控制台
控制台主要的处理类是 FlowControllerV1 。
@RestController
@RequestMapping(value = "/v1/flow")
public class FlowControllerV1 {
private final Logger logger = LoggerFactory.getLogger(FlowControllerV1.class);
@Autowired
private InMemoryRuleRepositoryAdapter<FlowRuleEntity> repository;
@Autowired
private SentinelApiClient sentinelApiClient;
@GetMapping("/rules")
@AuthAction(PrivilegeType.READ_RULE)
public Result<List<FlowRuleEntity>> apiQueryMachineRules(@RequestParam String app,
@RequestParam String ip,
@RequestParam Integer port) {
if (StringUtil.isEmpty(app)) {
return Result.ofFail(-1, "app can't be null or empty");
}
if (StringUtil.isEmpty(ip)) {
return Result.ofFail(-1, "ip can't be null or empty");
}
if (port == null) {
return Result.ofFail(-1, "port can't be null");
}
try {
List<FlowRuleEntity> rules = sentinelApiClient.fetchFlowRuleOfMachine(app, ip, port);
rules = repository.saveAll(rules);
return Result.ofSuccess(rules);
} catch (Throwable throwable) {
logger.error("Error when querying flow rules", throwable);
return Result.ofThrowable(-1, throwable);
}
}
private <R> Result<R> checkEntityInternal(FlowRuleEntity entity) {
if (StringUtil.isBlank(entity.getApp())) {
return Result.ofFail(-1, "app can't be null or empty");
}
if (StringUtil.isBlank(entity.getIp())) {
return Result.ofFail(-1, "ip can't be null or empty");
}
if (entity.getPort() == null) {
return Result.ofFail(-1, "port can't be null");
}
if (StringUtil.isBlank(entity.getLimitApp())) {
return Result.ofFail(-1, "limitApp can't be null or empty");
}
if (StringUtil.isBlank(entity.getResource())) {
return Result.ofFail(-1, "resource can't be null or empty");
}
if (entity.getGrade() == null) {
return Result.ofFail(-1, "grade can't be null");
}
if (entity.getGrade() != 0 && entity.getGrade() != 1) {
return Result.ofFail(-1, "grade must be 0 or 1, but " + entity.getGrade() + " got");
}
if (entity.getCount() == null || entity.getCount() < 0) {
return Result.ofFail(-1, "count should be at lease zero");
}
if (entity.getStrategy() == null) {
return Result.ofFail(-1, "strategy can't be null");
}
if (entity.getStrategy() != 0 && StringUtil.isBlank(entity.getRefResource())) {
return Result.ofFail(-1, "refResource can't be null or empty when strategy!=0");
}
if (entity.getControlBehavior() == null) {
return Result.ofFail(-1, "controlBehavior can't be null");
}
int controlBehavior = entity.getControlBehavior();
if (controlBehavior == 1 && entity.getWarmUpPeriodSec() == null) {
return Result.ofFail(-1, "warmUpPeriodSec can't be null when controlBehavior==1");
}
if (controlBehavior == 2 && entity.getMaxQueueingTimeMs() == null) {
return Result.ofFail(-1, "maxQueueingTimeMs can't be null when controlBehavior==2");
}
if (entity.isClusterMode() && entity.getClusterConfig() == null) {
return Result.ofFail(-1, "cluster config should be valid");
}
return null;
}
@PostMapping("/rule")
@AuthAction(PrivilegeType.WRITE_RULE)
public Result<FlowRuleEntity> apiAddFlowRule(@RequestBody FlowRuleEntity entity) {
Result<FlowRuleEntity> checkResult = checkEntityInternal(entity);
if (checkResult != null) {
return checkResult;
}
entity.setId(null);
Date date = new Date();
entity.setGmtCreate(date);
entity.setGmtModified(date);
entity.setLimitApp(entity.getLimitApp().trim());
entity.setResource(entity.getResource().trim());
try {
entity = repository.save(entity);
publishRules(entity.getApp(), entity.getIp(), entity.getPort()).get(5000, TimeUnit.MILLISECONDS);
return Result.ofSuccess(entity);
} catch (Throwable t) {
Throwable e = t instanceof ExecutionException ? t.getCause() : t;
logger.error("Failed to add new flow rule, app={}, ip={}", entity.getApp(), entity.getIp(), e);
return Result.ofFail(-1, e.getMessage());
}
}
@PutMapping("/save.json")
@AuthAction(PrivilegeType.WRITE_RULE)
public Result<FlowRuleEntity> apiUpdateFlowRule(Long id, String app,
String limitApp, String resource, Integer grade,
Double count, Integer strategy, String refResource,
Integer controlBehavior, Integer warmUpPeriodSec,
Integer maxQueueingTimeMs) {
if (id == null) {
return Result.ofFail(-1, "id can't be null");
}
FlowRuleEntity entity = repository.findById(id);
if (entity == null) {
return Result.ofFail(-1, "id " + id + " dose not exist");
}
if (StringUtil.isNotBlank(app)) {
entity.setApp(app.trim());
}
if (StringUtil.isNotBlank(limitApp)) {
entity.setLimitApp(limitApp.trim());
}
if (StringUtil.isNotBlank(resource)) {
entity.setResource(resource.trim());
}
if (grade != null) {
if (grade != 0 && grade != 1) {
return Result.ofFail(-1, "grade must be 0 or 1, but " + grade + " got");
}
entity.setGrade(grade);
}
if (count != null) {
entity.setCount(count);
}
if (strategy != null) {
if (strategy != 0 && strategy != 1 && strategy != 2) {
return Result.ofFail(-1, "strategy must be in [0, 1, 2], but " + strategy + " got");
}
entity.setStrategy(strategy);
if (strategy != 0) {
if (StringUtil.isBlank(refResource)) {
return Result.ofFail(-1, "refResource can't be null or empty when strategy!=0");
}
entity.setRefResource(refResource.trim());
}
}
if (controlBehavior != null) {
if (controlBehavior != 0 && controlBehavior != 1 && controlBehavior != 2) {
return Result.ofFail(-1, "controlBehavior must be in [0, 1, 2], but " + controlBehavior + " got");
}
if (controlBehavior == 1 && warmUpPeriodSec == null) {
return Result.ofFail(-1, "warmUpPeriodSec can't be null when controlBehavior==1");
}
if (controlBehavior == 2 && maxQueueingTimeMs == null) {
return Result.ofFail(-1, "maxQueueingTimeMs can't be null when controlBehavior==2");
}
entity.setControlBehavior(controlBehavior);
if (warmUpPeriodSec != null) {
entity.setWarmUpPeriodSec(warmUpPeriodSec);
}
if (maxQueueingTimeMs != null) {
entity.setMaxQueueingTimeMs(maxQueueingTimeMs);
}
}
Date date = new Date();
entity.setGmtModified(date);
try {
entity = repository.save(entity);
if (entity == null) {
return Result.ofFail(-1, "save entity fail: null");
}
publishRules(entity.getApp(), entity.getIp(), entity.getPort()).get(5000, TimeUnit.MILLISECONDS);
return Result.ofSuccess(entity);
} catch (Throwable t) {
Throwable e = t instanceof ExecutionException ? t.getCause() : t;
logger.error("Error when updating flow rules, app={}, ip={}, ruleId={}", entity.getApp(),
entity.getIp(), id, e);
return Result.ofFail(-1, e.getMessage());
}
}
@DeleteMapping("/delete.json")
@AuthAction(PrivilegeType.WRITE_RULE)
public Result<Long> apiDeleteFlowRule(Long id) {
if (id == null) {
return Result.ofFail(-1, "id can't be null");
}
FlowRuleEntity oldEntity = repository.findById(id);
if (oldEntity == null) {
return Result.ofSuccess(null);
}
try {
repository.delete(id);
} catch (Exception e) {
return Result.ofFail(-1, e.getMessage());
}
try {
publishRules(oldEntity.getApp(), oldEntity.getIp(), oldEntity.getPort()).get(5000, TimeUnit.MILLISECONDS);
return Result.ofSuccess(id);
} catch (Throwable t) {
Throwable e = t instanceof ExecutionException ? t.getCause() : t;
logger.error("Error when deleting flow rules, app={}, ip={}, id={}", oldEntity.getApp(),
oldEntity.getIp(), id, e);
return Result.ofFail(-1, e.getMessage());
}
}
private CompletableFuture<Void> publishRules(String app, String ip, Integer port) {
List<FlowRuleEntity> rules = repository.findAllByMachine(MachineInfo.of(app, ip, port));
return sentinelApiClient.setFlowRuleOfMachineAsync(app, ip, port, rules);
}
}
从上面可以看出来，dashboard 是通过一个叫 SentinelApiClient 的类去指定的 ip 和 port 处获取数据的。这个 ip 和 port 是前端页面直接提交给后端的，而前端页面又是通过 /app/{app}/machines.json 接口获取机器列表的。
连接 dashboard
sentinel-core 在初始化的时候，通过 JVM 参数中指定的 dashboard 的 ip 和 port，会主动向 dashboard 发起连接的请求，该请求是通过 HeartbeatSender 接口以心跳的方式发送的，并将自己的 ip 和 port 告知 dashboard。这里 sentinel-core 上报给 dashboard 的端口是 sentinel 对外暴露的自己的 CommandCenter 的端口。
HeartbeatSender 有两个实现类，一个是通过 http，另一个是通过 netty，我们看 http 的实现类：
SimpleHttpHeartbeatSender.java
private final HeartbeatMessage heartBeat = new HeartbeatMessage();
private final SimpleHttpClient httpClient = new SimpleHttpClient();
@Override
public boolean sendHeartbeat() throws Exception {
if (TransportConfig.getRuntimePort() <= 0) {
RecordLog.info("[SimpleHttpHeartbeatSender] Runtime port not initialized, won't send heartbeat");
return false;
}
InetSocketAddress addr = getAvailableAddress();
if (addr == null) {
return false;
}
SimpleHttpRequest request = new SimpleHttpRequest(addr, HEARTBEAT_PATH);
request.setParams(heartBeat.generateCurrentMessage());
try {
SimpleHttpResponse response = httpClient.post(request);
if (response.getStatusCode() == OK_STATUS) {
return true;
}
} catch (Exception e) {
RecordLog.warn("[SimpleHttpHeartbeatSender] Failed to send heartbeat to " + addr + " : ", e);
}
return false;
}
通过一个 HttpClient 向 dashboard 发送了自己的信息，包括 ip port 和版本号等信息。
其中consoleHost 和 consolePort 的值就是从 JVM 参数 csp.sentinel.dashboard.server 中获取的。
dashboard 在接收到 sentinel-core 的连接之后，就会与 sentinel-core 建立连接，并将 sentinel-core 上报的 ip 和 port 的信息包装成一个 MachineInfo 对象，然后通过 SimpleMachineDiscovery 将该对象保存在一个 map 中，如下图所示：
请求数据
当dashboard 有了具体的 sentinel-core 实例的 ip 和 port 之后，就可以去请求所需要的数据了。
让我们再回到最开始的地方，我在页面上查询某一台机器的限流的规则时，是将该机器的 ip 和 port 以及 appName 都传给了服务端，服务端通过这些信息去具体的远程实例中请求所需的数据，拿到数据后再封装成 dashboard 所需的格式返回给前端页面进行展示。
具体请求限流规则列表的代码在 SentinelApiClient 中，如下所示：
SentinelApiClient.java
public List<FlowRuleEntity> fetchFlowRuleOfMachine(String app, String ip, int port) {
String url = "http://" + ip + ":" + port + "/" + GET_RULES_PATH + "?type=" + FLOW_RULE_TYPE;
String body = httpGetContent(url);
logger.info("FlowRule Body:{}", body);
List<FlowRule> rules = RuleUtils.parseFlowRule(body);
if (rules != null) {
return rules.stream().map(rule -> FlowRuleEntity.fromFlowRule(app, ip, port, rule))
.collect(Collectors.toList());
} else {
return null;
}
}
可以看到也是通过一个 httpClient 请求的数据，然后再对结果进行转换，具体请求的过程是在 httpGetContent 方法中进行的，我们看下该方法，如下所示：
private String httpGetContent(String url) {
final HttpGet httpGet = new HttpGet(url);
final CountDownLatch latch = new CountDownLatch(1);
final AtomicReference<String> reference = new AtomicReference<>();
httpClient.execute(httpGet, new FutureCallback<HttpResponse>() {
@Override
public void completed(final HttpResponse response) {
try {
reference.set(getBody(response));
} catch (Exception e) {
logger.info("httpGetContent " + url + " error:", e);
} finally {
latch.countDown();
}
}
@Override
public void failed(final Exception ex) {
latch.countDown();
logger.info("httpGetContent " + url + " failed:", ex);
}
@Override
public void cancelled() {
latch.countDown();
}
});
try {
latch.await(5, TimeUnit.SECONDS);
} catch (Exception e) {
logger.info("wait http client error:", e);
}
return reference.get();
}
从代码中可以看到，是通过一个异步的 httpClient 再结合 CountDownLatch 等待 5 秒的超时时间去获取结果的。
客户端
sentinel-core 在启动的时候，执行了一个 InitExecutor.init 的方法，该方法会触发所有 InitFunc 实现类的 init 方法。
InitExecutor#doInit
public static void doInit() {
//InitExecutor只会初始化一次，并且初始化失败会退出
if (!initialized.compareAndSet(false, true)) {
return;
}
try {
//通过spi加载InitFunc子类
ServiceLoader<InitFunc> loader = ServiceLoader.load(InitFunc.class);
List<OrderWrapper> initList = new ArrayList<OrderWrapper>();
for (InitFunc initFunc : loader) {
RecordLog.info("[InitExecutor] Found init func: " + initFunc.getClass().getCanonicalName());
//给所有的initFunc排序，按@InitOrder从小到大进行排序
//然后封装成OrderWrapper对象
insertSorted(initList, initFunc);
}
for (OrderWrapper w : initList) {
w.func.init();
RecordLog.info(String.format("[InitExecutor] Executing %s with order %d",
w.func.getClass().getCanonicalName(), w.order));
}
} catch (Exception ex) {
RecordLog.warn("[InitExecutor] WARN: Initialization failed", ex);
ex.printStackTrace();
} catch (Error error) {
RecordLog.warn("[InitExecutor] ERROR: Initialization failed with fatal error", error);
error.printStackTrace();
}
}
因为这里我们引入了sentinel-transport-simple-http模块，所以使用spi加载InitFunc的子类的时候会新加载两个子类实例，分别是：CommandCenterInitFunc、HeartbeatSenderInitFunc。然后会遍历loader，根据@InitOrder的大小进行排序，并封装成OrderWrapper放入到initList中。
所以initList里面的对象顺序是：
- CommandCenterInitFunc
- HeartbeatSenderInitFunc
- MetricCallbackInit
然后遍历initList依次调用init方法。
CommandCenterInitFunc
CommandCenterInitFunc 则会启动一个 CommandCenter 对外提供 sentinel-core 的数据服务，而这些数据服务是通过一个一个的 CommandHandler 来提供的，如下图所示：
CommandCenterInitFunc#init
public void init() throws Exception {
//获取commandCenter对象
CommandCenter commandCenter = CommandCenterProvider.getCommandCenter();
if (commandCenter == null) {
RecordLog.warn("[CommandCenterInitFunc] Cannot resolve CommandCenter");
return;
}
//调用SimpleHttpCommandCenter的beforeStart方法
//用来设置CommandHandler的实现类
commandCenter.beforeStart();
commandCenter.start();
RecordLog.info("[CommandCenterInit] Starting command center: "
+ commandCenter.getClass().getCanonicalName());
}
这个方法里面的所有操作都是针对CommandCenter来进行的，所以我们先来看看CommandCenterProvider这个类。
CommandCenterProvider
static {
//初始化commandCenter对象
resolveInstance();
}
private static void resolveInstance() {
//获取SpiOrder更大的子类实现类
CommandCenter resolveCommandCenter = SpiLoader.loadHighestPriorityInstance(CommandCenter.class);
if (resolveCommandCenter == null) {
RecordLog.warn("[CommandCenterProvider] WARN: No existing CommandCenter found");
} else {
commandCenter = resolveCommandCenter;
RecordLog.info("[CommandCenterProvider] CommandCenter resolved: " + resolveCommandCenter.getClass()
.getCanonicalName());
}
}
CommandCenterProvider会在首次初始化的时候调用resolveInstance方法。在resolveInstance方法里面会调用SpiLoader.loadHighestPriorityInstance来获取CommandCenter，这里获取的是SimpleHttpCommandCenter这个实例，loadHighestPriorityInstance方法具体的实现非常简单，我就不去分析了。然后将commandCenter赋值SimpleHttpCommandCenter实例。
所以CommandCenterProvider.getCommandCenter()方法返回的是SimpleHttpCommandCenter实例。然后调用SimpleHttpCommandCenter的beforeStart方法。
SimpleHttpCommandCenter#beforeStart
public void beforeStart() throws Exception {
// Register handlers
//调用CommandHandlerProvider的namedHandlers方法
//获取CommandHandler的spi中设置的实现类
Map<String, CommandHandler> handlers = CommandHandlerProvider.getInstance().namedHandlers();
//将handlers中的数据设置到handlerMap中
registerCommands(handlers);
}
这个方法首先会调用CommandHandlerProvider的namedHandlers中获取所有的CommandHandler实现类。
CommandHandlerProvider#namedHandlers
private final ServiceLoader<CommandHandler> serviceLoader = ServiceLoader.load(CommandHandler.class);
public Map<String, CommandHandler> namedHandlers() {
Map<String, CommandHandler> map = new HashMap<String, CommandHandler>();
for (CommandHandler handler : serviceLoader) {
//获取实现类CommandMapping注解的name属性
String name = parseCommandName(handler);
if (!StringUtil.isEmpty(name)) {
map.put(name, handler);
}
}
return map;
}
这个类会通过spi先加载CommandHandler的实现类，然后将实现类按注解上面的name属性放入到map里面去。CommandHandler的实现类是用来和控制台进行交互的处理类，负责处理。
这也是策略模式的一种应用，根据map里面的不同策略来做不同的处理，例如SendMetricCommandHandler是用来统计调用信息然后发送给控制台用的，ModifyRulesCommandHandler是用来做实时修改限流策略的处理的等等。
然后我们再回到CommandCenterInitFunc中，继续往下走，调用commandCenter.start()方法。
SimpleHttpCommandCenter#start
public void start() throws Exception {
//获取当前机器的cpu线程数
int nThreads = Runtime.getRuntime().availableProcessors();
//创建一个cpu线程数大小的固定线程池，用来做业务线程池用
this.bizExecutor = new ThreadPoolExecutor(nThreads, nThreads, 0L, TimeUnit.MILLISECONDS,
new ArrayBlockingQueue<Runnable>(10),
new NamedThreadFactory("sentinel-command-center-service-executor"),
new RejectedExecutionHandler() {
@Override
public void rejectedExecution(Runnable r, ThreadPoolExecutor executor) {
CommandCenterLog.info("EventTask rejected");
throw new RejectedExecutionException();
}
});
Runnable serverInitTask = new Runnable() {
int port;
{
try {
//获取port
port = Integer.parseInt(TransportConfig.getPort());
} catch (Exception e) {
port = DEFAULT_PORT;
}
}
@Override
public void run() {
boolean success = false;
//创建一个ServerSocket
ServerSocket serverSocket = getServerSocketFromBasePort(port);
if (serverSocket != null) {
CommandCenterLog.info("[CommandCenter] Begin listening at port " + serverSocket.getLocalPort());
socketReference = serverSocket;
executor.submit(new ServerThread(serverSocket));
success = true;
port = serverSocket.getLocalPort();
} else {
CommandCenterLog.info("[CommandCenter] chooses port fail, http command center will not work");
}
if (!success) {
port = PORT_UNINITIALIZED;
}
TransportConfig.setRuntimePort(port);
//关闭线程池
executor.shutdown();
}
};
new Thread(serverInitTask).start();
}
1、 这个方法会创建一个固定大小的业务线程池；
2、 创建一个serverInitTask，里面负责建立serverSocket然后用executor去创建一个ServerThread异步执行serverSocket；
3、 executor用完之后会在serverInitTask里面调用executor的shutdown方法去关闭线程池；
其中executor是一个单线程的线程池：
private ExecutorService executor = Executors.newSingleThreadExecutor(
new NamedThreadFactory("sentinel-command-center-executor"));
ServerThread是SimpleHttpCommandCenter的内部类：
public void run() {
while (true) {
Socket socket = null;
try {
//建立连接
socket = this.serverSocket.accept();
//默认的超时时间是3s
setSocketSoTimeout(socket);
HttpEventTask eventTask = new HttpEventTask(socket);
//使用业务线程异步处理
bizExecutor.submit(eventTask);
} catch (Exception e) {
CommandCenterLog.info("Server error", e);
if (socket != null) {
try {
socket.close();
} catch (Exception e1) {
CommandCenterLog.info("Error when closing an opened socket", e1);
}
}
try {
// In case of infinite log.
Thread.sleep(10);
} catch (InterruptedException e1) {
// Indicates the task should stop.
break;
}
}
}
}
run方法会使用构造器传入的serverSocket建立连接后设置超时时间，封装成HttpEventTask类，然后使用上面创建的bizExecutor异步执行任务。
HttpEventTask是Runnable的实现类，所以调用bizExecutor的submit的时候会调用其中的run方法使用socket与控制台进行交互。
HttpEventTask#run
public void run() {
....
// Validate the target command.
//获取commandName
String commandName = HttpCommandUtils.getTarget(request);
if (StringUtil.isBlank(commandName)) {
badRequest(printWriter, "Invalid command");
return;
}
// Find the matching command handler.
//根据commandName获取处理器名字
CommandHandler<?> commandHandler = SimpleHttpCommandCenter.getHandler(commandName);
if (commandHandler != null) {
//调用处理器结果，然后返回给控制台
CommandResponse<?> response = commandHandler.handle(request);
handleResponse(response, printWriter, outputStream);
}
....
} catch (Throwable e) {
....
} finally {
....
}
}
HttpEventTask的run方法很长，但是很多都是有关输入输出流的，我们不关心，所以省略。只需要知道会把request请求最后转换成一个控制台发过来的指令，然后通过SimpleHttpCommandCenter调用getHandler得到处理器，然后处理数据就行了。
所以这个整个的处理流程就是：
通过这样的一个处理流程，然后实现了类似reactor的一个处理流程。
SimpleHttpCommandCenter#getHandler
public static CommandHandler getHandler(String commandName) {
return handlerMap.get(commandName);
}
handlerMap里面的数据是通过前面我们分析的调用beforeStart方法设置进来的。
然后通过commandName获取对应的控制台，例如：控制台发送过来metric指令，那么就会对应的调用SendMetricCommandHandler的handle方法来处理控制台的指令。
HeartbeatSenderInitFunc
HeartbeatSenderInitFunc主要是用来做心跳线程使用的，定期的和控制台进行心跳连接。
HeartbeatSenderInitFunc#init
public void init() {
//获取HeartbeatSender的实现类
HeartbeatSender sender = HeartbeatSenderProvider.getHeartbeatSender();
if (sender == null) {
RecordLog.warn("[HeartbeatSenderInitFunc] WARN: No HeartbeatSender loaded");
return;
}
//创建一个corepoolsize为2，maximumPoolSize为最大的线程池
initSchedulerIfNeeded();
//获取心跳间隔时间，默认10s
long interval = retrieveInterval(sender);
//设置间隔心跳时间
setIntervalIfNotExists(interval);
//开启一个定时任务，每隔interval时间发送一个心跳
scheduleHeartbeatTask(sender, interval);
}
1、 首先会调用HeartbeatSenderProvider.getHeartbeatSender方法，里面会根据spi创建实例，返回一个SimpleHttpHeartbeatSender实例；
2、 调用initSchedulerIfNeeded方法创建一个corepoolsize为2的线程池；
3、 获取心跳间隔时间，如果没有设置，那么是10s；
4、 调用scheduleHeartbeatTask方法开启一个定时线程调用；
我们来看看scheduleHeartbeatTask方法：
HeartbeatSenderInitFunc#scheduleHeartbeatTask
private void scheduleHeartbeatTask(/*@NonNull*/ final HeartbeatSender sender, /*@Valid*/ long interval) {
pool.scheduleAtFixedRate(new Runnable() {
@Override
public void run() {
try {
sender.sendHeartbeat();
} catch (Throwable e) {
RecordLog.warn("[HeartbeatSender] Send heartbeat error", e);
}
}
}, 5000, interval, TimeUnit.MILLISECONDS);
RecordLog.info("[HeartbeatSenderInit] HeartbeatSender started: "
+ sender.getClass().getCanonicalName());
}
默认的情况，创建的这个定时任务会每隔10s调用一次SimpleHttpHeartbeatSender的sendHeartbeat方法。
SimpleHttpHeartbeatSender#sendHeartbeat
public boolean sendHeartbeat() throws Exception {
if (TransportConfig.getRuntimePort() <= 0) {
RecordLog.info("[SimpleHttpHeartbeatSender] Runtime port not initialized, won't send heartbeat");
return false;
}
//获取控制台的ip和端口等信息
InetSocketAddress addr = getAvailableAddress();
if (addr == null) {
return false;
}
//设置http调用的ip和端口，还有访问的url
SimpleHttpRequest request = new SimpleHttpRequest(addr, HEARTBEAT_PATH);
//获取版本号，端口等信息
request.setParams(heartBeat.generateCurrentMessage());
try {
//发送post请求
SimpleHttpResponse response = httpClient.post(request);
if (response.getStatusCode() == OK_STATUS) {
return true;
}
} catch (Exception e) {
RecordLog.warn("[SimpleHttpHeartbeatSender] Failed to send heartbeat to " + addr + " : ", e);
}
return false;
}
这个心跳检测的方法就写的很简单了，通过Dcsp.sentinel.dashboard.server预先设置好的ip和端口号发送post请求到控制台，然后检测是否返回200，如果是则说明控制台正常，否则进行异常处理。
总结
现在我们已经知道了 dashboard 是如何获取到实时数据的了，具体的流程如下所示：
1、 首先sentinel-core向dashboard发送心跳包；
2、 dashboard将sentinel-core的机器信息保存在内存中；
3、 dashboard根据sentinel-core的机器信息通过httpClient获取实时的数据；
4、 sentinel-core接收到请求之后，会找到具体的CommandHandler来处理；
5、 sentinel-core将处理好的结果返回给dashboard；
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