0
点赞
收藏
分享

微信扫一扫

sentinel源码分析第十二篇一核心流程一FlowSlot流控

一叶随风_c94d 2022-12-19 阅读 85
sentinel限流源码分析AppLinux系统/运维


文章目录

  • ​​源码分析一入口checkFlow​​
  • ​​源码分析一canPassCheck​​
  • ​​源码分析一集群流控passClusterCheck​​
  • ​​源码分析一clusterService.requestToken​​
  • ​​源码分析一单机流控passLocalCheck​​
  • ​​快速失败DefaultController​​
  • ​​扩展点一外部配置图​​

源码分析一入口checkFlow

  • 获取所有的流控规则 通过flowRuleManager 获取资源的所有规则
  • 没有通过流控则抛出异常 流控规则和node中的统计信息比较

public class FlowRuleChecker {

    public void checkFlow(Function<String, Collection<FlowRule>> ruleProvider, ResourceWrapper resource,
                          Context context, DefaultNode node, int count, boolean prioritized) throws BlockException {
        if (ruleProvider == null || resource == null) {
            return;
        }
        获取所有的流控规则 通过flowRuleManager 获取资源的所有规则
        Collection<FlowRule> rules = ruleProvider.apply(resource.getName());
        if (rules != null) {
            for (FlowRule rule : rules) {
                没有通过流控则抛出异常  流控规则和node中的统计信息比较
                if (!canPassCheck(rule, context, node, count, prioritized)) {
                    throw new FlowException(rule.getLimitApp(), rule);
                }
            }
        }
    }
}

源码分析一canPassCheck

  • 集群模式则处理集群流控
  • 单机模式处理单机流控

public boolean canPassCheck(/*@NonNull*/ FlowRule rule, Context context, DefaultNode node, int acquireCount,
                                                    boolean prioritized) {

        // 来源 从规则中获取限定的来源 一般为default 来源null说明是发出请求 不是别人请求我的资源
        String limitApp = rule.getLimitApp();
        // 流控规则 flowRule默认default 其他规则基本没有默认值
        if (limitApp == null) {
            return true;
        }
        // 集群流控处理
        if (rule.isClusterMode()) {
            return passClusterCheck(rule, context, node, acquireCount, prioritized);
        }
        // 单机流控处理
        return passLocalCheck(rule, context, node, acquireCount, prioritized);
    }

源码分析一集群流控passClusterCheck

  • 直接或远程调用TokenService获取限流结果
  • 所有特殊情况则直接通过或者调用单机限流

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();
        如果clusterService是集群server 则直接调用
        如果clusterService是集群client 则远程调用
        TokenResult result = clusterService.requestToken(flowId, acquireCount, prioritized);
        /*
         若用户未引入集群限流 client 相关依赖，或者 client 未开启/连接失败/通信失败，则对于开启了集群模式的规则：
              根据配置直接通过限流或者退化到 local 模式的限流
         */
        return applyTokenResult(result, rule, context, node, acquireCount, prioritized);
    } catch (Throwable ex) {
    }
    根据配置直接通过限流或者退化到 local 模式的限流
    return fallbackToLocalOrPass(rule, context, node, acquireCount, prioritized);
}

源码分析一clusterService.requestToken

  • 通过ClusterFlowChecker.acquireClusterToken判断是否被限流或者排队

public TokenResult requestToken(Long ruleId, int acquireCount, boolean prioritized) {
    if (notValidRequest(ruleId, acquireCount)) {
        return badRequest();
    }
    FlowRule rule = ClusterFlowRuleManager.getFlowRuleById(ruleId);
    if (rule == null) {
        return new TokenResult(TokenResultStatus.NO_RULE_EXISTS);
    }
    通过ClusterFlowChecker获取流控结果
    return ClusterFlowChecker.acquireClusterToken(rule, acquireCount, prioritized);
}

  • 获取相关限流信息
  • 剩余token树大于0 返回成功并处理通过的流量统计信息
  • 如果需要等待返回等待时间
  • 如果发生block则返回被限流,并处理未通过的流量统计信息

final class ClusterFlowChecker {
    static TokenResult acquireClusterToken(/*@Valid*/ FlowRule rule, int acquireCount, boolean prioritized) {
        Long id = rule.getClusterConfig().getFlowId();
        // qps保护
        if (!allowProceed(id)) {
            return new TokenResult(TokenResultStatus.TOO_MANY_REQUEST);
        }
        // 获取度量信息
        ClusterMetric metric = ClusterMetricStatistics.getMetric(id);
        if (metric == null) {
            return new TokenResult(TokenResultStatus.FAIL);
        }
        // 获取相关限流信息
        double latestQps = metric.getAvg(ClusterFlowEvent.PASS);
        double globalThreshold = calcGlobalThreshold(rule) * ClusterServerConfigManager.getExceedCount();
        double nextRemaining = globalThreshold - latestQps - acquireCount;
        // 剩余token树大于0
        if (nextRemaining >= 0) {
            // 处理已经通过的流量统计信息
            metric.add(ClusterFlowEvent.PASS, acquireCount);
            metric.add(ClusterFlowEvent.PASS_REQUEST, 1);
            if (prioritized) {
                // Add prioritized pass.
                metric.add(ClusterFlowEvent.OCCUPIED_PASS, acquireCount);
            }
            // Remaining count is cut down to a smaller integer.
            // 返回限流结果
            return new TokenResult(TokenResultStatus.OK)
                .setRemaining((int) nextRemaining)
                .setWaitInMs(0);
        } else {
            if (prioritized) {
                // 需要排队则返回排队等待时间 
                double occupyAvg = metric.getAvg(ClusterFlowEvent.WAITING);
                if (occupyAvg <= ClusterServerConfigManager.getMaxOccupyRatio() * globalThreshold) {
                    int waitInMs = metric.tryOccupyNext(ClusterFlowEvent.PASS, acquireCount, globalThreshold);
                    // waitInMs > 0 indicates pre-occupy incoming buckets successfully.
                    if (waitInMs > 0) {
                        ClusterServerStatLogUtil.log("flow|waiting|" + id);
                        return new TokenResult(TokenResultStatus.SHOULD_WAIT)
                            .setRemaining(0)
                            .setWaitInMs(waitInMs);
                    }
                }
            }
            // 处理未通过的流量的统计信息
            metric.add(ClusterFlowEvent.BLOCK, acquireCount);
            metric.add(ClusterFlowEvent.BLOCK_REQUEST, 1);
            ClusterServerStatLogUtil.log("flow|block|" + id, acquireCount);
            ClusterServerStatLogUtil.log("flow|block_request|" + id, 1);
            if (prioritized) {
                // Add prioritized block.
                metric.add(ClusterFlowEvent.OCCUPIED_BLOCK, acquireCount);
                ClusterServerStatLogUtil.log("flow|occupied_block|" + id, 1);
            }
            // 返回被限流结果
            return blockedResult();
        }
    }
}

源码分析一单机流控passLocalCheck

  • 根据策略选择不同的单机限流controller处理

private static boolean passLocalCheck(FlowRule rule, Context context, DefaultNode node, int acquireCount,
                                      boolean prioritized) {
    根据规则[来源 限流策略等]获取正确的统计node
    Node selectedNode = selectNodeByRequesterAndStrategy(rule, context, node);
    if (selectedNode == null) {
        return true;
    }
    根据规则进行逐项检查 流控效果
    DefaultController 快速失败
    RateLimiterController
    WarmUpController 令牌桶
    WarmUpRateLimiterController
    return rule.getRater().canPass(selectedNode, acquireCount, prioritized);
}

快速失败DefaultController

  • 获取当前时间窗中已经统计的数据
  • 超过阈值则限流

public class DefaultController implements TrafficShapingController {

    public boolean canPass(Node node, int acquireCount, boolean prioritized) {

        获取当前时间窗中已经统计的数据
        int curCount = avgUsedTokens(node);
        超过阈值 限流
        if (curCount + acquireCount > count) {

            // 有优先权
            if (prioritized && grade == RuleConstant.FLOW_GRADE_QPS) {
                long currentTime;
                long waitInMs;
                currentTime = TimeUtil.currentTimeMillis();
                // 判断是不是要sleep一下, 可能提前获取了以后时间窗口的令牌,要sleep平滑下流量
                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;
    }
}

扩展点一外部配置图

sentinel源码分析第十二篇一核心流程一FlowSlot流控_sentinel


举报

相关推荐

sentinel源码分析第十一篇一核心流程一ParamFlowSlot流控

sentinel限流sedTimeLinux系统/运维

sentinel源码分析第十三篇一核心流程一DegradeSlot熔断

sentinelci源码分析时间戳Linux系统/运维

sentinel源码分析第十四篇一核心流程一集群架构

架构sentinel客户端服务端限流虚拟化云计算

【sentinel】Sentinel工作主流程以流控规则源码分析

sentineljavaspringbootspringcloudspringjQuery前端开发

sentinel源码分析第十五篇一核心流程一样本窗口数据结构

数据结构sentinel原理图源码分析窗口大小虚拟化云计算

Redis篇---第十二篇

redis数据库缓存

sentinel源码分析第九篇一核心流程一lookProcessChain查找资源处理链

sentinelide源码分析限流虚拟化云计算

rocketmq核心源码分析第十二篇一消息处理第六部分一broker端刷盘机制[同步,异步]

分布式rocketmq

JavaSE：第十二章：IO流

JavaSE对象流文件流序列化Java编程语言

第十二篇 读书笔记

分布式kafkajava
0 条评论