Hmily:高性能异步分布式事务TCC框架详解架构师

Hmily框架特性

  • 无缝集成Spring,Spring boot start。

  • 无缝集成Dubbo,SpringCloud,Motan等rpc框架。

  • 多种事务日志的存储方式(redis,mongdb,mysql等)。

  • 多种不同日志序列化方式(Kryo,protostuff,hession)。

  • 事务自动恢复。

  • 支持内嵌事务的依赖传递。

  • 代码零侵入,配置简单灵活。

Hmily为什么这么高性能?

1.采用disruptor进行事务日志的异步读写(disruptor是一个无锁,无GC的并发编程框架)

package com.hmily.tcc.core.disruptor.publisher; 
import com.hmily.tcc.common.bean.entity.TccTransaction; 
import com.hmily.tcc.common.enums.EventTypeEnum; 
import com.hmily.tcc.core.concurrent.threadpool.HmilyThreadFactory; 
import com.hmily.tcc.core.coordinator.CoordinatorService; 
import com.hmily.tcc.core.disruptor.event.HmilyTransactionEvent; 
import com.hmily.tcc.core.disruptor.factory.HmilyTransactionEventFactory; 
import com.hmily.tcc.core.disruptor.handler.HmilyConsumerDataHandler; 
import com.hmily.tcc.core.disruptor.translator.HmilyTransactionEventTranslator; 
import com.lmax.disruptor.BlockingWaitStrategy; 
import com.lmax.disruptor.IgnoreExceptionHandler; 
import com.lmax.disruptor.RingBuffer; 
import com.lmax.disruptor.dsl.Disruptor; 
import com.lmax.disruptor.dsl.ProducerType; 
import org.springframework.beans.factory.DisposableBean; 
import org.springframework.beans.factory.annotation.Autowired; 
import org.springframework.stereotype.Component; 
import java.util.concurrent.Executor; 
import java.util.concurrent.LinkedBlockingQueue; 
import java.util.concurrent.ThreadPoolExecutor; 
import java.util.concurrent.TimeUnit; 
import java.util.concurrent.atomic.AtomicInteger; 
/** 
* event publisher. 
* 
* @author xiaoyu(Myth) 
*/ 
@Component 
public class HmilyTransactionEventPublisher implements DisposableBean { 
private Disruptor<HmilyTransactionEvent> disruptor; 
private final CoordinatorService coordinatorService; 
@Autowired 
public HmilyTransactionEventPublisher(final CoordinatorService coordinatorService) { 
this.coordinatorService = coordinatorService; 
} 
/** 
* disruptor start. 
* 
* @param bufferSize this is disruptor buffer size. 
* @param threadSize this is disruptor consumer thread size. 
*/ 
public void start(final int bufferSize, final int threadSize) { 
disruptor = new Disruptor<>(new HmilyTransactionEventFactory(), bufferSize, r -> { 
AtomicInteger index = new AtomicInteger(1); 
return new Thread(null, r, "disruptor-thread-" + index.getAndIncrement()); 
}, ProducerType.MULTI, new BlockingWaitStrategy()); 
final Executor executor = new ThreadPoolExecutor(threadSize, threadSize, 0, TimeUnit.MILLISECONDS, 
new LinkedBlockingQueue<>(), 
HmilyThreadFactory.create("hmily-log-disruptor", false), 
new ThreadPoolExecutor.AbortPolicy()); 
HmilyConsumerDataHandler[] consumers = new HmilyConsumerDataHandler[threadSize]; 
for (int i = 0; i < threadSize; i++) { 
consumers[i] = new HmilyConsumerDataHandler(executor, coordinatorService); 
} 
disruptor.handleEventsWithWorkerPool(consumers); 
disruptor.setDefaultExceptionHandler(new IgnoreExceptionHandler()); 
disruptor.start(); 
} 
/** 
* publish disruptor event. 
* 
* @param tccTransaction {@linkplain com.hmily.tcc.common.bean.entity.TccTransaction } 
* @param type           {@linkplain EventTypeEnum} 
*/ 
public void publishEvent(final TccTransaction tccTransaction, final int type) { 
final RingBuffer<HmilyTransactionEvent> ringBuffer = disruptor.getRingBuffer(); 
ringBuffer.publishEvent(new HmilyTransactionEventTranslator(type), tccTransaction); 
} 
@Override 
public void destroy() { 
disruptor.shutdown(); 
} 
}

在这里bufferSize 的默认值是4094 * 4,用户可以根据自行的情况进行配置。

HmilyConsumerDataHandler[] consumers = new HmilyConsumerDataHandler[threadSize]; 
for (int i = 0; i < threadSize; i++) { 
consumers[i] = new HmilyConsumerDataHandler(executor, coordinatorService); 
} 
disruptor.handleEventsWithWorkerPool(consumers);

这里是采用多个消费者去处理队列里面的任务。

2.异步执行confrim,cancel方法。

package com.hmily.tcc.core.service.handler; 
import com.hmily.tcc.common.bean.context.TccTransactionContext; 
import com.hmily.tcc.common.bean.entity.TccTransaction; 
import com.hmily.tcc.common.enums.TccActionEnum; 
import com.hmily.tcc.core.concurrent.threadpool.HmilyThreadFactory; 
import com.hmily.tcc.core.service.HmilyTransactionHandler; 
import com.hmily.tcc.core.service.executor.HmilyTransactionExecutor; 
import org.aspectj.lang.ProceedingJoinPoint; 
import org.springframework.beans.factory.annotation.Autowired; 
import org.springframework.stereotype.Component; 
import java.util.concurrent.Executor; 
import java.util.concurrent.LinkedBlockingQueue; 
import java.util.concurrent.ThreadPoolExecutor; 
import java.util.concurrent.TimeUnit; 
/** 
* this is transaction starter. 
* 
* @author xiaoyu 
*/ 
@Component 
public class StarterHmilyTransactionHandler implements HmilyTransactionHandler { 
private static final int MAX_THREAD = Runtime.getRuntime().availableProcessors() << 1; 
private final HmilyTransactionExecutor hmilyTransactionExecutor; 
private final Executor executor = new ThreadPoolExecutor(MAX_THREAD, MAX_THREAD, 0, TimeUnit.MILLISECONDS, 
new LinkedBlockingQueue<>(), 
HmilyThreadFactory.create("hmily-execute", false), 
new ThreadPoolExecutor.AbortPolicy()); 
@Autowired 
public StarterHmilyTransactionHandler(final HmilyTransactionExecutor hmilyTransactionExecutor) { 
this.hmilyTransactionExecutor = hmilyTransactionExecutor; 
} 
@Override 
public Object handler(final ProceedingJoinPoint point, final TccTransactionContext context) 
throws Throwable { 
Object returnValue; 
try { 
TccTransaction tccTransaction = hmilyTransactionExecutor.begin(point); 
try { 
//execute try 
returnValue = point.proceed(); 
tccTransaction.setStatus(TccActionEnum.TRYING.getCode()); 
hmilyTransactionExecutor.updateStatus(tccTransaction); 
} catch (Throwable throwable) { 
//if exception ,execute cancel 
final TccTransaction currentTransaction = hmilyTransactionExecutor.getCurrentTransaction(); 
executor.execute(() -> hmilyTransactionExecutor 
.cancel(currentTransaction)); 
throw throwable; 
} 
//execute confirm 
final TccTransaction currentTransaction = hmilyTransactionExecutor.getCurrentTransaction(); 
executor.execute(() -> hmilyTransactionExecutor.confirm(currentTransaction)); 
} finally { 
hmilyTransactionExecutor.remove(); 
} 
return returnValue; 
} 
}

当try方法的AOP切面有异常的时候,采用线程池异步去执行cancel,无异常的时候去执行confrim方法。

这里有人可能会问:那么cancel方法异常,或者confrim方法异常怎么办呢?
答:首先这种情况是非常罕见的,因为你上一面才刚刚执行完try。其次如果出现这种情况,在try阶段会保存好日志,Hmily有内置的调度线程池来进行恢复,不用担心。

有人又会问:这里如果日志保存异常了怎么办?
答:首先这又是一个牛角尖问题,首先日志配置的参数,在框架启动的时候,会要求你配置的。其次,就算在运行过程中日志保存异常,这时候框架会取缓存中的,并不会影响程序正确执行。最后,万一日志保存异常了,系统又在很极端的情况下down机了,恭喜你,你可以去买彩票了,最好的解决办法就是不去解决它。

3.ThreadLocal缓存的使用。
/** 
* transaction begin. 
* 
* @param point cut point. 
* @return TccTransaction 
*/ 
public TccTransaction begin(final ProceedingJoinPoint point) { 
LogUtil.debug(LOGGER, () -> "......hmily transaction!start...."); 
//build tccTransaction 
final TccTransaction tccTransaction = buildTccTransaction(point, TccRoleEnum.START.getCode(), null); 
//save tccTransaction in threadLocal 
    CURRENT.set(tccTransaction); 
//publishEvent 
    hmilyTransactionEventPublisher.publishEvent(tccTransaction, EventTypeEnum.SAVE.getCode()); 
//set TccTransactionContext this context transfer remote 
TccTransactionContext context = new TccTransactionContext(); 
//set action is try 
    context.setAction(TccActionEnum.TRYING.getCode()); 
context.setTransId(tccTransaction.getTransId()); 
context.setRole(TccRoleEnum.START.getCode()); 
TransactionContextLocal.getInstance().set(context); 
return tccTransaction; 
}

首先要理解,threadLocal保存的发起者一方法的事务信息。这个很重要,不要会有点懵逼。rpc的调用,会形成调用链,进行保存。

/** 
* add participant. 
* 
* @param participant {@linkplain Participant} 
*/ 
public void enlistParticipant(final Participant participant) { 
if (Objects.isNull(participant)) { 
return; 
} 
Optional.ofNullable(getCurrentTransaction()) 
.ifPresent(c -> { 
c.registerParticipant(participant); 
updateParticipant(c); 
}); 
}
4.GuavaCache的使用
package com.hmily.tcc.core.cache; 
import com.google.common.cache.CacheBuilder; 
import com.google.common.cache.CacheLoader; 
import com.google.common.cache.LoadingCache; 
import com.google.common.cache.Weigher; 
import com.hmily.tcc.common.bean.entity.TccTransaction; 
import com.hmily.tcc.core.coordinator.CoordinatorService; 
import com.hmily.tcc.core.helper.SpringBeanUtils; 
import org.apache.commons.lang3.StringUtils; 
import java.util.Optional; 
import java.util.concurrent.ExecutionException; 
/** 
* use google guava cache. 
* @author xiaoyu 
*/ 
public final class TccTransactionCacheManager { 
private static final int MAX_COUNT = 10000; 
private static final LoadingCache<String, TccTransaction> LOADING_CACHE = 
CacheBuilder.newBuilder().maximumWeight(MAX_COUNT) 
.weigher((Weigher<String, TccTransaction>) (string, tccTransaction) -> getSize()) 
.build(new CacheLoader<String, TccTransaction>() { 
@Override 
public TccTransaction load(final String key) { 
return cacheTccTransaction(key); 
} 
}); 
private static CoordinatorService coordinatorService = SpringBeanUtils.getInstance().getBean(CoordinatorService.class); 
private static final TccTransactionCacheManager TCC_TRANSACTION_CACHE_MANAGER = new TccTransactionCacheManager(); 
private TccTransactionCacheManager() { 
} 
/** 
* TccTransactionCacheManager. 
* 
* @return TccTransactionCacheManager 
*/ 
public static TccTransactionCacheManager getInstance() { 
return TCC_TRANSACTION_CACHE_MANAGER; 
} 
private static int getSize() { 
return (int) LOADING_CACHE.size(); 
} 
private static TccTransaction cacheTccTransaction(final String key) { 
return Optional.ofNullable(coordinatorService.findByTransId(key)).orElse(new TccTransaction()); 
} 
/** 
* cache tccTransaction. 
* 
* @param tccTransaction {@linkplain TccTransaction} 
*/ 
public void cacheTccTransaction(final TccTransaction tccTransaction) { 
LOADING_CACHE.put(tccTransaction.getTransId(), tccTransaction); 
} 
/** 
* acquire TccTransaction. 
* 
* @param key this guava key. 
* @return {@linkplain TccTransaction} 
*/ 
public TccTransaction getTccTransaction(final String key) { 
try { 
return LOADING_CACHE.get(key); 
} catch (ExecutionException e) { 
return new TccTransaction(); 
} 
} 
/** 
* remove guava cache by key. 
* @param key guava cache key. 
*/ 
public void removeByKey(final String key) { 
if (StringUtils.isNotEmpty(key)) { 
LOADING_CACHE.invalidate(key); 
} 
} 
}

在参与者中,我们使用了ThreadLocal,而在参与者中,我们为什么不使用呢?

其实原因有二点:首先.因为try,和confrim 会不在一个线程里,会造成ThreadLocal失效。当考虑到RPC集群的时候,可能会负载到不同的机器上。

这里有一个细节就是:

private static TccTransaction cacheTccTransaction(final String key) { 
return Optional.ofNullable(coordinatorService.findByTransId(key)).orElse(new TccTransaction()); 
}

当GuavaCache里面没有的时候,会去查询日志返回,这样就保证了对集群环境的支持。以上4点造就了Hmily是一个异步的高性能分布式事务TCC框架的原因。

Hmily如何使用?
(https://github.com/yu199195/hmily/tree/master/hmily-tcc-demo)

首先因为之前的包命名问题,框架包并没有上传到maven中心仓库,固需要使用者自己拉取代码,编译deploy到自己的私服。

1.dubbo用户

  • 在你的Api接口项目引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-annotation</artifactId> 
    <version>{you version}</version> 
    </dependency>

  • 在你的服务提供者项目引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-dubbo</artifactId> 
    <version>{you version}</version> 
    </dependency>

  • 配置启动bean
    <!-- Aspect 切面配置,是否开启AOP切面--> 
    <aop:aspectj-autoproxy expose-proxy="true"/> 
    <!--扫描框架的包--> 
    <context:component-scan base-package="com.hmily.tcc.*"/> 
    <!--启动类属性配置--> 
    <bean id="hmilyTransactionBootstrap" class="com.hmily.tcc.core.bootstrap.HmilyTransactionBootstrap"> 
    <property name="serializer" value="kryo"/> 
    <property name="recoverDelayTime" value="120"/> 
    <property name="retryMax" value="3"/> 
    <property name="scheduledDelay" value="120"/> 
    <property name="scheduledThreadMax" value="4"/> 
    <property name="repositorySupport" value="db"/> 
    <property name="tccDbConfig"> 
    <bean class="com.hmily.tcc.common.config.TccDbConfig"> 
    <property name="url" 
    value="jdbc:mysql://192.168.1.98:3306/tcc?useUnicode=true&amp;characterEncoding=utf8"/> 
    <property name="driverClassName" value="com.mysql.jdbc.Driver"/> 
    <property name="username" value="root"/> 
    <property name="password" value="123456"/> 
    </bean> 
    </property> 
    </bean>

当然配置属性很多,这里只给出了demo,具体可以参考这个类:

package com.hmily.tcc.common.config; 
import com.hmily.tcc.common.enums.RepositorySupportEnum; 
import lombok.Data; 
/** 
* hmily config. 
* 
* @author xiaoyu 
*/ 
@Data 
public class TccConfig { 
/** 
* Resource suffix this parameter please fill in about is the transaction store path. 
* If it's a table store this is a table suffix, it's stored the same way. 
* If this parameter is not filled in, the applicationName of the application is retrieved by default 
*/ 
private String repositorySuffix; 
/** 
* log serializer. 
* {@linkplain com.hmily.tcc.common.enums.SerializeEnum} 
*/ 
private String serializer = "kryo"; 
/** 
* scheduledPool Thread size. 
*/ 
private int scheduledThreadMax = Runtime.getRuntime().availableProcessors() << 1; 
/** 
* scheduledPool scheduledDelay unit SECONDS. 
*/ 
private int scheduledDelay = 60; 
/** 
* retry max. 
*/ 
private int retryMax = 3; 
/** 
* recoverDelayTime Unit seconds 
* (note that this time represents how many seconds after the local transaction was created before execution). 
*/ 
private int recoverDelayTime = 60; 
/** 
* Parameters when participants perform their own recovery. 
* 1.such as RPC calls time out 
* 2.such as the starter down machine 
*/ 
private int loadFactor = 2; 
/** 
* repositorySupport. 
* {@linkplain RepositorySupportEnum} 
*/ 
private String repositorySupport = "db"; 
/** 
* disruptor bufferSize. 
*/ 
private int bufferSize = 4096 * 2 * 2; 
/** 
* this is disruptor consumerThreads. 
*/ 
private int consumerThreads = Runtime.getRuntime().availableProcessors() << 1; 
/** 
* db config. 
*/ 
private TccDbConfig tccDbConfig; 
/** 
* mongo config. 
*/ 
private TccMongoConfig tccMongoConfig; 
/** 
* redis config. 
*/ 
private TccRedisConfig tccRedisConfig; 
/** 
* zookeeper config. 
*/ 
private TccZookeeperConfig tccZookeeperConfig; 
/** 
* file config. 
*/ 
private TccFileConfig tccFileConfig; 
}

2.SpringCloud用户

  • 需要引入
     <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-springcloud</artifactId> 
    <version>{you version}</version> 
    </dependency>

  • 配置启动bean 如上。

3.Motan用户

  • 需要引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-motan</artifactId> 
    <version>{you version}</version> 
    </dependency>

  • 配置启动bean 如上。

hmily-spring-boot-start

  • 那这个就更容易了,只需要根据你的RPC框架去引入不同的jar包。
  • 如果你是dubbo用户,那么引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-spring-boot-starter-dubbo</artifactId> 
    <version>${your version}</version> 
    </dependency>

  • 如果你是SpringCloud用户,那么引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-spring-boot-starter-springcloud</artifactId> 
    <version>${your version}</version> 
    </dependency>

  • 如果你是Motan用户,那么引入
    <dependency> 
    <groupId>com.hmily.tcc</groupId> 
    <artifactId>hmily-tcc-spring-boot-starter-motan</artifactId> 
    <version>${your version}</version> 
    </dependency>

  • 然后在你的yml里面进行如下配置:
    hmily: 
    tcc : 
    serializer : kryo 
    recoverDelayTime : 128 
    retryMax : 3 
    scheduledDelay : 128 
    scheduledThreadMax :  10 
    repositorySupport : db 
    tccDbConfig : 
    driverClassName  : com.mysql.jdbc.Driver 
    url :  jdbc:mysql://192.168.1.98:3306/tcc?useUnicode=true&amp;characterEncoding=utf8 
    username : root 
    password : 123456 
    #repositorySupport : redis 
    #tccRedisConfig: 
    #masterName: mymaster 
    #sentinel : true 
    #sentinelUrl : 192.168.1.91:26379;192.168.1.92:26379;192.168.1.93:26379 
    #password  : foobaredbbexONE123 
    # repositorySupport : zookeeper 
    #         host      : 92.168.1.73:2181 
    #         sessionTimeOut      :  100000 
    #         rootPath  : /tcc 
    # repositorySupport : mongodb 
    #       mongoDbUrl  : 192.168.1.68:27017 
    #       mongoDbName  :  happylife 
    #       mongoUserName  : xiaoyu 
    #       mongoUserPwd   : 123456 
    # repositorySupport : file 
    #         path      : /account 
    #         prefix    :  account

就这么简单,[email protected],进行愉快的使用了。当然因为篇幅问题,很多东西只是简单的描述,尤其是逻辑方面的。

下面是github地址:https://github.com/yu199195/hmily

 

原创文章,作者:奋斗,如若转载,请注明出处:https://blog.ytso.com/7096.html

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