热点参数限流
热点参数限流会统计传入参数中的热点参数，并根据配置的限流阈值与模式，对包含热点参数的资源调用进行限流。热点参数限流可以看做是一种特殊的流量控制，仅对包含热点参数的资源调用生效。
@Override
public void entry(Context context, ResourceWrapper resourceWrapper, DefaultNode node, int count,
boolean prioritized, Object... args) throws Throwable {
//没有设置了热点数据流控规则直接执行后续逻辑
if (!ParamFlowRuleManager.hasRules(resourceWrapper.getName())) {
fireEntry(context, resourceWrapper, node, count, prioritized, args);
return;
}
checkFlow(resourceWrapper, count, args);
fireEntry(context, resourceWrapper, node, count, prioritized, args);
}
void checkFlow(ResourceWrapper resourceWrapper, int count, Object... args) throws BlockException {
//args就是设置的多个热点数据
if (args == null) {
return;
}
if (!ParamFlowRuleManager.hasRules(resourceWrapper.getName())) {
return;
}
//获取热点数据流控规则
List<ParamFlowRule> rules = ParamFlowRuleManager.getRulesOfResource(resourceWrapper.getName());
for (ParamFlowRule rule : rules) {
//设置热点参数索引
applyRealParamIdx(rule, args.length);
// Initialize the parameter metrics.
//初始化统计
ParameterMetricStorage.initParamMetricsFor(resourceWrapper, rule);
//返回false则需要执行限流
if (!ParamFlowChecker.passCheck(resourceWrapper, rule, count, args)) {
String triggeredParam = "";
if (args.length > rule.getParamIdx()) {
Object value = args[rule.getParamIdx()];
triggeredParam = String.valueOf(value);
}
throw new ParamFlowException(resourceWrapper.getName(), triggeredParam, rule);
}
}
}
这里获取热点数据流控规则，通过配置规则中的设置的索引获取对应需要限流的热点参数
public static void initParamMetricsFor(ResourceWrapper resourceWrapper, /*@Valid*/ ParamFlowRule rule) {
if (resourceWrapper == null || resourceWrapper.getName() == null) {
return;
}
//资源名称
String resourceName = resourceWrapper.getName();
ParameterMetric metric;
// Assume that the resource is valid.
if ((metric = metricsMap.get(resourceName)) == null) {
synchronized (LOCK) {
if ((metric = metricsMap.get(resourceName)) == null) {
//创建热点数据统计
metric = new ParameterMetric();
metricsMap.put(resourceWrapper.getName(), metric);
RecordLog.info("[ParameterMetricStorage] Creating parameter metric for: " + resourceWrapper.getName());
}
}
}
//初始化
metric.initialize(rule);
}
创建统计metric根据资源名称添加到缓存map中，然后执行初始化
public void initialize(ParamFlowRule rule) {
if (!ruleTimeCounters.containsKey(rule)) {
synchronized (lock) {
if (ruleTimeCounters.get(rule) == null) {
long size = Math.min(BASE_PARAM_MAX_CAPACITY * rule.getDurationInSec(), TOTAL_MAX_CAPACITY);
ruleTimeCounters.put(rule, new ConcurrentLinkedHashMapWrapper<Object, AtomicLong>(size));
}
}
}
if (!ruleTokenCounter.containsKey(rule)) {
synchronized (lock) {
if (ruleTokenCounter.get(rule) == null) {
long size = Math.min(BASE_PARAM_MAX_CAPACITY * rule.getDurationInSec(), TOTAL_MAX_CAPACITY);
ruleTokenCounter.put(rule, new ConcurrentLinkedHashMapWrapper<Object, AtomicLong>(size));
}
}
}
if (!threadCountMap.containsKey(rule.getParamIdx())) {
synchronized (lock) {
if (threadCountMap.get(rule.getParamIdx()) == null) {
threadCountMap.put(rule.getParamIdx(),
new ConcurrentLinkedHashMapWrapper<Object, AtomicInteger>(THREAD_COUNT_MAX_CAPACITY));
}
}
}
}
初始化了几个map用于存储令牌、存储添加的时间、线程总数
public static boolean passCheck(ResourceWrapper resourceWrapper, /*@Valid*/ ParamFlowRule rule, /*@Valid*/ int count,
Object... args) {
if (args == null) {
return true;
}
int paramIdx = rule.getParamIdx();
if (args.length <= paramIdx) {
return true;
}
// Get parameter value. If value is null, then pass.
//获取热点参数
Object value = args[paramIdx];
if (value == null) {
return true;
}
//集群限流
if (rule.isClusterMode() && rule.getGrade() == RuleConstant.FLOW_GRADE_QPS) {
return passClusterCheck(resourceWrapper, rule, count, value);
}
return passLocalCheck(resourceWrapper, rule, count, value);
}
获取热点参数执行限流逻辑
private static boolean passLocalCheck(ResourceWrapper resourceWrapper, ParamFlowRule rule, int count,
Object value) {
try {
//参数是集合类型
if (Collection.class.isAssignableFrom(value.getClass())) {
for (Object param : ((Collection)value)) {
if (!passSingleValueCheck(resourceWrapper, rule, count, param)) {
return false;
}
}
} else if (value.getClass().isArray()) {
int length = Array.getLength(value);
for (int i = 0; i < length; i++) {
Object param = Array.get(value, i);
if (!passSingleValueCheck(resourceWrapper, rule, count, param)) {
return false;
}
}
} else {
//单个参数处理
return passSingleValueCheck(resourceWrapper, rule, count, value);
}
} catch (Throwable e) {
RecordLog.warn("[ParamFlowChecker] Unexpected error", e);
}
return true;
}
根据热点参数类型执行限流逻辑，最终是调用passSingleValueCheck
static boolean passSingleValueCheck(ResourceWrapper resourceWrapper, ParamFlowRule rule, int acquireCount,
Object value) {
//根据QPS限流
if (rule.getGrade() == RuleConstant.FLOW_GRADE_QPS) {
//匀速通过
if (rule.getControlBehavior() == RuleConstant.CONTROL_BEHAVIOR_RATE_LIMITER) {
return passThrottleLocalCheck(resourceWrapper, rule, acquireCount, value);
} else {
//快速失败
return passDefaultLocalCheck(resourceWrapper, rule, acquireCount, value);
}
//根据线程数限流
} else if (rule.getGrade() == RuleConstant.FLOW_GRADE_THREAD) {
//单独设置限流阈值
Set<Object> exclusionItems = rule.getParsedHotItems().keySet();
//获取线程总数
long threadCount = getParameterMetric(resourceWrapper).getThreadCount(rule.getParamIdx(), value);
//根据单独设置的规则判断
if (exclusionItems.contains(value)) {
int itemThreshold = rule.getParsedHotItems().get(value);
return ++threadCount <= itemThreshold;
}
//正常逻辑
long threshold = (long)rule.getCount();
return ++threadCount <= threshold;
}
return true;
}
根据限流规则是根据QPS限流还是根据线程数量限流，默认是QPS。如果是QPS则根据限流策略执行不同逻辑，这里支持匀速通过和快速失败策略。
static boolean passDefaultLocalCheck(ResourceWrapper resourceWrapper, ParamFlowRule rule, int acquireCount,
Object value) {
//热点数据统计
ParameterMetric metric = getParameterMetric(resourceWrapper);
//令牌计数
CacheMap<Object, AtomicLong> tokenCounters = metric == null ? null : metric.getRuleTokenCounter(rule);
//时间计数
CacheMap<Object, AtomicLong> timeCounters = metric == null ? null : metric.getRuleTimeCounter(rule);
if (tokenCounters == null || timeCounters == null) {
return true;
}
// Calculate max token count (threshold)
//获取token计数
Set<Object> exclusionItems = rule.getParsedHotItems().keySet();
long tokenCount = (long)rule.getCount();
if (exclusionItems.contains(value)) {
tokenCount = rule.getParsedHotItems().get(value);
}
if (tokenCount == 0) {
return false;
}
//获取最大通过阈值
long maxCount = tokenCount + rule.getBurstCount();
if (acquireCount > maxCount) {
return false;
}
while (true) {
long currentTime = TimeUtil.currentTimeMillis();
//添加热点参数到缓存，并返回上次添加时间
AtomicLong lastAddTokenTime = timeCounters.putIfAbsent(value, new AtomicLong(currentTime));
if (lastAddTokenTime == null) {
// Token never added, just replenish the tokens and consume {@code acquireCount} immediately.
//令牌从未添加，只需补充令牌并立即使用
tokenCounters.putIfAbsent(value, new AtomicLong(maxCount - acquireCount));
return true;
}
// Calculate the time duration since last token was added.
//计算上次添加token距离当前的时间
long passTime = currentTime - lastAddTokenTime.get();
// A simplified token bucket algorithm that will replenish the tokens only when statistic window has passed.
//一种简化的令牌桶算法，仅在统计窗口过去时才补充令牌
if (passTime > rule.getDurationInSec() * 1000) {
//重新放入最大令牌并立即使用acquireCount个
AtomicLong oldQps = tokenCounters.putIfAbsent(value, new AtomicLong(maxCount - acquireCount));
if (oldQps == null) {
// Might not be accurate here.
//首次添加，记录添加时间
lastAddTokenTime.set(currentTime);
return true;
} else {
//剩余令牌数量
long restQps = oldQps.get();
//计算可以放入令牌数
long toAddCount = (passTime * tokenCount) / (rule.getDurationInSec() * 1000);
//计算请求后剩余令牌数
long newQps = toAddCount + restQps > maxCount ? (maxCount - acquireCount)
: (restQps + toAddCount - acquireCount);
//没有剩余令牌则返回false
if (newQps < 0) {
return false;
}
//设置剩余令牌数量
if (oldQps.compareAndSet(restQps, newQps)) {
//设置更新时间
lastAddTokenTime.set(currentTime);
return true;
}
Thread.yield();
}
} else {
//还在一个时间周期内，计算剩余令牌是否足够
AtomicLong oldQps = tokenCounters.get(value);
if (oldQps != null) {
long oldQpsValue = oldQps.get();
if (oldQpsValue - acquireCount >= 0) {
if (oldQps.compareAndSet(oldQpsValue, oldQpsValue - acquireCount)) {
return true;
}
} else {
return false;
}
}
Thread.yield();
}
}
}
这里是一个简单的令牌桶，每次执行逻辑查看是否需要限流时，计算上次添加令牌的时间距离当前时间差，如果时间大于一个周期，则重置令牌桶令牌数量，然后立即使用acquireCount个令牌，如果已经存在令牌桶则计算上次添加时间到当前时间差可以放入的令牌数和剩余令牌数之和是否能够支持acquireCount个令牌，如果达不到则需要限流。
static boolean passThrottleLocalCheck(ResourceWrapper resourceWrapper, ParamFlowRule rule, int acquireCount,
Object value) {
ParameterMetric metric = getParameterMetric(resourceWrapper);
CacheMap<Object, AtomicLong> timeRecorderMap = metric == null ? null : metric.getRuleTimeCounter(rule);
if (timeRecorderMap == null) {
return true;
}
// Calculate max token count (threshold)
//获取最大通过阈值
Set<Object> exclusionItems = rule.getParsedHotItems().keySet();
long tokenCount = (long)rule.getCount();
if (exclusionItems.contains(value)) {
tokenCount = rule.getParsedHotItems().get(value);
}
if (tokenCount == 0) {
return false;
}
//计算请求通过所要经过的时间
long costTime = Math.round(1.0 * 1000 * acquireCount * rule.getDurationInSec() / tokenCount);
while (true) {
long currentTime = TimeUtil.currentTimeMillis();
//首次
AtomicLong timeRecorder = timeRecorderMap.putIfAbsent(value, new AtomicLong(currentTime));
if (timeRecorder == null) {
return true;
}
//AtomicLong timeRecorder = timeRecorderMap.get(value);
//上次通过时间
long lastPassTime = timeRecorder.get();
//需要所有请求通过的时间
long expectedTime = lastPassTime + costTime;
//expectedTime <= currentTime 经过的时间已经满足所有请求通过或需要等待一段时间后通过
if (expectedTime <= currentTime || expectedTime - currentTime < rule.getMaxQueueingTimeMs()) {
AtomicLong lastPastTimeRef = timeRecorderMap.get(value);
if (lastPastTimeRef.compareAndSet(lastPassTime, currentTime)) {
//等待时间
long waitTime = expectedTime - currentTime;
if (waitTime > 0) {
lastPastTimeRef.set(expectedTime);
try {
TimeUnit.MILLISECONDS.sleep(waitTime);
} catch (InterruptedException e) {
RecordLog.warn("passThrottleLocalCheck: wait interrupted", e);
}
}
return true;
} else {
Thread.yield();
}
} else {
//超过了最大等待时间
return false;
}
}
}
匀速通过，这里就是计算请求通过所要经过的时间，如果这个时间点比当前时间小，如果可以正常通过，如果比当前时间点大说明需要等待一段时间才能通过，通过等待的时间不能超过等待的最大时间，否则就会返回false执行限流
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