Nacos 是如何同时实现AP与CP的
- 两种一致性策略如何在nacos中共存
- AP实现
- CP实现
- 重要的协议——RAFT
- nacos是如何实现CP(raft)的
- 为什么要同时实现CP和AP两套一致性策略模式?
两种一致性策略如何在nacos中共存
或许会有疑问,为什么早先的cp
模式的Zookeeper
或者AP
模式的Eureka
,都只有支持CAP
理论下大家常用的AP
实现或者CP
实现,而nacos却能够两个都实现呢?
其实CAP
理论,仅仅是针对分布式下数据的一致性而言,如果你对于数据的一致性要求不高,可忍受最终一致性,那么AP
模式的Eureka
就可以满足你了,如果说你对数据的一致性要求很高,那么就使用CP
模式的Zookeeper
,而追其根本,并不是说Eureka
是AP
的,或者说Zookeeper
是CP
的,而是他们存储的数据的一致性,满足AP
或者CP
,因此也就不难实现在一个组件中实现AP
模式与CP
模式共存
@Service("consistencyDelegate")
public class DelegateConsistencyServiceImpl implements ConsistencyService {
@Autowired
private PersistentConsistencyService persistentConsistencyService;
@Autowired
private EphemeralConsistencyService ephemeralConsistencyService;
}
DelegateConsistencyServiceImpl
是一个一致性策略选择的类,根据不同的策略触发条件(在nacos中,CP
与AP
切换的条件是注册的服务实例是否是临时实例),选择PersistentConsistencyService
策略或者EphemeralConsistencyService
策略,而EphemeralConsistencyService
对应的是DistroConsistencyServiceImpl
,采用的协议是阿里自研的Distro
,我个人觉得就像gossip
协议;PersistentConsistencyService
对应的是RaftConsistencyServiceImpl
,其底层采用的是Raft
协议;这两种一致性策略下的数据存储互不影响,所以nacos
实现了AP
模式与CP
模式在一个组件中同时存在
AP实现
Nacos中的DistroConsistencyServiceImpl工作浅析
Eureka 一致性策略
Eureka是一个AP模式的服务发现框架,在Eureka集群模式下,Eureka采取的是Server之间互相广播各自的数据进行数据复制、更新操作;并且Eureka在客户端与注册中心出现网络故障时,依然能够获取服务注册信息——Eureka实现了客户端对于服务注册信息的缓存
DiscoveryClient
private void fetchRegistryFromBackup() {
try {
@SuppressWarnings("deprecation")
BackupRegistry backupRegistryInstance = newBackupRegistryInstance();
if (null == backupRegistryInstance) { // backward compatibility with the old protected method, in case it is being used.
backupRegistryInstance = backupRegistryProvider.get();
}
if (null != backupRegistryInstance) {
Applications apps = null;
if (isFetchingRemoteRegionRegistries()) {
String remoteRegionsStr = remoteRegionsToFetch.get();
if (null != remoteRegionsStr) {
apps = backupRegistryInstance.fetchRegistry(remoteRegionsStr.split(","));
}
} else {
apps = backupRegistryInstance.fetchRegistry();
}
if (apps != null) {
final Applications applications = this.filterAndShuffle(apps);
applications.setAppsHashCode(applications.getReconcileHashCode());
localRegionApps.set(applications);
logTotalInstances();
logger.info("Fetched registry successfully from the backup");
}
} else {
logger.warn("No backup registry instance defined & unable to find any discovery servers.");
}
} catch (Throwable e) {
logger.warn("Cannot fetch applications from apps although backup registry was specified", e);
}
}
正因为Eureka为了能够在Eureka集群无法工作时不影响消费者调用服务提供者而设置的客户端缓存,因此Eureka无法保证服务注册信息的强一致性(CP模式),只能满足数据的最终一致性(AP模式)
Nacos一致性策略——Distro
Nacos在AP模式下的一致性策略就类似于Eureka,采用Server
之间互相的数据同步来实现数据在集群中的同步、复制操作。
触发数据广播
DistroConsistencyServiceImpl.java
@Override
public void put(String key, Record value) throws NacosException {
onPut(key, value);
taskDispatcher.addTask(key);
}
当调用ConsistencyService
接口定义的put
、remove
方法时,涉及到了Server
端数据的变更,此时会创建一个任务,将数据的key
传入taskDispatcher.addTask
方法中,用于后面数据变更时数据查找操作
TaskDispatcher.java
public void addTask(String key) {
taskSchedulerList.get(UtilsAndCommons.shakeUp(key, cpuCoreCount)).addTask(key);
}
这里有一个方法需要注意——shakeUp
,查看官方代码注解可知这是将key
(key
可以看作是一次数据变更事件)这里应该是将任务均匀的路由到不同的TaskScheduler
对象,确保每个TaskScheduler
所承担的任务都差不多。
public class TaskScheduler implements Runnable {
private int dataSize = 0;
private long lastDispatchTime = 0L;
private BlockingQueue<String> queue = new LinkedBlockingQueue<>(128 * 1024);
...
public void addTask(String key) {
queue.offer(key);
}
@Override
public void run() {
List<String> keys = new ArrayList<>();
while (true) {
try {
String key = queue.poll(partitionConfig.getTaskDispatchPeriod(),TimeUnit.MILLISECONDS);
if (Loggers.EPHEMERAL.isDebugEnabled() && StringUtils.isNotBlank(key)) {
Loggers.EPHEMERAL.debug("got key: {}", key);
}
if (dataSyncer.getServers() == null || dataSyncer.getServers().isEmpty()) {
continue;
}
if (StringUtils.isBlank(key)) {
continue;
}
if (dataSize == 0) {
keys = new ArrayList<>();
}
keys.add(key);
dataSize++;
if (dataSize == partitionConfig.getBatchSyncKeyCount() ||
(System.currentTimeMillis() - lastDispatchTime) > partitionConfig.getTaskDispatchPeriod()) {
for (Server member : dataSyncer.getServers()) {
// 自己不需要进行数据广播操作
if (NetUtils.localServer().equals(member.getKey())) {
continue;
}
SyncTask syncTask = new SyncTask();
syncTask.setKeys(keys);
syncTask.setTargetServer(member.getKey());
if (Loggers.EPHEMERAL.isDebugEnabled() && StringUtils.isNotBlank(key)) {
Loggers.EPHEMERAL.debug("add sync task: {}", JSON.toJSONString(syncTask));
}
dataSyncer.submit(syncTask, 0);
}
lastDispatchTime = System.currentTimeMillis();
dataSize = 0;
}
} catch (Exception e) {
Loggers.EPHEMERAL.error("dispatch sync task failed.", e);
}
}
}
}
核心方法就是for (Server member : dataSyncer.getServers()) {..}
循环体内的代码,此处就是将数据在Nacos Server
中进行广播操作;具体步骤如下
- 创建`SyncTask`,并设置事件集合(就是`key`集合)
- 将目标`Server`信息设置到`SyncTask`中——`syncTask.setTargetServer(member.getKey())`
- 将数据广播任务提交到`DataSyncer`中
执行数据广播——DataSyncer
public void submit(SyncTask task, long delay) {
// If it's a new task:
if (task.getRetryCount() == 0) {
Iterator<String> iterator = task.getKeys().iterator();
while (iterator.hasNext()) {
String key = iterator.next();
if (StringUtils.isNotBlank(taskMap.putIfAbsent(buildKey(key, task.getTargetServer()), key))) {
// associated key already exist:
if (Loggers.EPHEMERAL.isDebugEnabled()) {
Loggers.EPHEMERAL.debug("sync already in process, key: {}", key);
}
iterator.remove();
}
}
}
if (task.getKeys().isEmpty()) {
// all keys are removed:
return;
}
GlobalExecutor.submitDataSync(new Runnable() {
@Override
public void run() {
try {
if (servers == null || servers.isEmpty()) {
Loggers.SRV_LOG.warn("try to sync data but server list is empty.");
return;
}
List<String> keys = task.getKeys();
if (Loggers.EPHEMERAL.isDebugEnabled()) {
Loggers.EPHEMERAL.debug("sync keys: {}", keys);
}
Map<String, Datum> datumMap = dataStore.batchGet(keys);
if (datumMap == null || datumMap.isEmpty()) {
// clear all flags of this task:
for (String key : task.getKeys()) {
taskMap.remove(buildKey(key, task.getTargetServer()));
}
return;
}
byte[] data = serializer.serialize(datumMap);
long timestamp = System.currentTimeMillis();
boolean success = NamingProxy.syncData(data, task.getTargetServer());
if (!success) {
SyncTask syncTask = new SyncTask();
syncTask.setKeys(task.getKeys());
syncTask.setRetryCount(task.getRetryCount() + 1);
syncTask.setLastExecuteTime(timestamp);
syncTask.setTargetServer(task.getTargetServer());
retrySync(syncTask);
} else {
// clear all flags of this task:
for (String key : task.getKeys()) {
taskMap.remove(buildKey(key, task.getTargetServer()));
}
}
} catch (Exception e) {
Loggers.EPHEMERAL.error("sync data failed.", e);
}
}
}, delay);
}
GlobalExecutor.submitDataSync(Runnable runnable)
提交一个数据广播任务;首先通过SyncTask
中的key
集合去DataStore
中去查询key
所对应的数据集合,然后对数据进行序列化操作,转为byte[]
数组后,执行Http
请求操作——NamingProxy.syncData(data, task.getTargetServer())
;如果数据广播失败,则将任务重新打包再次压入GlobalExecutor
中
(这里有一个疑问,SyncTask记录了任务重试的次数,但是却没有根据该次数做一些判断,比如超过多少次server未响应可能是server挂掉了,这里仅仅是记录了重试的次数)
public static boolean syncData(byte[] data, String curServer) throws Exception {
try {
Map<String, String> headers = new HashMap<>(128);
headers.put("Client-Version", UtilsAndCommons.SERVER_VERSION);
headers.put("User-Agent", UtilsAndCommons.SERVER_VERSION);
headers.put("Accept-Encoding", "gzip,deflate,sdch");
headers.put("Connection", "Keep-Alive");
headers.put("Content-Encoding", "gzip");
HttpClient.HttpResult result = HttpClient.httpPutLarge("http://" + curServer + RunningConfig.getContextPath()
+ UtilsAndCommons.NACOS_NAMING_CONTEXT + DATA_ON_SYNC_URL, headers, data);
if (HttpURLConnection.HTTP_OK == result.code) {
return true;
}
if (HttpURLConnection.HTTP_NOT_MODIFIED == result.code) {
return true;
}
throw new IOException("failed to req API:" + "http://" + curServer
+ RunningConfig.getContextPath()
+ UtilsAndCommons.NACOS_NAMING_CONTEXT + DATA_ON_SYNC_URL + ". code:"
+ result.code + " msg: " + result.content);
} catch (Exception e) {
Loggers.SRV_LOG.warn("NamingProxy", e);
}
return false;
}
这里将数据提交到了URL为PUT http://ip:port/nacos/v1/ns//distro/datum
中,而该URL对应的处理器为DistroController
中的public String onSyncDatum(HttpServletRequest request, HttpServletResponse response)
方法
public String onSyncDatum(HttpServletRequest request, HttpServletResponse response) throws Exception {
String entity = IOUtils.toString(request.getInputStream(), "UTF-8");
if (StringUtils.isBlank(entity)) {
Loggers.EPHEMERAL.error("[onSync] receive empty entity!");
throw new NacosException(NacosException.INVALID_PARAM, "receive empty entity!");
}
Map<String, Datum<Instances>> dataMap = serializer.deserializeMap(entity.getBytes(), Instances.class);
for (Map.Entry<String, Datum<Instances>> entry : dataMap.entrySet()) {
if (KeyBuilder.matchEphemeralInstanceListKey(entry.getKey())) {
String namespaceId = KeyBuilder.getNamespace(entry.getKey());
String serviceName = KeyBuilder.getServiceName(entry.getKey());
if (!serviceManager.containService(namespaceId, serviceName) && switchDomain.isDefaultInstanceEphemeral()) {
serviceManager.createEmptyService(namespaceId, serviceName, true);
}
consistencyService.onPut(entry.getKey(), entry.getValue().value);
}
}
return "ok";
}
这里会调用consistencyService.onPut(entry.getKey(), entry.getValue().value)
方法进行数据的更新,注意,onPut
方法并不会涉及taskDispatcher.addTask(key);
操作,而是将数据更新压入了Notifier
的Task
列表中(Notifier
的作用看Nacos Server端注册一个服务实例流程);至此完成了Nacos Server
在AP模式下的数据的最终一致性操作。
CP实现
重要的协议——RAFT
动画演示地址:raft-protocol)
nacos是如何实现CP(raft)的
RaftController
RaftController
控制器是raft
集群内部节点间通信使用的,具体的信息如下
POST HTTP://{ip:port}/v1/ns/raft/vote : 进行投票请求
POST HTTP://{ip:port}/v1/ns/raft/beat : Leader向Follower发送心跳信息
GET HTTP://{ip:port}/v1/ns/raft/peer : 获取该节点的RaftPeer信息
PUT HTTP://{ip:port}/v1/ns/raft/datum/reload : 重新加载某日志信息
POST HTTP://{ip:port}/v1/ns/raft/datum : Leader接收传来的数据并存入
DELETE HTTP://{ip:port}/v1/ns/raft/datum : Leader接收传来的数据删除操作
GET HTTP://{ip:port}/v1/ns/raft/datum : 获取该节点存储的数据信息
GET HTTP://{ip:port}/v1/ns/raft/state : 获取该节点的状态信息{UP or DOWN}
POST HTTP://{ip:port}/v1/ns/raft/datum/commit : Follower节点接收Leader传来得到数据存入操作
DELETE HTTP://{ip:port}/v1/ns/raft/datum : Follower节点接收Leader传来的数据删除操作
GET HTTP://{ip:port}/v1/ns/raft/leader : 获取当前集群的Leader节点信息
GET HTTP://{ip:port}/v1/ns/raft/listeners : 获取当前Raft集群的所有事件监听者
RaftPeerSet
这个对象存储的是所有raft
协议下的节点信息,存储的元素如下
// 集群节点地址管理
private ServerListManager serverListManager;
// 周期数
private AtomicLong localTerm = new AtomicLong(0L);
// 当前周期内的Leader
private RaftPeer leader = null;
// 所有的节点信息
private Map<String, RaftPeer> peers = new HashMap<String, RaftPeer>();
// 暂时不清楚用途
private Set<String> sites = new HashSet<>();
// 本节点是否已准备完毕
private boolean ready = false;
同时还具备了raft
协议下必要的方法
// 当前IP对应的节点是否是Leader
public boolean isLeader(String ip) {
if (STANDALONE_MODE) {
return true;
}
if (leader == null) {
Loggers.RAFT.warn("[IS LEADER] no leader is available now!");
return false;
}
return StringUtils.equals(leader.ip, ip);
}
// 决定Leader节点,根据投票结果以及是否满足majorityCount机制
public RaftPeer decideLeader(RaftPeer candidate) {
peers.put(candidate.ip, candidate);
SortedBag ips = new TreeBag();
int maxApproveCount = 0;
String maxApprovePeer = null;
for (RaftPeer peer : peers.values()) {
if (StringUtils.isEmpty(peer.voteFor)) {
continue;
}
// 选票计数
ips.add(peer.voteFor);
// 如果某节点的得票数大于当前的最大得票数,则更新候选Leader信息
if (ips.getCount(peer.voteFor) > maxApproveCount) {
maxApproveCount = ips.getCount(peer.voteFor);
maxApprovePeer = peer.voteFor;
}
}
// 是否满足majorityCount数量的限制
if (maxApproveCount >= majorityCount()) {
// 若满足则设置Leader节点信息
RaftPeer peer = peers.get(maxApprovePeer);
peer.state = RaftPeer.State.LEADER;
if (!Objects.equals(leader, peer)) {
leader = peer;
Loggers.RAFT.info("{} has become the LEADER", leader.ip);
}
}
return leader;
}
public RaftPeer makeLeader(RaftPeer candidate) {
// 如果当前Leader与Candidate节点不一样,则进行Leader信息更改
if (!Objects.equals(leader, candidate)) {
leader = candidate;
Loggers.RAFT.info("{} has become the LEADER, local: {}, leader: {}",
leader.ip, JSON.toJSONString(local()), JSON.toJSONString(leader));
}
for (final RaftPeer peer : peers.values()) {
Map<String, String> params = new HashMap<String, String>(1);
// 如果当前节点与远程Leader节点不等且是Follower节点
if (!Objects.equals(peer, candidate) && peer.state == RaftPeer.State.LEADER) {
try {
// 获取每个节点的RaftPeer节点信息对象数据
String url = RaftCore.buildURL(peer.ip, RaftCore.API_GET_PEER);
HttpClient.asyncHttpGet(url, null, params, new AsyncCompletionHandler<Integer>() {
@Override
public Integer onCompleted(Response response) throws Exception {
if (response.getStatusCode() != HttpURLConnection.HTTP_OK) {
Loggers.RAFT.error("[NACOS-RAFT] get peer failed: {}, peer: {}", response.getResponseBody(), peer.ip);
peer.state = RaftPeer.State.FOLLOWER;
return 1;
}
update(JSON.parseObject(response.getResponseBody(), RaftPeer.class));
return 0;
}
});
} catch (Exception e) {
peer.state = RaftPeer.State.FOLLOWER;
Loggers.RAFT.error("[NACOS-RAFT] error while getting peer from peer: {}", peer.ip);
}
}
}
return update(candidate);
}
RaftCore
该对象是nacos
中raft
协议的主要实现,在启动之初,会进行一系列初始化的操作
@PostConstruct
public void init() throws Exception {
Loggers.RAFT.info("initializing Raft sub-system");
executor.submit(notifier);
long start = System.currentTimeMillis();
// 进行日志文件的加载到内存数据对象Datums的操作
datums = raftStore.loadDatums(notifier);
// 设置当前的周期数
setTerm(NumberUtils.toLong(raftStore.loadMeta().getProperty("term"), 0L));
Loggers.RAFT.info("cache loaded, datum count: {}, current term: {}", datums.size(), peers.getTerm());
while (true) {
// 等待上一步的数据加载任务全部完成
if (notifier.tasks.size() <= 0) {
break;
}
Thread.sleep(1000L);
}
// 初始化标识更改
initialized = true;
Loggers.RAFT.info("finish to load data from disk, cost: {} ms.", (System.currentTimeMillis() - start));
// 开启定时的Leader选举任务
GlobalExecutor.registerMasterElection(new MasterElection());
// 开启定时的Leader心跳服务
GlobalExecutor.registerHeartbeat(new HeartBeat());
Loggers.RAFT.info("timer started: leader timeout ms: {}, heart-beat timeout ms: {}",
GlobalExecutor.LEADER_TIMEOUT_MS, GlobalExecutor.HEARTBEAT_INTERVAL_MS);
}
初始化的一系列操作完成后,此时集群还无法对外提供服务,因为此时Leader
还未选举出来,需要在MasterElection
选举Leader
成功后才可以对外提供服务
// Leader 选举任务
public class MasterElection implements Runnable {
@Override
public void run() {
try {
// 当前节点是否已准备完毕
if (!peers.isReady()) {
return;
}
// 获取自身节点信息
RaftPeer local = peers.local();
// 本地存储的Leader任期时间
local.leaderDueMs -= GlobalExecutor.TICK_PERIOD_MS;
// 如果Leader任期时间还在允许范围内,则不进行Leader选举
if (local.leaderDueMs > 0) {
return;
}
// reset timeout
local.resetLeaderDue();
local.resetHeartbeatDue();
// 向其他节点发起投票请求
sendVote();
} catch (Exception e) {
Loggers.RAFT.warn("[RAFT] error while master election {}", e);
}
}
public void sendVote() {
RaftPeer local = peers.get(NetUtils.localServer());
Loggers.RAFT.info("leader timeout, start voting,leader: {}, term: {}",
JSON.toJSONString(getLeader()), local.term);
// Raft node cluster rest
peers.reset();
local.term.incrementAndGet();
// 设置给自己投票
local.voteFor = local.ip;
// update node status to CANDIDATE
local.state = RaftPeer.State.CANDIDATE;
Map<String, String> params = new HashMap<String, String>(1);
params.put("vote", JSON.toJSONString(local));
// 遍历所有的节点信息(除了自己之外)
for (final String server : peers.allServersWithoutMySelf()) {
final String url = buildURL(server, API_VOTE);
try {
HttpClient.asyncHttpPost(url, null, params, new AsyncCompletionHandler<Integer>() {
@Override
public Integer onCompleted(Response response) throws Exception {
if (response.getStatusCode() != HttpURLConnection.HTTP_OK) {
Loggers.RAFT.error("NACOS-RAFT vote failed: {}, url: {}", response.getResponseBody(), url);
return 1;
}
// 获取投票结果,并进行Leader的选举工作
RaftPeer peer = JSON.parseObject(response.getResponseBody(), RaftPeer.class);
Loggers.RAFT.info("received approve from peer: {}", JSON.toJSONString(peer));
peers.decideLeader(peer);
return 0;
}
});
} catch (Exception e) {
Loggers.RAFT.warn("error while sending vote to server: {}", server);
}
}
}
}
每个节点启动时,都会认为自己可以作为Leader
,因此都会以自去己作为被选举人,向其他节点发起投票请求,而其他节点在接收到投票请求后的工作流程如下
// 其他节点接收到投票请求后的反应
public RaftPeer receivedVote(RaftPeer remote) {
// 被选举人是否在raft集群节点列表中
if (!peers.contains(remote)) {
throw new IllegalStateException("can not find peer: " + remote.ip);
}
// 获取自身节点信息
RaftPeer local = peers.get(NetUtils.localServer());
// 如果被选举节点的周期数小于本节点的周期数,则将自己的投票投给自己并告诉被选举者
if (remote.term.get() <= local.term.get()) {
String msg = "received illegitimate vote" + ", voter-term:" + remote.term + ", votee-term:" + local.term;
Loggers.RAFT.info(msg);
if (StringUtils.isEmpty(local.voteFor)) {
local.voteFor = local.ip;
}
return local;
}
// 满足投票条件后,本节点确认将自己的票投给被选举者
local.resetLeaderDue();
local.state = RaftPeer.State.FOLLOWER;
local.voteFor = remote.ip;
local.term.set(remote.term.get());
Loggers.RAFT.info("vote {} as leader, term: {}", remote.ip, remote.term);
return local;
}
通过以上步骤,最终选举出了Leader
节点,接下来,就可以对外提供服务了
因为是CP
模式,所以操作都是通过Leader
节点进行传达的,Follower
节点本身不与Client
进行联系,Follower
只能接受来自Leader
的操作请求,因此就存在请求转发的问题。因此在RaftCore
中的singlePublish
以及singleDelete
中,存在着对Leader
节点的判断以及请求转发的逻辑
public void signalPublish(String key, Record value) throws Exception {
if (!isLeader()) {
JSONObject params = new JSONObject();
params.put("key", key);
params.put("value", value);
Map<String, String> parameters = new HashMap<>(1);
parameters.put("key", key);
// 请求转发
raftProxy.proxyPostLarge(getLeader().ip, API_PUB, params.toJSONString(), parameters);
return;
}
...
}
public void signalDelete(final String key) throws Exception {
OPERATE_LOCK.lock();
try {
if (!isLeader()) {
Map<String, String> params = new HashMap<>(1);
params.put("key", URLEncoder.encode(key, "UTF-8"));
// 删除请求进行转发给 leader 进行处理
raftProxy.proxy(getLeader().ip, API_DEL, params, HttpMethod.DELETE);
return;
}
...
}
}
同时,还有一个重要的机制——心跳机制,raft
通过心跳机制来维持Leader
以及Follower
的关系
// 心跳任务,如果成为Leader,需要对 follower 发送心跳信息
public class HeartBeat implements Runnable {
@Override
public void run() {
try {
// 程序是否已准备完毕
if (!peers.isReady()) {
return;
}
RaftPeer local = peers.local();
local.heartbeatDueMs -= GlobalExecutor.TICK_PERIOD_MS;
// 心跳周期判断
if (local.heartbeatDueMs > 0) {
return;
}
// 重置心跳发送周期
local.resetHeartbeatDue();
// 发送心跳信息
sendBeat();
} catch (Exception e) {
Loggers.RAFT.warn("[RAFT] error while sending beat {}", e);
}
}
public void sendBeat() throws IOException, InterruptedException {
RaftPeer local = peers.local();
// 如果自己不是Leader节点或者处于单机模式下,则直接返回
if (local.state != RaftPeer.State.LEADER && !STANDALONE_MODE) {
return;
}
Loggers.RAFT.info("[RAFT] send beat with {} keys.", datums.size());
// 重置Leader任期时间
local.resetLeaderDue();
// build data
JSONObject packet = new JSONObject();
packet.put("peer", local);
JSONArray array = new JSONArray();
if (switchDomain.isSendBeatOnly()) {
Loggers.RAFT.info("[SEND-BEAT-ONLY] {}", String.valueOf(switchDomain.isSendBeatOnly()));
}
if (!switchDomain.isSendBeatOnly()) {
// 如果开启了在心跳包中携带Leader存储的数据进行发送,则对数据进行打包操作
for (Datum datum : datums.values()) {
JSONObject element = new JSONObject();
if (KeyBuilder.matchServiceMetaKey(datum.key)) {
element.put("key", KeyBuilder.briefServiceMetaKey(datum.key));
} else if (KeyBuilder.matchInstanceListKey(datum.key)) {
element.put("key", KeyBuilder.briefInstanceListkey(datum.key));
}
element.put("timestamp", datum.timestamp);
array.add(element);
}
} else {
Loggers.RAFT.info("[RAFT] send beat only.");
}
packet.put("datums", array);
// broadcast
Map<String, String> params = new HashMap<String, String>(1);
params.put("beat", JSON.toJSONString(packet));
// 将参数信息进行 Gzip算法压缩,降低网络消耗
String content = JSON.toJSONString(params);
ByteArrayOutputStream out = new ByteArrayOutputStream();
GZIPOutputStream gzip = new GZIPOutputStream(out);
gzip.write(content.getBytes("UTF-8"));
gzip.close();
byte[] compressedBytes = out.toByteArray();
String compressedContent = new String(compressedBytes, "UTF-8");
Loggers.RAFT.info("raw beat data size: {}, size of compressed data: {}", content.length(), compressedContent.length());
// 遍历所有的Follower节点进行发送心跳数据包
for (final String server : peers.allServersWithoutMySelf()) {
try {
final String url = buildURL(server, API_BEAT);
Loggers.RAFT.info("send beat to server " + server);
// 采用异步HTTP请求进行心跳数据发送
HttpClient.asyncHttpPostLarge(url, null, compressedBytes, new AsyncCompletionHandler<Integer>() {
@Override
public Integer onCompleted(Response response) throws Exception {
if (response.getStatusCode() != HttpURLConnection.HTTP_OK) {
Loggers.RAFT.error("NACOS-RAFT beat failed: {}, peer: {}", response.getResponseBody(), server);
MetricsMonitor.getLeaderSendBeatFailedException().increment();
}
// 成功后接收Follower节点的心跳回复(Follower节点的当前信息)进行节点更新操作
peers.update(JSON.parseObject(response.getResponseBody(), RaftPeer.class));
Loggers.RAFT.info("receive beat response from: {}", url);
return 0;
}
@Override
public void onThrowable(Throwable t) {
Loggers.RAFT.error("NACOS-RAFT error while sending heart-beat to peer: {} {}", server, t);
MetricsMonitor.getLeaderSendBeatFailedException().increment();
}
});
} catch (Exception e) {
Loggers.RAFT.error("error while sending heart-beat to peer: {} {}", server, e);
MetricsMonitor.getLeaderSendBeatFailedException().increment();
}
}
}
}
至于心跳接收的回复操作基本就是Follower
节点将自己当前的信息进行数据打包发送给Leader
节点,同时也会重置当前Leader
的任期时间信息,并且根据接收到心跳信息,进行拉取Leader
节点的最新数据信息
为什么要同时实现CP和AP两套一致性策略模式?
或许有的人会问,为什么Nacos
要同时实现CP
以及AP
两种数据的一致性策略。其实在一个组件中同时实现两种数据一致性策略,我觉得这样在做服务注册中心选型时,就不必操心AP
选什么组件,CP
选什么组件,直接采用nacos
就好了,同时满足你AP
以及CP
的数据一致性需求;直接在一个组件中,享受Zookeeper
以及Eureka
组件的服务,避免了需要同时维护两种不同的组件的运维代价,只需要根据自己的实例需求,选择不同的注册模式即可。