Prometheus监控有所思：多标签埋点及Mbean

　　使用 grafana+prometheus+jmx 作为普通的监控手段，是比较有用的。我之前的文章介绍了相应的实现办法。https://www.cnblogs.com/yougewe/p/11140129.html

　　但是，按照之前的实现，我们更多的只能是监控 单值型的数据，如请求量，tps 等等，对于复杂组合型的指标却不容易监控。

　　这种情况一般带有一定的业务属性，比如想监控mq中的每个topic的消费情况，每类产品的实时订单情况等等。当然，对于看过完整的 prometheus 的监控数据的同学来说，会觉得很正常，因为你会看到如下的数据:

# HELP java_lang_MemoryPool_PeakUsage_max java.lang.management.MemoryUsage (java.lang<type=MemoryPool, name=Metaspace><PeakUsage>max)
# TYPE java_lang_MemoryPool_PeakUsage_max untyped
java_lang_MemoryPool_PeakUsage_max{name="Metaspace",} -1.0
java_lang_MemoryPool_PeakUsage_max{name="PS Old Gen",} 1.415053312E9
java_lang_MemoryPool_PeakUsage_max{name="PS Eden Space",} 6.96778752E8
java_lang_MemoryPool_PeakUsage_max{name="Code Cache",} 2.5165824E8
java_lang_MemoryPool_PeakUsage_max{name="Compressed Class Space",} 1.073741824E9
java_lang_MemoryPool_PeakUsage_max{name="PS Survivor Space",} 5242880.0

　　这里面的 name 就是普通标签嘛，同理于其他埋点咯。应该是可以实现的。

　　是的，prometheus 是方便实现这玩意的，但是我们之前不是使用 jmx_exportor 作为导出工具嘛，使用的埋点组件是 io.dropwizard.metrics:metrics-core 。

　　而它则是重在单值的监控，所以，用它我们是实现不了带指标的数据的监控了。

　　那怎么办呢？三个办法！

1. 直接替换原有的 metrics-core 组件为 prometheus 的client 组件，因为官方是支持这种操作的;
2. 使用 prometheus-client 组件与 metrics-core 组件配合，各自使用各自的功能;
3. 自行实现带标签的埋点，这可能是基于 MBean 的;

 

　　以上这几种方案，各有优劣。方案1可能改动太大，而且可能功能不兼容不可行; 方案2可能存在整合不了或者功能冲突情况，当然如果能整合，绝对是最好的; 方案3实现复杂度就高了，比如监控值维护、线程安全、MBean数据吐出方式等等。

　　好吧，不管怎么样，我们还是都看看吧。

 

一、 使用 prometheus-client 埋点实现带标签的监控

　　1. 引入 pom 依赖

        <dependency>
            <groupId>io.prometheus</groupId>
            <artifactId>simpleclient</artifactId>
            <version>0.8.0</version>
        </dependency>
        <dependency>
                <groupId>io.prometheus</groupId>
                <artifactId>simpleclient_hotspot</artifactId>
                <version>0.8.0</version>
        </dependency>
        <dependency>
                <groupId>io.prometheus</groupId>
                <artifactId>simpleclient_servlet</artifactId>
                <version>0.8.0</version>
        </dependency>

　　2. 框架注册监控

        @Configuration
        public class PrometheusConfig {
            @Bean
            public ServletRegistrationBean servletRegistrationBean(){
                // 将埋点指标吐出到 /metrics 节点
                return new ServletRegistrationBean(new MetricsServlet(), "/metrics");
            }
        }

　　3. 业务埋点数据

        // 注册指标实例
        io.prometheus.client.Counter c = io.prometheus.client.Counter.build()
                .name("jmx_test_abc_ffff")
                .labelNames("topic")
                .help("topic counter usage.")
                .register();
        public void incTopicMetric(String topic) {
            // c.labels("test").inc();  // for test
        }

　　4. 获取埋点数据信息

        curl http://localhost:8080/metrics
        # 对外暴露http接口调用，结果如下
        # HELP jmx_test_abc_ffff counter usage.
        # TYPE jmx_test_abc_ffff counter
        jmx_test_abc_ffff{topic="bbb",} 1.0
        jmx_test_abc_ffff{topic="2",} 2.0
        jmx_test_abc_ffff{topic="test",} 1.0

　　可以看出，效果咱们是实现了。但是，对于已经运行的东西，要改这玩意可能不是那么友好。主要有以下几点：

　　　　1. 暴露数据方式变更，原来由javaagent进行统一处理的数据，现在可能由于应用端口的不一，导致收集的配置会变更，不一定符合运维场景;
　　　　2. 需要将原来的埋点进行替换;

 

二、 prometheus-client 与 metrics-core 混合埋点

　　不处理以前的监控，将新监控带标签数据吐入到 jmx_exportor 中。

　　我们试着使用如上的埋点方式：

        // 注册指标实例
        io.prometheus.client.Counter c = io.prometheus.client.Counter.build()
                .name("jmx_test_abc_ffff")
                .labelNames("topic")
                .help("topic counter usage.")
                .register();
        public void incTopicMetric(String topic) {
            // c.labels("test").inc();  // for test
        }

　　好像数据是不会进入的到 jmx_exportor 的。这也不奇怪，毕竟咱们也不了解其原理，难道想靠运气取胜？？

　　细去查看 metrics-core 组件的埋点实现方案，发现其是向 MBean 中吐入数据，从而被 jmx_exportor 抓取的。

        // com.codahale.metrics.jmx.JmxReporter.JmxListener#onCounterAdded
        @Override
        public void onCounterAdded(String name, Counter counter) {
            try {
                if (filter.matches(name, counter)) {
                    final ObjectName objectName = createName("counters", name);
                    registerMBean(new JmxCounter(counter, objectName), objectName);
                }
            } catch (InstanceAlreadyExistsException e) {
                LOGGER.debug("Unable to register counter", e);
            } catch (JMException e) {
                LOGGER.warn("Unable to register counter", e);
            }
        }
        // 向 mBeanServer 注册监控实例
        // 默认情况下 mBeanServer = ManagementFactory.getPlatformMBeanServer();
        private void registerMBean(Object mBean, ObjectName objectName) throws InstanceAlreadyExistsException, JMException {
            ObjectInstance objectInstance = mBeanServer.registerMBean(mBean, objectName);
            if (objectInstance != null) {
                // the websphere mbeanserver rewrites the objectname to include
                // cell, node & server info
                // make sure we capture the new objectName for unregistration
                registered.put(objectName, objectInstance.getObjectName());
            } else {
                registered.put(objectName, objectName);
            }
        }

　　而 prometheus-client 则是通过 CollectorRegistry.defaultRegistry 进行注册实例的。

    // io.prometheus.client.SimpleCollector.Builder#register()
    /**
     * Create and register the Collector with the default registry.
     */
    public C register() {
      return register(CollectorRegistry.defaultRegistry);
    }
    /**
     * Create and register the Collector with the given registry.
     */
    public C register(CollectorRegistry registry) {
      C sc = create();
      registry.register(sc);
      return sc;
    }

　　所以，好像原理上来讲是不同的。至于到底为什么不能监控到数据，那还不好说。至少，你可以学习 metrics-core 使用 MBean 的形式将数据导出。这是我们下一个方案要讨论的事。

　　这里我可以给到一个最终简单又不失巧合的方式，实现两个监控组件的兼容，同时向 jmx_exportor 进行导出。如下：

　　1. 引入 javaagent 依赖包

        <!-- javaagent 包，与 外部使用的 jmx_exportor 一致 -->
        <dependency>
            <groupId>io.prometheus.jmx</groupId>
            <artifactId>jmx_prometheus_javaagent</artifactId>
            <version>0.12.0</version>
        </dependency>

　　2. 使用 agent 的工具类进行埋点

　　因为 javaagent 里面提供一套完整的 client 工具包，所以，我们可以使用。

        // 注册指标实例
        // 将 io.prometheus.client.Counter 包替换为 io.prometheus.jmx.shaded.io.prometheus.client.Counter
        io.prometheus.client.Counter c = io.prometheus.client.Counter.build()
                .name("jmx_test_abc_ffff")
                .labelNames("topic")
                .help("topic counter usage.")
                .register();
        public void incTopicMetric(String topic) {
            // c.labels("test").inc();  // for test
        }

　　3. 原样使用 jmx_exportor 就可以导出监控数据了

　　为什么换一个包这样就可以了？

　　因为 jmx_exportor 也是通过注册 CollectorRegistry.defaultRegistry 来进行收集数据的，我们只要保持与其实例一致，就可以做到在同一个jvm内共享数据了。

 

三、 基于 MBean自行实现带标签的埋点

// 测试类
public class PrometheusMbeanMetricsMain {
    private static ConcurrentHashMap<String, AtomicInteger> topicContainer = new ConcurrentHashMap<>();
    private static MBeanServer mBeanServer = ManagementFactory.getPlatformMBeanServer();

    public static void main(String[] args) throws Exception {
        // 模拟某个topic
        String commingTopic = "test_topic";
        AtomicInteger myTopic1Counter = getMetricCounter(commingTopic);
        System.out.println("jmx started!");
        while(true){
            System.out.println("---");
            // 计数增加
            myTopic1Counter.incrementAndGet();
            Thread.sleep(10000);
        }
    }

    private static AtomicInteger getMetricCounter(String topic) throws MalformedObjectNameException, NotCompliantMBeanException, InstanceAlreadyExistsException, MBeanRegistrationException {
        AtomicInteger myTopic1Counter = topicContainer.get(topic);
        if(myTopic1Counter == null) {
            myTopic1Counter = new AtomicInteger(0);
            Hashtable<String, String> tab = new Hashtable<>();
            tab.put("topic", topic);
            // 占位符，虽然不知道什么意思，但是感觉很厉害的样子
            tab.put("_", "_value");
            ObjectName objectName = new ObjectName("mydomain_test", tab);
            // 注册监控实例 到 MBeanServer 中
            ObjectInstance objectInstance = mBeanServer.registerMBean(new JmxCounter(myTopic1Counter, objectName), objectName);
        }
        return myTopic1Counter;
    }
}
// JmxCounter, MBean 要求: 1. 接口必须定义成Public的;  2. 接口命名规范符合要求, 即接口名叫 XYZMBean ，那么实现名就必须一定是XYZ;
// DynamicMBean
public interface JmxCounterMBean {
    public Object getCount() throws Exception;
}
public class JmxCounter implements JmxCounterMBean {
    private AtomicInteger metric;
    private ObjectName objectName;

    public JmxCounter(AtomicInteger metric, ObjectName objectName) {
        this.objectName = objectName;
        this.metric = metric;
    }

    @Override
    public Object getCount() throws Exception {
        // 返回监控结果
        return metric.get();
    }

}

　　最后，见证奇迹的时刻。结果如下：

# HELP mydomain_test_value_Count Attribute exposed for management (mydomain_test<_=_value, topic=b_topic><>Count)
# TYPE mydomain_test_value_Count untyped
mydomain_test_value_Count{topic="b_topic",} 1.0
mydomain_test_value_Count{topic="a_topic",} 88.0

　　很明显，这是一个糟糕的实现，不要学他。仅为了演示效果。

　　所以，总结下来，自然是使用方案2了。两个组件兼容，实现简单，性能也不错。如果只是为了使用，到此就可以了。不过你得明白，以上方案有取巧的成分在。

 

四、 原理: jmx_exportor 是如何获取数据的？

　　jmx_exportor 也是可以通过 http_server 暴露数据。

    // io.prometheus.client.exporter.HTTPServer
    /**
     * Start a HTTP server serving Prometheus metrics from the given registry.
     */
    public HTTPServer(InetSocketAddress addr, CollectorRegistry registry, boolean daemon) throws IOException {
        server = HttpServer.create();
        server.bind(addr, 3);
        // 使用 HTTPMetricHandler 处理请求
        HttpHandler mHandler = new HTTPMetricHandler(registry);
        // 绑定到 /metrics 地址上
        server.createContext("/", mHandler);
        server.createContext("/metrics", mHandler);
        executorService = Executors.newFixedThreadPool(5, DaemonThreadFactory.defaultThreadFactory(daemon));
        server.setExecutor(executorService);
        start(daemon);
    }    
    /**
     * Start a HTTP server by making sure that its background thread inherit proper daemon flag.
     */
    private void start(boolean daemon) {
        if (daemon == Thread.currentThread().isDaemon()) {
            server.start();
        } else {
            FutureTask<Void> startTask = new FutureTask<Void>(new Runnable() {
                @Override
                public void run() {
                    server.start();
                }
            }, null);
            DaemonThreadFactory.defaultThreadFactory(daemon).newThread(startTask).start();
            try {
                startTask.get();
            } catch (ExecutionException e) {
                throw new RuntimeException("Unexpected exception on starting HTTPSever", e);
            } catch (InterruptedException e) {
                // This is possible only if the current tread has been interrupted,
                // but in real use cases this should not happen.
                // In any case, there is nothing to do, except to propagate interrupted flag.
                Thread.currentThread().interrupt();
            }
        }
    }

　　所以，可以主要逻辑是 HTTPMetricHandler 处理。来看看。

        // io.prometheus.client.exporter.HTTPServer.HTTPMetricHandler#handle
        public void handle(HttpExchange t) throws IOException {
            String query = t.getRequestURI().getRawQuery();

            ByteArrayOutputStream response = this.response.get();
            response.reset();
            OutputStreamWriter osw = new OutputStreamWriter(response);
            // 主要由该 TextFormat 进行格式化输出
            // registry.filteredMetricFamilySamples() 进行数据收集
            TextFormat.write004(osw,
                    registry.filteredMetricFamilySamples(parseQuery(query)));
            osw.flush();
            osw.close();
            response.flush();
            response.close();

            t.getResponseHeaders().set("Content-Type",
                    TextFormat.CONTENT_TYPE_004);
            if (shouldUseCompression(t)) {
                t.getResponseHeaders().set("Content-Encoding", "gzip");
                t.sendResponseHeaders(HttpURLConnection.HTTP_OK, 0);
                final GZIPOutputStream os = new GZIPOutputStream(t.getResponseBody());
                response.writeTo(os);
                os.close();
            } else {
                t.getResponseHeaders().set("Content-Length",
                        String.valueOf(response.size()));
                t.sendResponseHeaders(HttpURLConnection.HTTP_OK, response.size());
                // 写向客户端
                response.writeTo(t.getResponseBody());
            }
            t.close();
        }

    }

 

五、 原理: jmx_exportor 是如何获取Mbean 的数据的？

　　jmx_exportor 有一个 JmxScraper, 专门用于处理 MBean 的值。

    // io.prometheus.jmx.JmxScraper#doScrape
    /**
      * Get a list of mbeans on host_port and scrape their values.
      *
      * Values are passed to the receiver in a single thread.
      */
    public void doScrape() throws Exception {
        MBeanServerConnection beanConn;
        JMXConnector jmxc = null;
        // 默认直接获取本地的 jmx 信息
        // 即是通过共享 ManagementFactory.getPlatformMBeanServer() 变量来实现通信的
        if (jmxUrl.isEmpty()) {
          beanConn = ManagementFactory.getPlatformMBeanServer();
        } else {
          Map<String, Object> environment = new HashMap<String, Object>();
          if (username != null && username.length() != 0 && password != null && password.length() != 0) {
            String[] credent = new String[] {username, password};
            environment.put(javax.management.remote.JMXConnector.CREDENTIALS, credent);
          }
          if (ssl) {
              environment.put(Context.SECURITY_PROTOCOL, "ssl");
              SslRMIClientSocketFactory clientSocketFactory = new SslRMIClientSocketFactory();
              environment.put(RMIConnectorServer.RMI_CLIENT_SOCKET_FACTORY_ATTRIBUTE, clientSocketFactory);
              environment.put("com.sun.jndi.rmi.factory.socket", clientSocketFactory);
          }
          // 如果是远程获取，则会通过 rmi 进行远程通信获取
          jmxc = JMXConnectorFactory.connect(new JMXServiceURL(jmxUrl), environment);
          beanConn = jmxc.getMBeanServerConnection();
        }
        try {
            // Query MBean names, see #89 for reasons queryMBeans() is used instead of queryNames()
            Set<ObjectName> mBeanNames = new HashSet<ObjectName>();
            for (ObjectName name : whitelistObjectNames) {
                for (ObjectInstance instance : beanConn.queryMBeans(name, null)) {
                    mBeanNames.add(instance.getObjectName());
                }
            }

            for (ObjectName name : blacklistObjectNames) {
                for (ObjectInstance instance : beanConn.queryMBeans(name, null)) {
                    mBeanNames.remove(instance.getObjectName());
                }
            }

            // Now that we have *only* the whitelisted mBeans, remove any old ones from the cache:
            jmxMBeanPropertyCache.onlyKeepMBeans(mBeanNames);

            for (ObjectName objectName : mBeanNames) {
                long start = System.nanoTime();
                scrapeBean(beanConn, objectName);
                logger.fine("TIME: " + (System.nanoTime() - start) + " ns for " + objectName.toString());
            }
        } finally {
          if (jmxc != null) {
            jmxc.close();
          }
        }
    }
    
    // io.prometheus.jmx.JmxScraper#scrapeBean
    private void scrapeBean(MBeanServerConnection beanConn, ObjectName mbeanName) {
        MBeanInfo info;
        try {
          info = beanConn.getMBeanInfo(mbeanName);
        } catch (IOException e) {
          logScrape(mbeanName.toString(), "getMBeanInfo Fail: " + e);
          return;
        } catch (JMException e) {
          logScrape(mbeanName.toString(), "getMBeanInfo Fail: " + e);
          return;
        }
        MBeanAttributeInfo[] attrInfos = info.getAttributes();

        Map<String, MBeanAttributeInfo> name2AttrInfo = new LinkedHashMap<String, MBeanAttributeInfo>();
        for (int idx = 0; idx < attrInfos.length; ++idx) {
            MBeanAttributeInfo attr = attrInfos[idx];
            if (!attr.isReadable()) {
                logScrape(mbeanName, attr, "not readable");
                continue;
            }
            name2AttrInfo.put(attr.getName(), attr);
        }
        final AttributeList attributes;
        try {
            // 通过 MBean 调用对象，获取所有属性值，略去不说
            attributes = beanConn.getAttributes(mbeanName, name2AttrInfo.keySet().toArray(new String[0]));
        } catch (Exception e) {
            logScrape(mbeanName, name2AttrInfo.keySet(), "Fail: " + e);
            return;
        }
        for (Attribute attribute : attributes.asList()) {
            MBeanAttributeInfo attr = name2AttrInfo.get(attribute.getName());
            logScrape(mbeanName, attr, "process");
            // 处理单个key的属性值, 如 topic=aaa,ip=1 将会进行再次循环处理
            processBeanValue(
                    mbeanName.getDomain(),
                    // 获取有效的属性列表, 我们可以简单看一下过滤规则, 如下文
                    jmxMBeanPropertyCache.getKeyPropertyList(mbeanName),
                    new LinkedList<String>(),
                    attr.getName(),
                    attr.getType(),
                    attr.getDescription(),
                    attribute.getValue()
            );
        }
    }
    // 处理每个 mBean 的属性，写入到 receiver 中
    // io.prometheus.jmx.JmxScraper#processBeanValue
    /**
     * Recursive function for exporting the values of an mBean.
     * JMX is a very open technology, without any prescribed way of declaring mBeans
     * so this function tries to do a best-effort pass of getting the values/names
     * out in a way it can be processed elsewhere easily.
     */
    private void processBeanValue(
            String domain,
            LinkedHashMap<String, String> beanProperties,
            LinkedList<String> attrKeys,
            String attrName,
            String attrType,
            String attrDescription,
            Object value) {
        if (value == null) {
            logScrape(domain + beanProperties + attrName, "null");
        } 
        // 单值情况，数字型，字符串型，可以处理
        else if (value instanceof Number || value instanceof String || value instanceof Boolean) {
            logScrape(domain + beanProperties + attrName, value.toString());
            // 解析出的数据存入 receiver 中，可以是 jmx, 或者 控制台
            this.receiver.recordBean(
                    domain,
                    beanProperties,
                    attrKeys,
                    attrName,
                    attrType,
                    attrDescription,
                    value);
        } 
        // 多值型情况
        else if (value instanceof CompositeData) {
            logScrape(domain + beanProperties + attrName, "compositedata");
            CompositeData composite = (CompositeData) value;
            CompositeType type = composite.getCompositeType();
            attrKeys = new LinkedList<String>(attrKeys);
            attrKeys.add(attrName);
            for(String key : type.keySet()) {
                String typ = type.getType(key).getTypeName();
                Object valu = composite.get(key);
                processBeanValue(
                        domain,
                        beanProperties,
                        attrKeys,
                        key,
                        typ,
                        type.getDescription(),
                        valu);
            }
        } 
        // 更复杂型对象
        else if (value instanceof TabularData) {
            // I don't pretend to have a good understanding of TabularData.
            // The real world usage doesn't appear to match how they were
            // meant to be used according to the docs. I've only seen them
            // used as 'key' 'value' pairs even when 'value' is itself a
            // CompositeData of multiple values.
            logScrape(domain + beanProperties + attrName, "tabulardata");
            TabularData tds = (TabularData) value;
            TabularType tt = tds.getTabularType();

            List<String> rowKeys = tt.getIndexNames();

            CompositeType type = tt.getRowType();
            Set<String> valueKeys = new TreeSet<String>(type.keySet());
            valueKeys.removeAll(rowKeys);

            LinkedList<String> extendedAttrKeys = new LinkedList<String>(attrKeys);
            extendedAttrKeys.add(attrName);
            for (Object valu : tds.values()) {
                if (valu instanceof CompositeData) {
                    CompositeData composite = (CompositeData) valu;
                    LinkedHashMap<String, String> l2s = new LinkedHashMap<String, String>(beanProperties);
                    for (String idx : rowKeys) {
                        Object obj = composite.get(idx);
                        if (obj != null) {
                            // Nested tabulardata will repeat the 'key' label, so
                            // append a suffix to distinguish each.
                            while (l2s.containsKey(idx)) {
                              idx = idx + "_";
                            }
                            l2s.put(idx, obj.toString());
                        }
                    }
                    for(String valueIdx : valueKeys) {
                        LinkedList<String> attrNames = extendedAttrKeys;
                        String typ = type.getType(valueIdx).getTypeName();
                        String name = valueIdx;
                        if (valueIdx.toLowerCase().equals("value")) {
                            // Skip appending 'value' to the name
                            attrNames = attrKeys;
                            name = attrName;
                        } 
                        processBeanValue(
                            domain,
                            l2s,
                            attrNames,
                            name,
                            typ,
                            type.getDescription(),
                            composite.get(valueIdx));
                    }
                } else {
                    logScrape(domain, "not a correct tabulardata format");
                }
            }
        } else if (value.getClass().isArray()) {
            logScrape(domain, "arrays are unsupported");
        } else {
            // 多半会返回不支持的对象然后得不到jmx监控值
            // mydomain_test{3=3, topic=aaa} java.util.Hashtable is not exported
            logScrape(domain + beanProperties, attrType + " is not exported");
        }
    }
    
    // 我们看下prometheus 对 mbeanName 的转换操作，会将各种特殊字符转换为 属性列表
    // io.prometheus.jmx.JmxMBeanPropertyCache#getKeyPropertyList
    public LinkedHashMap<String, String> getKeyPropertyList(ObjectName mbeanName) {
        LinkedHashMap<String, String> keyProperties = keyPropertiesPerBean.get(mbeanName);
        if (keyProperties == null) {
            keyProperties = new LinkedHashMap<String, String>();
            // 转化为 string 格式
            String properties = mbeanName.getKeyPropertyListString();
            // 此处为 prometheus 认识的格式，已经匹配上了
            Matcher match = PROPERTY_PATTERN.matcher(properties);
            while (match.lookingAt()) {
                keyProperties.put(match.group(1), match.group(2));
                properties = properties.substring(match.end());
                if (properties.startsWith(",")) {
                    properties = properties.substring(1);
                }
                match.reset(properties);
            }
            keyPropertiesPerBean.put(mbeanName, keyProperties);
        }
        return keyProperties;
    }
        // io.prometheus.jmx.JmxMBeanPropertyCache#PROPERTY_PATTERN
        private static final Pattern PROPERTY_PATTERN = Pattern.compile(
            "([^,=:\\*\\?]+)" + // Name - non-empty, anything but comma, equals, colon, star, or question mark
                    "=" +  // Equals
                    "(" + // Either
                    "\"" + // Quoted
                    "(?:" + // A possibly empty sequence of
                    "[^\\\\\"]*" + // Greedily match anything but backslash or quote
                    "(?:\\\\.)?" + // Greedily see if we can match an escaped sequence
                    ")*" +
                    "\"" +
                    "|" + // Or
                    "[^,=:\"]*" + // Unquoted - can be empty, anything but comma, equals, colon, or quote
                    ")");

 

六、 原理: jmx_exportor 为什么输出的格式是这样的？

　　prometheus 的数据格式如下，如何从埋点数据转换？

# HELP mydomain_test_value_Count Attribute exposed for management (mydomain_test<_=_value, topic=b_topic><>Count)
# TYPE mydomain_test_value_Count untyped
mydomain_test_value_Count{topic="b_topic",} 1.0
mydomain_test_value_Count{topic="a_topic",} 132.0

　　是一个输出格式问题，也是一协议问题。

  // io.prometheus.client.exporter.common.TextFormat#write004
  public static void write004(Writer writer, Enumeration<Collector.MetricFamilySamples> mfs) throws IOException {
    /* See http://prometheus.io/docs/instrumenting/exposition_formats/
     * for the output format specification. */
    while(mfs.hasMoreElements()) {
      Collector.MetricFamilySamples metricFamilySamples = mfs.nextElement();
      writer.write("# HELP ");
      writer.write(metricFamilySamples.name);
      writer.write(' ');
      writeEscapedHelp(writer, metricFamilySamples.help);
      writer.write('\n');

      writer.write("# TYPE ");
      writer.write(metricFamilySamples.name);
      writer.write(' ');
      writer.write(typeString(metricFamilySamples.type));
      writer.write('\n');

      for (Collector.MetricFamilySamples.Sample sample: metricFamilySamples.samples) {
        writer.write(sample.name);
        // 带 labelNames 的，依次输出对应的标签
        if (sample.labelNames.size() > 0) {
          writer.write('{');
          for (int i = 0; i < sample.labelNames.size(); ++i) {
            writer.write(sample.labelNames.get(i));
            writer.write("=\"");
            writeEscapedLabelValue(writer, sample.labelValues.get(i));
            writer.write("\",");
          }
          writer.write('}');
        }
        writer.write(' ');
        writer.write(Collector.doubleToGoString(sample.value));
        if (sample.timestampMs != null){
          writer.write(' ');
          writer.write(sample.timestampMs.toString());
        }
        writer.write('\n');
      }
    }
  }

 

　　done.