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<h1 id="metrics">应用性能监控（metrics）</h1>
<p>应用性能监控通过周期性的统计数据，帮助开发者分析业务性能瓶颈，在性能优化、故障定位的时候非常有帮助。应用性能监控的指标包括
请求各个环节的时延、线程池使用情况、连接池使用情况、CPU和网络使用情况等。</p>
<p>统计数据是一个周期性时效数据，只在统计周期内具有意义。可以选择通过 <code>REST</code> 接口查询实时的统计数据，也可以通过日志开关，将
统计数据输出到日志文件里面，帮助分析性能问题。</p>
<h2 id="_1">使用方法</h2>
<p>使用应用性能监控非常简单， 只需要在应用中包含相关的依赖包，通过配置项开启和关闭相关功能。 </p>
<ol>
<li>开启应用性能监控功能， 需要引入下面的依赖：<pre><code>```
&lt;dependency&gt;
&lt;groupId&gt;org.apache.servicecomb&lt;/groupId&gt;
&lt;artifactId&gt;metrics-core&lt;/artifactId&gt;
&lt;/dependency&gt;
```
</code></pre>
</li>
</ol>
<p>通过在 <code>microservice.yaml</code> 中增加下面的配置项，就可以将性能统计数据输出到日志文件中。</p>
<pre><code> ```
servicecomb:
metrics:
window_time: 60000
invocation:
latencyDistribution: 0,1,10,100,1000
Consumer.invocation.slow:
enabled: true
msTime: 1000
Provider.invocation.slow:
enabled: true
msTime: 1000
publisher.defaultLog:
enabled: true
endpoints.client.detail.enabled: true
```
</code></pre>
<p>上述配置开启了慢调用检测，如果存在慢调用，则会立即输出相应日志：</p>
<pre><code> ```
2019-04-02 23:01:09,103\[WARN]\[pool-7-thread-74]\[5ca37935c00ff2c7-350076] - slow(40 ms) invocation, CONSUMER highway perf1.impl.syncQuery
http method: GET
url : /v1/syncQuery/{id}/
server : highway://192.168.0.152:7070?login=true
status code: 200
total : 50.760 ms
prepare : 0.0 ms
handlers request : 0.0 ms
client filters request : 0.0 ms
send request : 0.5 ms
get connection : 0.0 ms
write to buf : 0.5 ms
wait response : 50.727 ms
wake consumer : 0.23 ms
client filters response: 0.2 ms
handlers response : 0.0 ms (SlowInvocationLogger.java:121)
```
</code></pre>
<p>其中 5ca37935c00ff2c7-350076 是 <code>${traceId}-${invocationId}</code> 的结构，在log4j2 或 logback 的输出格式中通过 %marker 引用。</p>
<p>也可以通过 <code>REST</code> 接口查询性能统数据。使用浏览器访问http://ip:port/metrics 即可，将会得到类似下面格式的json数据：</p>
<pre><code> ```
{
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=connectCount,type=client)": 0.0,
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=disconnectCount,type=client)": 0.0,
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=connections,type=client)": 1.0,
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=bytesRead,type=client)": 508011.0,
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=bytesWritten,type=client)": 542163.0,
"servicecomb.vertx.endpoints(address=192.168.0.124:7070,statistic=queueCount,type=client)": 0.0,
"servicecomb.vertx.endpoints(address=0.0.0.0:7070,statistic=connectCount,type=server)": 0.0,
"servicecomb.vertx.endpoints(address=0.0.0.0:7070,statistic=disconnectCount,type=server)": 0.0,
"servicecomb.vertx.endpoints(address=0.0.0.0:7070,statistic=connections,type=server)": 1.0,
"servicecomb.vertx.endpoints(address=0.0.0.0:7070,statistic=bytesRead,type=server)": 542163.0
... ...
}
```
</code></pre>
<h2 id="prometheus">集成Prometheus</h2>
<p><a href="https://prometheus.io/">Prometheus</a> (普罗米修斯)是一个名字非常酷的开源监控系统。</p>
<p>它支持多维度的指标数据模型，服务端通过HTTP协议定时拉取数据后，通过灵活的查询语言，实现监控的目的。</p>
<p>servicecomb的应用性能监控功能支持对接Prometheus,首先需要在您的业务项目中加入如下依赖:</p>
<pre><code> ```
&lt;dependency&gt;
&lt;groupId&gt;org.apache.servicecomb&lt;/groupId&gt;
&lt;artifactId&gt;metrics-prometheus&lt;/artifactId&gt;
&lt;/dependency&gt;
```
</code></pre>
<p>然后在 microservice.yaml 中增加下面的配置项:</p>
<pre><code> ```
servicecomb:
metrics:
prometheus:
address: ${ip}:${port}
```
</code></pre>
<p>该配置项是设置普罗米修斯框架监听的端口,您只需使用浏览器访问对应的ip地址加该端口即http://ip:port/metrics 就可以获得如下格式的监控数据:</p>
<pre><code> ```
# HELP ServiceComb_Metrics ServiceComb Metrics
# TYPE ServiceComb_Metrics untyped
threadpool_rejectedCount{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} 0.0
threadpool_rejectedCount{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} 0.0
threadpool_taskCount{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} 0.0
threadpool_currentThreadsBusy{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} NaN
threadpool_taskCount{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} 0.0
threadpool_currentThreadsBusy{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} NaN
threadpool_poolSize{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} NaN
threadpool_poolSize{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} NaN
threadpool_completedTaskCount{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="connectCount",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="disconnectCount",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="connections",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="bytesRead",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="bytesWritten",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="requests",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="latency",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:7070",statistic="rejectByConnectionLimit",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="connectCount",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="disconnectCount",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="connections",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="bytesRead",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="bytesWritten",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="requests",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="latency",type="server",} 0.0
servicecomb_vertx_endpoints{appId="springmvctest",address="0.0.0.0:8080",statistic="rejectByConnectionLimit",type="server",} 0.0
threadpool_completedTaskCount{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} 0.0
threadpool_maxThreads{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} NaN
threadpool_maxThreads{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} NaN
threadpool_queueSize{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} NaN
threadpool_queueSize{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} NaN
threadpool_corePoolSize{appId="springmvctest",id="cse.executor.groupThreadPool-group0",} NaN
threadpool_corePoolSize{appId="springmvctest",id="cse.executor.groupThreadPool-group1",} NaN
```
</code></pre>
<p>注:默认端口是9696，您也可以根据实际业务要求进行修改。</p>
<h2 id="_2">配置说明</h2>
<div class="metrics-cfg"></div>
<table>
<thead>
<tr>
<th align="left">配置项</th>
<th align="left">默认值</th>
<th align="left">含义</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left">servicecomb.metrics.window_time</td>
<td align="left">60000</td>
<td align="left">统计周期，单位为毫秒<br>TPS、时延等等周期性的数据，每周期更新一次，在周期内获取到的值，实际是上一周期的值</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.invocation.latencyDistribution</td>
<td align="left"></td>
<td align="left">时延分布时间段定义，单位为毫秒<br>例如：0,1,10,100,1000<br>表示定义了下列时延段[0, 1),[1, 10),[10, 100),[100, 1000),[1000, )</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.Consumer.invocation.slow.enabled</td>
<td align="left">false</td>
<td align="left">是否开启Consumer端的慢调用检测<br>通过增加后缀.${service}.${schema}.${operation}，可以支持4级优先级定义</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.Consumer.invocation.slow.msTime</td>
<td align="left">1000</td>
<td align="left">时延超过配置值，则会立即输出日志，记录本次调用的stage耗时信息<br>通过增加后缀.${service}.${schema}.${operation}，可以支持4级优先级定义</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.Provider.invocation.slow.enabled</td>
<td align="left">false</td>
<td align="left">是否开启Provide端的慢调用检测<br>通过增加后缀.${service}.${schema}.${operation}，可以支持4级优先级定义</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.Provider.invocation.slow.msTime</td>
<td align="left">1000</td>
<td align="left">时延超过配置值，则会立即输出日志，记录本次调用的stage耗时信息<br>通过增加后缀.${service}.${schema}.${operation}，可以支持4级优先级定义</td>
</tr>
<tr>
<td align="left">servicecomb.metrics<br>.prometheus.address</td>
<td align="left">0.0.0.0:9696</td>
<td align="left">prometheus监听地址</td>
</tr>
<tr>
<td align="left">servicecomb.metrics.publisher.defaultLog<br>.enabled</td>
<td align="left">false</td>
<td align="left">是否输出默认的统计日志</td>
</tr>
<tr>
<td align="left">servicecomb.metrics.publisher.defaultLog<br>.endpoints.client.detail.enabled</td>
<td align="left">false</td>
<td align="left">是否输出每一条client endpoint统计日志，因为跟目标的ip:port数有关，可能会有很多数据，所以默认不输出</td>
</tr>
</tbody>
</table>
<h2 id="_3">统计指标含义说明</h2>
<ul>
<li>CPU 统计信息</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="2">os</td>
<td rowspan="2">type</td>
<td>cpu</td>
<td>当前周期内系统CPU使用率，Solaris模式</td>
</tr>
<tr>
<td>processCpu</td>
<td>当前周期内微服务进程CPU使用率，IRIX模式<br>
processCpu除以cpu近似等于系统CPU数</td>
</tr>
</table>
<ul>
<li>网络统计信息</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="6">os</td>
<td>type</td>
<td>net</td>
<td></td>
</tr>
<tr>
<td rowspan="4">statistic</td>
<td>send</td>
<td>当前周期内平均每秒发送的字节数(Bps)</td>
</tr>
<tr>
<td>receive</td>
<td>当前周期内平均每秒接收的字节数(Bps)</td>
</tr>
<tr>
<td>sendPackets</td>
<td>当前周期内平均每秒发送的包数(pps)</td>
</tr>
<tr>
<td>receivePackets</td>
<td>当前周期内平均每秒接收的包数(pps)</td>
</tr>
<tr>
<td>interface</td>
<td></td>
<td>网卡设备名</td>
</tr>
</table>
<ul>
<li>vertx client endpoints 统计信息 </li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="8">servicecomb<br>.vertx<br>.endpoints</td>
<td>type</td>
<td>client</td>
<td></td>
</tr>
<tr>
<td>address</td>
<td>${ip}:${port}</td>
<td>服务端的ip:port</td>
</tr>
<tr>
<td rowspan="6">statistic</td>
<td>connectCount</td>
<td>当前周期内共发起多少次连接</td>
</tr>
<tr>
<td>disconnectCount</td>
<td>当前周期内断连的次数</td>
</tr>
<tr>
<td>queueCount</td>
<td>http连接池中正在等待获取连接的请求数</td>
</tr>
<tr>
<td>connections</td>
<td>当前时刻的连接数</td>
</tr>
<tr>
<td>bytesRead</td>
<td>当前周期内平均每秒接收的字节数(Bps)<br>
业务层的统计，相对从网卡获取的数据，这里的数据不包括包头的大小<br>
对于http消息，不包括http header大小</td>
</tr>
<tr>
<td>bytesWritten</td>
<td>当前周期内平均每秒发送的字节数(Bps)<br>
业务层的统计，相对从网卡获取的数据，这里的数据不包括包头的大小<br>
对于http消息，不包括http header大小</td>
</tr>
</table>
<ul>
<li>vertx server endpoints 统计信息</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="8">servicecomb<br>.vertx<br>.endpoints</td>
<td>type</td>
<td>server</td>
<td></td>
</tr>
<tr>
<td>address</td>
<td>${ip}:${port}</td>
<td>监听的ip:port</td>
</tr>
<tr>
<td rowspan="6">statistic</td>
<td>connectCount</td>
<td>当前周期内共接入多少次连接</td>
</tr>
<tr>
<td>disconnectCount</td>
<td>当前周期内断连的次数</td>
</tr>
<tr>
<td>rejectByConnectionLimit</td>
<td>当前周期内因超出连接数限制而主动断连的次数</td>
</tr>
<tr>
<td>connections</td>
<td>当前时刻的连接数</td>
</tr>
<tr>
<td>bytesRead</td>
<td>当前周期内平均每秒发送的字节数(Bps)<br>
业务层的统计，相对从网卡获取的数据，这里的数据不包括包头的大小<br>
对于http消息，不包括http header大小</td>
</tr>
<tr>
<td>bytesWritten</td>
<td>当前周期内平均每秒接收的字节数(Bps)<br>
业务层的统计，相对从网卡获取的数据，这里的数据不包括包头的大小<br>
对于http消息，不包括http header大小</td>
</tr>
</table>
<ul>
<li>时延分布概览</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="11">servicecomb<br>.invocation</td>
<td>role</td>
<td>CONSUMER、PRODUCER、EDGE</td>
<td>是CONSUMER、PRODUCER还是EDGE端的统计</td>
</tr>
<tr>
<td>operation</td>
<td>${microserviceName}<br>.${schemaId}<br>.${operationName}</td>
<td>调用的方法名</td>
</tr>
<tr>
<td>transport</td>
<td>highway或rest</td>
<td>调用是在哪个传输通道上发生的</td>
</tr>
<tr>
<td>status</td>
<td>http status code</td>
<td></td>
</tr>
<tr>
<td>type</td>
<td>latencyDistribution</td>
<td>调用时延分布</td>
</tr>
<tr>
<td>scope</td>
<td>[${min}, ${max})</td>
<td>当前周期内调用时延大于等于min，小于max的次数<br>
[${min},)表示max为无限大</td>
</tr>
</table>
<ul>
<li>consumer 详细时延分布</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="19">servicecomb<br>.invocation</td>
<td>role</td>
<td>CONSUMER</td>
<td>CONSUMER端的统计</td>
</tr>
<tr>
<td>operation</td>
<td>${microserviceName}<br>.${schemaId}<br>.${operationName}</td>
<td>调用的方法名</td>
</tr>
<tr>
<td>transport</td>
<td>highway或rest</td>
<td>调用是在哪个传输通道上发生的</td>
</tr>
<tr>
<td>status</td>
<td>http status code</td>
<td></td>
</tr>
<tr>
<td>type</td>
<td>stage</td>
<td>stage时延</td>
</tr>
<tr>
<td rowspan="11">stage</td>
<td>total</td>
<td>全流程</td>
</tr>
<tr>
<td>prepare</td>
<td>初始化</td>
</tr>
<tr>
<td>handlers_request</td>
<td>handler链请求流程</td>
</tr>
<tr>
<td>client_filters_request</td>
<td>http client filter链请求流程<br>
只有走rest transport才有本阶段</td>
</tr>
<tr>
<td>consumer_send_request</td>
<td>发送请求阶段，包括consumer_get_connection和consumer_write_to_buf</td>
</tr>
<tr>
<td>consumer_get_connection</td>
<td>从连接池获取连接</td>
</tr>
<tr>
<td>consumer_write_to_buf</td>
<td>向网络缓冲区写数据</td>
</tr>
<tr>
<td>consumer_wait_response</td>
<td>等待服务端应答</td>
</tr>
<tr>
<td>consumer_wake_consumer</td>
<td>同步流程中，收到应答后，从唤醒等待线程，到等待线程开始处理应答的耗时</td>
</tr>
<tr>
<td>client_filters_response</td>
<td>http client filter链应答流程</td>
</tr>
<tr>
<td>handlers_response</td>
<td>handler链应答流程</td>
</tr>
<tr>
<td rowspan="3">statistic</td>
<td>count</td>
<td>平均每秒调用次数，即TPS<br>
count=统计周期内的调用次数/周期（秒）</td>
</tr>
<tr>
<td>totalTime</td>
<td>单位为秒<br>
totalTime=当前周期内的调用耗时总时长/周期（秒）<br>
totalTime除以count即可得到平均时延</td>
</tr>
<tr>
<td>max</td>
<td>单位为秒<br>
当前周期内最大耗时</td>
</tr>
</table>
<ul>
<li>provider 详细时延分布</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="17">servicecomb<br>.invocation</td>
<td>role</td>
<td>PRODUCER</td>
<td>PRODUCER端的统计</td>
</tr>
<tr>
<td>operation</td>
<td>${microserviceName}<br>.${schemaId}<br>.${operationName}</td>
<td>调用的方法名</td>
</tr>
<tr>
<td>transport</td>
<td>highway或rest</td>
<td>调用是在哪个传输通道上发生的</td>
</tr>
<tr>
<td>status</td>
<td>http status code</td>
<td></td>
</tr>
<tr>
<td>type</td>
<td>stage</td>
<td>stage时延</td>
</tr>
<tr>
<td rowspan="9">stage</td>
<td>total</td>
<td>全流程</td>
</tr>
<tr>
<td>prepare</td>
<td>初始化</td>
</tr>
<tr>
<td>queue</td>
<td>仅在使用线程池时有意义<br>
表示调用在线程池中排队的时长</td>
</tr>
<tr>
<td>server_filters_request</td>
<td>http server filter链请求流程<br>
只有走rest transport才有本阶段</td>
</tr>
<tr>
<td>handlers_request</td>
<td>handler链请求流程</td>
</tr>
<tr>
<td>execution</td>
<td>业务方法</td>
</tr>
<tr>
<td>handlers_response</td>
<td>handler链应答流程</td>
</tr>
<tr>
<td>server_filters_response</td>
<td>http server filter链应答流程</td>
</tr>
<tr>
<td>producer_send_response</td>
<td>发送应答</td>
</tr>
<tr>
<td rowspan="3">statistic</td>
<td>count</td>
<td>平均每秒调用次数，即TPS<br>
count=统计周期内的调用次数/周期（秒）</td>
</tr>
<tr>
<td>totalTime</td>
<td>单位为秒<br>
totalTime=当前周期内的调用耗时总时长/周期（秒）<br>
totalTime除以count即可得到平均时延</td>
</tr>
<tr>
<td>max</td>
<td>单位为秒<br>
当前周期内最大耗时</td>
</tr>
</table>
<ul>
<li>edge service 详细时延分布</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td rowspan="23">servicecomb<br>.invocation</td>
<td>role</td>
<td>EDGE</td>
<td>EDGE的统计</td>
</tr>
<tr>
<td>operation</td>
<td>${microserviceName}<br>.${schemaId}<br>.${operationName}</td>
<td>调用的方法名</td>
</tr>
<tr>
<td>transport</td>
<td>highway或rest</td>
<td>调用是在哪个传输通道上发生的</td>
</tr>
<tr>
<td>status</td>
<td>http status code</td>
<td></td>
</tr>
<tr>
<td>type</td>
<td>stage</td>
<td>stage时延</td>
</tr>
<tr>
<td rowspan="15">stage</td>
<td>total</td>
<td>全流程</td>
</tr>
<tr>
<td>prepare</td>
<td>初始化</td>
</tr>
<tr>
<td>queue</td>
<td>仅在使用线程池时有意义<br>
表示调用在线程池中排队的时长</td>
</tr>
<tr>
<td>server_filters_request</td>
<td>http server filter链请求流程</td>
</tr>
<tr>
<td>handlers_request</td>
<td>handler链请求流程</td>
</tr>
<tr>
<td>client_filters_request</td>
<td>http client filter链请求流程</td>
</tr>
<tr>
<td>consumer_send_request</td>
<td>发送请求阶段，包括consumer_get_connection和consumer_write_to_buf</td>
</tr>
<tr>
<td>consumer_get_connection</td>
<td>从连接池获取连接</td>
</tr>
<tr>
<td>consumer_write_to_buf</td>
<td>向网络缓冲区写数据</td>
</tr>
<tr>
<td>consumer_wait_response</td>
<td>等待服务端应答</td>
</tr>
<tr>
<td>consumer_wake_consumer</td>
<td>同步流程中，收到应答后，从唤醒等待线程，到等待线程开始处理应答的耗时</td>
</tr>
<tr>
<td>client_filters_response</td>
<td>http client filter链应答流程</td>
</tr>
<tr>
<td>handlers_response</td>
<td>handler链应答流程</td>
</tr>
<tr>
<td>server_filters_response</td>
<td>http server filter链应答流程</td>
</tr>
<tr>
<td>producer_send_response</td>
<td>发送应答</td>
</tr>
<tr>
<td rowspan="3">statistic</td>
<td>count</td>
<td>平均每秒调用次数，即TPS<br>
count=统计周期内的调用次数/周期（秒）</td>
</tr>
<tr>
<td>totalTime</td>
<td>单位为秒<br>
totalTime=当前周期内的调用耗时总时长/周期（秒）<br>
totalTime除以count即可得到平均时延</td>
</tr>
<tr>
<td>max</td>
<td>单位为秒<br>
当前周期内最大耗时</td>
</tr>
</table>
<ul>
<li>线程池信息</li>
</ul>
<table class="metrics-table">
<tr>
<th>Name</th>
<th>Tag keys</th>
<th>Tag values</th>
<th>含义</th>
</tr>
<tr>
<td>threadpool.corePoolSize </td>
<td rowspan="8">id</td>
<td rowspan="8">${threadPoolName}</td>
<td>最小线程数</td>
</tr>
<tr>
<td>threadpool.maxThreads </td>
<td>最大允许的线程数</td>
</tr>
<tr>
<td>threadpool.poolSize </td>
<td>当前实际线程数</td>
</tr>
<tr>
<td>threadpool.currentThreadsBusy </td>
<td>当前的活动线程数，即当前正在执行的任务数</td>
</tr>
<tr>
<td>threadpool.queueSize </td>
<td>当前正在排队的任务数</td>
</tr>
<tr>
<td>threadpool.rejectedCount </td>
<td>当前周期内平均每秒被拒绝的任务数</td>
</tr>
<tr>
<td>threadpool.taskCount</td>
<td>统计周期内平均每秒提交的任务数<br>taskCount=（completed + queue + active）/周期（秒）</td>
</tr>
<tr>
<td>threadpool.completedTaskCount </td>
<td>统计周期内平均每秒完成的任务数<br>completedTaskCount=completed/周期（秒）</td>
</tr>
</table>
<h2 id="_4">开发者信息和高级课题</h2>
<ul>
<li>实现原理</li>
</ul>
<p><img alt="" src="../assets/metrics/logicDiagram.png" /></p>
<pre><code>1. 基于[netflix spectator](https://github.com/Netflix/spectator)
2. Foundation-metrics通过SPI机制加载所有MetricsInitializer实现，实现者可以通过MetricsInitializer中的getOrder规划执行顺序，order数字越小，越先执行。
3. Metrics-core实现3类MetricsInitializer：
1. DefaultRegistryInitializer: 实例化并注册spectator-reg-servo，设置较小的order，保证比下面2类MetricsInitializer先执行
2. Meters Initializer: 实现TPS、时延、线程池、jvm资源等等数据的统计
3. Publisher: 输出统计结果，内置了日志输出，以及通过RESTful接口输出
4. Metrics-prometheus提供与prometheus对接的能力
</code></pre>
<ul>
<li>业务定制</li>
</ul>
<p>因为ServiceComb已经初始化了servo的registry，所以业务不必再创建registry。实现MetricsInitializer接口，定
义业务级的Meters，或实现定制的Publisher，再通过SPI机制声明自己的实现即可。</p>
<p>1.Meters</p>
<pre><code>创建Meters能力均由spectator提供，可查阅[netflix spectator](https://github.com/Netflix/spectator)文档
</code></pre>
<p>2.Publisher</p>
<pre><code>周期性输出的场景，比如日志场景，通过eventBus订阅org.apache.servicecomb.foundation.metrics.PolledEvent，PolledEvent.getMeters()即是本周期的统计结果
非周期性输出的场景，比如通过RESTful接口访问，通过globalRegistry.iterator()即可得到本周期的统计结果
</code></pre>
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