服务降级熔断 - 请求合并
请求合并
前言
今日继续摸鱼Hystrix的请求合并部分，可能不如请求缓存分析的详细，但是我感觉足够表达实现原理了。
本文选择了较为简单的请求合并的用例进行切入并分析，即CommandCollapserGetValueForKey，而非ObservableCollapserGetWordForNumber，原理都是一致的，只是ObservableCollapserGetWordForNumber提供了更为丰富的接口，供业务实现。
请求合并的使用
从CommandCollapserGetValueForKey例子看，只要做如下3件事，就能实现请求合并。
1、继承HystrixCollapser<BatchReturnType, ResponseType, RequestArgumentType>。 2、重写三个方法，分别为
getRequestArgument、createCommand、mapResponseToRequests。 3、写一个BatchCommand，即请求合并后的一个HystrixCommand。
接下来，可以从源码层面上看，如何通过这三步操作实现请求合并。
HystrixCollapser
- 需要注意
<BatchReturnType, ResponseType, RequestArgumentType>泛型的含义，已经在代码块中添加了相应注释。
**
* 根据设定时间参数以及合并请求数，将多个HystrixCommand合并成一次的HystrixCommand，从而将短时间调用服务的次数减少。
* <p>
* 通常将时间窗口设为10ms左右
*
* @param <BatchReturnType>
* 合并后的HystrixCommand的返回类型，例如String变成List<String>。
* @param <ResponseType>
* 需要合并的HystrixCommand的返回类型。
* @param <RequestArgumentType>
* 需要合并的HystrixCommand的请求参数类型。
*/
public abstract class HystrixCollapser<BatchReturnType, ResponseType, RequestArgumentType> implements HystrixExecutable<ResponseType>, HystrixObservable<ResponseType> {
}
请求合并的过程
-
请求合并的过程，从例子可以看出，合并后的
BatchCommand的参数为Collection<CollapsedRequest<String, Integer>> requests，即请求合并的过程就是从单个请求的参数合并成Collection<CollapsedRequest<ResponseType, RequestArgumentType>>。 -
因此，可以从
getRequestArgument的调用入手，就找到了HystrixCollapser.toObservable。
// 提交请求，直接返回结果了。。。
Observable<ResponseType> response = requestCollapser.submitRequest(getRequestArgument());
/**
* Submit a request to a batch. If the batch maxSize is hit trigger the batch immediately.
* 和清楚了将时间窗口内的请求提交，如果到了设定的合并阈值，触发一次合并请求
* @param arg argument to a {@link RequestCollapser}
* @return Observable<ResponseType>
* @throws IllegalStateException
* if submitting after shutdown
*/
public Observable<ResponseType> submitRequest(final RequestArgumentType arg) {
/*
* 启动计时器，时间窗口阈值到了，则触发一次合并请求
*/
if (!timerListenerRegistered.get() && timerListenerRegistered.compareAndSet(false, true)) {
/* schedule the collapsing task to be executed every x milliseconds (x defined inside CollapsedTask) */
timerListenerReference.set(timer.addListener(new CollapsedTask()));
}
// loop until succeed (compare-and-set spin-loop)
// 等待-通知模型
while (true) {
// 拿到RequestBatch
final RequestBatch<BatchReturnType, ResponseType, RequestArgumentType> b = batch.get();
if (b == null) {
return Observable.error(new IllegalStateException("Submitting requests after collapser is shutdown"));
}
final Observable<ResponseType> response;
// 添加到RequestBatch
if (arg != null) {
response = b.offer(arg);
} else {
response = b.offer( (RequestArgumentType) NULL_SENTINEL);
}
// it will always get an Observable unless we hit the max batch size
// 添加成功，返回 Observable
if (response != null) {
return response;
} else {
// this batch can't accept requests so create a new one and set it if another thread doesn't beat us
// 添加失败，执行 RequestBatch ，并创建新的 RequestBatch
createNewBatchAndExecutePreviousIfNeeded(b);
}
}
}
-
offer方法
public Observable<ResponseType> offer(RequestArgumentType arg) {
2: // 执行已经开始，添加失败
3: /* short-cut - if the batch is started we reject the offer */
4: if (batchStarted.get()) {
5: return null;
6: }
7:
8: /*
9: * The 'read' just means non-exclusive even though we are writing.
10: */
11: if (batchLock.readLock().tryLock()) {
12: try {
13: // 执行已经开始，添加失败
14: /* double-check now that we have the lock - if the batch is started we reject the offer */
15: if (batchStarted.get()) {
16: return null;
17: }
18:
19: // 超过队列最大长度，添加失败
20: if (argumentMap.size() >= maxBatchSize) {
21: return null;
22: } else {
23: // 创建 CollapsedRequestSubject ，并添加到队列
24: CollapsedRequestSubject<ResponseType, RequestArgumentType> collapsedRequest = new CollapsedRequestSubject<ResponseType, RequestArgumentType>(arg, this);
25: final CollapsedRequestSubject<ResponseType, RequestArgumentType> existing = (CollapsedRequestSubject<ResponseType, RequestArgumentType>) argumentMap.putIfAbsent(arg, collapsedRequest);
26: /**
27: * If the argument already exists in the batch, then there are 2 options:
28: * A) If request caching is ON (the default): only keep 1 argument in the batch and let all responses
29: * be hooked up to that argument
30: * B) If request caching is OFF: return an error to all duplicate argument requests
31: *
32: * This maintains the invariant that each batch has no duplicate arguments. This prevents the impossible
33: * logic (in a user-provided mapResponseToRequests for HystrixCollapser and the internals of HystrixObservableCollapser)
34: * of trying to figure out which argument of a set of duplicates should get attached to a response.
35: *
36: * See https://github.com/Netflix/Hystrix/pull/1176 for further discussion.
37: */
38: if (existing != null) {
39: boolean requestCachingEnabled = properties.requestCacheEnabled().get();
40: if (requestCachingEnabled) {
41: return existing.toObservable();
42: } else {
43: return Observable.error(new IllegalArgumentException("Duplicate argument in collapser batch : [" + arg + "] This is not supported. Please turn request-caching on for HystrixCollapser:" + commandCollapser.getCollapserKey().name() + " or prevent duplicates from making it into the batch!"));
44: }
45: } else {
46: return collapsedRequest.toObservable();
47: }
48:
49: }
50: } finally {
51: batchLock.readLock().unlock();
52: }
53: } else {
54: return null;
55: }
56: }
-
第 38 至 47 行 ：返回
Observable。当argumentMap已经存在arg对应的Observable时，必须开启缓存 (HystrixCollapserProperties.requestCachingEnabled = true) 功能。原因是，如果在相同的arg，并且未开启缓存，同时第 43 行实现的是collapsedRequest.toObservable()，那么相同的arg将有多个Observable执行命令，此时HystrixCollapserBridge.mapResponseToRequests方法无法将执行(Response)赋值到arg对应的命令请求(CollapsedRequestSubject) 。 -
回过头看
HystrixCollapser#toObservable()方法的代码，这里也有对缓存功能，是不是重复了呢？argumentMap针对的是RequestBatch级的缓存，HystrixCollapser:RequestCollapser:RequestBatch是 1 : 1 : N 的关系，通过HystrixCollapser#toObservable()对缓存的处理逻辑，保证RequestBatch切换后，依然有缓存。 -
CollapsedTask负责触发时间窗口内合并请求的处理，其实关键方法就是createNewBatchAndExecutePreviousIfNeeded，并且也调用了executeBatchIfNotAlreadyStarted。
/**
* Executed on each Timer interval execute the current batch if it has requests in it.
*/
private class CollapsedTask implements TimerListener {
...
@Override
public Void call() throws Exception {
try {
// we fetch current so that when multiple threads race
// we can do compareAndSet with the expected/new to ensure only one happens
// 拿到合并请求
RequestBatch<BatchReturnType, ResponseType, RequestArgumentType> currentBatch = batch.get();
// 1) it can be null if it got shutdown
// 2) we don't execute this batch if it has no requests and let it wait until next tick to be executed
// 处理合并请求
if (currentBatch != null && currentBatch.getSize() > 0) {
// do execution within context of wrapped Callable
createNewBatchAndExecutePreviousIfNeeded(currentBatch);
}
} catch (Throwable t) {
logger.error("Error occurred trying to execute the batch.", t);
t.printStackTrace();
// ignore error so we don't kill the Timer mainLoop and prevent further items from being scheduled
}
return null;
}
});
}
-
executeBatchIfNotAlreadyStarted中对请求进行了合并及执行！！！
1、调用
HystrixCollapserBridge.shardRequests方法，将多个命令请求分片成 N 个【多个命令请求】。默认实现下，不进行分片。 2、循环 N 个【多个命令请求】。 3、调用HystrixCollapserBridge.createObservableCommand方法，将多个命令请求合并，创建一个 HystrixCommand 。
...
// shard batches
Collection<Collection<CollapsedRequest<ResponseType, RequestArgumentType>>> shards = commandCollapser.shardRequests(argumentMap.values());
// for each shard execute its requests
for (final Collection<CollapsedRequest<ResponseType, RequestArgumentType>> shardRequests : shards) {
try {
// create a new command to handle this batch of requests
Observable<BatchReturnType> o = commandCollapser.createObservableCommand(shardRequests);
commandCollapser.mapResponseToRequests(o, shardRequests).doOnError(new Action1<Throwable>() {
...
}
- 最后执行就很简单了，调用重写的
mapResponseToRequests的方法将一个HystrixCommand的执行结果，映射回对应的命令请求们。
总结
- 简单来讲，就是讲一定时间窗口内的请求，放入“队列”，有一个定时触发的任务，将“队列”里的请求（“队列”里的请求也有可能被分片，成为“请求队列集合”,即Collection<Collection<>>)，通过自己实现的
BatchCommand执行，将最后结果再映射为合并前HystrixCommand的结果返回。
PS：又见到了好多concurrentHashMap，CAS，Atomic变量。。。
