Sentinel基本使用--基于QPS流量控制(二), 采用Warm Up预热/冷启动方式控制突增流量
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一, Warm Up
Sentinel的Warm Up（RuleConstant.CONTROL_BEHAVIOR_WARM_UP）方式，即预热/冷启动方式。当系统长期处于低水位的情况下，当流量突然增加时，直接把系统拉升到高水位可能瞬间把系统压垮。通过"冷启动"，让通过的流量缓慢增加，在一定时间内逐渐增加到阈值上限，给冷系统一个预热的时间，避免冷系统被压垮。warm up冷启动主要用于启动需要额外开销的场景，例如建立数据库连接等。
二, 实例
本文结合sentinel提供的示例, 通过dashboard控制台展示warm up方式启动流量曲线变化,
WarmUpFlowDemo类说明:
1 初始化基于QPS流控规则, 流控效果使用warm up; 阈值 : 1000, 预热时间60s;
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private static void initFlowRule() {
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List<FlowRule> rules = new ArrayList<FlowRule>();
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FlowRule rule1 = new FlowRule();
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rule1.setResource(KEY);
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// 这里设置QPS最大的阈值1000, 尽量设置大一点, 便于在监控台查看流量变化曲线
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rule1.setCount( 1000);
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// 基于QPS流控规则
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rule1.setGrade(RuleConstant.FLOW_GRADE_QPS);
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// 默认不区分调用来源
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rule1.setLimitApp( "default");
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// 流控效果, 采用warm up冷启动方式
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rule1.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_WARM_UP);
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// 在一定时间内逐渐增加到阈值上限，给冷系统一个预热的时间，避免冷系统被压垮。
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// warmUpPeriodSec 代表期待系统进入稳定状态的时间（即预热时长）。
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// 这里预热时间为1min, 便于在dashboard控制台实时监控查看QPS的pass和block变化曲线
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rule1.setWarmUpPeriodSec( 60); // 默认值为10s
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rules. add(rule1);
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FlowRuleManager.loadRules(rules);
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}
2 启动一个TimerTask线程, 统计每一秒的pass, block, total这三个指标;
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static class TimerTask implements Runnable {
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@ Override
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public void run() {
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long start = System.currentTimeMillis();
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System. out.println("begin to statistic!!!");
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long oldTotal = 0;
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long oldPass = 0;
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long oldBlock = 0;
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while (!stop) {
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try {
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TimeUnit.SECONDS.sleep( 1);
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} catch (InterruptedException e) {
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}
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long globalTotal = total.get();
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long oneSecondTotal = globalTotal - oldTotal;
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oldTotal = globalTotal;
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long globalPass = pass.get();
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long oneSecondPass = globalPass - oldPass;
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oldPass = globalPass;
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long globalBlock = block.get();
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long oneSecondBlock = globalBlock - oldBlock;
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oldBlock = globalBlock;
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System. out.println("currentTimeMillis:" + TimeUtil.currentTimeMillis() + ", totalSeconds:"
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+ TimeUtil.currentTimeMillis() / 1000 + ", currentSecond:"
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+ (TimeUtil.currentTimeMillis() / 1000) % 60 + ", total:" + oneSecondTotal
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+ ", pass:" + oneSecondPass + ", block:" + oneSecondBlock);
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if (seconds-- <= 0) {
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stop = true;
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}
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}
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long cost = System.currentTimeMillis() - start;
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System. out.println("time cost: " + cost + " ms");
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System. out.println("total:" + total.get() + ", pass:" + pass.get() + ", block:" + block.get());
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System.exit( 0);
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}
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}
3 同时启动三个WarmUpTask线程, 设置其休眠时间小于2s, 使系统访问资源处于一个较低的流量 .
①同时启动3个WarmUpTask线程
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for (int i = 0; i < 3; i++) {
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Thread t = new Thread(new WarmUpTask());
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t.setName( "sentinel-warmup-task");
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t.start();
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}
②WarmUpTask线程休眠小于2s, 通过控制休眠时间, 达到控制访问资源的流量处于一个较低的水平.
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static class WarmUpTask implements Runnable {
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public void run() {
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while (!stop) {
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Entry entry = null;
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try {
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entry = SphU.entry(KEY);
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// token acquired, means pass
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pass.addAndGet( 1);
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} catch (BlockException e1) {
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block.incrementAndGet();
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} catch (Exception e2) {
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// biz exception
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} finally {
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total.incrementAndGet();
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if (entry != null) {
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entry.exit();
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}
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}
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Random random2 = new Random();
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try {
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// 随机休眠时间<2s, 通过设置休眠时间, 模拟访问资源的流量大小
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TimeUnit.MILLISECONDS.sleep(random2.nextInt( 2000));
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} catch (InterruptedException e) {
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// ignore
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}
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}
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}
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}
4 WarmUpTask线程运行20s后,再同时启动100个线程, 设置其休眠时间小于50ms, 这样就模拟造成了访问资源的流量突增, 一是可以查看后台console观察流量变化数值, 而是查看监控台的实时监控, 能比较直观的看见warm up过程.
①20s后, 再同时启动100个线程
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// 20s开始有突增的流量进来, 访问资源
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Thread.sleep( 20000);
②再同时启动100个线程, 模拟突增的流量访问资源
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// 创建一个100线程, 模拟突增的流量访问被保护的资源
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for (int i = 0; i < threadCount; i++) {
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Thread t = new Thread(new RunTask());
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t.setName( "sentinel-run-task");
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t.start();
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}
③RunTask线程休眠时间小于50ms, 这样每个线程就能多次的访问资源, 模拟造成资源被突增的流量访问. 这样对资源的访问流量就处于一个较高的水平.
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static class RunTask implements Runnable {
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public void run() {
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while (!stop) {
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Entry entry = null;
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try {
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entry = SphU.entry(KEY);
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pass.addAndGet( 1);
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} catch (BlockException e1) {
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block.incrementAndGet();
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} catch (Exception e2) {
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// biz exception
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} finally {
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total.incrementAndGet();
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if (entry != null) {
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entry.exit();
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}
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}
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Random random2 = new Random();
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try {
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// 随机休眠时间<50ms, 通过设置休眠时间, 模拟访问资源的流量大小
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TimeUnit.MILLISECONDS.sleep(random2.nextInt( 50));
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} catch (InterruptedException e) {
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// ignore
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}
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}
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}
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}
三, 后台console端每秒展示pass, block, total数据.
①从下图可以很明显的看出, 有一个很明显的流量激增, total由原来的几或者几十, 突然增加到了4000左右, 而pass也是陡然的增加到了几百, block也由原来的0变成了3500左右.
②接着往下看, 由于我们设置的阈值为1000, 所以最终的pass值是稳定在1000没有问题; 流控效果采用warm up方式, pass的值不是一下子增加到1000, 而是由300-->400-->500-->600-->700-->800-->900-->1000逐渐增加的.
③最终QPS流量稳定在最大阈值1000, 如下图:
四, dashboard控制台流量曲线展示
① 下图展示的是, 访问资源的流量刚开始处于一个较低的水平, QPS大概只有3左右;
②下图可以明显的看到绿曲线p_qps是一个逐渐上升的过程, 代表着访问资源的流量逐渐变大, 最终稳定在阈值1000QPS.
③下图, 展示的是38分51秒左右, 经过60s的预热, QPS最终达到阈值1000.
完整代码:
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public class WarmUpFlowDemo {
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private static final String KEY = "abc";
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private static AtomicInteger pass = new AtomicInteger();
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private static AtomicInteger block = new AtomicInteger();
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private static AtomicInteger total = new AtomicInteger();
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private static volatile boolean stop = false;
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private static final int threadCount = 100;
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private static int seconds = 60 + 40;
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public static void main(String[] args) throws Exception {
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initFlowRule();
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// trigger Sentinel internal init
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Entry entry = null;
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try {
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entry = SphU.entry(KEY);
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} catch (Exception e) {
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} finally {
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if (entry != null) {
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entry.exit();
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}
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}
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Thread timer = new Thread(new TimerTask());
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timer.setName( "sentinel-timer-task");
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timer.start();
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// first make the system run on a very low condition
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// 创建3个线程, 模拟一个系统处于一个低水平流量
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for (int i = 0; i < 3; i++) {
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Thread t = new Thread(new WarmUpTask());
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t.setName( "sentinel-warmup-task");
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t.start();
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}
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// 20s开始有突增的流量进来, 访问资源
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Thread.sleep( 20000);
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/*
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* Start more thread to simulate more qps. Since we use {@link RuleConstant.CONTROL_BEHAVIOR_WARM_UP} as {@link
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* FlowRule#controlBehavior}, real passed qps will increase to {@link FlowRule#count} in {@link
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* FlowRule#warmUpPeriodSec} seconds.
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*/
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// 创建一个100线程, 模拟突增的流量访问被保护的资源
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for (int i = 0; i < threadCount; i++) {
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Thread t = new Thread(new RunTask());
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t.setName( "sentinel-run-task");
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t.start();
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}
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}
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private static void initFlowRule() {
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List<FlowRule> rules = new ArrayList<FlowRule>();
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FlowRule rule1 = new FlowRule();
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rule1.setResource(KEY);
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// 设置最大阈值为20
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// rule1.setCount(20);
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// 这里设置QPS最大的阈值1000, 便于查看变化曲线
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rule1.setCount( 1000);
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rule1.setGrade(RuleConstant.FLOW_GRADE_QPS);
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rule1.setLimitApp( "default");
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rule1.setControlBehavior(RuleConstant.CONTROL_BEHAVIOR_WARM_UP);
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// 在一定时间内逐渐增加到阈值上限，给冷系统一个预热的时间，避免冷系统被压垮。
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// warmUpPeriodSec 代表期待系统进入稳定状态的时间（即预热时长）。
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// 这里预热时间为1min, 便于在dashboard控制台实时监控查看QPS的pass和block变化曲线
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rule1.setWarmUpPeriodSec( 60); // 默认值为10s
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rules.add(rule1);
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FlowRuleManager.loadRules(rules);
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}
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static class WarmUpTask implements Runnable {
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public void run() {
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while (!stop) {
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Entry entry = null;
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try {
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entry = SphU.entry(KEY);
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// token acquired, means pass
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pass.addAndGet( 1);
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} catch (BlockException e1) {
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block.incrementAndGet();
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} catch (Exception e2) {
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// biz exception
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} finally {
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total.incrementAndGet();
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if (entry != null) {
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entry.exit();
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}
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}
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Random random2 = new Random();
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try {
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// 随机休眠时间<2s, 通过设置休眠时间, 模拟访问资源的流量大小
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TimeUnit.MILLISECONDS.sleep(random2.nextInt( 2000));
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} catch (InterruptedException e) {
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// ignore
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}
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}
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}
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}
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static class RunTask implements Runnable {
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public void run() {
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while (!stop) {
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Entry entry = null;
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try {
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entry = SphU.entry(KEY);
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pass.addAndGet( 1);
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} catch (BlockException e1) {
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block.incrementAndGet();
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} catch (Exception e2) {
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// biz exception
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} finally {
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total.incrementAndGet();
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if (entry != null) {
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entry.exit();
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}
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}
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Random random2 = new Random();
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try {
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// 随机休眠时间<50ms, 通过设置休眠时间, 模拟访问资源的流量大小
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TimeUnit.MILLISECONDS.sleep(random2.nextInt( 50));
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} catch (InterruptedException e) {
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// ignore
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}
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}
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}
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}
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static class TimerTask implements Runnable {
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public void run() {
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long start = System.currentTimeMillis();
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System.out.println( "begin to statistic!!!");
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long oldTotal = 0;
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long oldPass = 0;
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long oldBlock = 0;
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while (!stop) {
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try {
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TimeUnit.SECONDS.sleep( 1);
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} catch (InterruptedException e) {
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}
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long globalTotal = total.get();
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long oneSecondTotal = globalTotal - oldTotal;
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oldTotal = globalTotal;
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long globalPass = pass.get();
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long oneSecondPass = globalPass - oldPass;
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oldPass = globalPass;
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long globalBlock = block.get();
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long oneSecondBlock = globalBlock - oldBlock;
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oldBlock = globalBlock;
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System.out.println( "currentTimeMillis:" + TimeUtil.currentTimeMillis() + ", totalSeconds:"
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+ TimeUtil.currentTimeMillis() / 1000 + ", currentSecond:"
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+ (TimeUtil.currentTimeMillis() / 1000) % 60 + ", total:" + oneSecondTotal
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+ ", pass:" + oneSecondPass + ", block:" + oneSecondBlock);
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if (seconds-- <= 0) {
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stop = true;
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}
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}
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long cost = System.currentTimeMillis() - start;
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System.out.println( "time cost: " + cost + " ms");
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System.out.println( "total:" + total.get() + ", pass:" + pass.get() + ", block:" + block.get());
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try {
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TimeUnit.SECONDS.sleep( 60);
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} catch (InterruptedException e) {
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// TODO Auto-generated catch block
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e.printStackTrace();
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}
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System.exit( 0);
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}
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}
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}
要想在将变化数据展示在dashboard控制台, 启动时需要配置:
-Dcsp.sentinel.dashboard.server=127.0.0.1:8080
-Dcsp.sentinel.api.port=8719
-Dproject.name=WarmUpFlowDemo
具体接入dashboard, 可参考上一篇博客, Sentinel基本使用--基于QPS流量控制(一), 采用默认快速失败/直接拒绝策略控制超过阈值的流量(结合Dashboard使用)
五, 总结
上面主要讲述了QPS流量控制, 采用Warm Up预热/冷启动方式控制突增流量, 通过在后台console观察数据以及结合dashboard图表的形式, 能很清晰的了解到warm up冷启动方式控制突增流量, 保护资源, 维护系统的稳定性的.