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README
Apache-2.0

UidGenerator

In English

UidGenerator是Java实现的, 基于Snowflake算法的唯一ID生成器。UidGenerator以组件形式工作在应用项目中, 支持自定义workerId位数和初始化策略, 从而适用于docker等虚拟化环境下实例自动重启、漂移等场景。 在实现上, UidGenerator通过借用未来时间来解决sequence天然存在的并发限制; 采用RingBuffer来缓存已生成的UID, 并行化UID的生产和消费, 同时对CacheLine补齐,避免了由RingBuffer带来的硬件级「伪共享」问题. 最终单机QPS可达600万。

依赖版本:Java8及以上版本, MySQL(内置WorkerID分配器, 启动阶段通过DB进行分配; 如自定义实现, 则DB非必选依赖)

Snowflake算法

Snowflake
Snowflake算法描述:指定机器 & 同一时刻 & 某一并发序列,是唯一的。据此可生成一个64 bits的唯一ID(long)。默认采用上图字节分配方式:

  • sign(1bit)
    固定1bit符号标识,即生成的UID为正数。

  • delta seconds (28 bits)
    当前时间,相对于时间基点"2016-05-20"的增量值,单位:秒,最多可支持约8.7年

  • worker id (22 bits)
    机器id,最多可支持约420w次机器启动。内置实现为在启动时由数据库分配,默认分配策略为用后即弃,后续可提供复用策略。

  • sequence (13 bits)
    每秒下的并发序列,13 bits可支持每秒8192个并发。

以上参数均可通过Spring进行自定义

CachedUidGenerator

RingBuffer环形数组,数组每个元素成为一个slot。RingBuffer容量,默认为Snowflake算法中sequence最大值,且为2^N。可通过boostPower配置进行扩容,以提高RingBuffer 读写吞吐量。

Tail指针、Cursor指针用于环形数组上读写slot:

  • Tail指针
    表示Producer生产的最大序号(此序号从0开始,持续递增)。Tail不能超过Cursor,即生产者不能覆盖未消费的slot。当Tail已赶上curosr,此时可通过rejectedPutBufferHandler指定PutRejectPolicy

  • Cursor指针
    表示Consumer消费到的最小序号(序号序列与Producer序列相同)。Cursor不能超过Tail,即不能消费未生产的slot。当Cursor已赶上tail,此时可通过rejectedTakeBufferHandler指定TakeRejectPolicy

RingBuffer

CachedUidGenerator采用了双RingBuffer,Uid-RingBuffer用于存储Uid、Flag-RingBuffer用于存储Uid状态(是否可填充、是否可消费)

由于数组元素在内存中是连续分配的,可最大程度利用CPU cache以提升性能。但同时会带来「伪共享」FalseSharing问题,为此在Tail、Cursor指针、Flag-RingBuffer中采用了CacheLine 补齐方式。

FalseSharing

RingBuffer填充时机

  • 初始化预填充
    RingBuffer初始化时,预先填充满整个RingBuffer.

  • 即时填充
    Take消费时,即时检查剩余可用slot量(tail - cursor),如小于设定阈值,则补全空闲slots。阈值可通过paddingFactor来进行配置,请参考Quick Start中CachedUidGenerator配置

  • 周期填充
    通过Schedule线程,定时补全空闲slots。可通过scheduleInterval配置,以应用定时填充功能,并指定Schedule时间间隔

Quick Start

这里介绍如何在基于Spring的项目中使用UidGenerator, 具体流程如下:

步骤1: 安装依赖

先下载Java8, MySQLMaven

设置环境变量

maven无须安装, 设置好MAVEN_HOME即可. 可像下述脚本这样设置JAVA_HOME和MAVEN_HOME, 如已设置请忽略.

export MAVEN_HOME=/xxx/xxx/software/maven/apache-maven-3.3.9
export PATH=$MAVEN_HOME/bin:$PATH
JAVA_HOME="/Library/Java/JavaVirtualMachines/jdk1.8.0_91.jdk/Contents/Home";
export JAVA_HOME;

步骤2: 创建表WORKER_NODE

运行sql脚本以导入表WORKER_NODE, 脚本如下:

DROP DATABASE IF EXISTS `xxxx`;
CREATE DATABASE `xxxx` ;
use `xxxx`;
DROP TABLE IF EXISTS WORKER_NODE;
CREATE TABLE WORKER_NODE
(
ID BIGINT NOT NULL AUTO_INCREMENT COMMENT 'auto increment id',
HOST_NAME VARCHAR(64) NOT NULL COMMENT 'host name',
PORT VARCHAR(64) NOT NULL COMMENT 'port',
TYPE INT NOT NULL COMMENT 'node type: ACTUAL or CONTAINER',
LAUNCH_DATE DATE NOT NULL COMMENT 'launch date',
MODIFIED TIMESTAMP NOT NULL COMMENT 'modified time',
CREATED TIMESTAMP NOT NULL COMMENT 'created time',
PRIMARY KEY(ID)
)
 COMMENT='DB WorkerID Assigner for UID Generator',ENGINE = INNODB;

修改mysql.properties配置中, jdbc.url, jdbc.username和jdbc.password, 确保库地址, 名称, 端口号, 用户名和密码正确.

步骤3: 修改Spring配置

提供了两种生成器: DefaultUidGeneratorCachedUidGenerator。如对UID生成性能有要求, 请使用CachedUidGenerator
对应Spring配置分别为: default-uid-spring.xmlcached-uid-spring.xml

DefaultUidGenerator配置

<!-- DefaultUidGenerator -->
<bean id="defaultUidGenerator" class="com.baidu.fsg.uid.impl.DefaultUidGenerator" lazy-init="false">
    <property name="workerIdAssigner" ref="disposableWorkerIdAssigner"/>

    <!-- Specified bits & epoch as your demand. No specified the default value will be used -->
    <property name="timeBits" value="29"/>
    <property name="workerBits" value="21"/>
    <property name="seqBits" value="13"/>
    <property name="epochStr" value="2016-09-20"/>
</bean>
 
<!-- 用完即弃的WorkerIdAssigner,依赖DB操作 -->
<bean id="disposableWorkerIdAssigner" class="com.baidu.fsg.uid.worker.DisposableWorkerIdAssigner" />

CachedUidGenerator配置

<!-- CachedUidGenerator -->
<bean id="cachedUidGenerator" class="com.baidu.fsg.uid.impl.CachedUidGenerator">
    <property name="workerIdAssigner" ref="disposableWorkerIdAssigner" />
 
    <!-- 以下为可选配置, 如未指定将采用默认值 -->
    <!-- Specified bits & epoch as your demand. No specified the default value will be used -->
    <property name="timeBits" value="29"/>
    <property name="workerBits" value="21"/>
    <property name="seqBits" value="13"/>
    <property name="epochStr" value="2016-09-20"/>
 
    <!-- RingBuffer size扩容参数, 可提高UID生成的吞吐量. -->
    <!-- 默认:3, 原bufferSize=8192, 扩容后bufferSize= 8192 << 3 = 65536 -->
    <property name="boostPower" value="3"></property>
 
    <!-- 指定何时向RingBuffer中填充UID, 取值为百分比(0, 100), 默认为50 -->
    <!-- 举例: bufferSize=1024, paddingFactor=50 -> threshold=1024 * 50 / 100 = 512. -->
    <!-- 当环上可用UID数量 < 512时, 将自动对RingBuffer进行填充补全 -->
    <property name="paddingFactor" value="50"></property>
 
    <!-- 另外一种RingBuffer填充时机, 在Schedule线程中, 周期性检查填充 -->
    <!-- 默认:不配置此项, 即不实用Schedule线程. 如需使用, 请指定Schedule线程时间间隔, 单位:秒 -->
    <property name="scheduleInterval" value="60"></property>
 
    <!-- 拒绝策略: 当环已满, 无法继续填充时 -->
    <!-- 默认无需指定, 将丢弃Put操作, 仅日志记录. 如有特殊需求, 请实现RejectedPutBufferHandler接口(支持Lambda表达式) -->
    <property name="rejectedPutBufferHandler" ref="XxxxYourPutRejectPolicy"></property>
 
    <!-- 拒绝策略: 当环已空, 无法继续获取时 -->
    <!-- 默认无需指定, 将记录日志, 并抛出UidGenerateException异常. 如有特殊需求, 请实现RejectedTakeBufferHandler接口(支持Lambda表达式) -->
    <property name="rejectedTakeBufferHandler" ref="XxxxYourTakeRejectPolicy"></property>
 
</bean>
 
<!-- 用完即弃的WorkerIdAssigner, 依赖DB操作 -->
<bean id="disposableWorkerIdAssigner" class="com.baidu.fsg.uid.worker.DisposableWorkerIdAssigner" />
 

Mybatis配置

mybatis-spring.xml配置说明如下:

<!-- Spring annotation扫描 -->
<context:component-scan base-package="com.baidu.fsg.uid" />

<bean id="sqlSessionFactory" class="org.mybatis.spring.SqlSessionFactoryBean">
    <property name="dataSource" ref="dataSource" />
    <property name="mapperLocations" value="classpath:/META-INF/mybatis/mapper/M_WORKER*.xml" />
</bean>

<!-- 事务相关配置 -->
<tx:annotation-driven transaction-manager="transactionManager" order="1" />

<bean id="transactionManager" class="org.springframework.jdbc.datasource.DataSourceTransactionManager">
	<property name="dataSource" ref="dataSource" />
</bean>

<!-- Mybatis Mapper扫描 -->
<bean class="org.mybatis.spring.mapper.MapperScannerConfigurer">
	<property name="annotationClass" value="org.springframework.stereotype.Repository" />
	<property name="basePackage" value="com.baidu.fsg.uid.worker.dao" />
	<property name="sqlSessionFactoryBeanName" value="sqlSessionFactory" />
</bean>

<!-- 数据源配置 -->
<bean id="dataSource" parent="abstractDataSource">
	<property name="driverClassName" value="${mysql.driver}" />
	<property name="maxActive" value="${jdbc.maxActive}" />
	<property name="url" value="${jdbc.url}" />
	<property name="username" value="${jdbc.username}" />
	<property name="password" value="${jdbc.password}" />
</bean>

<bean id="abstractDataSource" class="com.alibaba.druid.pool.DruidDataSource" destroy-method="close">
	<property name="filters" value="${datasource.filters}" />
	<property name="defaultAutoCommit" value="${datasource.defaultAutoCommit}" />
	<property name="initialSize" value="${datasource.initialSize}" />
	<property name="minIdle" value="${datasource.minIdle}" />
	<property name="maxWait" value="${datasource.maxWait}" />
	<property name="testWhileIdle" value="${datasource.testWhileIdle}" />
	<property name="testOnBorrow" value="${datasource.testOnBorrow}" />
	<property name="testOnReturn" value="${datasource.testOnReturn}" />
	<property name="validationQuery" value="${datasource.validationQuery}" />
	<property name="timeBetweenEvictionRunsMillis" value="${datasource.timeBetweenEvictionRunsMillis}" />
	<property name="minEvictableIdleTimeMillis" value="${datasource.minEvictableIdleTimeMillis}" />
	<property name="logAbandoned" value="${datasource.logAbandoned}" />
	<property name="removeAbandoned" value="${datasource.removeAbandoned}" />
	<property name="removeAbandonedTimeout" value="${datasource.removeAbandonedTimeout}" />
</bean>

<bean id="batchSqlSession" class="org.mybatis.spring.SqlSessionTemplate">
	<constructor-arg index="0" ref="sqlSessionFactory" />
	<constructor-arg index="1" value="BATCH" />
</bean>

步骤4: 运行示例单测

运行单测CachedUidGeneratorTest, 展示UID生成、解析等功能

@Resource
private UidGenerator uidGenerator;

@Test
public void testSerialGenerate() {
    // Generate UID
    long uid = uidGenerator.getUID();

    // Parse UID into [Timestamp, WorkerId, Sequence]
    // {"UID":"180363646902239241","parsed":{    "timestamp":"2017-01-19 12:15:46",    "workerId":"4",    "sequence":"9"        }}
    System.out.println(uidGenerator.parseUID(uid));

}

关于UID比特分配的建议

对于并发数要求不高、期望长期使用的应用, 可增加timeBits位数, 减少seqBits位数. 例如节点采取用完即弃的WorkerIdAssigner策略, 重启频率为12次/天, 那么配置成{"workerBits":23,"timeBits":31,"seqBits":9}时, 可支持28个节点以整体并发量14400 UID/s的速度持续运行68年.

对于节点重启频率频繁、期望长期使用的应用, 可增加workerBitstimeBits位数, 减少seqBits位数. 例如节点采取用完即弃的WorkerIdAssigner策略, 重启频率为24*12次/天, 那么配置成{"workerBits":27,"timeBits":30,"seqBits":6}时, 可支持37个节点以整体并发量2400 UID/s的速度持续运行34年.

吞吐量测试

在MacBook Pro(2.7GHz Intel Core i5, 8G DDR3)上进行了CachedUidGenerator(单实例)的UID吞吐量测试.
首先固定住workerBits为任选一个值(如20), 分别统计timeBits变化时(如从25至32, 总时长分别对应1年和136年)的吞吐量, 如下表所示:

timeBits 25 26 27 28 29 30 31 32
throughput 6,831,465 7,007,279 6,679,625 6,499,205 6,534,971 7,617,440 6,186,930 6,364,997

throughput1

再固定住timeBits为任选一个值(如31), 分别统计workerBits变化时(如从20至29, 总重启次数分别对应1百万和500百万)的吞吐量, 如下表所示:

workerBits 20 21 22 23 24 25 26 27 28 29
throughput 6,186,930 6,642,727 6,581,661 6,462,726 6,774,609 6,414,906 6,806,266 6,223,617 6,438,055 6,435,549

throughput1

由此可见, 不管如何配置, CachedUidGenerator总能提供600万/s的稳定吞吐量, 只是使用年限会有所减少. 这真的是太棒了.

最后, 固定住workerBits和timeBits位数(如23和31), 分别统计不同数目(如1至8,本机CPU核数为4)的UID使用者情况下的吞吐量,

workerBits 1 2 3 4 5 6 7 8
throughput 6,462,726 6,542,259 6,077,717 6,377,958 7,002,410 6,599,113 7,360,934 6,490,969

throughput1

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