一、Sentinel簡介
Sentinel 以流量為切入點，從流量控制、熔斷降級、系統負載保護等多個次元保護服務的穩定性。
Sentinel 具有以下特征:
- 豐富的應用場景：秒殺（即突發流量控制在系統容量可以承受的範圍）、消息削峰填谷、叢集流量控制、實時熔斷下遊不可用應用等。
- 完備的實時監控：Sentinel 同時提供實時的監控功能。您可以在控制台中看到接入應用的單台機器秒級資料，甚至 500 台以下規模的叢集的彙總運作情況。
- 廣泛的開源生态：Sentinel 提供開箱即用的與其它開源架構/庫的整合子產品，例如與 Spring Cloud、Apache Dubbo、gRPC、Quarkus 的整合。您隻需要引入相應的依賴并進行簡單的配置即可快速地接入 Sentinel。同時 Sentinel 提供 Java/Go/C++ 等多語言的原生實作。
- 完善的 SPI 擴充機制：Sentinel 提供簡單易用、完善的 SPI 擴充接口。您可以通過實作擴充接口來快速地定制邏輯。例如定制規則管理、适配動态資料源等
有關Sentinel的詳細介紹以及和Hystrix的差別可以自行網上檢索，推薦一篇文章：https://mp.weixin.qq.com/s/Q7Xv8cypQFrrOQhbd9BOXw
本次主要使用了Sentinel的降級、限流、系統負載保護功能
二、Sentinel關鍵技術源碼解解析
無論是限流、降級、負載等控制手段，大緻流程如下：
- StatisticSlot 則用于記錄、統計不同次元的 runtime 名額監控資訊
- 責任鍊依次觸發後續 slot 的 entry 方法，如 SystemSlot、FlowSlot、DegradeSlot 等的規則校驗；
- 當後續的 slot 通過，沒有抛出 BlockException 異常，說明該資源被成功調用，則增加執行線程數和通過的請求數等資訊。
- 關于資料統計，主要會牽扯到 ArrayMetric、BucketLeapArray、MetricBucket、WindowWrap 等類。
項目結構
以下主要分析core包裡的内容
2.1注解入口
2.1.1 Entry、Context、Node
SphU門面類的方法出參都是Entry，Entry可以了解為每次進入資源的一個憑證，如果調用SphO.entry()或者SphU.entry()能擷取Entry對象，代表擷取了憑證，沒有被限流，否則抛出一個BlockException。
Entry中持有本次對資源調用的相關資訊：
- createTime：建立該Entry的時間戳。
- curNode：Entry目前是在哪個節點。
- orginNode：Entry的調用源節點。
- resourceWrapper：Entry關聯的資源資訊。
Entry是一個抽象類，CtEntry是Entry的實作，CtEntry持有Context和調用鍊的資訊
Context的源碼注釋如下，
This class holds metadata of current invocation
Node的源碼注釋
Holds real-time statistics for resources
Node中儲存了對資源的實時資料的統計，Sentinel中的限流或者降級等功能就是通過Node中的資料進行判斷的。Node是一個接口，裡面定義了各種操作request、exception、rt、qps、thread的方法。
在細看Node實作時，不難發現LongAddr的使用，關于LongAddr和DoubleAddr都是java8 java.util.concurrent.atomic裡的内容，感興趣的小夥伴可以再深入研究一下，這兩個是高并發下計數功能非常優秀的資料結構，實際應用場景裡需要計數時可以考慮使用。
關于Node的介紹後續還會深入，此處大緻先提一下這個概念。
2.2 初始化
2.2.1 Context初始化
在初始化slot責任鍊部分前，還執行了context的初始化，裡面涉及幾個重要概念，需要解釋一下：
可以發現在Context初始化的過程中，會把EntranceNode加入到Root子節點中（實際Root本身是一個特殊的EntranceNode），并把EntranceNode放到contextNameNodeMap中。
之前簡單提到過Node，是用來統計資料用的，不同Node功能如下：
- Node：用于完成資料統計的接口
- StatisticNode：統計節點，是Node接口的實作類，用于完成資料統計
- EntranceNode：入口節點，一個Context會有一個入口節點，用于統計目前Context的總體流量資料
- DefaultNode：預設節點，用于統計一個資源在目前Context中的流量資料
- ClusterNode：叢集節點，用于統計一個資源在所有Context中的總體流量資料
protected static Context trueEnter(String name, String origin) {
Context context = contextHolder.get();
if (context == null) {
Map<String, DefaultNode> localCacheNameMap = contextNameNodeMap;
DefaultNode node = localCacheNameMap.get(name);
if (node == null) {
if (localCacheNameMap.size() > Constants.MAX_CONTEXT_NAME_SIZE) {
setNullContext();
return NULL_CONTEXT;
} else {
LOCK.lock();
try {
node = contextNameNodeMap.get(name);
if (node == null) {
if (contextNameNodeMap.size() > Constants.MAX_CONTEXT_NAME_SIZE) {
setNullContext();
return NULL_CONTEXT;
} else {
node = new EntranceNode(new StringResourceWrapper(name, EntryType.IN), null);
// Add entrance node.
Constants.ROOT.addChild(node);
Map<String, DefaultNode> newMap = new HashMap<>(contextNameNodeMap.size() + 1);
newMap.putAll(contextNameNodeMap);
newMap.put(name, node);
contextNameNodeMap = newMap;
}
}
} finally {
LOCK.unlock();
}
}
}
context = new Context(node, name);
context.setOrigin(origin);
contextHolder.set(context);
}
return context;
}
2.2.2 通過SpiLoader預設初始化8個slot
每個slot的主要職責如下：
- NodeSelectorSlot 負責收集資源的路徑，并将這些資源的調用路徑，以樹狀結構存儲起來，用于根據調用路徑來限流降級；
- ClusterBuilderSlot 則用于存儲資源的統計資訊以及調用者資訊，例如該資源的 RT, QPS, thread count 等等，這些資訊将用作為多元度限流，降級的依據；
- StatisticSlot 則用于記錄、統計不同緯度的 runtime 名額監控資訊；
- FlowSlot 則用于根據預設的限流規則以及前面 slot 統計的狀态，來進行流量控制；
- AuthoritySlot 則根據配置的黑白名單和調用來源資訊，來做黑白名單控制；
- DegradeSlot 則通過統計資訊以及預設的規則，來做熔斷降級；
- SystemSlot 則通過系統的狀态，例如 叢集QPS、線程數、RT、負載 等，來控制總的入口流量；
2.3 StatisticSlot
2.3.1 Node
深入看一下Node，因為統計資訊都在裡面，後面不論是限流、熔斷、負載保護等都是結合規則+統計資訊判斷是否要執行
從Node的源碼注釋看，它會持有資源次元的實時統計資料，以下是接口裡的方法定義，可以看到totalRequest、totalPass、totalSuccess、blockRequest、totalException、passQps等很多request、qps、thread的相關方法：
/**
* Holds real-time statistics for resources.
*
* @author qinan.qn
* @author leyou
* @author Eric Zhao
*/
public interface Node extends OccupySupport, DebugSupport {
long totalRequest();
long totalPass();
long totalSuccess();
long blockRequest();
long totalException();
double passQps();
double blockQps();
double totalQps();
double successQps();
……
}
2.3.2 StatisticNode
我們先從最基礎的StatisticNode開始看，源碼給出的定位是：
The statistic node keep three kinds of real-time statistics metrics：
metrics in second level ({@code rollingCounterInSecond})
metrics in minute level ({@code rollingCounterInMinute})
thread count
StatisticNode隻有四個屬性，除了之前提到過的LongAddr類型的curThreadNum外，還有兩個屬性是Metric對象，通過入參已經屬性命名可以看出，一個用于秒級，一個用于分鐘級統計。接下來我們就要看看Metric
// StatisticNode持有兩個Metric，一個秒級一個分鐘級，由入參可知，秒級統計劃分了兩個時間視窗，視窗程度是500ms
private transient volatile Metric rollingCounterInSecond = new ArrayMetric(SampleCountProperty.SAMPLE_COUNT,
IntervalProperty.INTERVAL);
// 分鐘級統計劃分了60個時間視窗，視窗長度是1000ms
private transient Metric rollingCounterInMinute = new ArrayMetric(60, 60 * 1000, false);
/**
* The counter for thread count.
*/
private LongAdder curThreadNum = new LongAdder();
/**
* The last timestamp when metrics were fetched.
*/
private long lastFetchTime = -1;
ArrayMetric隻有一個屬性LeapArray<MetricBucket>，其餘都是用于統計的方法，LeapArray是sentinel中統計最基本的資料結構，這裡有必要詳細看一下，總體就是根據timeMillis去擷取一個bucket，分為：沒有建立、有直接傳回、被廢棄後的reset三種場景。
//以分鐘級的統計屬性為例，看一下時間視窗初始化過程
private transient Metric rollingCounterInMinute = new ArrayMetric(60, 60 * 1000, false);
public LeapArray(int sampleCount, int intervalInMs) {
AssertUtil.isTrue(sampleCount > 0, "bucket count is invalid: " + sampleCount);
AssertUtil.isTrue(intervalInMs > 0, "total time interval of the sliding window should be positive");
AssertUtil.isTrue(intervalInMs % sampleCount == 0, "time span needs to be evenly divided");
// windowLengthInMs = 60*1000 / 60 = 1000 滑動視窗時間長度，可見sentinel預設将機關時間分為了60個滑動視窗進行資料統計
this.windowLengthInMs = intervalInMs / sampleCount;
// 60*1000
this.intervalInMs = intervalInMs;
// 60
this.intervalInSecond = intervalInMs / 1000.0;
// 60
this.sampleCount = sampleCount;
// 數組長度60
this.array = new AtomicReferenceArray<>(sampleCount);
}
/**
* Get bucket item at provided timestamp.
*
* @param timeMillis a valid timestamp in milliseconds
* @return current bucket item at provided timestamp if the time is valid; null if time is invalid
*/
public WindowWrap<T> currentWindow(long timeMillis) {
if (timeMillis < 0) {
return null;
}
// 根據目前時間戳算一個數組索引
int idx = calculateTimeIdx(timeMillis);
// Calculate current bucket start time.
// timeMillis % 1000
long windowStart = calculateWindowStart(timeMillis);
/*
* Get bucket item at given time from the array.
*
* (1) Bucket is absent, then just create a new bucket and CAS update to circular array.
* (2) Bucket is up-to-date, then just return the bucket.
* (3) Bucket is deprecated, then reset current bucket.
*/
while (true) {
WindowWrap<T> old = array.get(idx);
if (old == null) {
/*
* B0 B1 B2 NULL B4
* ||_______|_______|_______|_______|_______||___
* 200 400 600 800 1000 1200 timestamp
* ^
* time=888
* bucket is empty, so create new and update
*
* If the old bucket is absent, then we create a new bucket at {@code windowStart},
* then try to update circular array via a CAS operation. Only one thread can
* succeed to update, while other threads yield its time slice.
*/
// newEmptyBucket 方法重寫，秒級和分鐘級統計對象實作不同
WindowWrap<T> window = new WindowWrap<T>(windowLengthInMs, windowStart, newEmptyBucket(timeMillis));
if (array.compareAndSet(idx, null, window)) {
// Successfully updated, return the created bucket.
return window;
} else {
// Contention failed, the thread will yield its time slice to wait for bucket available.
Thread.yield();
}
} else if (windowStart == old.windowStart()) {
/*
* B0 B1 B2 B3 B4
* ||_______|_______|_______|_______|_______||___
* 200 400 600 800 1000 1200 timestamp
* ^
* time=888
* startTime of Bucket 3: 800, so it's up-to-date
*
* If current {@code windowStart} is equal to the start timestamp of old bucket,
* that means the time is within the bucket, so directly return the bucket.
*/
return old;
} else if (windowStart > old.windowStart()) {
/*
* (old)
* B0 B1 B2 NULL B4
* |_______||_______|_______|_______|_______|_______||___
* ... 1200 1400 1600 1800 2000 2200 timestamp
* ^
* time=1676
* startTime of Bucket 2: 400, deprecated, should be reset
*
* If the start timestamp of old bucket is behind provided time, that means
* the bucket is deprecated. We have to reset the bucket to current {@code windowStart}.
* Note that the reset and clean-up operations are hard to be atomic,
* so we need a update lock to guarantee the correctness of bucket update.
*
* The update lock is conditional (tiny scope) and will take effect only when
* bucket is deprecated, so in most cases it won't lead to performance loss.
*/
if (updateLock.tryLock()) {
try {
// Successfully get the update lock, now we reset the bucket.
return resetWindowTo(old, windowStart);
} finally {
updateLock.unlock();
}
} else {
// Contention failed, the thread will yield its time slice to wait for bucket available.
Thread.yield();
}
} else if (windowStart < old.windowStart()) {
// Should not go through here, as the provided time is already behind.
return new WindowWrap<T>(windowLengthInMs, windowStart, newEmptyBucket(timeMillis));
}
}
}
// 持有一個時間視窗對象的資料，會根據目前時間戳除以時間視窗長度然後散列到數組中
private int calculateTimeIdx(/*@Valid*/ long timeMillis) {
long timeId = timeMillis / windowLengthInMs;
// Calculate current index so we can map the timestamp to the leap array.
return (int)(timeId % array.length());
}
WindowWrap持有了windowLengthInMs, windowStart和LeapArray（分鐘統計實作是BucketLeapArray，秒級統計實作是OccupiableBucketLeapArray），對于分鐘級别的統計，MetricBucket維護了一個longAddr數組和一個配置的minRT
/**
* The fundamental data structure for metric statistics in a time span.
*
* @author jialiang.linjl
* @author Eric Zhao
* @see LeapArray
*/
public class BucketLeapArray extends LeapArray<MetricBucket> {
public BucketLeapArray(int sampleCount, int intervalInMs) {
super(sampleCount, intervalInMs);
}
@Override
public MetricBucket newEmptyBucket(long time) {
return new MetricBucket();
}
@Override
protected WindowWrap<MetricBucket> resetWindowTo(WindowWrap<MetricBucket> w, long startTime) {
// Update the start time and reset value.
w.resetTo(startTime);
w.value().reset();
return w;
}
} 對于秒級統計，QPS=20場景下，如何準确統計的問題，此處用到了另外一個LeapArry實作FutureBucketLeapArray，至于秒級統計如何保證沒有統計誤差，讀者可以再研究一下FutureBucketLeapArray的上下文就好。
2.4 FlowSlot
2.4.1 常見限流算法
介紹sentinel限流實作前，先介紹一下常見限流算法，基本分為三種：計數器、漏鬥、令牌桶。
計數器算法
顧名思義，計數器算法就是統計某個時間段内的請求，每機關時間加1，然後與配置的限流值（最大QPS）進行比較，如果超出則觸發限流。但是這種算法不能做到“平滑限流”，以1s為機關時間，100QPS為限流值為例，如下圖，會出現某時段超出限流值的情況
是以在單純計數器算法上，又出現了滑動視窗計數器算法，我們将統計時間細分，比如将1s統計時長分為5個時間視窗，通過滾動統計所有時間視窗的QPS作為系統實際的QPS的方式，就能解決上述臨界統計問題，後續我們看sentinel源碼時也能看到類似操作。
漏鬥算法
不論流量有多大都會先到漏桶中，然後以均勻的速度流出。如何在代碼中實作這個勻速呢？比如我們想讓勻速為100q/s，那麼我們可以得到每流出一個流量需要消耗10ms，類似一個隊列，每隔10ms從隊列頭部取出流量進行放行，而我們的隊列也就是漏桶，當流量大于隊列的長度的時候，我們就可以拒絕超出的部分。
漏鬥算法同樣的也有一定的缺點：無法應對突發流量。比如一瞬間來了100個請求，在漏桶算法中隻能一個一個的過去，當最後一個請求流出的時候時間已經過了一秒了，是以漏鬥算法比較适合請求到達比較均勻，需要嚴格控制請求速率的場景。
令牌桶算法
令牌桶算法和漏鬥算法比較類似，差別是令牌桶存放的是令牌數量不是請求數量，令牌桶可以根據自身需求多樣性得管理令牌的生産和消耗，可以解決突發流量的問題。
2.4.2 單機限流模式
接下來我們看一下Sentinel中的限流實作，相比上述基本限流算法，Sentinel限流的第一個特性就是引入“資源”的概念，可以細粒度多樣性的支援特定資源、關聯資源、指定鍊路的限流。
FlowSlot的主要邏輯都在FlowRuleChecker裡，介紹之前，我們先看一下Sentinel關于規則的模型描述，下圖分别是限流、通路控制規則、系統保護規則（Linux負載）、降級規則
/**
* 流量控制兩種模式
* 0: thread count（當調用該api的線程數達到門檻值的時候，進行限流）
* 1: QPS（當調用該api的QPS達到門檻值的時候，進行限流）
*/
private int grade = RuleConstant.FLOW_GRADE_QPS;
/**
* 流量控制門檻值，值含義與grade有關
*/
private double count;
/**
* 調用關系限流政策（可以支援關聯資源或指定鍊路的多樣性限流需求）
* 直接（api 達到限流條件時，直接限流）
* 關聯（當關聯的資源達到限流門檻值時，就限流自己）
* 鍊路（隻記錄指定鍊路上的流量）
* {@link RuleConstant#STRATEGY_DIRECT} for direct flow control (by origin);
* {@link RuleConstant#STRATEGY_RELATE} for relevant flow control (with relevant resource);
* {@link RuleConstant#STRATEGY_CHAIN} for chain flow control (by entrance resource).
*/
private int strategy = RuleConstant.STRATEGY_DIRECT;
/**
* Reference resource in flow control with relevant resource or context.
*/
private String refResource;
/**
* 流控效果：
* 0. default(reject directly),直接拒絕，抛異常FlowException
* 1. warm up, 慢啟動模式（根據coldFactor（冷加載因子，預設3）的值，從門檻值/coldFactor，經過預熱時長，才達到設定的QPS門檻值）
* 2. rate limiter 排隊等待
* 3. warm up + rate limiter
*/
private int controlBehavior = RuleConstant.CONTROL_BEHAVIOR_DEFAULT;
private int warmUpPeriodSec = 10;
/**
* Max queueing time in rate limiter behavior.
*/
private int maxQueueingTimeMs = 500;
/**
* 是否叢集限流，預設為否
*/
private boolean clusterMode;
/**
* Flow rule config for cluster mode.
*/
private ClusterFlowConfig clusterConfig;
/**
* The traffic shaping (throttling) controller.
*/
private TrafficShapingController controller;
接着我們繼續分析FlowRuleChecker
canPassCheck第一步會好看limitApp，這個是結合通路授權限制規則使用的，預設是所有
private static boolean passLocalCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
boolean prioritized) {
// 根據政策選擇Node來進行統計（可以是本身Node、關聯的Node、指定的鍊路）
Node selectedNode = selectNodeByRequesterAndStrategy(rule, context, node);
if (selectedNode == null) {
return true;
}
return rule.getRater().canPass(selectedNode, acquireCount, prioritized);
}
static Node selectNodeByRequesterAndStrategy(/*@NonNull*/ FlowRule rule, Context context, DefaultNode node) {
// limitApp是通路控制使用的，預設是default，不限制來源
String limitApp = rule.getLimitApp();
// 拿到限流政策
int strategy = rule.getStrategy();
String origin = context.getOrigin();
// 基于調用來源做鑒權
if (limitApp.equals(origin) && filterOrigin(origin)) {
if (strategy == RuleConstant.STRATEGY_DIRECT) {
// Matches limit origin, return origin statistic node.
return context.getOriginNode();
}
//
return selectReferenceNode(rule, context, node);
} else if (RuleConstant.LIMIT_APP_DEFAULT.equals(limitApp)) {
if (strategy == RuleConstant.STRATEGY_DIRECT) {
// Return the cluster node.
return node.getClusterNode();
}
return selectReferenceNode(rule, context, node);
} else if (RuleConstant.LIMIT_APP_OTHER.equals(limitApp)
&& FlowRuleManager.isOtherOrigin(origin, rule.getResource())) {
if (strategy == RuleConstant.STRATEGY_DIRECT) {
return context.getOriginNode();
}
return selectReferenceNode(rule, context, node);
}
return null;
}
static Node selectReferenceNode(FlowRule rule, Context context, DefaultNode node) {
String refResource = rule.getRefResource();
int strategy = rule.getStrategy();
if (StringUtil.isEmpty(refResource)) {
return null;
}
if (strategy == RuleConstant.STRATEGY_RELATE) {
return ClusterBuilderSlot.getClusterNode(refResource);
}
if (strategy == RuleConstant.STRATEGY_CHAIN) {
if (!refResource.equals(context.getName())) {
return null;
}
return node;
}
// No node.
return null;
}
// 此代碼是load限流規則時根據規則初始化流量整形控制器的邏輯，rule.getRater()傳回TrafficShapingController
private static TrafficShapingController generateRater(/*@Valid*/ FlowRule rule) {
if (rule.getGrade() == RuleConstant.FLOW_GRADE_QPS) {
switch (rule.getControlBehavior()) {
// 預熱模式傳回WarmUpController
case RuleConstant.CONTROL_BEHAVIOR_WARM_UP:
return new WarmUpController(rule.getCount(), rule.getWarmUpPeriodSec(),
ColdFactorProperty.coldFactor);
// 排隊模式傳回ThrottlingController
case RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER:
return new ThrottlingController(rule.getMaxQueueingTimeMs(), rule.getCount());
// 預熱+排隊模式傳回WarmUpRateLimiterController
case RuleConstant.CONTROL_BEHAVIOR_WARM_UP_RATE_LIMITER:
return new WarmUpRateLimiterController(rule.getCount(), rule.getWarmUpPeriodSec(),
rule.getMaxQueueingTimeMs(), ColdFactorProperty.coldFactor);
case RuleConstant.CONTROL_BEHAVIOR_DEFAULT:
default:
// Default mode or unknown mode: default traffic shaping controller (fast-reject).
}
}
// 預設是DefaultController
return new DefaultController(rule.getCount(), rule.getGrade());
}
Sentinel單機限流算法
上面我們看到根據限流規則controlBehavior屬性（流控效果），會初始化以下實作：
- DefaultController：是一個非常典型的滑動視窗計數器算法實作，将目前統計的qps和請求進來的qps進行求和，小于限流值則通過，大于則計算一個等待時間，稍後再試
- ThrottlingController：是漏鬥算法的實作，實作思路已經在源碼片段中加了備注
- WarmUpController：實作參考了Guava的帶預熱的RateLimiter，差別是Guava側重于請求間隔，類似前面提到的令牌桶，而Sentinel更關注于請求數，和令牌桶算法有點類似
- WarmUpRateLimiterController：低水位使用預熱算法，高水位使用滑動視窗計數器算法排隊。
DefaultController
@Override
public boolean canPass(Node node, int acquireCount, boolean prioritized) {
int curCount = avgUsedTokens(node);
if (curCount + acquireCount > count) {
if (prioritized && grade == RuleConstant.FLOW_GRADE_QPS) {
long currentTime;
long waitInMs;
currentTime = TimeUtil.currentTimeMillis();
waitInMs = node.tryOccupyNext(currentTime, acquireCount, count);
if (waitInMs < OccupyTimeoutProperty.getOccupyTimeout()) {
node.addWaitingRequest(currentTime + waitInMs, acquireCount);
node.addOccupiedPass(acquireCount);
sleep(waitInMs);
// PriorityWaitException indicates that the request will pass after waiting for {@link @waitInMs}.
throw new PriorityWaitException(waitInMs);
}
}
return false;
}
return true;
}
ThrottlingController
public ThrottlingController(int queueingTimeoutMs, double maxCountPerStat) {
this(queueingTimeoutMs, maxCountPerStat, 1000);
}
public ThrottlingController(int queueingTimeoutMs, double maxCountPerStat, int statDurationMs) {
AssertUtil.assertTrue(statDurationMs > 0, "statDurationMs should be positive");
AssertUtil.assertTrue(maxCountPerStat >= 0, "maxCountPerStat should be >= 0");
AssertUtil.assertTrue(queueingTimeoutMs >= 0, "queueingTimeoutMs should be >= 0");
this.maxQueueingTimeMs = queueingTimeoutMs;
this.count = maxCountPerStat;
this.statDurationMs = statDurationMs;
// Use nanoSeconds when durationMs%count != 0 or count/durationMs> 1 (to be accurate)
// 可見配置限流值count大于1000時useNanoSeconds會是true否則是false
if (maxCountPerStat > 0) {
this.useNanoSeconds = statDurationMs % Math.round(maxCountPerStat) != 0 || maxCountPerStat / statDurationMs > 1;
} else {
this.useNanoSeconds = false;
}
}
@Override
public boolean canPass(Node node, int acquireCount) {
return canPass(node, acquireCount, false);
}
private boolean checkPassUsingNanoSeconds(int acquireCount, double maxCountPerStat) {
final long maxQueueingTimeNs = maxQueueingTimeMs * MS_TO_NS_OFFSET;
long currentTime = System.nanoTime();
// Calculate the interval between every two requests.
final long costTimeNs = Math.round(1.0d * MS_TO_NS_OFFSET * statDurationMs * acquireCount / maxCountPerStat);
// Expected pass time of this request.
long expectedTime = costTimeNs + latestPassedTime.get();
if (expectedTime <= currentTime) {
// Contention may exist here, but it's okay.
latestPassedTime.set(currentTime);
return true;
} else {
final long curNanos = System.nanoTime();
// Calculate the time to wait.
long waitTime = costTimeNs + latestPassedTime.get() - curNanos;
if (waitTime > maxQueueingTimeNs) {
return false;
}
long oldTime = latestPassedTime.addAndGet(costTimeNs);
waitTime = oldTime - curNanos;
if (waitTime > maxQueueingTimeNs) {
latestPassedTime.addAndGet(-costTimeNs);
return false;
}
// in race condition waitTime may <= 0
if (waitTime > 0) {
sleepNanos(waitTime);
}
return true;
}
}
// 漏鬥算法具體實作
private boolean checkPassUsingCachedMs(int acquireCount, double maxCountPerStat) {
long currentTime = TimeUtil.currentTimeMillis();
// 計算兩次請求的間隔（分為秒級和納秒級）
long costTime = Math.round(1.0d * statDurationMs * acquireCount / maxCountPerStat);
// 請求的期望的時間
long expectedTime = costTime + latestPassedTime.get();
if (expectedTime <= currentTime) {
// latestPassedTime是AtomicLong類型，支援volatile語義
latestPassedTime.set(currentTime);
return true;
} else {
// 計算等待時間
long waitTime = costTime + latestPassedTime.get() - TimeUtil.currentTimeMillis();
// 如果大于最大排隊時間，則觸發限流
if (waitTime > maxQueueingTimeMs) {
return false;
}
long oldTime = latestPassedTime.addAndGet(costTime);
waitTime = oldTime - TimeUtil.currentTimeMillis();
if (waitTime > maxQueueingTimeMs) {
latestPassedTime.addAndGet(-costTime);
return false;
}
// in race condition waitTime may <= 0
if (waitTime > 0) {
sleepMs(waitTime);
}
return true;
}
}
@Override
public boolean canPass(Node node, int acquireCount, boolean prioritized) {
// Pass when acquire count is less or equal than 0.
if (acquireCount <= 0) {
return true;
}
// Reject when count is less or equal than 0.
// Otherwise, the costTime will be max of long and waitTime will overflow in some cases.
if (count <= 0) {
return false;
}
if (useNanoSeconds) {
return checkPassUsingNanoSeconds(acquireCount, this.count);
} else {
return checkPassUsingCachedMs(acquireCount, this.count);
}
}
private void sleepMs(long ms) {
try {
Thread.sleep(ms);
} catch (InterruptedException e) {
}
}
private void sleepNanos(long ns) {
LockSupport.parkNanos(ns);
}
long costTime = Math.round(1.0d * statDurationMs * acquireCount / maxCountPerStat); 由上述計算兩次請求間隔的公式我們可以發現，當maxCountPerStat（規則配置的限流值QPS）超過1000後，就無法準确計算出勻速排隊模式下的請求間隔時長。
是以對應前面介紹的，當規則配置限流值超過1000QPS後，會采用checkPassUsingNanoSeconds，小于1000QPS會采用checkPassUsingCachedMs，對比一下checkPassUsingNanoSeconds和checkPassUsingCachedMs，可以發現主體思路沒變，隻是統計次元從毫秒換算成了納秒，是以隻看checkPassUsingCachedMs實作就可以。
WarmUpController
@Override
public boolean canPass(Node node, int acquireCount, boolean prioritized) {
long passQps = (long) node.passQps();
long previousQps = (long) node.previousPassQps();
syncToken(previousQps);
// 開始計算它的斜率
// 如果進入了警戒線，開始調整他的qps
long restToken = storedTokens.get();
if (restToken >= warningToken) {
long aboveToken = restToken - warningToken;
// 消耗的速度要比warning快，但是要比慢
// current interval = restToken*slope+1/count
double warningQps = Math.nextUp(1.0 / (aboveToken * slope + 1.0 / count));
if (passQps + acquireCount <= warningQps) {
return true;
}
} else {
if (passQps + acquireCount <= count) {
return true;
}
}
return false;
}
protected void syncToken(long passQps) {
long currentTime = TimeUtil.currentTimeMillis();
currentTime = currentTime - currentTime % 1000;
long oldLastFillTime = lastFilledTime.get();
if (currentTime <= oldLastFillTime) {
return;
}
long oldValue = storedTokens.get();
long newValue = coolDownTokens(currentTime, passQps);
if (storedTokens.compareAndSet(oldValue, newValue)) {
long currentValue = storedTokens.addAndGet(0 - passQps);
if (currentValue < 0) {
storedTokens.set(0L);
}
lastFilledTime.set(currentTime);
}
}
private long coolDownTokens(long currentTime, long passQps) {
long oldValue = storedTokens.get();
long newValue = oldValue;
// 添加令牌的判斷前提條件:
// 當令牌的消耗程度遠遠低于警戒線的時候
if (oldValue < warningToken) {
newValue = (long)(oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
} else if (oldValue > warningToken) {
if (passQps < (int)count / coldFactor) {
newValue = (long)(oldValue + (currentTime - lastFilledTime.get()) * count / 1000);
}
}
return Math.min(newValue, maxToken);
}
2.4.3 叢集限流
passClusterCheck方法(因為clusterService找不到會降級到非叢集限流)
private static boolean passClusterCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
boolean prioritized) {
try {
// 擷取目前節點是Token Client還是Token Server
TokenService clusterService = pickClusterService();
if (clusterService == null) {
return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}
long flowId = rule.getClusterConfig().getFlowId();
// 根據擷取的flowId通過TokenService進行申請token。從上面可知，它可能是TokenClient調用的，也可能是ToeknServer調用的。分别對應的類是DefaultClusterTokenClient和DefaultTokenService
TokenResult result = clusterService.requestToken(flowId, acquireCount, prioritized);
return applyTokenResult(result, rule, context, node, acquireCount, prioritized);
// If client is absent, then fallback to local mode.
} catch (Throwable ex) {
RecordLog.warn("[FlowRuleChecker] Request cluster token unexpected failed", ex);
}
// Fallback to local flow control when token client or server for this rule is not available.
// If fallback is not enabled, then directly pass.
return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}
//擷取目前節點是Token Client還是Token Server。
//1） 如果目前節點的角色是Client，傳回的TokenService為DefaultClusterTokenClient；
//2）如果目前節點的角色是Server，則預設傳回的TokenService為DefaultTokenService。
private static TokenService pickClusterService() {
if (ClusterStateManager.isClient()) {
return TokenClientProvider.getClient();
}
if (ClusterStateManager.isServer()) {
return EmbeddedClusterTokenServerProvider.getServer();
}
return null;
}
叢集限流模式
Sentinel 叢集限流服務端有兩種啟動方式：
- 嵌入模式（Embedded）适合應用級别的限流，部署簡單，但對應用性能有影響
- 獨立模式（Alone）适合全局限流，需要獨立部署
考慮到文章篇幅，叢集限流有機會再展開詳細介紹。
叢集限流模式降級
private static boolean passClusterCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
boolean prioritized) {
try {
TokenService clusterService = pickClusterService();
if (clusterService == null) {
return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}
long flowId = rule.getClusterConfig().getFlowId();
TokenResult result = clusterService.requestToken(flowId, acquireCount, prioritized);
return applyTokenResult(result, rule, context, node, acquireCount, prioritized);
// If client is absent, then fallback to local mode.
} catch (Throwable ex) {
RecordLog.warn("[FlowRuleChecker] Request cluster token unexpected failed", ex);
}
// Fallback to local flow control when token client or server for this rule is not available.
// If fallback is not enabled, then directly pass.
// 可以看到如果叢集限流有異常，會降級到單機限流模式，如果配置不允許降級，那麼直接會跳過此次校驗
return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}
2.5 DegradeSlot
CircuitBreaker
大神對斷路器的解釋：https://martinfowler.com/bliki/CircuitBreaker.html
首先就看到了根據資源名稱擷取斷路器清單，Sentinel的斷路器有兩個實作：RT模式使用ResponseTimeCircuitBreaker、異常模式使用ExceptionCircuitBreaker
public interface CircuitBreaker {
/**
* Get the associated circuit breaking rule.
*
* @return associated circuit breaking rule
*/
DegradeRule getRule();
/**
* Acquires permission of an invocation only if it is available at the time of invoking.
*
* @param context context of current invocation
* @return {@code true} if permission was acquired and {@code false} otherwise
*/
boolean tryPass(Context context);
/**
* Get current state of the circuit breaker.
*
* @return current state of the circuit breaker
*/
State currentState();
/**
* <p>Record a completed request with the context and handle state transformation of the circuit breaker.</p>
* <p>Called when a <strong>passed</strong> invocation finished.</p>
*
* @param context context of current invocation
*/
void onRequestComplete(Context context);
/**
* Circuit breaker state.
*/
enum State {
/**
* In {@code OPEN} state, all requests will be rejected until the next recovery time point.
*/
OPEN,
/**
* In {@code HALF_OPEN} state, the circuit breaker will allow a "probe" invocation.
* If the invocation is abnormal according to the strategy (e.g. it's slow), the circuit breaker
* will re-transform to the {@code OPEN} state and wait for the next recovery time point;
* otherwise the resource will be regarded as "recovered" and the circuit breaker
* will cease cutting off requests and transform to {@code CLOSED} state.
*/
HALF_OPEN,
/**
* In {@code CLOSED} state, all requests are permitted. When current metric value exceeds the threshold,
* the circuit breaker will transform to {@code OPEN} state.
*/
CLOSED
}
}
以ExceptionCircuitBreaker為例看一下具體實作
public class ExceptionCircuitBreaker extends AbstractCircuitBreaker {
// 異常模式有兩種，異常率和異常數
private final int strategy;
// 最小請求數
private final int minRequestAmount;
// 門檻值
private final double threshold;
// LeapArray是sentinel統計資料非常重要的一個結構，主要封裝了時間視窗相關的操作
private final LeapArray<SimpleErrorCounter> stat;
public ExceptionCircuitBreaker(DegradeRule rule) {
this(rule, new SimpleErrorCounterLeapArray(1, rule.getStatIntervalMs()));
}
ExceptionCircuitBreaker(DegradeRule rule, LeapArray<SimpleErrorCounter> stat) {
super(rule);
this.strategy = rule.getGrade();
boolean modeOk = strategy == DEGRADE_GRADE_EXCEPTION_RATIO || strategy == DEGRADE_GRADE_EXCEPTION_COUNT;
AssertUtil.isTrue(modeOk, "rule strategy should be error-ratio or error-count");
AssertUtil.notNull(stat, "stat cannot be null");
this.minRequestAmount = rule.getMinRequestAmount();
this.threshold = rule.getCount();
this.stat = stat;
}
@Override
protected void resetStat() {
// Reset current bucket (bucket count = 1).
stat.currentWindow().value().reset();
}
@Override
public void onRequestComplete(Context context) {
Entry entry = context.getCurEntry();
if (entry == null) {
return;
}
Throwable error = entry.getError();
SimpleErrorCounter counter = stat.currentWindow().value();
if (error != null) {
counter.getErrorCount().add(1);
}
counter.getTotalCount().add(1);
handleStateChangeWhenThresholdExceeded(error);
}
private void handleStateChangeWhenThresholdExceeded(Throwable error) {
if (currentState.get() == State.OPEN) {
return;
}
if (currentState.get() == State.HALF_OPEN) {
// In detecting request
if (error == null) {
fromHalfOpenToClose();
} else {
fromHalfOpenToOpen(1.0d);
}
return;
}
List<SimpleErrorCounter> counters = stat.values();
long errCount = 0;
long totalCount = 0;
for (SimpleErrorCounter counter : counters) {
+= counter.errorCount.sum();
totalCount += counter.totalCount.sum();
}
if (totalCount < minRequestAmount) {
return;
}
double curCount = errCount;
if (strategy == DEGRADE_GRADE_EXCEPTION_RATIO) {
// Use errorRatio
curCount = errCount * 1.0d / totalCount;
}
if (curCount > threshold) {
transformToOpen(curCount);
}
}
static class SimpleErrorCounter {
private LongAdder errorCount;
private LongAdder totalCount;
public SimpleErrorCounter() {
this.errorCount = new LongAdder();
this.totalCount = new LongAdder();
}
public LongAdder getErrorCount() {
return errorCount;
}
public LongAdder getTotalCount() {
return totalCount;
}
public SimpleErrorCounter reset() {
errorCount.reset();
totalCount.reset();
return this;
}
@Override
public String toString() {
return "SimpleErrorCounter{" +
"errorCount=" + errorCount +
", totalCount=" + totalCount +
'}';
}
}
static class SimpleErrorCounterLeapArray extends LeapArray<SimpleErrorCounter> {
public SimpleErrorCounterLeapArray(int sampleCount, int intervalInMs) {
super(sampleCount, intervalInMs);
}
@Override
public SimpleErrorCounter newEmptyBucket(long timeMillis) {
return new SimpleErrorCounter();
}
@Override
protected WindowWrap<SimpleErrorCounter> resetWindowTo(WindowWrap<SimpleErrorCounter> w, long startTime) {
// Update the start time and reset value.
w.resetTo(startTime);
w.value().reset();
return w;
}
}
}
2.6 SystemSlot
校驗邏輯主要集中在com.alibaba.csp.sentinel.slots.system.SystemRuleManager#checkSystem，以下是片段，可以看到，作為負載保護規則校驗，實作了叢集的QPS、線程、RT（響應時間）、系統負載的控制，除系統負載以外，其餘統計都是依賴StatisticSlot實作，系統負載是通過SystemRuleManager定時排程SystemStatusListener，通過OperatingSystemMXBean去擷取
/**
* Apply {@link SystemRule} to the resource. Only inbound traffic will be checked.
*
* @param resourceWrapper the resource.
* @throws BlockException when any system rule's threshold is exceeded.
*/
public static void checkSystem(ResourceWrapper resourceWrapper, int count) throws BlockException {
if (resourceWrapper == null) {
return;
}
// Ensure the checking switch is on.
if (!checkSystemStatus.get()) {
return;
}
// for inbound traffic only
if (resourceWrapper.getEntryType() != EntryType.IN) {
return;
}
// total qps 此處是拿到某個資源在叢集中的QPS總和，相關概念可以會看初始化關于Node的介紹
double currentQps = Constants.ENTRY_NODE.passQps();
if (currentQps + count > qps) {
throw new SystemBlockException(resourceWrapper.getName(), "qps");
}
// total thread
int currentThread = Constants.ENTRY_NODE.curThreadNum();
if (currentThread > maxThread) {
throw new SystemBlockException(resourceWrapper.getName(), "thread");
}
double rt = Constants.ENTRY_NODE.avgRt();
if (rt > maxRt) {
throw new SystemBlockException(resourceWrapper.getName(), "rt");
}
// load. BBR algorithm.
if (highestSystemLoadIsSet && getCurrentSystemAvgLoad() > highestSystemLoad) {
if (!checkBbr(currentThread)) {
throw new SystemBlockException(resourceWrapper.getName(), "load");
}
}
// cpu usage
if (highestCpuUsageIsSet && getCurrentCpuUsage() > highestCpuUsage) {
throw new SystemBlockException(resourceWrapper.getName(), "cpu");
}
}
private static boolean checkBbr(int currentThread) {
if (currentThread > 1 &&
currentThread > Constants.ENTRY_NODE.maxSuccessQps() * Constants.ENTRY_NODE.minRt() / 1000) {
return false;
}
return true;
}
public static double getCurrentSystemAvgLoad() {
return statusListener.getSystemAverageLoad();
}
public static double getCurrentCpuUsage() {
return statusListener.getCpuUsage();
}
public class SystemStatusListener implements Runnable {
volatile double currentLoad = -1;
volatile double currentCpuUsage = -1;
volatile String reason = StringUtil.EMPTY;
volatile long processCpuTime = 0;
volatile long processUpTime = 0;
public double getSystemAverageLoad() {
return currentLoad;
}
public double getCpuUsage() {
return currentCpuUsage;
}
@Override
public void run() {
try {
OperatingSystemMXBean osBean = ManagementFactory.getPlatformMXBean(OperatingSystemMXBean.class);
currentLoad = osBean.getSystemLoadAverage();
/*
* Java Doc copied from {@link OperatingSystemMXBean#getSystemCpuLoad()}:</br>
* Returns the "recent cpu usage" for the whole system. This value is a double in the [0.0,1.0] interval.
* A value of 0.0 means that all CPUs were idle during the recent period of time observed, while a value
* of 1.0 means that all CPUs were actively running 100% of the time during the recent period being
* observed. All values between 0.0 and 1.0 are possible depending of the activities going on in the
* system. If the system recent cpu usage is not available, the method returns a negative value.
*/
double systemCpuUsage = osBean.getSystemCpuLoad();
// calculate process cpu usage to support application running in container environment
RuntimeMXBean runtimeBean = ManagementFactory.getPlatformMXBean(RuntimeMXBean.class);
long newProcessCpuTime = osBean.getProcessCpuTime();
long newProcessUpTime = runtimeBean.getUptime();
int cpuCores = osBean.getAvailableProcessors();
long processCpuTimeDiffInMs = TimeUnit.NANOSECONDS
.toMillis(newProcessCpuTime - processCpuTime);
long processUpTimeDiffInMs = newProcessUpTime - processUpTime;
double processCpuUsage = (double) processCpuTimeDiffInMs / processUpTimeDiffInMs / cpuCores;
processCpuTime = newProcessCpuTime;
processUpTime = newProcessUpTime;
currentCpuUsage = Math.max(processCpuUsage, systemCpuUsage);
if (currentLoad > SystemRuleManager.getSystemLoadThreshold()) {
writeSystemStatusLog();
}
} catch (Throwable e) {
RecordLog.warn("[SystemStatusListener] Failed to get system metrics from JMX", e);
}
}
private void writeSystemStatusLog() {
StringBuilder sb = new StringBuilder();
sb.append("Load exceeds the threshold: ");
sb.append("load:").append(String.format("%.4f", currentLoad)).append("; ");
sb.append("cpuUsage:").append(String.format("%.4f", currentCpuUsage)).append("; ");
sb.append("qps:").append(String.format("%.4f", Constants.ENTRY_NODE.passQps())).append("; ");
sb.append("rt:").append(String.format("%.4f", Constants.ENTRY_NODE.avgRt())).append("; ");
sb.append("thread:").append(Constants.ENTRY_NODE.curThreadNum()).append("; ");
sb.append("success:").append(String.format("%.4f", Constants.ENTRY_NODE.successQps())).append("; ");
sb.append("minRt:").append(String.format("%.2f", Constants.ENTRY_NODE.minRt())).append("; ");
sb.append("maxSuccess:").append(String.format("%.2f", Constants.ENTRY_NODE.maxSuccessQps())).append("; ");
RecordLog.info(sb.toString());
}
}
三、京東版最佳實踐
3.1 使用方式
Sentinel使用方式本身非常簡單，就是一個注解，但是要考慮規則加載和規則持久化的方式，現有的方式有：
•使用Sentinel-dashboard功能：使用面闆接入需要維護一個配置規則的管理端，考慮到偏後端的系統需要額外維護一個面闆成本較大，如果是像RPC架構這種本身有管理端的接入可以考慮次方案。
•中間件（如：zookepper、nacos、eureka、redis等）：Sentinel源碼extension包裡提供了類似的實作，如下圖
結合京東實際，我實作了一個規則熱部署的Sentinel元件，實作方式類似zookeeper的方式，将規則記錄到ducc的一個key上，在spring容器啟動時做第一次規則加載和監聽器注冊，元件也做一了一些規則讀取，校驗、執行個體化不同規則對象的工作
插件使用方式：注解+配置
第一步 引入元件
<dependency>
<groupId>com.jd.ldop.tools</groupId>
<artifactId>sentinel-tools</artifactId>
<version>1.0.0-SNAPSHOT</version>
</dependency>
第二步 初始化sentinelProcess
支援ducc、本地檔案讀取、直接寫入三種方式規則寫入方式
目前支援限流規則、熔斷降級規則兩種模式，系統負載保護模式待開發和驗證
<!-- 基于sentinel的降級、限流、熔斷元件 -->
<bean id="sentinelProcess" class="com.jd.ldop.sentinel.SentinelProcess">
<property name="ruleResourceWrappers">
<list>
<ref bean="degradeRule"/>
</list>
</property>
</bean>
<!-- 降級或限流規則配置 -->
<bean id="degradeRule" class="com.jd.ldop.sentinel.dto.RuleResourceWrapper">
<constructor-arg index="0" value="ducc.degradeRule"/>
<constructor-arg index="1" value="0"/>
<constructor-arg index="2" value="0"/>
</bean>
ducc上配置如下：
第三步 定義資源和關聯類型
通過@SentinelResource可以直接在任意位置定義資源名以及對應的熔斷降級或者限流方式、回調方法等，同時也可以指定關聯類型，支援直接、關聯、指定鍊路三種
@Override
@SentinelResource(value = "modifyGetWaybillState", fallback = "executeDegrade")
public ExecutionResult<List<Integer>> execute(@NotNull Model imodel) {
// 業務邏輯處理
}
public ExecutionResult<List<Integer>> executeDegrade(@NotNull Model imodel) {
// 降級業務邏輯處理
}
3.2 應用場景
元件支援任意的業務降級、限流、負載保護
四、Sentinel壓測資料
4.1 壓測目标
調用量：1.2W/m
應用機器記憶體穩定在50%以内
機器規格： 8C16G50G磁盤*2
Sentinel降級規則：
count=350-------慢調用臨界門檻值350ms
timeWindow=180------熔斷時間視窗180s
grade=0-----降級模式 慢調用
statIntervalMs=60000------統計時長1min
4.2 壓測結果
應用機器監控：
壓測分為了兩個階段，分别是元件開啟群組件關閉兩次，前半部分是元件開啟的情況，後半部分是元件關閉的情況
應用程序記憶體分析,和sentinel有關的前三對象是
com.alibaba.csp.sentinel.node.metric.MetricNode
com.alibaba.csp.sentinel.CtEntry
com.alibaba.csp.sentinel.context.Context
4.3 壓測結論
使Sentinel元件實作系統服務自動降級或限流，由于sentinel會按照滑動視窗周期性統計資料，是以會占用一定的機器記憶體，使用時應設定合理的規則，如：合理的統計時長、避免過多的Sentinel資源建立等。
總體來說，使用sentinel元件對應用cpu和記憶體影響不大。
作者：京東物流 楊建民
内容來源：京東雲開發者社群