Hmily框架特性
[https://github.com/yu199195/hmily]
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無縫集成Spring,Spring boot start。
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無縫集成Dubbo,SpringCloud,Motan等rpc框架。
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多種事務日誌的存儲方式(redis,mongdb,mysql等)。
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多種不同日誌序列化方式(Kryo,protostuff,hession)。
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事務自動恢復。
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支持內嵌事務的依賴傳遞。
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代碼零侵入,配置簡單靈活。
Hmily爲什麼這麼高性能?
1.採用disruptor進行事務日誌的異步讀寫(disruptor是一個無鎖,無GC的併發編程框架)
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();
}
}
在這裏bufferSize 的默認值是4094 * 4,用戶可以根據自行的情況進行配置。
HmilyConsumerDataHandler[] consumers = new HmilyConsumerDataHandler[threadSize];
for (int i = 0; i < threadSize; i++) {
consumers[i] = new HmilyConsumerDataHandler(executor, coordinatorService);
}
disruptor.handleEventsWithWorkerPool(consumers);
這裏是採用多個消費者去處理隊列裏面的任務。
2.異步執行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;
}
}
當try方法的AOP切面有異常的時候,採用線程池異步去執行cancel,無異常的時候去執行confrim方法。
這裏有人可能會問:那麼cancel方法異常,或者confrim方法異常怎麼辦呢?
答:首先這種情況是非常罕見的,因爲你上一面纔剛剛執行完try。其次如果出現這種情況,在try階段會保存好日誌,Hmily有內置的調度線程池來進行恢復,不用擔心。
有人又會問:這裏如果日誌保存異常了怎麼辦?
答:首先這又是一個牛角尖問題,首先日誌配置的參數,在框架啓動的時候,會要求你配置的。其次,就算在運行過程中日誌保存異常,這時候框架會取緩存中的,並不會影響程序正確執行。最後,萬一日誌保存異常了,系統又在很極端的情況下down機了,恭喜你,你可以去買彩票了,最好的解決辦法就是不去解決它。
3.ThreadLocal緩存的使用。
/**
* 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保存的發起者一方法的事務信息。這個很重要,不要會有點懵逼。rpc的調用,會形成調用鏈,進行保存。
/**
* 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);
}
}
}
在參與者中,我們使用了ThreadLocal,而在參與者中,我們爲什麼不使用呢?
其實原因有二點:首先.因爲try,和confrim 會不在一個線程裏,會造成ThreadLocal失效。當考慮到RPC集羣的時候,可能會負載到不同的機器上。
這裏有一個細節就是:
private static TccTransaction cacheTccTransaction(final String key) {
return Optional.ofNullable(coordinatorService.findByTransId(key)).orElse(new TccTransaction());
}
當GuavaCache裏面沒有的時候,會去查詢日誌返回,這樣就保證了對集羣環境的支持。
以上4點造就了Hmily是一個異步的高性能分佈式事務TCC框架的原因。
Hmily如何使用?
(https://github.com/yu199195/hmily/tree/master/hmily-tcc-demo)
首先因爲之前的包命名問題,框架包並沒有上傳到maven中心倉庫,固需要使用者自己拉取代碼,編譯deploy到自己的私服。
1.dubbo用戶
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在你的Api接口項目引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-annotation</artifactId>
<version>{you version}</version>
</dependency>
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在你的服務提供者項目引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-dubbo</artifactId>
<version>{you version}</version>
</dependency>
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配置啓動bean
<!-- Aspect 切面配置,是否開啓AOP切面-->
<aop:aspectj-autoproxy expose-proxy="true"/>
<!--掃描框架的包-->
<context:component-scan base-package="com.hmily.tcc.*"/>
<!--啓動類屬性配置-->
<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>
當然配置屬性很多,這裏我只給出了demo,具體可以參考這個類:
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用戶
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需要引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-springcloud</artifactId>
<version>{you version}</version>
</dependency>
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配置啓動bean 如上。
3.Motan用戶
-
需要引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-motan</artifactId>
<version>{you version}</version>
</dependency>
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配置啓動bean 如上。
hmily-spring-boot-start
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那這個就更容易了,只需要根據你的RPC框架去引入不同的jar包。
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如果你是dubbo用戶,那麼引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-spring-boot-starter-dubbo</artifactId>
<version>${your version}</version>
</dependency>
-
如果你是SpringCloud用戶,那麼引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-spring-boot-starter-springcloud</artifactId>
<version>${your version}</version>
</dependency>
-
如果你是Motan用戶,那麼引入
<dependency>
<groupId>com.hmily.tcc</groupId>
<artifactId>hmily-tcc-spring-boot-starter-motan</artifactId>
<version>${your version}</version>
</dependency>
-
然後在你的yml裏面進行如下配置:
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
就這麼簡單,然後就可以在接口方法上加上@Tcc註解,進行愉快的使用了。
當然因爲篇幅問題,很多東西只是簡單的描述,尤其是邏輯方面的。
下面是github地址:https://github.com/yu199195/hmily
最後再次感謝大家,如果有興趣的朋友,可以提供你的優秀牛逼轟轟的PR。