关于Prometheus监控的思考：多标签埋点及Mbean
等你归去来 人气:2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。
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>
<https://img.qb5200.com/download-x/dependency>
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_hotspot</artifactId>
<version>0.8.0</version>
<https://img.qb5200.com/download-x/dependency>
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_servlet</artifactId>
<version>0.8.0</version>
<https://img.qb5200.com/download-x/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>
<https://img.qb5200.com/download-x/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.iohttps://img.qb5200.com/download-x/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.
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