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<div><h1>19 RocketMQ 集群监控（一）</h1>
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<h3>前言</h3>
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<p>在 RocketMQ 体系中，有集群、主题、消费组，集群又包括 NameSrv 和 Broker。本篇主要介绍 RocketMQ 的集群监控设计应该考虑哪些方面，以及如何实现。下一篇文章介绍主题、消费组方面的监控。本篇的介绍基于实战中 4 主 4 从，主从异步复制的架构模式。</p>
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<h3>监控项设计</h3>
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<p>集群监控的目的记录集群健康状态，具体监控项见下图：</p>
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<p><img src="assets/20200822095602855.png" alt="img" /></p>
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<h4><strong>节点数量</strong></h4>
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<p>如果集群中是 4 主 4 从架构，那么集群中会有 8 个 Broker 节点，下面通过 clusterList 命令可以看到有 8 个节点。当集群中节点数量小于 8 时，说明集群中有节点掉线。</p>
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<pre><code>$ bin/mqadmin clusterList -n x.x.x.x:9876
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RocketMQLog:WARN No appenders could be found for logger (io.netty.util.internal.PlatformDependent0).
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RocketMQLog:WARN Please initialize the logger system properly.
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#Cluster Name #Broker Name #BID #Addr #Version #InTPS(LOAD) #OutTPS(LOAD) #PCWait(ms) #Hour #SPACE
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demo_mq demo_mq_a 0 10.111.89.111:10911 V4_7_0 380.96(0,0ms) 383.16(0,0ms) 0 557.15 0.2298
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demo_mq demo_mq_a 1 10.111.89.110:10915 V4_7_0 380.76(0,0ms) 0.00(0,0ms) 0 557.15 0.4734
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demo_mq demo_mq_b 0 10.111.89.112:10911 V4_7_0 391.86(0,0ms) 381.66(0,0ms) 0 557.22 0.2437
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demo_mq demo_mq_b 1 10.111.89.110:10925 V4_7_0 391.26(0,0ms) 0.00(0,0ms) 0 557.22 0.4734
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demo_mq demo_mq_c 0 10.111.26.96:10911 V4_7_0 348.37(0,0ms) 342.77(0,0ms) 0 557.22 0.2428
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demo_mq demo_mq_c 1 10.111.26.91:10925 V4_7_0 357.66(0,0ms) 0.00(0,0ms) 0 557.22 0.4852
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demo_mq demo_mq_d 0 10.111.26.81:10911 V4_7_0 421.16(0,0ms) 409.86(0,0ms) 0 557.18 0.2424
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demo_mq demo_mq_d 1 10.111.26.91:10915 V4_7_0 423.30(0,0ms) 0.00(0,0ms) 0 557.18 0.4852
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</code></pre>
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<h4><strong>节点可用性</strong></h4>
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<p>检测集群中节点的是否可用也很重要，Broker 节点数量或者进程的检测不能保证节点是否可用。这个容易理解，比如 Broker 进程在，但是可能不能提供正常服务或者假死状态。我们可以通过定时向集群中各个 Broker 节点发送心跳的方式来检测。另外，记录发送的响应时间也很关键，响应时间过长，例如超过 5 秒，往往伴随着集群抖动，具体体现为客户端发送超时。</p>
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<p>可用性心跳检测：</p>
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<ul>
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<li>发送成功：表示该节点运行正常</li>
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<li>发送失败：表示该节点运行异常</li>
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</ul>
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<p>响应时间检测：</p>
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<ul>
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<li>响应正常：响应时间在几毫秒到几十毫秒，是比较合理的范围</li>
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<li>响应过长：响应时间大于 1 秒，甚至超过 5 秒，是不正常的，需要介入调查</li>
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</ul>
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<h4><strong>集群写入 TPS</strong></h4>
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<p>在前面的文章中介绍了 RocketMQ 集群的性能摸高，文章中测试场景最高为 12 万多 TPS。那我们预计承载范围 4 万~6 万，留有一定的增长空间。持续监测集群写入的 TPS，使集群保持在我们预计的承载范围。从 clusterList 命令中，可以看到每个节点的 InTPS，将各个 Master 节点求和即为集群的 TPS。</p>
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<h4><strong>集群写入 TPS 变化率</strong></h4>
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<p>考虑到过高的瞬时流量会使集群发生流控，那么集群写入的 TPS 变化率监控就比较重要了。我们可以在集群写入 TPS 监控数据的基础上通过时序数据库函数统计集群 TPS 在某一段时间内的变化率。</p>
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<h3>监控开发实战</h3>
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<p>本小节中会给出监控设计的架构图示和示例代码，通过采集服务采集 RocketMQ 监控指标，并将其存储在时序数据库中，例如 InfluxDB。</p>
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<p><img src="assets/20200822150652834.png" alt="img" /></p>
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<h4><strong>准备工作</strong></h4>
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<p>\1. 定时任务调度，以 10 秒钟为例：</p>
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<pre><code>ScheduledExecutorService executorService = Executors.newScheduledThreadPool(1, new ThreadFactory() {
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@Override
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public Thread newThread(Runnable r) {
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return new Thread(r, "rocketMQ metrics collector");
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}
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});
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executorService.scheduleAtFixedRate(new Runnable() {
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@Override
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public void run() {
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// 指标收集方法 1 collectClusterNum()
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// 指标收集方法 2 collectMetric2()
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}
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}, 60, 10, TimeUnit.SECONDS);
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</code></pre>
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<p>\2. 获取 Broker TPS 时用到了 MQAdmin，下面是初始化代码：</p>
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<pre><code>public DefaultMQAdminExt getMqAdmin() throws MQClientException {
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DefaultMQAdminExt defaultMQAdminExt = new DefaultMQAdminExt();
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defaultMQAdminExt.setNamesrvAddr("x.x.x.x:9876");
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defaultMQAdminExt.setInstanceName(Long.toString(System.currentTimeMillis()));
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defaultMQAdminExt.setVipChannelEnabled(false);
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defaultMQAdminExt.start();
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return defaultMQAdminExt;
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}
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</code></pre>
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<p>\3. 发送 Producer 启动代码：</p>
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<pre><code>public DefaultMQProducer getMqProducer(){
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DefaultMQProducer producer = new DefaultMQProducer("rt_collect_producer");
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producer.setNamesrvAddr("");
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producer.setVipChannelEnabled(false);
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producer.setClientIP("mq producer-client-id-1");
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try {
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producer.start();
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} catch (MQClientException e) {
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e.getErrorMessage();
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}
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return producer;
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}
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</code></pre>
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<h4><strong>收集集群节点数量</strong></h4>
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<p>下面代码中统计了集群中的主节点和从节点总数量，定时调用该收集方法，并将其记录在时序数据中。</p>
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<pre><code>public void collectClusterNum() throws Exception {
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DefaultMQAdminExt mqAdmin = getMqAdmin();
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ClusterInfo clusterInfo = mqAdmin.examineBrokerClusterInfo();
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int brokers = 0;
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Set<Map.Entry<String, BrokerData>> entries = clusterInfo.getBrokerAddrTable().entrySet();
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for (Map.Entry<String, BrokerData> entry : entries) {
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brokers += entry.getValue().getBrokerAddrs().entrySet().size();
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}
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// 将 brokers 存储到时序数据库即可
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System.out.println(brokers);
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}
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</code></pre>
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<h4><strong>收集节点可用性</strong></h4>
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<p>集群中的每个 Broker 的可用性，可以通过定时发送信息到该 Broker 特定的主题来实现。例如：集群中有 broker-a、broker-b、broker-c、broker-d。那每个 broker-a 上有一个名字为“broker-a”的主题，其他节点同理。通过定时向该主题发送心跳来实现可用性。</p>
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<p>下面两个 ClusterRtTime 和 RtTime 分别为集群和 Broker 的收集的数据填充类。</p>
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<pre><code>public class ClusterRtTime {
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private String cluster;
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private List<RtTime> times;
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private long timestamp = System.currentTimeMillis();
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public long getTimestamp() {
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return timestamp;
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}
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public void setTimestamp(long timestamp) {
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this.timestamp = timestamp;
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}
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public String getCluster() {
|
||
return cluster;
|
||
}
|
||
public void setCluster(String cluster) {
|
||
this.cluster = cluster;
|
||
}
|
||
public List<RtTime> getTimes() {
|
||
return times;
|
||
}
|
||
public void setTimes(List<RtTime> times) {
|
||
this.times = times;
|
||
}
|
||
}
|
||
public class RtTime {
|
||
private long rt;
|
||
private String brokerName;
|
||
private String status;
|
||
private int result;
|
||
public int getResult() {
|
||
return result;
|
||
}
|
||
public void setResult(int result) {
|
||
this.result = result;
|
||
}
|
||
public String getStatus() {
|
||
return status;
|
||
}
|
||
public void setStatus(String status) {
|
||
this.status = status;
|
||
}
|
||
public long getRt() {
|
||
return rt;
|
||
}
|
||
public void setRt(long rt) {
|
||
this.rt = rt;
|
||
}
|
||
public String getBrokerName() {
|
||
return brokerName;
|
||
}
|
||
public void setBrokerName(String brokerName) {
|
||
this.brokerName = brokerName;
|
||
}
|
||
}
|
||
</code></pre>
|
||
<p>以下方法为同步发送心跳检测实现，以 broker-a 为例，time.setRt 表示每次发送心跳的耗时，time.setResult 表示每次发送心跳的结果，成功还是失败。</p>
|
||
<pre><code> public void collectRtTime() throws Exception {
|
||
DefaultMQAdminExt mqAdmin = getMqAdmin();
|
||
ClusterRtTime clusterRtTime = new ClusterRtTime();
|
||
ClusterInfo clusterInfo = null;
|
||
try {
|
||
clusterInfo = mqAdmin.examineBrokerClusterInfo();
|
||
} catch (Exception e) {
|
||
e.printStackTrace();
|
||
return;
|
||
}
|
||
clusterRtTime.setCluster("demo_mq");
|
||
List<RtTime> times = Lists.newArrayList();
|
||
for (Map.Entry<String, BrokerData> stringBrokerDataEntry : clusterInfo.getBrokerAddrTable().entrySet()) {
|
||
BrokerData brokerData = stringBrokerDataEntry.getValue();
|
||
String brokerName = brokerData.getBrokerName();
|
||
long begin = System.currentTimeMillis();
|
||
SendResult sendResult = null;
|
||
RtTime time = new RtTime();
|
||
time.setBrokerName(brokerName);
|
||
try {
|
||
byte[] TEST_MSG = "helloworld".getBytes();
|
||
sendResult = getMqProducer().send(new Message(brokerName, TEST_MSG));
|
||
long end = System.currentTimeMillis() - begin;
|
||
SendStatus sendStatus = sendResult.getSendStatus();
|
||
// 记录发送耗时情况
|
||
time.setRt(end);
|
||
// 记录发送是否成功情况
|
||
time.setStatus(sendStatus.name());
|
||
time.setResult(sendStatus.ordinal());
|
||
} catch (Exception e) {
|
||
time.setRt(-1);
|
||
time.setStatus("FAILED");
|
||
time.setResult(5);
|
||
}
|
||
times.add(time);
|
||
}
|
||
clusterRtTime.setTimes(times);
|
||
// 将 clusterRtTime 信息存储到时序数据库即可
|
||
}
|
||
</code></pre>
|
||
<h4><strong>收集集群 TPS</strong></h4>
|
||
<p>结合定时任务调度下面的收集集群 TPS 方法，将其存储到时序数据库中。如果 10 秒收集一次，那么 1 分钟可以收集 6 次集群 TPS。</p>
|
||
<pre><code> public void collectClusterTps() throws Exception {
|
||
DefaultMQAdminExt mqAdmin = getMqAdmin();
|
||
ClusterInfo clusterInfo = mqAdmin.examineBrokerClusterInfo();
|
||
double totalTps = 0d;
|
||
for (Map.Entry<String, BrokerData> stringBrokerDataEntry : clusterInfo.getBrokerAddrTable().entrySet()) {
|
||
BrokerData brokerData = stringBrokerDataEntry.getValue();
|
||
// 选择 Master 节点
|
||
String brokerAddr = brokerData.getBrokerAddrs().get(MixAll.MASTER_ID);
|
||
if (StringUtils.isBlank(brokerAddr)) continue;
|
||
KVTable runtimeStatsTable = mqAdmin.fetchBrokerRuntimeStats(brokerAddr);
|
||
HashMap<String, String> runtimeStatus = runtimeStatsTable.getTable();
|
||
Double putTps = Math.ceil(Double.valueOf(runtimeStatus.get("putTps").split(" ")[0]));
|
||
totalTps = totalTps + putTps;
|
||
}
|
||
// 将 totalTps 存储到时序数据库即可
|
||
System.out.println(totalTps);
|
||
}
|
||
</code></pre>
|
||
<h4><strong>计算集群 TPS 的变化率</strong></h4>
|
||
<p>集群 TPS 的变化情况，我们可以通过时序数据库函数来实现。假设我们上面采集到的集群 TPS 写入到 InfluxDB 的 cluster_number_info 表中。下面语句表示 5 分钟内集群 Tps 的变化率。示例中 5 分钟内集群 TPS 变化了 12%，如果变化超过 50%，甚至 200%、300%，是需要我们去关注的，以免瞬时流量过高使集群发生流控，对业务造成超时影响。</p>
|
||
<p>写入 TPS 的变化率 = (最大值 - 最小值)/中位数</p>
|
||
<pre><code>> select SPREAD(value)/MEDIAN(value) from cluster_number_info where clusterName='demo_mq' and "name"='totalTps' and "time" > now()-5m ;
|
||
name: cluster_number_info
|
||
time spread_median
|
||
---- -------------
|
||
1572941783075915928 0.12213740458015267
|
||
</code></pre>
|
||
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