    public Executor threadPoolTaskExecutor() {
        ThreadPoolTaskExecutor taskExecutor = new ThreadPoolTaskExecutor();
        taskExecutor.setCorePoolSize(5);
        taskExecutor.setMaxPoolSize(200);
        taskExecutor.setQueueCapacity(200);
        taskExecutor.setKeepAliveSeconds(100);
        taskExecutor.setThreadNamePrefix("LocalDbTaskThreadPool");
        taskExecutor.setRejectedExecutionHandler((Runnable r, ThreadPoolExecutor executor) -> {
                    if (!executor.isShutdown()) {
                        try {
                            Thread.sleep(300);
                            executor.getQueue().put(r);
                        } catch (InterruptedException e) {
                            log.error(e.toString(), e);
                            Thread.currentThread().interrupt();
                        }
                    }
                }
        );
        taskExecutor.initialize();
        return taskExecutor;
    }


}

四、业务拆分

能够将比较耗时或者不一样的业务拆分出来提供单节点的吞吐量

五、集成消息队列

有不少场景对数据实时性要求不那么强的，或者对业务进行业务容错处理时能够将消息发送到kafka，而后延时消费。举个例子，根据条件查询指定用户发送推送消息，这里能够时按时、按天、按月等等，这时就 </string,></string>