基于Grafana和Prometheus的监视系统(3):java客户端使用
0.基本说明
- 如何在代码中进行指标埋点: prometheus java client的使用
- 如何生成jvm 指标数据: hotspot expoter的使用
- 在spring boot 中进行指标采集
- 非指标数据的采集: Elasticsearch的使用
1. java client的使用
[https://github.com/prometheus/client_java]
使用方式和log4j的使用很相似
<!-- The client -->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient</artifactId>
<version>0.4.0</version>
</dependency>
<!-- Hotspot JVM metrics-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_hotspot</artifactId>
<version>0.4.0</version>
</dependency>
<!-- Exposition HTTPServer-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_httpserver</artifactId>
<version>0.4.0</version>
</dependency>
<!-- Pushgateway exposition-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_pushgateway</artifactId>
<version>0.4.0</version>
</dependency>
class A {
static CollectorRegistry registry = new CollectorRegistry();
// 请求总数统计
static final Counter counterRequest = Counter.build().name("TsHistoryData_RequestTotal").help("xx").
labelNames("request").register(registry);
// 请求时间
static final Gauge costTime = Gauge.build().name("TsHistoryData_CostTime").help("xx").
labelNames("id").register(registry);
// 直方图, 统计在某个bucket时间的请求的个数.(linearBuckets | exponentialBuckets)
static final Histogram requestLatency_hgm = Histogram.build()
.name("TsHistoryData_RequestLatency_hgm").exponentialBuckets(0.5,2,10).help("Request latency in seconds.").register(registry);
static final Summary requestLatency_suy = Summary.build()
.name("TsHistoryData_RequestLatency_suy").
quantile(0.1, 0.01).
quantile(0.3, 0.01).
quantile(0.5,0.01).
quantile(0.7, 0.01).
quantile(0.9, 0.01).
quantile(0.95, 0.01).help("Request latency in seconds.").register(registry);
void function1() {
String requestId = System.currentTimeMillis() + "";
counterRequest.labels("request").inc();
Gauge.Timer costTimeTimer = costTime.labels(requestId).startTimer();
Histogram.Timer requestTimer = requestLatency_hgm.startTimer();
Summary.Timer requestTimer_suy = requestLatency_suy.startTimer();
......
costTimeTimer.setDuration();
requestTimer.observeDuration();
requestTimer_suy.observeDuration();
Monitor.pushMetricToPushGateway(registry, "TS_HISTORY_DATA");
}
}
2. hotspot expoter
对于程序运行时的jvm指标进行监控
public static void jvmExport() throws Exception {
DefaultExports.initialize();
InetSocketAddress address = new InetSocketAddress("192.168.1.222", 9201);
Server server = new Server(address);
ServletContextHandler context = new ServletContextHandler();
context.setContextPath("/");
server.setHandler(context);
context.addServlet(new ServletHolder(new MetricsServlet()), "/metrics");
server.start();
// server.join();
}
3.在spring boot中的使用
1.添加Maven的依赖
<!-- Hotspot JVM metrics-->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_hotspot</artifactId>
<version>0.4.0</version>
</dependency>
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_spring_boot</artifactId>
<version>0.4.0</version>
</dependency>
<!-- Exposition servlet -->
<dependency>
<groupId>io.prometheus</groupId>
<artifactId>simpleclient_servlet</artifactId>
<version>0.4.0</version>
</dependency>
- 启用Prometheus Metrics Endpoint
添加注解@EnablePrometheusEndpoint启用Prometheus Endpoint,这里同时使用了simpleclient_hotspot中提供的DefaultExporter,该Exporter展示当前应用JVM的相关信息
@SpringBootApplication
@EnablePrometheusEndpoint
public class CoreApplication extends WebMvcConfigurerAdapter implements CommandLineRunner {
public static void main(String[] args) {
SpringApplication springApplication = new SpringApplication(CoreApplication.class);
springApplication.run(args);
}
@Override
public void run(String... strings) throws Exception {
DefaultExports.initialize();
}
}
向外暴露指标的接口
@Configuration
public class MonitoringConfig {
@Bean
ServletRegistrationBean servletRegistrationBean() {
return new ServletRegistrationBean(new MetricsServlet(), "/metrics");
}
}
这样访问 localhost:8080/metrics 可以看到jvm相关的指标.
3.添加拦截器，为监控埋点做准备
除了获取应用JVM相关的状态以外，我们还可能需要添加一些自定义的监控Metrics实现对系统性能，以及业务状态进行采集，以提供日后优化的相关支撑数据。首先我们使用拦截器处理对应用的所有请求。
继承WebMvcConfigurerAdapter类，复写addInterceptors方法，对所有请求/**添加拦截器
@SpringBootApplication
@EnablePrometheusEndpoint
public class CoreApplication extends WebMvcConfigurerAdapter implements CommandLineRunner {
@Override
public void addInterceptors(InterceptorRegistry registry) {
registry.addInterceptor(new PrometheusMetricsInterceptor()).addPathPatterns("/**");
}
}
PrometheusMetricsInterceptor集成HandlerInterceptorAdapter，通过复写父方法，实现对请求处理前/处理完成的处理。
@Component
public class PrometheusMetricsInterceptor extends HandlerInterceptorAdapter {
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
return super.preHandle(request, response, handler);
}
@Override
public void afterCompletion(HttpServletRequest request, HttpServletResponse response, Object handler, Exception ex) throws Exception {
super.afterCompletion(request, response, handler, ex);
}
}
4.自定义指标
@Component
public class PrometheusMetricsInterceptor extends HandlerInterceptorAdapter {
static final Counter requestCounter = Counter.build()
.name("module_core_http_requests_total").labelNames("path", "method", "code")
.help("Total requests.").register();
static final Gauge inprogressRequests = Gauge.build()
.name("module_core_http_inprogress_requests").labelNames("path", "method", "code")
.help("Inprogress requests.").register();
static final Gauge requestTime = Gauge.build()
.name("module_core_http_requests_costTime").labelNames("path", "method", "code")
.help("requests cost time.").register();
static final Histogram requestLatencyHistogram = Histogram.build().labelNames("path", "method", "code")
.name("module_core_http_requests_latency_seconds_histogram").help("Request latency in seconds.")
.register();
static final Summary requestLatency = Summary.build()
.name("module_core_http_requests_latency_seconds_summary")
.quantile(0.5, 0.05)
.quantile(0.9, 0.01)
.labelNames("path", "method", "code")
.help("Request latency in seconds.").register();
private Histogram.Timer histogramRequestTimer;
private Summary.Timer summaryTimer;
private Gauge.Timer gaugeTimer;
@Override
public boolean preHandle(HttpServletRequest request, HttpServletResponse response, Object handler) throws Exception {
String requestURI = request.getRequestURI();
String method = request.getMethod();
int status = response.getStatus();
inprogressRequests.labels(requestURI, method, String.valueOf(status)).inc();
histogramRequestTimer = requestLatencyHistogram.labels(requestURI, method, String.valueOf(status)).startTimer();
summaryTimer = requestLatency.labels(requestURI, method, String.valueOf(status)).startTimer();
gaugeTimer = requestTime.labels(requestURI, method, String.valueOf(status)).startTimer();
return super.preHandle(request, response, handler);
}
@Override
public void afterCompletion(HttpServletRequest request, HttpServletResponse response, Object handler, Exception ex) throws Exception {
String requestURI = request.getRequestURI();
String method = request.getMethod();
int status = response.getStatus();
requestCounter.labels(requestURI, method, String.valueOf(status)).inc();
inprogressRequests.labels(requestURI, method, String.valueOf(status)).dec();
histogramRequestTimer.observeDuration();
summaryTimer.observeDuration();
gaugeTimer.setDuration();
super.afterCompletion(request, response, handler, ex);
}
}
5.使用Collector暴露业务指标
除了在拦截器中使用Prometheus提供的Counter,Summary,Gauage等构造监控指标以外，我们还可以通过自定义的Collector实现对相关业务指标的暴露
@SpringBootApplication
@EnablePrometheusEndpoint
public class CoreApplication extends WebMvcConfigurerAdapter implements CommandLineRunner {
@Autowired
private CustomExporter customExporter;
...省略的代码
@Override
public void run(String... args) throws Exception {
...省略的代码
customExporter.register();
}
}
CustomExporter集成自io.prometheus.client.Collector，在调用Collector的register()方法后，当访问/metrics时，则会自动从Collector的collection()方法中获取采集到的监控指标。
由于这里CustomExporter存在于Spring的IOC容器当中，这里可以直接访问业务代码，返回需要的业务相关的指标。
@Component
public class CustomExporter extends Collector {
@Override
public List<MetricFamilySamples> collect() {
List<MetricFamilySamples> mfs = new ArrayList<>();
GaugeMetricFamily labeledGauge =
new GaugeMetricFamily("module_core_custom_metrics", "custom metrics", Collections.singletonList("labelname"));
labeledGauge.addMetric(Collections.singletonList("labelvalue"), 1);
mfs.add(labeledGauge);
return mfs;
}
}
4. ES的使用
docker compose [https://www.elastic.co/guide/en/elasticsearch/reference/5.6/docker.html]
java client
<dependency>
<groupId>org.elasticsearch.client</groupId>
<artifactId>elasticsearch-rest-high-level-client</artifactId>
<version>6.0.1</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.elasticsearch/elasticsearch -->
<dependency>
<groupId>org.elasticsearch</groupId>
<artifactId>elasticsearch</artifactId>
<version>6.0.0</version>
</dependency>
public class EsConfig {
private static RestHighLevelClient client;
public static RestHighLevelClient init() throws Exception{
client = new RestHighLevelClient(
RestClient.builder(
new HttpHost("192.168.1.223", 9200, "http")
));
return client;
}
public static void close() throws Exception{
client.close();
}
}
try {
client = EsConfig.init();
Map<String, Object> jsonMap = new HashMap<>();
jsonMap.put("Id", requestId);
jsonMap.put("Date", new Date());
jsonMap.put("Value", tt);
IndexRequest indexRequest = new IndexRequest("ts_data_history", "doc", requestId)
.source(jsonMap);
IndexResponse indexResponse = client.index(indexRequest);
client.close();
} catch (Exception e) {
e.printStackTrace();
}
