Prometheus監控有所思：多標簽埋點及Mbean

　　使用 grafana+prometheus+jmx 作為普通的監控手段，是比較有用的。我之前的文章介紹了相應的實現辦法。https://www.cnblogs.com/yougewe/p/11140129.html

　　但是，按照之前的實現，我們更多的只能是監控 單值型的數據，如請求量，tps 等等，對於復雜組合型的指標卻不容易監控。

　　這種情況一般帶有一定的業務屬性，比如想監控mq中的每個topic的消費情況，每類產品的實時訂單情況等等。當然，對於看過完整的 prometheus 的監控數據的同學來說，會覺得很正常，因為你會看到如下的數據:

# 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

　　這里面的 name 就是普通標簽嘛，同理於其他埋點咯。應該是可以實現的。

　　是的，prometheus 是方便實現這玩意的，但是我們之前不是使用 jmx_exportor 作為導出工具嘛，使用的埋點組件是 io.dropwizard.metrics:metrics-core 。

　　而它則是重在單值的監控，所以，用它我們是實現不了帶指標的數據的監控了。

　　那怎么辦呢？三個辦法！

1. 直接替換原有的 metrics-core 組件為 prometheus 的client 組件，因為官方是支持這種操作的;
2. 使用 prometheus-client 組件與 metrics-core 組件配合，各自使用各自的功能;
3. 自行實現帶標簽的埋點，這可能是基於 MBean 的;

 

　　以上這幾種方案，各有優劣。方案1可能改動太大，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沖突情況，當然如果能整合，絕對是最好的; 方案3實現復雜度就高了，比如監控值維護、線程安全、MBean數據吐出方式等等。

　　好吧，不管怎么樣，我們還是都看看吧。

 

一、 使用 prometheus-client 埋點實現帶標簽的監控

　　1. 引入 pom 依賴

        <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. 框架注冊監控

        @Configuration
        public class PrometheusConfig {
            @Bean
            public ServletRegistrationBean servletRegistrationBean(){
                // 將埋點指標吐出到 /metrics 節點
                return new ServletRegistrationBean(new MetricsServlet(), "/metrics");
            }
        }

　　3. 業務埋點數據

        // 注冊指標實例
        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. 獲取埋點數據信息

        curl http://localhost:8080/metrics
        # 對外暴露http接口調用，結果如下
        # 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，效果咱們是實現了。但是，對於已經運行的東西，要改這玩意可能不是那么友好。主要有以下幾點：

　　　　1. 暴露數據方式變更，原來由javaagent進行統一處理的數據，現在可能由於應用端口的不一，導致收集的配置會變更，不一定符合運維場景;
　　　　2. 需要將原來的埋點進行替換;

 

二、 prometheus-client 與 metrics-core 混合埋點

　　不處理以前的監控，將新監控帶標簽數據吐入到 jmx_exportor 中。

　　我們試着使用如上的埋點方式：

        // 注冊指標實例
        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
        }

　　好像數據是不會進入的到 jmx_exportor 的。這也不奇怪，畢竟咱們也不了解其原理，難道想靠運氣取勝？？

　　細去查看 metrics-core 組件的埋點實現方案，發現其是向 MBean 中吐入數據，從而被 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 注冊監控實例
        // 默認情況下 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 則是通過 CollectorRegistry.defaultRegistry 進行注冊實例的。

    // 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，好像原理上來講是不同的。至於到底為什么不能監控到數據，那還不好說。至少，你可以學習 metrics-core 使用 MBean 的形式將數據導出。這是我們下一個方案要討論的事。

　　這里我可以給到一個最終簡單又不失巧合的方式，實現兩個監控組件的兼容，同時向 jmx_exportor 進行導出。如下：

　　1. 引入 javaagent 依賴包

        <!-- javaagent 包，與 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 的工具類進行埋點

　　因為 javaagent 里面提供一套完整的 client 工具包，所以，我們可以使用。

        // 注冊指標實例
        // 將 io.prometheus.client.Counter 包替換為 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. 原樣使用 jmx_exportor 就可以導出監控數據了

　　為什么換一個包這樣就可以了？

　　因為 jmx_exportor 也是通過注冊 CollectorRegistry.defaultRegistry 來進行收集數據的，我們只要保持與其實例一致，就可以做到在同一個jvm內共享數據了。

 

三、 基於 MBean自行實現帶標簽的埋點

// 測試類
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 {
        // 模擬某個topic
        String commingTopic = "test_topic";
        AtomicInteger myTopic1Counter = getMetricCounter(commingTopic);
        System.out.println("jmx started!");
        while(true){
            System.out.println("---");
            // 計數增加
            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，雖然不知道什么意思，但是感覺很厲害的樣子
            tab.put("_", "_value");
            ObjectName objectName = new ObjectName("mydomain_test", tab);
            // 注冊監控實例 到 MBeanServer 中
            ObjectInstance objectInstance = mBeanServer.registerMBean(new JmxCounter(myTopic1Counter, objectName), objectName);
        }
        return myTopic1Counter;
    }
}
// JmxCounter, MBean 要求: 1. 接口必須定義成Public的;  2. 接口命名規范符合要求, 即接口名叫 XYZMBean ，那么實現名就必須一定是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 {
        // 返回監控結果
        return metric.get();
    }

}

　　最后，見證奇跡的時刻。結果如下：

# 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

　　很明顯，這是一個糟糕的實現，不要學他。僅為了演示效果。

　　所以，總結下來，自然是使用方案2了。兩個組件兼容，實現簡單，性能也不錯。如果只是為了使用，到此就可以了。不過你得明白，以上方案有取巧的成分在。

 

四、 原理: jmx_exportor 是如何獲取數據的？

　　jmx_exportor 也是可以通過 http_server 暴露數據。

    // 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 處理請求
        HttpHandler mHandler = new HTTPMetricHandler(registry);
        // 綁定到 /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，可以主要邏輯是 HTTPMetricHandler 處理。來看看。

        // 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);
            // 主要由該 TextFormat 進行格式化輸出
            // registry.filteredMetricFamilySamples() 進行數據收集
            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());
                // 寫向客戶端
                response.writeTo(t.getResponseBody());
            }
            t.close();
        }

    }

 

五、 原理: jmx_exportor 是如何獲取Mbean 的數據的？

　　jmx_exportor 有一個 JmxScraper, 專門用於處理 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;
        // 默認直接獲取本地的 jmx 信息
        // 即是通過共享 ManagementFactory.getPlatformMBeanServer() 變量來實現通信的
        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);
          }
          // 如果是遠程獲取，則會通過 rmi 進行遠程通信獲取
          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 {
            // 通過 MBean 調用對象，獲取所有屬性值，略去不說
            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");
            // 處理單個key的屬性值, 如 topic=aaa,ip=1 將會進行再次循環處理
            processBeanValue(
                    mbeanName.getDomain(),
                    // 獲取有效的屬性列表, 我們可以簡單看一下過濾規則, 如下文
                    jmxMBeanPropertyCache.getKeyPropertyList(mbeanName),
                    new LinkedList<String>(),
                    attr.getName(),
                    attr.getType(),
                    attr.getDescription(),
                    attribute.getValue()
            );
        }
    }
    // 處理每個 mBean 的屬性，寫入到 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");
        } 
        // 單值情況，數字型，字符串型，可以處理
        else if (value instanceof Number || value instanceof String || value instanceof Boolean) {
            logScrape(domain + beanProperties + attrName, value.toString());
            // 解析出的數據存入 receiver 中，可以是 jmx, 或者 控制台
            this.receiver.recordBean(
                    domain,
                    beanProperties,
                    attrKeys,
                    attrName,
                    attrType,
                    attrDescription,
                    value);
        } 
        // 多值型情況
        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);
            }
        } 
        // 更復雜型對象
        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 {
            // 多半會返回不支持的對象然后得不到jmx監控值
            // mydomain_test{3=3, topic=aaa} java.util.Hashtable is not exported
            logScrape(domain + beanProperties, attrType + " is not exported");
        }
    }
    
    // 我們看下prometheus 對 mbeanName 的轉換操作，會將各種特殊字符轉換為 屬性列表
    // 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>();
            // 轉化為 string 格式
            String properties = mbeanName.getKeyPropertyListString();
            // 此處為 prometheus 認識的格式，已經匹配上了
            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 為什么輸出的格式是這樣的？

　　prometheus 的數據格式如下，如何從埋點數據轉換？

# 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

　　是一個輸出格式問題，也是一協議問題。

  // 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);
        // 帶 labelNames 的，依次輸出對應的標簽
        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.