Hmily框架特性
[https://github.com/yu199195/hmily]
-
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。
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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>
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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&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，这里我只给出了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&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，然后就可以在接口方法上加上@Tcc注解，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
最后再次感谢大家，如果有兴趣的朋友，可以提供你的优秀牛逼轰轰的PR。
原文发布时间为：2018-10-12
本文作者：xiaoyu
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