一、简单使用（源码分析基石）
1.引入 Sentinel 依赖
<dependency>
<groupId>com.alibaba.cloud</groupId>
<artifactId>spring-cloud-starter-alibaba-sentinel</artifactId>
</dependency>
2.对Controller方法打上注解
@SentinelResource(value = "/order/create", blockHandler = "blockHandlerFunc", fallback = "fallbackFunc")
@GetMapping("/test-sentinel-resource")
public String testSentinelResource(@RequestParam(required = false) String a)
throws InterruptedException {
// 模拟执行被保护的业务逻辑耗时
Thread.sleep(100);
return a;
}
二、注解解析-----切面
通过一个切面类SentinelResourceAspect，去解析我们写的@SentinelResource
@Aspect
public class SentinelResourceAspect extends AbstractSentinelAspectSupport {
//切点，SentinelResource注解
@Pointcut("@annotation(com.alibaba.csp.sentinel.annotation.SentinelResource)")
public void sentinelResourceAnnotationPointcut() {
}
//环绕通知
@Around("sentinelResourceAnnotationPointcut()")
public Object invokeResourceWithSentinel(ProceedingJoinPoint pjp) throws Throwable {
Method originMethod = resolveMethod(pjp);
SentinelResource annotation = originMethod.getAnnotation(SentinelResource.class);
if (annotation == null) {
// Should not go through here.
throw new IllegalStateException("Wrong state for SentinelResource annotation");
}
String resourceName = getResourceName(annotation.value(), originMethod);
EntryType entryType = annotation.entryType();
int resourceType = annotation.resourceType();
Entry entry = null;
try {
entry = SphU.entry(resourceName, resourceType, entryType, pjp.getArgs());
Object result = pjp.proceed();
return result;
} catch (BlockException ex) {
return handleBlockException(pjp, annotation, ex);
} catch (Throwable ex) {
Class<? extends Throwable>[] exceptionsToIgnore = annotation.exceptionsToIgnore();
// The ignore list will be checked first.
if (exceptionsToIgnore.length > 0 && exceptionBelongsTo(ex, exceptionsToIgnore)) {
throw ex;
}
if (exceptionBelongsTo(ex, annotation.exceptionsToTrace())) {
traceException(ex, annotation);
return handleFallback(pjp, annotation, ex);
}
throw ex;
} finally {
if (entry != null) {
entry.exit(1, pjp.getArgs());
}
}
}
}
三、处理方式----try/catch
通过一个大大的try/catch，包裹住我们的方法，在之前执行SphU.entry(通过在这规则校验抛出异常而阻止后续方法正常执行达到限流熔断)，执行之后根据方法执行的情况进行记录处理等：
try {
entry = SphU.entry(resourceName, resourceType, entryType, pjp.getArgs());
Object result = pjp.proceed();
return result;
} catch (BlockException ex) {
return handleBlockException(pjp, annotation, ex);
} catch (Throwable ex) {
if (exceptionBelongsTo(ex, annotation.exceptionsToTrace())) {
traceException(ex, annotation);
return handleFallback(pjp, annotation, ex);
}
throw ex;
} finally {
if (entry != null) {
entry.exit(1, pjp.getArgs());
}
}
四、重点看看SphU.entry的规则校验记录怎么实现的？
跟进上面的那行代码：
entry = SphU.entry(resourceName, resourceType, entryType, pjp.getArgs());
不断跟进会来到entryWithPriority方法：
private Entry entryWithPriority(ResourceWrapper resourceWrapper, int count, boolean prioritized, Object... args)
throws BlockException {
//。。。略
ProcessorSlot<Object> chain = lookProcessChain(resourceWrapper);
if (chain == null) {
return new CtEntry(resourceWrapper, null, context);
}
Entry e = new CtEntry(resourceWrapper, chain, context);
try {
chain.entry(context, resourceWrapper, null, count, prioritized, args);
} catch (BlockException e1) {
e.exit(count, args);
throw e1;
} catch (Throwable e1) {
// This should not happen, unless there are errors existing in Sentinel internal.
RecordLog.info("Sentinel unexpected exception", e1);
}
return e;
}
主要是lookProcessChain获取一个“链条”，然后执行“链条”的entry的方法（实际上是lookProcessChain取出很多个ProcessorSlot，然后依次执行，责任链设计模式）
4.1 lookProcessChain
ProcessorSlot<Object> lookProcessChain(ResourceWrapper resourceWrapper) {
ProcessorSlotChain chain = chainMap.get(resourceWrapper);
if (chain == null) {
synchronized (LOCK) {
chain = chainMap.get(resourceWrapper);
if (chain == null) {
// Entry size limit.
if (chainMap.size() >= Constants.MAX_SLOT_CHAIN_SIZE) {
return null;
}
chain = SlotChainProvider.newSlotChain();
Map<ResourceWrapper, ProcessorSlotChain> newMap = new HashMap<ResourceWrapper, ProcessorSlotChain>(
chainMap.size() + 1);
newMap.putAll(chainMap);
newMap.put(resourceWrapper, chain);
chainMap = newMap;
}
}
}
return chain;
}
单例设计模式DCL去创建ProcessorSlotChain，主要是：
chain = SlotChainProvider.newSlotChain();
查看源码会发现是通过建造者模式builder去创建的，而建造者本身builde方法：
public ProcessorSlotChain build() {
ProcessorSlotChain chain = new DefaultProcessorSlotChain();
//加入下面这些slot形成链表，之后调用的时候依次执行
chain.addLast(new NodeSelectorSlot());
chain.addLast(new ClusterBuilderSlot());
chain.addLast(new LogSlot());
chain.addLast(new StatisticSlot());
chain.addLast(new SystemSlot());
chain.addLast(new AuthoritySlot());
chain.addLast(new FlowSlot());
chain.addLast(new DegradeSlot());
return chain;
}
真相大白，这个执行“执行链条”就是这些各种‘Slot’，核心的是：StatisticSlot（统计相关）、FlowSlot（限流相关）、DegradeSlot（降级相关）。
4.2 chain.entry依次执行各个Slot
回到之前的entryWithPriority方法：
private Entry entryWithPriority(ResourceWrapper resourceWrapper, int count, boolean prioritized, Object... args)
throws BlockException {
//。。。略
ProcessorSlot<Object> chain = lookProcessChain(resourceWrapper);
if (chain == null) {
return new CtEntry(resourceWrapper, null, context);
}
Entry e = new CtEntry(resourceWrapper, chain, context);
try {
chain.entry(context, resourceWrapper, null, count, prioritized, args);
} catch (BlockException e1) {
e.exit(count, args);
throw e1;
} catch (Throwable e1) {
// This should not happen, unless there are errors existing in Sentinel internal.
RecordLog.info("Sentinel unexpected exception", e1);
}
return e;
}
那么拿到执行链条之后，就是依次执行各个Slot的entry方法了！
五、StatisticSlot（统计相关）
稍后补充todo
六、FlowSlot（限流相关）
稍后补充todo
七、DegradeSlot（降级相关）
稍后补充todo
八、集群限流设计方案-ClusterBuilderSlot
8.1 基本思想
引入了Token Server和Token Client的概念（相当引入了个管理中心）
Token Server：即集群流控服务端，处理来自 Token Client 的请求，根据配置的集群规则判断是否应该发放 token（是否允许通过）。服务端采用的还是滑动窗口。
Token Client：集群流控客户端，就是我们的应用本身，用于向所属 Token Server 通信请求 token（以及统计信息）。集群限流服务端会返回给客户端结果，决定是否限流。
8.2 基本流程
ClusterBuilderSlot用来统计各种集群相关信息啥的，真正判断限流还是在流控FlowRuleChecker类（FlowSlot持有的属性对象）的canPassCheck方法中：
public boolean canPassCheck(/*@NonNull*/ FlowRule rule, Context context, DefaultNode node, int acquireCount,
boolean prioritized) {
String limitApp = rule.getLimitApp();
if (limitApp == null) {
return true;
}
//判断是集群模式，采用从Token Server获取是否给通过
if (rule.isClusterMode()) {
return passClusterCheck(rule, context, node, acquireCount, prioritized);
}
return passLocalCheck(rule, context, node, acquireCount, prioritized);
}
会根据是否是集群模式，采用从Token Server获取是否给通过
8.3 可用性
目前针对token server高可用，sentinel并没有对应的解决方案，默认会降级走本地流控。
8.4 总结
仅靠单机维度去限制的话可能会无法精确地限制总体流量。而集群流控可以精确地控制整个集群的调用总量，结合单机限流兜底，可以更好地发挥流量控制的效果。