作者：京東物流 楊建民
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維度保護服務的穩定性。
Sentinel 具有以下特徵:
- 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）、訊息削峰填谷、叢集流量控制、實時熔斷下游不可用應用等。
- 完備的實時監控：Sentinel 同時提供實時的監控功能。您可以在控制檯中看到接入應用的單臺機器秒級資料，甚至 500 臺以下規模的叢集的彙總執行情況。
- 廣泛的開源生態：Sentinel 提供開箱即用的與其它開源框架/庫的整合模組，例如與 Spring Cloud、Apache Dubbo、gRPC、Quarkus 的整合。您只需要引入相應的依賴並進行簡單的配置即可快速地接入 Sentinel。同時 Sentinel 提供 Java/Go/C++ 等多語言的原生實現。
- 完善的 SPI 擴充套件機制：Sentinel 提供簡單易用、完善的 SPI 擴充套件介面。您可以透過實現擴充套件介面來快速地定製邏輯。例如定製規則管理、適配動態資料來源等
有關Sentinel的詳細介紹以及和Hystrix的區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關鍵技術原始碼解析
無論是限流、降級、負載等控制手段，大致流程如下：
•StatisticSlot 則用於記錄、統計不同維度的 runtime 指標監控資訊
•責任鏈依次觸發後續 slot 的 entry 方法，如 SystemSlot、FlowSlot、DegradeSlot 等的規則校驗；
•當後續的 slot 透過，沒有丟擲 BlockException 異常，說明該資源被成功呼叫，則增加執行執行緒數和透過的請求數等資訊。
關於資料統計，主要會牽扯到 ArrayMetric、BucketLeapArray、MetricBucket、WindowWrap 等類。
專案結構
以下主要分析core包裡的內容
2.1註解入口
2.1.1 Entry、Context、Node
SphU門面類的方法出參都是Entry，Entry可以理解為每次進入資源的一個憑證，如果呼叫SphO.entry()或者SphU.entry()能獲取Entry物件，代表獲取了憑證，沒有被限流，否則丟擲一個BlockException。
Entry中持有本次對資源呼叫的相關資訊：
•createTime：建立該Entry的時間戳。
•curNode：Entry當前是在哪個節點。
•orginNode：Entry的呼叫源節點。
•resourceWrapper：Entry關聯的資源資訊。
Entry是一個抽象類，CtEntry是Entry的實現，CtEntry持有Context和呼叫鏈的資訊
Context的原始碼註釋如下，
This class holds metadata of current invocation
Node的原始碼註釋
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透過Node中的資料進行判斷的。Node是一個介面，裡面定義了各種操作request、exception、rt、qps、thread的方法。
在細看Node實現時，不難發現LongAddr的使用，關於LongAddr和DoubleAddr都是java8 java.util.concurrent.atomic裡的內容，感興趣的小夥伴可以再深入研究一下，這兩個是高併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。
關於Node的介紹後續還會深入，此處大致先提一下這個概念。
2.2 初始化
2.2.1 Context初始化
在初始化slot責任鏈部分前，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），並把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：用於完成資料統計的介面
•StatisticNode：統計節點，是Node介面的實現類，用於完成資料統計
•EntranceNode：入口節點，一個Context會有一個入口節點，用於統計當前Context的總體流量資料
•DefaultNode：預設節點，用於統計一個資源在當前Context中的流量資料
•ClusterNode：叢集節點，用於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 透過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，並將這些資源的呼叫路徑，以樹狀結構儲存起來，用於根據呼叫路徑來限流降級；
•ClusterBuilderSlot 則用於儲存資源的統計資訊以及呼叫者資訊，例如該資源的 RT, QPS, thread count 等等，這些資訊將用作為多維度限流，降級的依據；
•StatisticSlot 則用於記錄、統計不同緯度的 runtime 指標監控資訊；
•FlowSlot 則用於根據預設的限流規則以及前面 slot 統計的狀態，來進行流量控制；
•AuthoritySlot 則根據配置的黑白名單和呼叫來源資訊，來做黑白名單控制；
•DegradeSlot 則透過統計資訊以及預設的規則，來做熔斷降級；
•SystemSlot 則透過系統的狀態，例如 叢集QPS、執行緒數、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的原始碼註釋看，它會持有資源維度的實時統計資料，以下是介面裡的方法定義，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，原始碼給出的定位是：
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只有四個屬性，除了之前提到過的LongAddr型別的curThreadNum外，還有兩個屬性是Metric物件，透過入參已經屬性命名可以看出，一個用於秒級，一個用於分鐘級統計。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只有一個屬性LeapArray
//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預設將單位時間分為了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;
// 陣列長度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;
}
// 根據當前時間戳算一個陣列索引
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，秒級和分鐘級統計物件實現不同
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));
}
}
}
// 持有一個時間視窗物件的資料，會根據當前時間戳除以時間視窗長度然後雜湊到陣列中
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（分鐘統計實現是BucketLeapArray，秒級統計實現是OccupiableBucketLeapArray），對於分鐘級別的統計，MetricBucket維護了一個longAddr陣列和一個配置的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;
}
}
對於秒級統計，QPS=20場景下，如何準確統計的問題，此處用到了另外一個LeapArry實現FutureBucketLeapArray，至於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 常見限流演算法
介紹sentinel限流實現前，先介紹一下常見限流演算法，基本分為三種：計數器、漏斗、令牌桶。
計數器演算法
顧名思義，計數器演算法就是統計某個時間段內的請求，每單位時間加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。但是這種演算法不能做到“平滑限流”，以1s為單位時間，100QPS為限流值為例，如下圖，會出現某時段超出限流值的情況
因此在單純計數器演算法上，又出現了滑動視窗計數器演算法，我們將統計時間細分，比如將1s統計時長分為5個時間視窗，透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原始碼時也能看到類似操作。
漏斗演算法
不論流量有多大都會先到漏桶中，然後以均勻的速度流出。如何在程式碼中實現這個勻速呢？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，類似一個佇列，每隔10ms從佇列頭部取出流量進行放行，而我們的佇列也就是漏桶，當流量大於佇列的長度的時候，我們就可以拒絕超出的部分。
漏斗演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，在漏桶演算法中只能一個一個的過去，當最後一個請求流出的時候時間已經過了一秒了，所以漏斗演算法比較適合請求到達比較均勻，需要嚴格控制請求速率的場景。
令牌桶演算法
令牌桶演算法和漏斗演算法比較類似，區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產和消耗，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中的限流實現，相比上述基本限流演算法，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、指定鏈路的限流。
FlowSlot的主要邏輯都在FlowRuleChecker裡，介紹之前，我們先看一下Sentinel關於規則的模型描述，下圖分別是限流、訪問控制規則、系統保護規則（Linux負載）、降級規則
/**
* 流量控制兩種模式
* 0: thread count（當呼叫該api的執行緒數達到閾值的時候，進行限流）
* 1: QPS（當呼叫該api的QPS達到閾值的時候，進行限流）
*/
private int grade = RuleConstant.FLOW_GRADE_QPS;
/**
* 流量控制閾值，值含義與grade有關
*/
private double count;
/**
* 呼叫關係限流策略（可以支援關聯資源或指定鏈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閾值時，就限流自己）
* 鏈路（只記錄指定鏈路上的流量）
* {@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，拋異常FlowException
* 1. warm up, 慢啟動模式（根據coldFactor（冷載入因子，預設3）的值，從閾值/coldFactor，經過預熱時長，才達到設定的QPS閾值）
* 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;
接著我們繼續分析FlowRuleChecker
canPassCheck第一步會好看limitApp，這個是結合訪問授許可權制規則使用的，預設是所有。
private static boolean passLocalCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
boolean prioritized) {
// 根據策略選擇Node來進行統計（可以是本身Node、關聯的Node、指定的鏈路）
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是訪問控制使用的，預設是default，不限制來源
String limitApp = rule.getLimitApp();
// 拿到限流策略
int strategy = rule.getStrategy();
String origin = context.getOrigin();
// 基於呼叫來源做鑑權
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;
}
// 此程式碼是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()返回TrafficShapingController
private static TrafficShapingController generateRater(/*@Valid*/ FlowRule rule) {
if (rule.getGrade() == RuleConstant.FLOW_GRADE_QPS) {
switch (rule.getControlBehavior()) {
// 預熱模式返回WarmUpController
case RuleConstant.CONTROL_BEHAVIOR_WARM_UP:
return new WarmUpController(rule.getCount(), rule.getWarmUpPeriodSec(),
ColdFactorProperty.coldFactor);
// 排隊模式返回ThrottlingController
case RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER:
return new ThrottlingController(rule.getMaxQueueingTimeMs(), rule.getCount());
// 預熱+排隊模式返回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單機限流演算法
上面我們看到根據限流規則controlBehavior屬性（流控效果），會初始化以下實現：
•DefaultController：是一個非常典型的滑動視窗計數器演算法實現，將當前統計的qps和請求進來的qps進行求和，小於限流值則透過，大於則計算一個等待時間，稍後再試
•ThrottlingController：是漏斗演算法的實現，實現思路已經在原始碼片段中加了備註
•WarmUpController：實現參考了Guava的帶預熱的RateLimiter，區別是Guava側重於請求間隔，類似前面提到的令牌桶，而Sentinel更關注於請求數，和令牌桶演算法有點類似
•WarmUpRateLimiterController：低水位使用預熱演算法，高水位使用滑動視窗計數器演算法排隊。
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大於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;
}
}
// 漏斗演算法具體實現
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型別，支援volatile語義
latestPassedTime.set(currentTime);
return true;
} else {
// 計算等待時間
long waitTime = costTime + latestPassedTime.get() - TimeUtil.currentTimeMillis();
// 如果大於最大排隊時間，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，就無法準確計算出勻速排隊模式下的請求間隔時長，因此對應前面介紹的，當規則配置限流值超過1000QPS後，會採用checkPassUsingNanoSeconds，小於1000QPS會採用checkPassUsingCachedMs，對比一下checkPassUsingNanoSeconds和checkPassUsingCachedMs，可以發現主體思路沒變，只是統計維度從毫秒換算成了納秒，因此只看checkPassUsingCachedMs實現就可以
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;
// 新增令牌的判斷前提條件:
// 當令牌的消耗程度遠遠低於警戒線的時候
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 {
// 獲取當前節點是Token Client還是Token Server
TokenService clusterService = pickClusterService();
if (clusterService == null) {
return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}
long flowId = rule.getClusterConfig().getFlowId();
// 根據獲取的flowId透過TokenService進行申請token。從上面可知，它可能是TokenClient呼叫的，也可能是ToeknServer呼叫的。分別對應的類是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);
}
//獲取當前節點是Token Client還是Token Server。
//1） 如果當前節點的角色是Client，返回的TokenService為DefaultClusterTokenClient；
//2）如果當前節點的角色是Server，則預設返回的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）適合應用級別的限流，部署簡單，但對應用效能有影響
•獨立模式（Alone）適合全侷限流，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獲取斷路器列表，Sentinel的斷路器有兩個實現：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為例看一下具體實現
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;
// 閾值
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，實現了叢集的QPS、執行緒、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實現，系統負載是透過SystemRuleManager定時排程SystemStatusListener，透過OperatingSystemMXBean去獲取
/**
* 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於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，就是一個註解，但是要考慮規則載入和規則持久化的方式，現有的方式有：
•使用Sentinel-dashboard功能：使用皮膚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皮膚成本較大，如果是像RPC框架這種本身有管理端的接入可以考慮次方案。
•中介軟體（如：zookepper、nacos、eureka、redis等）：Sentinel原始碼extension包裡提供了類似的實現，如下圖
結合京東實際，我實現了一個規則熱部署的Sentinel元件，實現方式類似zookeeper的方式，將規則記錄到ducc的一個key上，在spring容器啟動時做第一次規則載入和監聽器註冊，元件也做一了一些規則讀取，校驗、例項化不同規則物件的工作
外掛使用方式：註解+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
<!-- 基於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 定義資源和關聯型別
透過@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、回撥方法等，同時也可以指定關聯型別，支援直接、關聯、指定鏈路三種
@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 壓測目標
呼叫量：1.2W/m
應用機器記憶體穩定在50%以內
機器規格： 8C16G50G磁碟*2
Sentinel降級規則：
count=350-------慢呼叫臨界閾值350ms
timeWindow=180------熔斷時間視窗180s
grade=0-----降級模式 慢呼叫
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，分別是元件開啟和元件關閉兩次，前半部分是元件開啟的情況，後半部分是元件關閉的情況
應用程式記憶體分析,和sentinel有關的前三物件是
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元件實現系統服務自動降級或限流，由於sentinel會按照滑動視窗週期性統計資料，因此會佔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。