WillemJiang closed pull request #39: Update metrics doc for 1.0.0-m1 release URL: https://github.com/apache/incubator-servicecomb-website/pull/39
This is a PR merged from a forked repository. As GitHub hides the original diff on merge, it is displayed below for the sake of provenance: As this is a foreign pull request (from a fork), the diff is supplied below (as it won't show otherwise due to GitHub magic): diff --git a/_users/cn/metrics-in-1.0.0-m1.md b/_users/cn/metrics-in-1.0.0-m1.md index 9c93f3e..bd08c41 100644 --- a/_users/cn/metrics-in-1.0.0-m1.md +++ b/_users/cn/metrics-in-1.0.0-m1.md @@ -31,40 +31,41 @@ redirect_from: 4. 没有提供通用数据发布接口，难以和更多的第三方监控系统做集成； 5. 由于foundation-metrics模块过于底层，用户无法以可选的方式决定是否启用； -因此，从0.5.0版本升级到1.0.0-m1版本，我们进行了一次全面的重构，重构后的Metrics将分为如下几个模块 +因此，从0.5.0版本升级到1.0.0-m1版本，我们进行了一次全面的重构，重构后的Metrics将分为如下两个个模块 | Module名 | 描述 | | :------------------ | :------------------------------- | +| foundation-metrics | Metrics机制层模块，提供Metrics基础能力 | | metrics-core | Metrics核心模块，引入后即启用Metrics数据收集功能 | -| metrics-common | Metrics通用模块，主要包含Metric DTO用于数据发布 | -| metrics-extension | 包含Metrics的一些扩展功能 | | metrics-integration | 包含Metrics与其它三方系统集成 | -它们的依赖关系如下图所示： +它们的依赖关系如下图所示： +  ### 数据采集不再依赖Hystrix（handler-bizkeeper），使用事件埋点收集与调用相关的所有数据 -1.0.0-m1版本不再从Hystrix获取调用的TPS和Latency，避免了不配置Java Chassis Bizkeeper Handler就不会输出这两项数据的问题；使用foundation-common中的EventBus作为事件总线，metrics-core中的DefaultEventListenerManager初始化后会立即注册三个事件监听处理类： +1.0.0-m1版本不再从Hystrix获取调用的TPS和Latency，避免了不配置Java Chassis Bizkeeper Handler就不会输出这两项数据的问题；使用foundation-common中的EventBus作为事件总线，EventBus初始化的时候会通过SPI（Service Provider Interface）的机制将所有的EventListener注册进来，已实现的EventListener包括： -| 事件监听处理类名 | 功能 | +| 事件监听处理类名 | 功能 | | :------------------------------------- | :------------------------ | -| InvocationStartedEventListener | Consumer调用或Producer接收开始 | -| InvocationStartProcessingEventListener | Producer从队列中取出调用开始处理 | -| InvocationFinishedEventListener | Consumer调用返回或Producer处理完毕 | +| InvocationStartedEventListener | 处理Consumer调用或Producer接收开始时触发的InvocationStartedEvent | +| InvocationStartExecutionEventListener | 处理Producer从队列中取出调用开始处理时触发的InvocationStartExecutionEvent | +| InvocationFinishedEventListener | 处理Consumer调用返回或Producer处理完毕触发的InvocationFinishedEvent | -*特别说明，Java Chassis的Reactor框架基于[Vertx](http://vertx.io/)，在同步调用模式下，微服务Producer端收到Invocation后，并不会马上同步处理请求，而是将它放入一个处理队列中，Invocation在队列中的时间称为**LifeTimeInQueue**，队列的长度称为**waitInQueue**，这是衡量微服务压力的两个重要指标，可以参考操作系统磁盘读写队列的概念；Consumer端并不会有队列，因此永远不会触发InvocationStartProcessingEvent。* +*特别说明，Java Chassis的Reactor框架基于[Vertx](http://vertx.io/)，在同步调用模式下，微服务Producer端收到Invocation后，并不会马上同步处理请求，而是将它放入一个处理队列中，Invocation在队列中的时间称为**LifeTimeInQueue**，队列的长度称为**waitInQueue**，这是衡量微服务压力的两个重要指标，可以参考操作系统磁盘读写队列的概念；Consumer端并不会有队列，因此永远不会触发InvocationStartExecutionEvent。* 事件触发的代码分布在Java Chassis的RestInvocation、HighwayServerInvoke和InvokerUtils中，如果微服务没有启用Metrics，EventBus中就不会注册Metrics事件监听处理器，因此对性能的影响微乎其微。 ### 使用Netflix Servo作为Metric的计数器 -[Netflix Servo](https://github.com/Netflix/servo)具有性能极高的计数器（Monitor），我们使用了四种： +[Netflix Servo](https://github.com/Netflix/servo)具有性能极高的计数器（Monitor），我们使用了五种： -| Monitor名 | 描述 | +| Monitor名 | 描述 | | :----------- | :------------------------------- | -| BasicCounter | 基本累积计数器（永续累加） | +| BasicCounter | 基本累加计数器（永续累加） | | StepCounter | 周期累加计数器（以前曾经称为ResettableCounter） | -| MinGauge | 周期最小值计数器 | -| MaxGauge | 周期最大值计数器 | +| BasicTimer | 时间计数器 | +| BasicGauge | 基本计量器 | +| MaxGauge | 周期最大值计数器 | *依赖的Servo版本为0.10.1* @@ -79,40 +80,34 @@ Metrics有很多种分类方式，在技术实现上我们偏向以取值方式 c) 与个数相关的，比如累加平均值、方差等等； 获取此类Metrics的值，返回的是上一个周期的统计结果，具有一定的延后性。在Servo中，这个时间被称为[“Polling Intervals”](https://github.com/Netflix/servo/wiki/Getting-Started)。 从1.0.0-m1开始，可以通过microservice.yaml中的servicecomb.metrics.window_time配置设置周期，效果与servo.pollers一致。 -## Metric列表 -从1.0.0-m1开始，支持微服务Operation级别的Metric输出，列表如下： - -| Group | Level | Catalog | Metrics | Item | -| :---------- | :--------------------- | :------- | :-------------- | :------------- | -| servicecomb | instance | system | cpu | load | -| servicecomb | instance | system | cpu | runningThreads | -| servicecomb | instance | system | heap | init | -| servicecomb | instance | system | heap | max | -| servicecomb | instance | system | heap | commit | -| servicecomb | instance | system | heap | used | -| servicecomb | instance | system | nonHeap | init | -| servicecomb | instance | system | nonHeap | max | -| servicecomb | instance | system | nonHeap | commit | -| servicecomb | instance | system | nonHeap | used | -| servicecomb | instance | operationName | producer | waitInQueue | count | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | average | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | max | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | min | -| servicecomb | instance | operationName | producer | executionTime | average | -| servicecomb | instance | operationName | producer | executionTime | max | -| servicecomb | instance | operationName | producer | executionTime | min | -| servicecomb | instance | operationName | producer | producerLatency | average | -| servicecomb | instance | operationName | producer | producerLatency | max | -| servicecomb | instance | operationName | producer | producerLatency | min | -| servicecomb | instance | operationName | producer | producerCall | total | -| servicecomb | instance | operationName | producer | producerCall | tps | -| servicecomb | instance | operationName | consumer | consumerLatency | average | -| servicecomb | instance | operationName | consumer | consumerLatency | max | -| servicecomb | instance | operationName | consumer | consumerLatency | min | -| servicecomb | instance | operationName | consumer | consumerCall | total | -| servicecomb | instance | operationName | consumer | consumerCall | tps | - -**当Level的值是“instance”的时候，代表微服务实例级别的Metric，否则代表微服务具体Operation的Metric，operationName使用的是Java Chassis MicroserviceQualifiedName，它是微服务名.SchemaID.操作方法名的组合。** + +**提示：Servo已经被Netflix标记为DEPRECATED，我们将在1.0.0-m2中使用Netflix spectator替换，将不需要设置周期** + +## Metrics数据ID格式 +Java Chassis Metrics内置两种类型的Metric输出： +### JVM信息 +输出ID格式为：*jvm(statistic=gauge,name={name})* +name包括： + +| name | 描述 | +| :----------- | :------------------------------- | +| cpuLoad | CPU使用率 | +| cpuRunningThreads | 线程数 | +| heapInit,heapMax,heapCommit,heapUsed | 内存heap使用情况 | +| nonHeapInit,nonHeapMax,nonHeapCommit,nonHeapUsed | 内存nonHeap使用情况 | + +### Invocation信息 +输出ID格式为：*servicecomb.invocation(operation={operationName},role={role},stage={stage},statistic={statistic},status={status},unit={unit})* +标签含义及值如下： + +| Tag名 | 描述 | 值 | +| :---------- | :---------- | :--------------------- | +| operationName | Operation全名 | MicroserviceQualifiedName | +| role | Consumer端统计还是Producer端统计 |consume,producer | +| stage | 统计的阶段 |queue（在队列中，仅producer）,execution（执行阶段，仅producer）,total（整体） | +| statistic | 统计项 |tps,count（总调用次数）,max,waitInQueue（在队列中等待数，仅producer）,latency | +| status | 调用结果状态值 |200, 404等等| +| unit| 如果是时延统计，单位 | MILLISECONDS，SECONDS等等 | ## 如何配置 ### 全局配置 @@ -125,13 +120,10 @@ service_description: servicecomb: metrics: - #时间窗间隔，与servo.pollers设置效果一致，单位毫秒 - #支持多个时间窗间隔，使用逗号（,）将多个分隔开，例如5000,10000，代表设置两个时间窗 - window_time: 5000,10000 + #时间窗间隔，单位毫秒,默认为5000（5秒） + window_time: 5000 ``` -*时间窗设置对于统计结果获取的影响，附上代码中包含的一段注释如下：* - - +**为了降低Metrics理解和使用难度，我们暂时不支持多周期** ### 依赖配置 只需要添加metrics-core依赖即可： @@ -143,43 +135,21 @@ servicecomb: </dependency> ``` -## 数据发布 +## 如何获取数据 配置好Metrics后，你可以通过如下两种方式获取Metrics数据： -### 内置的发布接口 +### 通过发布接口获取 当微服务启动后，metrics-core会自动以Springmvc的方式发布服务： ```java @RestSchema(schemaId = "metricsEndpoint") @RequestMapping(path = "/metrics") -public class DefaultMetricsPublisher implements MetricsPublisher { - - private final DataSource dataSource; - - public DefaultMetricsPublisher(DataSource dataSource) { - this.dataSource = dataSource; - } - - @RequestMapping(path = "/appliedWindowTime", method = RequestMethod.GET) - @CrossOrigin - @Override - public List<Long> getAppliedWindowTime() { - return dataSource.getAppliedWindowTime(); - } - - @RequestMapping(path = "/", method = RequestMethod.GET) - @CrossOrigin - @Override - public RegistryMetric metrics() { - return dataSource.getRegistryMetric(); - } - +public class MetricsPublisher { @ApiResponses({ @ApiResponse(code = 400, response = String.class, message = "illegal request content"), }) - @RequestMapping(path = "/{windowTime}", method = RequestMethod.GET) + @RequestMapping(path = "/", method = RequestMethod.GET) @CrossOrigin - @Override - public RegistryMetric metricsWithWindowTime(@PathVariable(name = "windowTime") long windowTime) { - return dataSource.getRegistryMetric(windowTime); + public Map<String, Double> measure() { + return MonitorManager.getInstance().measure(); } } ``` @@ -193,14 +163,123 @@ cse: address: 0.0.0.0:8080 ``` 你就可以通过http://localhost:8080/metrics 直接获取到数据，打开浏览器输入此URL就可以看到返回结果。 -### 直接代码获取 -从上面的代码可以看到，数据提供Bean接口是org.apache.servicecomb.metrics.core.publish.DataSource，因此如果你希望自己开发数据发布程序，只需要注入它即可。 + +### 直接获取 +从上面的代码可以看到，数据提供的入口是org.apache.servicecomb.metrics.core.MonitorManager，因此如果你希望自己开发数据发布程序，只需要获取它即可。 ```java -@Autowired -private DataSource dataSource; +MonitorManager manager = MonitorManager.getInstance(); +Map<String, Double> metrics = manager.measure(); ``` +**提示：Servo已经被Netflix标记为DEPRECATED，我们将在1.0.0-m2中使用Netflix spectator替换，获取数据的接口会有调整** + +## 如何使用数据 +Metrics数据将以Map<String, Double>的形式输出，为了能够方便用户获取指定Metric的值，提供了org.apache.servicecomb.foundation.metrics.publish.MetricsLoader工具类： +```java + //模拟MonitorManager.getInstance().measure()获取所有的Metrics值 + Map<String, Double> metrics = new HashMap<>(); + metrics.put("X(K1=1,K2=2,K3=3)", 100.0); + metrics.put("X(K1=1,K2=20,K3=30)", 200.0); + metrics.put("X(K1=2,K2=200,K3=300)", 300.0); + metrics.put("X(K1=2,K2=2000,K3=3000)", 400.0); + + metrics.put("Y(K1=1,K2=2,K3=3)", 500.0); + metrics.put("Y(K1=10,K2=20,K3=30)", 600.0); + metrics.put("Y(K1=100,K2=200,K3=300)", 700.0); + metrics.put("Y(K1=1000,K2=2000,K3=3000)", 800.0); + + //创建一个MetricsLoader加载所有的Metrics值 + MetricsLoader loader = new MetricsLoader(metrics); + + //获取name为X的所有Metrics并且按K1,K2两个Tag层次分组 + MetricNode node = loader.getMetricTree("X","K1","K2"); + + //获取K1=1且K2=20的所有Metrics，因为node是按K1和K2的层次分组的 + node.getChildrenNode("1").getChildrenNode("20").getMetrics(); + + //从层次结构中通过Tag匹配获取Metric的值 + node.getChildrenNode("1").getChildrenNode("20").getFirstMatchMetricValue("K3","30"); +``` +*demo/perf/PerfMetricsFilePublisher.java提供了MetricsLoader更详细的使用示例* + +## 如何扩展 +Java Chassis Metrics支持自定义Metrics扩展，MonitorManager包含一组获取各类Monitor的方法： + +| 方法名 | 描述 | +| :---------- | :---------- | +| getCounter | 获取一个计数器类的Monitor | +| getMaxGauge | 获取一个最大值统计Monitor | +| getGauge | 获取基本计量Monitor | +| getTimer | 获取一个时间计数器类的Monitor | + +以处理订单这个场景为例： +```java +public class OrderController { + private final Counter orderCount; + private final Counter orderTps; + private final Timer averageLatency; + private final MaxGauge maxLatency; + + OrderController() { + MonitorManager manager = MonitorManager.getInstance(); + //"商品名","levis jeans"与"型号","512" 是两个自定义Tag的name和value，支持定义多Tag + this.orderCount = manager.getCounter("订单数量", "商品名", "levis jeans", "型号", "512"); + this.orderTps = manager.getCounter(StepCounter::new, "生成订单", "统计项", "事务每秒"); + this.averageLatency = manager.getTimer("生成订单", "统计项", "平均生成时间", "单位", "MILLISECONDS"); + this.maxLatency = manager.getMaxGauge("生成订单", "统计项", "最大生成时间", "单位", "MILLISECONDS"); + } + + public void makeOrder() { + long startTime = System.nanoTime(); + //处理订单逻辑 + //... + //处理完毕 + long totalTime = System.nanoTime() - startTime； + + //增加订单数量 + this.orderCount.increment(); + + //更新Tps + this.orderTps.increment(); + + //记录订单生成处理时间 + this.averageLatency.record(totalTime, TimeUnit.NANOSECONDS); + + //记录最大订单生成时间，因为惯用毫秒作为最终输出，因此我们转换一下单位 + this.maxLatency.update(TimeUnit.NANOSECONDS.toMillis(totalTime)); + } +} +``` + +注意事项： +1. 通过MonitorManager获取Monitor传递name和tag数组，最终输出的ID是它们连接后的字符串，所以请保持唯一性，上面的例子输出的Metrics为： +```java +Map<String,Double> metrics = MonitorManager.getInstance().measure(); + +//metrics的keySet()将包含： +// 订单数量(商品名=levis jeans,型号=512) +// 生成订单(统计项=事务每秒) +// 生成订单(统计项=平均生成时间,单位=MILLISECONDS) +// 生成订单(统计项=最大生成时间,单位=MILLISECONDS) +``` + +2. MonitorManager获取Monitor的方法均为**获取或创建**，因此多次传递相同的name和tag数组返回的是同一个计数器： +```java + Counter counter1 = MonitorManager.getInstance().getCounter("订单数量", "商品名", "levis jeans", "型号", "512"); + Counter counter2 = MonitorManager.getInstance().getCounter("订单数量", "商品名", "levis jeans", "型号", "512"); + + counter1.increment(); + counter2.increment(); + + Assert.assertEquals(2,counter1.getValue()); + Assert.assertEquals(2,counter2.getValue()); + Assert.assertEquals(2.0,MonitorManager.getInstance().measure().get("订单数量(商品名=levis jeans,型号=512)"),0); +``` +**获取Monitor的方法性能较低，请在初始化阶段一次获取所需的Monitor，然后将它们缓存起来，请参照前面OrderController的做法。** + +**提示：Servo已经被Netflix标记为DEPRECATED，我们将在1.0.0-m2中使用Netflix spectator替换，扩展自定义Metrics的方式会有调整** + ## 参考示例 我们已经开发完成了两个使用场景可以作为参考： -1. metrics-wirte-file：将Metrics数据写入文件，代码在metrics-extension中； -2. metrics-prometheus：将Metrics发布为prometheus Producer。 +1. demo/perf：在Console里打印Metrics； +2. metrics-prometheus：将Metrics发布为prometheus Producer。 \ No newline at end of file diff --git a/_users/cn/metrics-integration-with-prometheus-in-1.0.0-m1.md b/_users/cn/metrics-integration-with-prometheus-in-1.0.0-m1.md index 559ad62..b5100dc 100644 --- a/_users/cn/metrics-integration-with-prometheus-in-1.0.0-m1.md +++ b/_users/cn/metrics-integration-with-prometheus-in-1.0.0-m1.md @@ -35,51 +35,6 @@ Prometheus推荐Pull模式拉取Metrics数据，被监控微服务作为Producer </dependency> ``` 因此一旦集成Prometheus引入了metrics-prometheus依赖后，不再需要添加metrics-core的依赖。 -### 与metrics-core Publish的关系 -文档[1.0.0-m1版本中的监控](/cn/users/metrics-in-1.0.0-m1/)中已经提到，metrics-core会伴随微服务启动内置的数据发布，如果你在microservice.yaml中配置了rest provider，例如： -```yaml -cse: - service: - registry: - address: http://127.0.0.1:30100 - rest: - address: 0.0.0.0:8080 -``` -你就可以通过http://localhost:8080/metrics 直接获取到Metrics数据，它返回的是org.apache.servicecomb.metrics.common.RegistryMetric实体对象，输出格式为： -```json -{"instanceMetric":{ -"systemMetric":{"cpuLoad":10.0,"cpuRunningThreads":39,"heapInit":266338304,"heapMax":3786407936,"heapCommit":626524160,"heapUsed":338280024,"nonHeapInit":2555904,"nonHeapMax":-1,"nonHeapCommit":60342272,"nonHeapUsed":58673152}, -"consumerMetric":{"operationName":"instance","prefix":"servicecomb.instance.consumer","consumerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"consumerCall":{"total":0,"tps":0.0}}, -"producerMetric":{"operationName":"instance","prefix":"servicecomb.instance.producer","waitInQueue":0,"lifeTimeInQueue":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"executionTime":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerCall":{"total":1,"tps":0.0}}}, -"consumerMetrics":{}, -"producerMetrics":{"calculator.metricsEndpoint.metrics":{"operationName":"calculator.metricsEndpoint.metrics","prefix":"servicecomb.calculator.metricsEndpoint.metrics.producer","waitInQueue":0,"lifeTimeInQueue":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"executionTime":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerCall":{"total":1,"tps":0.0}} -}} -``` -使用Prometheus Simple HTTP Server接口发布的数据是Prometheus采集的标准格式： -```text -# HELP Instance Level Instance Level Metrics -# TYPE Instance Level untyped -servicecomb_instance_producer_producerLatency_average 0.0 -servicecomb_instance_producer_producerLatency_total 0.0 -servicecomb_instance_consumer_producerLatency_count 0.0 -... -servicecomb_instance_producer_producerLatency_min 0.0 -servicecomb_instance_producer_lifeTimeInQueue_average 0.0 -servicecomb_instance_producer_lifeTimeInQueue_count 0.0 -servicecomb_instance_system_heap_init 2.66338304E8 -# HELP calculator.metricsEndpoint.metrics Producer Side calculator.metricsEndpoint.metrics Producer Side Metrics -# TYPE calculator.metricsEndpoint.metrics Producer Side untyped -servicecomb_calculator_metricsEndpoint_metrics_producer_lifeTimeInQueue_average 0.0 -... -servicecomb_calculator_metricsEndpoint_metrics_producer_executionTime_total 0.0 -servicecomb_calculator_metricsEndpoint_metrics_producer_waitInQueue_count 0.0 -servicecomb_calculator_metricsEndpoint_metrics_producer_lifeTimeInQueue_count 0.0 -``` -所以它们两个是完全独立各有用途的。 - -*Prometheus Simple HTTP Server同样使用/metrics作为默认URL，metrics-prometheus会使用9696作为默认端口，因此微服务启动后你可以使用http://localhost:9696/metrics 访问它。* - -我们可以看到在Prometheus的Metric命名统一使用下划线代替了点，因为需要遵守它的[命名规则](https://prometheus.io/docs/practices/naming/)。 ## 如何配置 开启对接普罗米修斯非常简单： @@ -129,6 +84,32 @@ scrape_configs: - targets: ['localhost:9696'] ``` 其中job_name: 'servicecomb'即自定义的job配置，目标是本地微服务localhost:9696，关于prometheus.yml的配置更多信息可以参考[这篇文章](https://prometheus.io/docs/prometheus/latest/configuration/configuration/)。 + +### 验证输出 +Prometheus Simple HTTP Server使用/metrics作为默认URL，metrics-prometheus会使用9696作为默认端口，微服务启动后你可以使用http://localhost:9696/metrics 访问它。 +使用Prometheus Simple HTTP Server接口发布的数据是Prometheus采集的标准格式： +```text +# HELP ServiceComb Metrics ServiceComb Metrics +# TYPE ServiceComb Metrics untyped +jvm{name="cpuRunningThreads",statistic="gauge",} 45.0 +jvm{name="heapMax",statistic="gauge",} 3.786407936E9 +jvm{name="heapCommit",statistic="gauge",} 6.12892672E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="max",status="200",unit="MILLISECONDS",} 1.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="tps",status="200",} 0.4 +jvm{name="nonHeapCommit",statistic="gauge",} 6.1104128E7 +jvm{name="nonHeapInit",statistic="gauge",} 2555904.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="execution",statistic="max",status="200",unit="MILLISECONDS",} 0.0 +jvm{name="heapUsed",statistic="gauge",} 2.82814088E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="latency",status="200",unit="MILLISECONDS",} 1.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="execution",statistic="latency",status="200",unit="MILLISECONDS",} 0.0 +jvm{name="heapInit",statistic="gauge",} 2.66338304E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="waitInQueue",} 0.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="count",status="200",} 39.0 +jvm{name="nonHeapUsed",statistic="gauge",} 5.9361032E7 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="latency",status="200",unit="MILLISECONDS",} 0.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="max",status="200",unit="MILLISECONDS",} 0.0 +``` + ### 配置Grafana（可选） 如何在Grafana中添加Prometheus作为数据源请参考[这篇文章](https://prometheus.io/docs/visualization/grafana/)。 ## 运行效果 diff --git a/_users/cn/metrics-write-file-extension-and-sample-in-1.0.0-m1.md b/_users/cn/metrics-write-file-extension-and-sample-in-1.0.0-m1.md index 81de0f1..bb9056e 100644 --- a/_users/cn/metrics-write-file-extension-and-sample-in-1.0.0-m1.md +++ b/_users/cn/metrics-write-file-extension-and-sample-in-1.0.0-m1.md @@ -90,7 +90,7 @@ Java Chassis集成了Spring Boot Starter，如果使用Spring Boot Starter启动 Spring Boot Starter中包含了log4j-over-slf4j，这个Log Bridge并没有完全实现log4j的所有接口，包括RollingFileAppender，所以我们需要排除它让slf4j直接调用log4j而不是这个Log Bridge，请确定这种排除对你的系统不会有影响，关于log4j-over-slf4j的更多信息可以参考[这篇文章](https://www.slf4j.org/legacy.html#log4j-over-slf4j)。 ## 运行示例 -metrics-write-file-config-log4j-springboot和metrics-write-file-config-log4j2-springboot都是可以直接运行的示例项目，使用ServiceApplication启动完成后，观察输出目录target/metric/下会生成很多Metrics文件，如果在浏览器中刷新几下http://localhost:8080/f 请求，则可以看到对应的Operation级别的Metrics文件也会在目录下自动生成。 +metrics-write-file-config-log4j-springboot和metrics-write-file-config-log4j2-springboot都是可以直接运行的示例项目，使用ServiceApplication启动完成后，观察输出目录target/metric/下会生成很多Metrics文件，如果在浏览器中刷新几下http://localhost:8080/f 请求，则可以看到对应的Operation的Metrics文件也会在目录下自动生成。  ## Q & A diff --git a/_users/cn/setup-environment.md b/_users/cn/setup-environment.md index ea0060f..11d9edb 100644 --- a/_users/cn/setup-environment.md +++ b/_users/cn/setup-environment.md @@ -31,19 +31,19 @@ last_modified_at: 2017-09-03T10:01:43-04:00 <div class="tab-content"> <div id="windows" class="tab-pane active" markdown="1"> - 1. 下载[服务注册中心可执行文件压缩包](https://github.com/apache/incubator-servicecomb-service-center/releases/download/0.5.0/service-center-0.5.0-windows-amd64.zip) + 1. 下载[服务注册中心可执行文件压缩包](http://apache.org/dyn/closer.cgi/incubator/servicecomb/incubator-servicecomb-service-center/1.0.0-m1/apache-servicecomb-incubating-service-center-1.0.0-m1-windows-amd64.tar.gz) 2. 解压缩到当前文件夹 - 3. 进入解压缩后的目录，然后双击运行**start**文件 + 3. 进入解压缩后的目录，然后双击运行**start-service-center.bat**文件 </div> <div id="linux" class="tab-pane fade" markdown="1"> 1. 下载服务注册中心可执行文件压缩包并解压缩 ```bash - wget https://github.com/apache/incubator-servicecomb-service-center/releases/download/0.5.0/service-center-0.5.0-linux-amd64.tar.gz - tar xvf service-center-0.5.0-linux-amd64.tar.gz + wget http://apache.org/dyn/closer.cgi/incubator/servicecomb/incubator-servicecomb-service-center/1.0.0-m1/apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64.tar.gz + tar xvf apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64.tar.gz ``` 2. 运行服务注册中心 ```bash - bash service-center-0.5.0-linux-amd64/start.sh + bash apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64/start-service-center.sh ``` </div> </div> diff --git a/_users/metrics-in-1.0.0-m1.md b/_users/metrics-in-1.0.0-m1.md index 38f042a..f46d845 100644 --- a/_users/metrics-in-1.0.0-m1.md +++ b/_users/metrics-in-1.0.0-m1.md @@ -35,35 +35,36 @@ So,upgrading from 0.5.0 to 1.0.0-m1,we had done a fully reconstruction,now it's | Module Name | Description | | :------------------ | :--------------------------------------- | +| foundation-metrics | Metrics mechanism module | | metrics-core | Metrics core module,work immediately after imported | -| metrics-common | Metrics common module,include DTO classes | -| metrics-extension | Include some metrics extension module | | metrics-integration | Include metrics Integration with other monitor system | -The dependency of this modules is: +The dependency of this modules is: +  ### Use event collect invocation data,not from Hystrix(handler-bizkeeper)any more -From 1.0.0-m1 invocation data such as TPS and latency are collected from invocation event,not from Hystrix（handler-bizkeeper） any more,so you don't need add Java Chassis Bizkeeper Handler only for metrics.we use EventBus in foundation-common,when DefaultEventListenerManager in metrics-core had initialized,three event listener class will be auto registered: +From 1.0.0-m1 invocation data such as TPS and latency are collected from invocation event,not from Hystrix（handler-bizkeeper） any more,so you don't need add Java Chassis Bizkeeper Handler only for metrics.we use EventBus in foundation-common,when EventBus had initialized,three build-in event listener class will be auto registered via SPI(Service Provider Interface): | Event Listener Name | Description | | :------------------------------------- | :--------------------------------------- | -| InvocationStartedEventListener | Trigger when consumer or producer called | -| InvocationStartProcessingEventListener | Trigger when producer fetch invocation from queue and start process | -| InvocationFinishedEventListener | Trigger when consumer call returned or producer process finished | +| InvocationStartedEventListener | Process InvocationStartedEvent when consumer or producer called | +| InvocationStartExecutionEventListener | Process InvocationStartExecutionEvent when producer fetch invocation from queue and start process | +| InvocationFinishedEventListener | Process InvocationFinishedEvent when consumer call returned or producer process finished | *ServiceComb java chassis had used [Vertx](http://vertx.io/) as Reactor framework,in synchronous call mode when producer received invocation from consumer,it won't start process immediately but put it into a queue,this queue called invocation queue(like disk queue in operation system),time waiting in the queue called **LifeTimeInQueue**,the length of the queue called **waitInQueue**,this two metrics are very important for measure stress of the microservice;consumer not has this queue,so InvocationStartProcessingEvent will never be triggered at consumer side.* -The code for trigger event write in RestInvocation,HighwayServerInvoke and InvokerUtils,if microservice don't import metrics,event listener of metrics won't be registered,the impact on performance is little. +The code for trigger event write in RestInvocation,HighwayServerInvoke and InvokerUtils,if microservice don't import metrics,event listeners of metrics won't be registered,the impact on performance is little. ### Use Netflix Servo as Monitor of Metric -[Netflix Servo](https://github.com/Netflix/servo) had implement a collection of high performance monitor,we had used four of them: +[Netflix Servo](https://github.com/Netflix/servo) had implement a collection of high performance monitor,we had used five of them: | Monitor Name | Description | | :----------- | :-------------------------------- | | BasicCounter | As name of it,always increment | | StepCounter | Called 'ResettableCounter' before | -| MinGauge | Mark min value in step | +| BasicTimer | Time (Latency) monitor | +| BasicGauge | Return a Callable call result monitor | | MaxGauge | Mark max value in step | *The version of Servo we used is 0.10.1* @@ -80,42 +81,35 @@ Metrics had many classifications,we can divided them into two major types by how If get value of this type,the result returned is the last 'Step Cycle' counted.in Servo,this time called ['Polling Intervals'](https://github.com/Netflix/servo/wiki/Getting-Started). From 1.0.0-m1,can set **servicecomb.metrics.window_time** in microservice.yaml,it has same effect as set **servo.pollers**. -## Metric List -From 1.0.0-m1,start support output metrics of operation level: - -| Group | Level | Catalog | Metrics | Item | -| :---------- | :--------------------- | :------- | :-------------- | :------------- | -| servicecomb | instance | system | cpu | load | -| servicecomb | instance | system | cpu | runningThreads | -| servicecomb | instance | system | heap | init | -| servicecomb | instance | system | heap | max | -| servicecomb | instance | system | heap | commit | -| servicecomb | instance | system | heap | used | -| servicecomb | instance | system | nonHeap | init | -| servicecomb | instance | system | nonHeap | max | -| servicecomb | instance | system | nonHeap | commit | -| servicecomb | instance | system | nonHeap | used | -| servicecomb | instance | operationName | producer | waitInQueue | count | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | average | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | max | -| servicecomb | instance | operationName | producer | lifeTimeInQueue | min | -| servicecomb | instance | operationName | producer | executionTime | average | -| servicecomb | instance | operationName | producer | executionTime | max | -| servicecomb | instance | operationName | producer | executionTime | min | -| servicecomb | instance | operationName | producer | producerLatency | average | -| servicecomb | instance | operationName | producer | producerLatency | max | -| servicecomb | instance | operationName | producer | producerLatency | min | -| servicecomb | instance | operationName | producer | producerCall | total | -| servicecomb | instance | operationName | producer | producerCall | tps | -| servicecomb | instance | operationName | consumer | consumerLatency | average | -| servicecomb | instance | operationName | consumer | consumerLatency | max | -| servicecomb | instance | operationName | consumer | consumerLatency | min | -| servicecomb | instance | operationName | consumer | consumerCall | total | -| servicecomb | instance | operationName | consumer | consumerCall | tps | - -**When the value of Level is 'instance',it's means microservice instance metric,otherwise specific operation metric,operationName same as Java Chassis MicroserviceQualifiedName,it's joined with microservice appId.SchemaID.methodName.** - -## How Configuration +**Notice: Servo had marked with DEPRECATED by Netflix, we will use Netflix spectator instead in 1.0.0-m2, no need to set the window_time any more** + +## Metrics ID Format +From 1.0.0-m1,build-in two type Metric output: +### JVM Information +ID format is : *jvm(statistic=gauge,name={name})* +name include: + +| Name | Description | +| :----------- | :------------------------------- | +| cpuLoad | CPU load rate | +| cpuRunningThreads | Running thread count | +| heapInit,heapMax,heapCommit,heapUsed | Memory heap usage | +| nonHeapInit,nonHeapMax,nonHeapCommit,nonHeapUsed | Memory nonHeap usage | + +### Invocation Information +ID format is : *servicecomb.invocation(operation={operationName},role={role},stage={stage},statistic={statistic},status={status},unit={unit})* +Tag name and value below: + +| Tag Name | Description | Options or Values | +| :---------- | :---------- | :--------------------- | +| operationName | Operation full name | MicroserviceQualifiedName | +| role | Consumer side or Producer side |consume,producer | +| stage | Stage of metrics | queue(producer only),execution(producer only,total | +| statistic | Normally metric type | tps,count(total call count),max,waitInQueue(producer),latency | +| status | Call result code | 200, 404 etc..| +| unit | TimeUint of latency | MILLISECONDS，SECONDS etc.. | + +## How to Configuration ### Global Configuration Please add window time config in microservice.yaml: ```yaml @@ -126,14 +120,10 @@ service_description: servicecomb: metrics: - #window time,same as servo.pollers,unit is millisecond - #support multi window time and use ',' split them,like 5000,10000 - window_time: 5000,10000 + #window time,same as servo.pollers,unit is millisecond,default value is 5000 (5 seconds) + window_time: 5000 ``` - -*The setting of window time is very important to getting value of metrics,here is a comment show how it effect* - - +**In order to decrease difficulty for understand and usage of metrics,we temporary do not support multi window time** ### Maven Configuration We just only need add metrics-core dependency: @@ -145,43 +135,21 @@ We just only need add metrics-core dependency: </dependency> ``` -## Metrics Publish -After configuration completed,you can get collected metrics data via this method: +## How to Get Metrics Data +After configuration completed,you can get collected metrics data via this two method: ### Embedded publish interface When microservice start-up,metrics-core will auto publish data service using Springmvc provider: ```java @RestSchema(schemaId = "metricsEndpoint") @RequestMapping(path = "/metrics") -public class DefaultMetricsPublisher implements MetricsPublisher { - - private final DataSource dataSource; - - public DefaultMetricsPublisher(DataSource dataSource) { - this.dataSource = dataSource; - } - - @RequestMapping(path = "/appliedWindowTime", method = RequestMethod.GET) - @CrossOrigin - @Override - public List<Long> getAppliedWindowTime() { - return dataSource.getAppliedWindowTime(); - } - - @RequestMapping(path = "/", method = RequestMethod.GET) - @CrossOrigin - @Override - public RegistryMetric metrics() { - return dataSource.getRegistryMetric(); - } - +public class MetricsPublisher { @ApiResponses({ @ApiResponse(code = 400, response = String.class, message = "illegal request content"), }) - @RequestMapping(path = "/{windowTime}", method = RequestMethod.GET) + @RequestMapping(path = "/", method = RequestMethod.GET) @CrossOrigin - @Override - public RegistryMetric metricsWithWindowTime(@PathVariable(name = "windowTime") long windowTime) { - return dataSource.getRegistryMetric(windowTime); + public Map<String, Double> measure() { + return MonitorManager.getInstance().measure(); } } ``` @@ -195,14 +163,122 @@ cse: address: 0.0.0.0:8080 ``` You can open a browser and input http://localhost:8080/metrics direct get metrics data. + ### Direct programming get -From above code you can known,the interface of data provider bean is org.apache.servicecomb.metrics.core.publish.DataSource,so if you want develop your own metrics publisher,autowired it is enough. +From above code you can known,the entry of data provider is org.apache.servicecomb.metrics.core.MonitorManager,so if you want develop your own metrics publisher,direct get it is enough. +```java +MonitorManager manager = MonitorManager.getInstance(); +Map<String, Double> metrics = manager.measure(); +``` + +**Notice: Servo had marked with DEPRECATED by Netflix, we will use Netflix spectator instead in 1.0.0-m2, publish interface will be adjusted** + +## How to Use Metrics Data +Metrics data will output as Map<String,Double>,in order to let user easier fetch certain metric value,we provide org.apache.servicecomb.foundation.metrics.publish.MetricsLoader tool class: +```java + //simulate MonitorManager.getInstance().measure() get all metrics data + Map<String, Double> metrics = new HashMap<>(); + metrics.put("X(K1=1,K2=2,K3=3)", 100.0); + metrics.put("X(K1=1,K2=20,K3=30)", 200.0); + metrics.put("X(K1=2,K2=200,K3=300)", 300.0); + metrics.put("X(K1=2,K2=2000,K3=3000)", 400.0); + + metrics.put("Y(K1=1,K2=2,K3=3)", 500.0); + metrics.put("Y(K1=10,K2=20,K3=30)", 600.0); + metrics.put("Y(K1=100,K2=200,K3=300)", 700.0); + metrics.put("Y(K1=1000,K2=2000,K3=3000)", 800.0); + + //new MetricsLoader load all metrics data + MetricsLoader loader = new MetricsLoader(metrics); + + //get name of 'X' Metrics then group by K1,K2 + MetricNode node = loader.getMetricTree("X","K1","K2"); + + //get all Metrics of K1=1 and K2=20 + node.getChildrenNode("1").getChildrenNode("20").getMetrics(); + + //get K3=30 Metric from node + node.getChildrenNode("1").getChildrenNode("20").getFirstMatchMetricValue("K3","30"); +``` +*More detail can be found in demo/perf/PerfMetricsFilePublisher.java* + +## How to Extend Custom Metrics +Java Chassis Metrics support user extend custom metrics,MonitorManager had a set of method get different type of Monitor: + +| Method Name | Description | +| :---------- | :---------- | +| getCounter | Get a counter monitor | +| getMaxGauge | Get a max monitor | +| getGauge | Get a gauge monitor | +| getTimer | Get a timer monitor | + +Let us use Process Order make an example: ```java -@Autowired -private DataSource dataSource; +public class OrderController { + private final Counter orderCount; + private final Counter orderTps; + private final Timer averageLatency; + private final MaxGauge maxLatency; + + OrderController() { + MonitorManager manager = MonitorManager.getInstance(); + //"product","levis jeans" and "model","512" are two custom Tag,support multi Tags + this.orderCount = manager.getCounter("orderCount", "product", "levis jeans", "model", "512"); + this.orderTps = manager.getCounter(StepCounter::new, "orderGenerated", "statistic", "tps"); + this.averageLatency = manager.getTimer("orderGenerated", "statistic", "latency", "unit", "MILLISECONDS"); + this.maxLatency = manager.getMaxGauge("orderGenerated", "statistic", "max", "unit", "MILLISECONDS"); + } + + public void makeOrder() { + long startTime = System.nanoTime(); + //process order logic + //... + //process finished + long totalTime = System.nanoTime() - startTime; + + //increase order count + this.orderCount.increment(); + + //increase tps + this.orderTps.increment(); + + //record latency for average + this.averageLatency.record(totalTime, TimeUnit.NANOSECONDS); + + //record max latency + this.maxLatency.update(TimeUnit.NANOSECONDS.toMillis(totalTime)); + } +} +``` +Notice: +1. Metric ID is join name and all tags that pass to MonitorManager when getting monitor,so please keep uniqueness,metrics output of front example are: +```java +Map<String,Double> metrics = MonitorManager.getInstance().measure(); + +//metrics.keySet() include： +// orderCount(product=levis jeans,model=512) +// orderGenerated(statistic=tps) +// orderGenerated(statistic=latency,unit=MILLISECONDS) +// orderGenerated(statistic=max,unit=MILLISECONDS) ``` +2. All get monitor method in MonitorManager act as **get or new**,so use same name and tags will return same one monitor: +```java + Counter counter1 = MonitorManager.getInstance().getCounter("orderGenerated", "product", "levis jeans", "model", "512"); + Counter counter2 = MonitorManager.getInstance().getCounter("orderGenerated", "product", "levis jeans", "model", "512"); + + counter1.increment(); + counter2.increment(); + + Assert.assertEquals(2,counter1.getValue()); + Assert.assertEquals(2,counter2.getValue()); + Assert.assertEquals(2.0,MonitorManager.getInstance().measure().get("orderGenerated(product=levis jeans,model=512)"),0); +``` +**Performance of get monitor from MonitorManager is slightly lower,so please get all monitors what needed when init,then cache them for later use,like OrderController example.** + +**Notice: Servo had marked with DEPRECATED by Netflix, we will use Netflix spectator instead in 1.0.0-m2, the way of extending custom metrics will be adjusted** + ## Other Reference We had developed two use case for reference: -1. metrics-wirte-file:ouput metrics data into files,code is at metrics-extension; +1. demo/perf:print Metrics in Console; 2. metrics-prometheus:integration with prometheus,publish metrics as prometheus producer. \ No newline at end of file diff --git a/_users/metrics-integration-with-prometheus-in-1.0.0-m1.md b/_users/metrics-integration-with-prometheus-in-1.0.0-m1.md index 657789a..e78275c 100644 --- a/_users/metrics-integration-with-prometheus-in-1.0.0-m1.md +++ b/_users/metrics-integration-with-prometheus-in-1.0.0-m1.md @@ -35,50 +35,6 @@ As an integration(optional) module,the implementation code is in metrics-integra </dependency> ``` So if we import metrics-prometheus,no longer need to add metrics-core dependence. -### Relation between metrics-core Publish -[Metrics in 1.0.0-m1](/users/metrics-in-1.0.0-m1/) had already been mentioned,metrics-core will auto start up a embedded publish interface,so if you had configured rest provider in microservice.yaml like: -```yaml -cse: - service: - registry: - address: http://127.0.0.1:30100 - rest: - address: 0.0.0.0:8080 -``` -You can direct get metrics data at http://localhost:8080/metrics ,it will return a entity of org.apache.servicecomb.metrics.common.RegistryMetric,the output is: -```json -{"instanceMetric":{ -"systemMetric":{"cpuLoad":10.0,"cpuRunningThreads":39,"heapInit":266338304,"heapMax":3786407936,"heapCommit":626524160,"heapUsed":338280024,"nonHeapInit":2555904,"nonHeapMax":-1,"nonHeapCommit":60342272,"nonHeapUsed":58673152}, -"consumerMetric":{"operationName":"instance","prefix":"servicecomb.instance.consumer","consumerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"consumerCall":{"total":0,"tps":0.0}}, -"producerMetric":{"operationName":"instance","prefix":"servicecomb.instance.producer","waitInQueue":0,"lifeTimeInQueue":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"executionTime":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerCall":{"total":1,"tps":0.0}}}, -"consumerMetrics":{}, -"producerMetrics":{"calculator.metricsEndpoint.metrics":{"operationName":"calculator.metricsEndpoint.metrics","prefix":"servicecomb.calculator.metricsEndpoint.metrics.producer","waitInQueue":0,"lifeTimeInQueue":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"executionTime":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerLatency":{"total":0,"count":0,"min":0,"max":0,"average":0.0},"producerCall":{"total":1,"tps":0.0}} -}} -``` -But use Prometheus Simple HTTP Server provider interface will publish the standard format which prometheus needed: -```text -# HELP Instance Level Instance Level Metrics -# TYPE Instance Level untyped -servicecomb_instance_producer_producerLatency_average 0.0 -servicecomb_instance_producer_producerLatency_total 0.0 -servicecomb_instance_consumer_producerLatency_count 0.0 -... -servicecomb_instance_producer_producerLatency_min 0.0 -servicecomb_instance_producer_lifeTimeInQueue_average 0.0 -servicecomb_instance_producer_lifeTimeInQueue_count 0.0 -servicecomb_instance_system_heap_init 2.66338304E8 -# HELP calculator.metricsEndpoint.metrics Producer Side calculator.metricsEndpoint.metrics Producer Side Metrics -# TYPE calculator.metricsEndpoint.metrics Producer Side untyped -servicecomb_calculator_metricsEndpoint_metrics_producer_lifeTimeInQueue_average 0.0 -... -servicecomb_calculator_metricsEndpoint_metrics_producer_executionTime_total 0.0 -servicecomb_calculator_metricsEndpoint_metrics_producer_waitInQueue_count 0.0 -servicecomb_calculator_metricsEndpoint_metrics_producer_lifeTimeInQueue_count 0.0 -``` -So they are two independent,different for use. - -*Prometheus Simple HTTP Server also use /metrics as default URL,metrics-prometheus will use 9696 as default port,after microservice start up you can get metrics data at http://localhost:9696/metrics .* -The metrics name in prometheus we replace all dot with underline,because we must follow its [naming rules](https://prometheus.io/docs/practices/naming/). ## How Configuration Enable prometheus integration is very easy: @@ -129,6 +85,31 @@ scrape_configs: ``` The job_name: 'servicecomb' is our custom job,it will collect metrics data from local microservice localhost:9696,more information about configuration of prometheus can found [here](https://prometheus.io/docs/prometheus/latest/configuration/configuration/). +### Verify Output +Prometheus Simple HTTP Server use /metrics as default URL,metrics-prometheus will use 9696 as default port,after microservice start up you can get metrics data at http://localhost:9696/metrics . +Prometheus Simple HTTP Server provider interface will publish the standard format which prometheus needed: +```text +# HELP ServiceComb Metrics ServiceComb Metrics +# TYPE ServiceComb Metrics untyped +jvm{name="cpuRunningThreads",statistic="gauge",} 45.0 +jvm{name="heapMax",statistic="gauge",} 3.786407936E9 +jvm{name="heapCommit",statistic="gauge",} 6.12892672E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="max",status="200",unit="MILLISECONDS",} 1.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="tps",status="200",} 0.4 +jvm{name="nonHeapCommit",statistic="gauge",} 6.1104128E7 +jvm{name="nonHeapInit",statistic="gauge",} 2555904.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="execution",statistic="max",status="200",unit="MILLISECONDS",} 0.0 +jvm{name="heapUsed",statistic="gauge",} 2.82814088E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="latency",status="200",unit="MILLISECONDS",} 1.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="execution",statistic="latency",status="200",unit="MILLISECONDS",} 0.0 +jvm{name="heapInit",statistic="gauge",} 2.66338304E8 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="waitInQueue",} 0.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="total",statistic="count",status="200",} 39.0 +jvm{name="nonHeapUsed",statistic="gauge",} 5.9361032E7 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="latency",status="200",unit="MILLISECONDS",} 0.0 +servicecomb_invocation_calculator_calculatorRestEndpoint_calculate{role="producer",stage="queue",statistic="max",status="200",unit="MILLISECONDS",} 0.0 +``` + ### Config Grafana(optional) How add prometheus as a datasource in grafana can found [here](https://prometheus.io/docs/visualization/grafana/). ## Effect Show diff --git a/_users/setup-environment.md b/_users/setup-environment.md index 5446f54..9888da2 100644 --- a/_users/setup-environment.md +++ b/_users/setup-environment.md @@ -31,19 +31,19 @@ There are two ways to start a service center service: <div class="tab-content"> <div id="windows" class="tab-pane active" markdown="1"> - 1. Download [the installation package of ServiceCenter](https://github.com/apache/incubator-servicecomb-service-center/releases/download/0.5.0/service-center-0.5.0-windows-amd64.zip) + 1. Download [the installation package of ServiceCenter](http://apache.org/dyn/closer.cgi/incubator/servicecomb/incubator-servicecomb-service-center/1.0.0-m1/apache-servicecomb-incubating-service-center-1.0.0-m1-windows-amd64.tar.gz) 2. Decompress the installation package into the installation directory. - 3. Start Local ServiceCenter from **start** file - </div> - <div id="linux" class="tab-pane fade" markdown="1"> - 4. Download and Decompress command: + 3. Start Local ServiceCenter from **start-service-center.bat** file + </div> + <div id="linux" class="tab-pane fade" markdown="1"> + 1. Download and Decompress command: ```bash - wget https://github.com/apache/incubator-servicecomb-service-center/releases/download/0.5.0/service-center-0.5.0-linux-amd64.tar.gz - tar xvf service-center-0.5.0-linux-amd64.tar.gz + wget http://apache.org/dyn/closer.cgi/incubator/servicecomb/incubator-servicecomb-service-center/1.0.0-m1/apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64.tar.gz + tar xvf apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64.tar.gz ``` 2. Start local service center command: ```bash - bash service-center-0.5.0-linux-amd64/start.sh + bash apache-servicecomb-incubating-service-center-1.0.0-m1-linux-amd64/start-service-center.sh ``` </div> </div> diff --git a/assets/images/MetricsDependency.png b/assets/images/MetricsDependency.png index 8133e13..9cfb146 100644 Binary files a/assets/images/MetricsDependency.png and b/assets/images/MetricsDependency.png differ diff --git a/assets/images/MetricsInGrafana.png b/assets/images/MetricsInGrafana.png index 99d381c..3e9c73c 100644 Binary files a/assets/images/MetricsInGrafana.png and b/assets/images/MetricsInGrafana.png differ diff --git a/assets/images/MetricsInPrometheus.png b/assets/images/MetricsInPrometheus.png index 136eb1f..2ac6a95 100644 Binary files a/assets/images/MetricsInPrometheus.png and b/assets/images/MetricsInPrometheus.png differ diff --git a/assets/images/MetricsWriteFileResult.png b/assets/images/MetricsWriteFileResult.png index 28a2449..66bb5e7 100644 Binary files a/assets/images/MetricsWriteFileResult.png and b/assets/images/MetricsWriteFileResult.png differ ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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