MQTT---HiveMQ源码详解(十七)Cluster-Consistent Hashing Ring & Node Lifecycle

源博客地址:http://blog.csdn.net/pipinet123

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Consistent Hashing Ring

基本上只要做Cluster，都会使用到一致性Hash环，具体作用此处就不细讲，我们只了解HiveMQ怎么用它，怎么实现它，这样实现能够带来什么好处。

• HiveMQ没有Master/Slave,它只由JGroup View(详情请查阅JGroup)第一个node作为Coordinator，这样就可以达到一个node也可以做集群(虽然这样的集群没有什么卵用)。

• HiveMQ采用两个一致性Hash环,来解决脑裂问题，以及脑裂后merge的问题。

• 每个node 500个虚拟节点，来增加node变化带来的动荡问题。

• Primary环:排除joining的node,即只添加RUNNING状态的node。

• Minority环:包含joining的node,即添加JOINING、RUNNING、MERGING状态的node。

• 它的hash算法由net.openhft.hashing.LongHashFunction.xx_r39()提供

ConsistentHashingRing源码

相对来说比较简单，我就不一行一行写注释了，网上针对一致性hash环实现各种版本到处都是，详细讲解也到处都是。

@Singleton
public class ConsistentHashingRing {
    private static final Logger LOGGER = LoggerFactory.getLogger(ConsistentHashingRing.class);
    private final String name;
    public static final int NODE_BUCKET_COUNT = 500;
    private final LongHashFunction hashFunction;
    private final ReadWriteLock readWriteLock = new ReentrantReadWriteLock(true);
    @VisibleForTesting
    final NavigableMap buckets;
    @VisibleForTesting
    final ConcurrentHashMap bucketNodes = new ConcurrentHashMap<>();
    final Set nodes = Sets.newConcurrentHashSet();

    public ConsistentHashingRing(String name, LongHashFunction hashFunction) {
        this.name = name;
        this.buckets = new ConcurrentSkipListMap();
        this.hashFunction = hashFunction;
    }

    public void add(@NotNull String node) {
        Preconditions.checkNotNull(node, "Name must not be null");
        LOGGER.trace("Add node {} to the {}.", node, this.name);
        Lock lock = this.readWriteLock.writeLock();
        lock.lock();
        try {
            for (int bucketIndex = 0; bucketIndex < NODE_BUCKET_COUNT; bucketIndex++) {
                long bucketHash = this.hashFunction.hashChars(node + bucketIndex);
                if (this.buckets.containsKey(bucketHash)) {
                    if (this.buckets.get(bucketHash).compareTo(node + 1) > 0) {
                        this.buckets.put(bucketHash, node + bucketIndex);
                        this.nodes.add(node);
                        this.bucketNodes.put(node + bucketIndex, node);
                    }
                } else {
                    this.buckets.put(bucketHash, node + bucketIndex);
                    this.nodes.add(node);
                    this.bucketNodes.put(node + bucketIndex, node);
                }
            }
        } finally {
            lock.unlock();
        }
    }

    public void remove(@NotNull String node) {
        Preconditions.checkNotNull(node, "Name must not be null");
        LOGGER.trace("Remove node {} from the {}.", node, this.name);
        Lock lock = this.readWriteLock.writeLock();
        lock.lock();
        try {
            for (int bucketIndex = 0; bucketIndex < NODE_BUCKET_COUNT; bucketIndex++) {
                long bucketHash = this.hashFunction.hashChars(node + bucketIndex);
                this.buckets.remove(bucketHash);
                this.bucketNodes.remove(node + bucketIndex);
            }
            this.nodes.remove(node);
        } finally {
            lock.unlock();
        }
    }

    public Set getReplicaNodes(@NotNull String key, int replicateCount) {
        Preconditions.checkNotNull(key, "key must not be null");
        int nodeCount = this.nodes.size();
        if (replicateCount > nodeCount - 1) {
            LOGGER.trace("There are not enough buckets in the consistent hash ring for {} replicas.", replicateCount);
            replicateCount = nodeCount - 1;
        }
        String bucket = getBucket(key);
        long bucketHash = this.hashFunction.hashChars(bucket);
        Lock lock = this.readWriteLock.readLock();
        lock.lock();
        Set buckets = new HashSet<>();
        try {
            for (Map.Entry entry = this.buckets.higherEntry(bucketHash);
                 buckets.size() < replicateCount;
                 entry = this.buckets.higherEntry(entry.getKey())) {
                if (entry == null) {
                    entry = this.buckets.firstEntry();
                }
                if (!this.bucketNodes.get(entry.getValue()).equals(this.bucketNodes.get(bucket))) {
                    buckets.add(this.bucketNodes.get(entry.getValue()));
                }
            }
            return buckets;
        } finally {
            lock.unlock();
        }
    }

    public Set getNodes() {
        ImmutableSet.Builder builder = ImmutableSet.builder();
        Lock lock = this.readWriteLock.readLock();
        lock.lock();
        try {
            return builder.addAll(this.nodes).build();
        } finally {
            lock.unlock();
        }
    }

    public String getBucket(@NotNull String key) {
        Preconditions.checkNotNull(key, "key must not be null");
        if (this.buckets.isEmpty()) {
            throw new IllegalStateException("Consistent hash ring is empty.");
        }
        long keyHash = this.hashFunction.hashChars(key);
        Lock lock = this.readWriteLock.readLock();
        lock.lock();
        try {
            Map.Entry entry = this.buckets.ceilingEntry(keyHash);
            if (entry != null) {
                return entry.getValue();
            }
            return this.buckets.ceilingEntry(Long.MIN_VALUE).getValue();
        } finally {
            lock.unlock();
        }
    }

    public String getNode(@NotNull String key) {
        Preconditions.checkNotNull(key, "key must not be null");
        if (this.buckets.isEmpty()) {
            throw new IllegalStateException("Consistent hash ring is empty.");
        }
        long keyHash = this.hashFunction.hashChars(key);
        Lock lock = this.readWriteLock.readLock();
        lock.lock();
        try {
            Map.Entry entry = this.buckets.ceilingEntry(keyHash);
            if (entry != null) {
                return this.bucketNodes.get(entry.getValue());
            }
            return this.bucketNodes.get(this.buckets.ceilingEntry(Long.MIN_VALUE).getValue());
        } finally {
            lock.unlock();
        }
    }
}

Node Lifecycle

其实了解了上面HiveMQ Cluster的基础之后，再来看node的生命周期，就是一件简单的事情了。

废话少说，我们直接上状态变化图。

各种状态简介

UNKNOWN

当JGroup通知新的node连接,但在本地不存在,则该node状态标记为UNKNOWN

NOT_JOINED

当node连接上JGroup后,若它不是唯一的node,则它将自己主动标记为NOT_JOINED

JOINING

当node将自己的状态更新至Cluster完成后,它将自己主动标记为JOINING

MERGE_MINORITY

当脑裂后与Coordinator在同组的其他node都将被标记为MERGE_MINORITY;或者加入Primary Group失败后它将自己主动标记为MERGE_MINORITY

MERGING

MERGE_MINORITY会一直去尝试主动将自己标记为MERGING

RUNNING

当MERGING成功后,node将会进行Replicate操作,当Replicate操作完成,就主动将自己标记为RUNNING

SHUTTING_DOWN/SHUTDOWN_FINISHED/DEAD

这三种状态在源码中未被使用,但HiveMQ还这样定义，或许是保留吧，反正博主未搞懂，不过不重要，不懂就算了，^_^。