Spring Boot如何使用HikariCP连接池详解_Java教程

前言

springboot让java开发更加美好，更加简洁，更加简单。spring boot 2.x中使用hikaricp作为默认的数据连接池。 hikaricp使用javassist字节码操作库来实现动态代理，优化并精简了字节码，同时内部使用 com.zaxxer.hikari.util.fastlist 代替arraylist、使用了更好的并发集合类 com.zaxxer.hikari.util.concurrentbag ，“号称”是目前最快的数据库连接池。

下面话不多说了，来一起看看详细的介绍吧

基本使用

在spring boot 2.x中使用hikaricp十分简单，只需引入依赖 implementation 'org.springframework.boot:spring-boot-starter-jdbc' ：

				?

									pluginmanagement {

									    repositories {

									        gradlepluginportal()

									    }

									}

									rootproject.name = 'datasource-config'

									plugins {

									    id 'org.springframework.boot' version '2.1.3.release'

									    id 'java'

									}

									apply plugin: 'io.spring.dependency-management'

									group = 'spring-boot-guides'

									version = '0.0.1-snapshot'

									sourcecompatibility = '1.8'

									repositories {

									    mavencentral()

									}

									dependencies {

									    implementation 'org.springframework.boot:spring-boot-starter-jdbc'

									    runtimeonly 'com.h2database:h2'

									    testimplementation 'org.springframework.boot:spring-boot-starter-test'

									}

配置文件如下：

				?

									spring:

									 datasource:

									 url: jdbc:h2:mem:demodb

									 username: sa

									 password:

									 hikari: # https://github.com/brettwooldridge/hikaricp (uses milliseconds for all time values)

									 maximumpoolsize: 10

									 minimumidle: 2

									 idletimeout: 600000

									 connectiontimeout: 30000

									 maxlifetime: 1800000

关于连接池的具体配置参数详见 hikaricp 。

示例代码如下：

				?

									package springbootguides.datasourceconfig;

									import org.springframework.beans.factory.annotation.autowired;

									import org.springframework.boot.commandlinerunner;

									import org.springframework.boot.springapplication;

									import org.springframework.boot.autoconfigure.springbootapplication;

									import javax.sql.datasource;

									import java.sql.connection;

									@springbootapplication

									public class datasourceconfigapplication implements commandlinerunner {

									    @autowired

									    private datasource datasource;

									    @override

									    public void run(string... args) throws exception {

									        try(connection conn = datasource.getconnection()) {

									            system.out.println(conn);

									        }

									    }

									    public static void main(string[] args) {

									        springapplication.run(datasourceconfigapplication.class, args);

									    }

									}

实现原理

spring boot使用如下方式整合hikaricp：入口是 org.springframework.boot.autoconfigure.jdbc.datasourceautoconfiguration ，通过 org.springframework.boot.autoconfigure.jdbc.datasourceconfiguration.hikari 中的 @bean 方式创建 com.zaxxer.hikari.hikaridatasource ：

				?

									/**

									     * hikari datasource configuration.

									     */

									    @conditionalonclass(hikaridatasource.class)

									    @conditionalonmissingbean(datasource.class)

									    @conditionalonproperty(name = "spring.datasource.type", havingvalue = "com.zaxxer.hikari.hikaridatasource", matchifmissing = true)

									    static class hikari {

									        @bean

									        @configurationproperties(prefix = "spring.datasource.hikari")

									        public hikaridatasource datasource(datasourceproperties properties) {

									            hikaridatasource datasource = createdatasource(properties,

									                    hikaridatasource.class);

									            if (stringutils.hastext(properties.getname())) {

									                datasource.setpoolname(properties.getname());

									            }

									            return datasource;

									        }

									    }

@configurationproperties(prefix = "spring.datasource.hikari") 会自动把 spring.datasource.hikari.* 相关的连接池配置信息注入到创建的hikaridatasource实例中。

hikaricp的监控和遥测

因为在我们的微服务体系中使用的监控系统是prometheus，这里以prometheus为例。

注意spring boot 2.0对spring boot 1.x的metrics进行了重构，不再向后兼容，主要是在spring-boot-acutator中使用了micrometer，支持了更多的监控系统：atlas、datadog、ganglia、graphite、influx、jmx、newrelic、prometheus、signalfx、statsd、wavefront。spring boot 2.0的metrics对比spring boot 1.x除了引入micrometer外，更大的体现是支持了tag，这也说明prometheus、influx等支持tag的时序监控数据模型的监控系统已经成为主流。

在前面示例中的build.gradle中加入如下依赖：

				?

									implementation 'org.springframework.boot:spring-boot-starter-web'

									implementation 'org.springframework.boot:spring-boot-starter-actuator'

									implementation 'io.micrometer:micrometer-registry-prometheus'

配置文件applycation.yaml中加入对actuator的配置：

				?

									management:

									 endpoints:

									 web:

									  exposure:

									  include: "health,info,prometheus"

									 server:

									 port: 8079

									 servlet:

									  context-path: /

注意这里引入了web和actuator依赖，通过配置 management.server.port 指定actuator的web端点为8089端口，通过 management.endpoints.include 对外开放 /actuator/prometheus ，在引入 io.micrometer:micrometer-registry-prometheus 依赖之后，端点 /actuator/prometheus 当即生效。

				?

									curl http://localhost:8079/actuator/prometheus | grep hikari

									# type hikaricp_connections_acquire_seconds summary

									hikaricp_connections_acquire_seconds_count{pool="hikaripool-1",} 3.0

									hikaricp_connections_acquire_seconds_sum{pool="hikaripool-1",} 0.001230082

									# help hikaricp_connections_acquire_seconds_max connection acquire time

									# type hikaricp_connections_acquire_seconds_max gauge

									hikaricp_connections_acquire_seconds_max{pool="hikaripool-1",} 0.0

									# help hikaricp_connections_min min connections

									# type hikaricp_connections_min gauge

									hikaricp_connections_min{pool="hikaripool-1",} 2.0

									# type hikaricp_connections_timeout_total counter

									hikaricp_connections_timeout_total{pool="hikaripool-1",} 0.0

									# help hikaricp_connections_pending pending threads

									# type hikaricp_connections_pending gauge

									hikaricp_connections_pending{pool="hikaripool-1",} 0.0

									# help hikaricp_connections_usage_seconds connection usage time

									# type hikaricp_connections_usage_seconds summary

									hikaricp_connections_usage_seconds_count{pool="hikaripool-1",} 3.0

									hikaricp_connections_usage_seconds_sum{pool="hikaripool-1",} 0.06

									# help hikaricp_connections_usage_seconds_max connection usage time

									# type hikaricp_connections_usage_seconds_max gauge

									hikaricp_connections_usage_seconds_max{pool="hikaripool-1",} 0.0

									# help hikaricp_connections_max max connections

									# type hikaricp_connections_max gauge

									hikaricp_connections_max{pool="hikaripool-1",} 10.0

									# help hikaricp_connections total connections

									# type hikaricp_connections gauge

									hikaricp_connections{pool="hikaripool-1",} 2.0

									# help hikaricp_connections_creation_seconds_max connection creation time

									# type hikaricp_connections_creation_seconds_max gauge

									hikaricp_connections_creation_seconds_max{pool="hikaripool-1",} 0.0

									# help hikaricp_connections_creation_seconds connection creation time

									# type hikaricp_connections_creation_seconds summary

									hikaricp_connections_creation_seconds_count{pool="hikaripool-1",} 1.0

									hikaricp_connections_creation_seconds_sum{pool="hikaripool-1",} 0.001

									# help hikaricp_connections_idle idle connections

									# type hikaricp_connections_idle gauge

									hikaricp_connections_idle{pool="hikaripool-1",} 2.0

									# help hikaricp_connections_active active connections

									# type hikaricp_connections_active gauge

									hikaricp_connections_active{pool="hikaripool-1",} 0.0