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Elasticsearch之client源码简要分析

问题

让我们带着问题去学习，效率会更高

1 es集群只配置一个节点，client是否能够自动发现集群中的所有节点？是如何发现的？

2 es client如何做到负载均衡？

3 一个es node挂掉之后，es client如何摘掉该节点？

4 es client node检测分为两种模式（SimpleNodeSampler和SniffNodesSampler），有什么不同？

核心类

TransportClient es client对外API类
TransportClientNodesService 维护node节点的类
ScheduledNodeSampler 定期维护正常节点类
NettyTransport 进行数据传输
NodeSampler 节点嗅探器

Client初始化过程

初始化代码

1  Settings.Builder builder = Settings.settingsBuilder()
                                   .put("cluster.name", clusterName)
                                   .put("client.transport.sniff", true);
Settings settings = builder.build(); 
2  TransportClient client = TransportClient.builder().settings(settings).build(); 
3  for (TransportAddress transportAddress : transportAddresses) {
    client.addTransportAddress(transportAddress);
}

1 ES 通过builder模式构造了基础的配置参数；

2 通过build构造了client，这个时候包括构造client、初始化ThreadPool、构造TransportClientNodesService、启动定时任务、定制化嗅探类型；

3 添加集群可用地址，比如我只配了集群中的一个节点；

构建client

调用build API

其中，关于依赖注入的简单说明：Guice 是 Google 用于 Java™ 开发的开放源码依赖项注入框架（感兴趣的可以了解下，这里不做重点讲解），具体可参考下边链接：

初始化TransportClientNodesService

在上一幅图的 modules.createInjector对TransportClientNodesService进行实例化，在TransportClient进行注入，可以看到TransportClient里边的绝大部分API都是通过TransportClientNodesService进行代理的

Guice通过注解进行注入

在上图中：注入了集群名称、线程池等，重点是如下代码：该段代码选择了节点嗅探器的类型嗅探同一集群中的所有节点（SniffNodesSampler）或者是只关注配置文件配置的节点（SimpleNodeSampler）

if (this.settings.getAsBoolean("client.transport.sniff", false)) {
    this.nodesSampler = new SniffNodesSampler();
} else {
    this.nodesSampler = new SimpleNodeSampler();
}

特点：

SniffNodesSampler：client会主动发现集群里的其他节点，会创建fully connect(什么叫fully connect?后边说)

SimpleNodeSampler：ping listedNodes中的所有node，区别在于这里创建的都是light connect；

其中TransportClientNodesService维护了三个节点存储数据结构：

// nodes that are added to be discovered
1 private volatile List<DiscoveryNode> listedNodes = Collections.emptyList(); 
2 private volatile List<DiscoveryNode> nodes = Collections.emptyList(); 
3 private volatile List<DiscoveryNode> filteredNodes = Collections.emptyList();

1 代表配置文件中主动加入的节点；

2 代表参与请求的节点；

3 过滤掉的不能进行请求处理的节点；

Client如何做到负载均衡

如上图，我们发现每次 execute 的时候，是从 nodes 这个数据结构中获取节点，然后通过简单的 rouund-robbin 获取节点服务器；核心代码如下：

private final AtomicInteger randomNodeGenerator = new AtomicInteger();
......
private int getNodeNumber() {
    int index = randomNodeGenerator.incrementAndGet();
    if (index < 0) {
        index = 0;
        randomNodeGenerator.set(0);
    }
    return index;
}

然后通过netty的channel将数据写入，核心代码如下：

public void sendRequest(final DiscoveryNode node, final long requestId, final String action, final TransportRequest request, TransportRequestOptions options) throws IOException, TransportException {
 
1    Channel targetChannel = nodeChannel(node, options); 
 
    if (compress) {
        options = TransportRequestOptions.builder(options).withCompress(true).build();
    }
 
    byte status = 0;
    status = TransportStatus.setRequest(status);
 
    ReleasableBytesStreamOutput bStream = new ReleasableBytesStreamOutput(bigArrays);
    boolean addedReleaseListener = false;
    try {
        bStream.skip(NettyHeader.HEADER_SIZE);
        StreamOutput stream = bStream;
        // only compress if asked, and, the request is not bytes, since then only
        // the header part is compressed, and the "body" can't be extracted as compressed
        if (options.compress() && (!(request instanceof BytesTransportRequest))) {
            status = TransportStatus.setCompress(status);
            stream = CompressorFactory.defaultCompressor().streamOutput(stream);
        }
 
        // we pick the smallest of the 2, to support both backward and forward compatibility
        // note, this is the only place we need to do this, since from here on, we use the serialized version
        // as the version to use also when the node receiving this request will send the response with
        Version version = Version.smallest(this.version, node.version());
 
        stream.setVersion(version);
        stream.writeString(action);
 
        ReleasablePagedBytesReference bytes;
        ChannelBuffer buffer;
        // it might be nice to somehow generalize this optimization, maybe a smart "paged" bytes output
        // that create paged channel buffers, but its tricky to know when to do it (where this option is
        // more explicit).
        if (request instanceof BytesTransportRequest) {
            BytesTransportRequest bRequest = (BytesTransportRequest) request;
            assert node.version().equals(bRequest.version());
            bRequest.writeThin(stream);
            stream.close();
            bytes = bStream.bytes();
            ChannelBuffer headerBuffer = bytes.toChannelBuffer();
            ChannelBuffer contentBuffer = bRequest.bytes().toChannelBuffer();
            buffer = ChannelBuffers.wrappedBuffer(NettyUtils.DEFAULT_GATHERING, headerBuffer, contentBuffer);
        } else {
            request.writeTo(stream);
            stream.close();
            bytes = bStream.bytes();
            buffer = bytes.toChannelBuffer();
        }
        NettyHeader.writeHeader(buffer, requestId, status, version);
2        ChannelFuture future = targetChannel.write(buffer);
        ReleaseChannelFutureListener listener = new ReleaseChannelFutureListener(bytes);
        future.addListener(listener);
        addedReleaseListener = true;
        transportServiceAdapter.onRequestSent(node, requestId, action, request, options);
    } finally {
        if (!addedReleaseListener) {
            Releasables.close(bStream.bytes());
        }
    }
}

View Code

其中最重要的就是1和2，中间一段是处理数据和进行一些必要的步骤

1代表拿到一个连接；

2代表通过拿到的连接写数据；

这时候就会有新的问题

1 nodes的数据是何时写入的？

2 连接是什么时候创建的？

Nodes数据何时写入

核心是调用doSampler，代码如下：

protected void doSample() {
    // the nodes we are going to ping include the core listed nodes that were added
    // and the last round of discovered nodes
    Set<DiscoveryNode> nodesToPing = Sets.newHashSet();
    for (DiscoveryNode node : listedNodes) {
        nodesToPing.add(node);
    }
    for (DiscoveryNode node : nodes) {
        nodesToPing.add(node);
    }
 
    final CountDownLatch latch = new CountDownLatch(nodesToPing.size());
    final ConcurrentMap<DiscoveryNode, ClusterStateResponse> clusterStateResponses = ConcurrentCollections.newConcurrentMap();
    for (final DiscoveryNode listedNode : nodesToPing) {
        threadPool.executor(ThreadPool.Names.MANAGEMENT).execute(new Runnable() {
            @Override
            public void run() {
                try {
                    if (!transportService.nodeConnected(listedNode)) {
                        try {
 
                            // if its one of the actual nodes we will talk to, not to listed nodes, fully connect
                            if (nodes.contains(listedNode)) {
                                logger.trace("connecting to cluster node [{}]", listedNode);
                                transportService.connectToNode(listedNode);
                            } else {
                                // its a listed node, light connect to it...
                                logger.trace("connecting to listed node (light) [{}]", listedNode);
                                transportService.connectToNodeLight(listedNode);
                            }
                        } catch (Exception e) {
                            logger.debug("failed to connect to node [{}], ignoring...", e, listedNode);
                            latch.countDown();
                            return;
                        }
                    }
                    //核心是在这里，刚刚开始初始化的时候，可能只有配置的一个节点，这个时候会通过这个地址发送一个state状态监测
                    //"cluster:monitor/state"
                    transportService.sendRequest(listedNode, ClusterStateAction.NAME,
                            headers.applyTo(Requests.clusterStateRequest().clear().nodes(true).local(true)),
                            TransportRequestOptions.builder().withType(TransportRequestOptions.Type.STATE).withTimeout(pingTimeout).build(),
                            new BaseTransportResponseHandler<ClusterStateResponse>() {
 
                                @Override
                                public ClusterStateResponse newInstance() {
                                    return new ClusterStateResponse();
                                }
 
                                @Override
                                public String executor() {
                                    return ThreadPool.Names.SAME;
                                }
 
                                @Override
                                public void handleResponse(ClusterStateResponse response) {
/*通过回调，会在这个地方返回集群中类似下边所有节点的信息
{
  "version" : 27,
  "state_uuid" : "YSI9d_HiQJ-FFAtGFCVOlw",
  "master_node" : "TXHHx-XRQaiXAxtP1EzXMw",
  "blocks" : { },
  "nodes" : {
    "7" : {
      "name" : "es03",
      "transport_address" : "1.1.1.1:9300",
      "attributes" : {
        "data" : "false",
        "master" : "true"
      }
    },
    "6" : {
      "name" : "common02",
      "transport_address" : "1.1.1.2:9300",
      "attributes" : {
        "master" : "false"
      }
    },
    "5" : {
      "name" : "es02",
      "transport_address" : "1.1.1.3:9300",
      "attributes" : {
        "data" : "false",
        "master" : "true"
      }
    },
    "4" : {
      "name" : "common01",
      "transport_address" : "1.1.1.4:9300",
      "attributes" : {
        "master" : "false"
      }
    },
    "3" : {
      "name" : "common03",
      "transport_address" : "1.1.1.5:9300",
      "attributes" : {
        "master" : "false"
      }
    },
    "2" : {
      "name" : "es01",
      "transport_address" : "1.1.1.6:9300",
      "attributes" : {
        "data" : "false",
        "master" : "true"
      }
    },
    "1" : {
      "name" : "common04",
      "transport_address" : "1.1.1.7:9300",
      "attributes" : {
        "master" : "false"
      }
    }
  },
  "metadata" : {
    "cluster_uuid" : "_na1x_",
    "templates" : { },
    "indices" : { }
  },
  "routing_table" : {
    "indices" : { }
  },
  "routing_nodes" : {
    "unassigned" : [ ],
  }
}
*/
                                    clusterStateResponses.put(listedNode, response);
                                    latch.countDown();
                                }
 
                                @Override
                                public void handleException(TransportException e) {
                                    logger.info("failed to get local cluster state for {}, disconnecting...", e, listedNode);
                                    transportService.disconnectFromNode(listedNode);
                                    latch.countDown();
                                }
                            });
                } catch (Throwable e) {
                    logger.info("failed to get local cluster state info for {}, disconnecting...", e, listedNode);
                    transportService.disconnectFromNode(listedNode);
                    latch.countDown();
                }
            }
        });
    }
 
    try {
        latch.await();
    } catch (InterruptedException e) {
        return;
    }
 
    HashSet<DiscoveryNode> newNodes = new HashSet<>();
    HashSet<DiscoveryNode> newFilteredNodes = new HashSet<>();
    for (Map.Entry<DiscoveryNode, ClusterStateResponse> entry : clusterStateResponses.entrySet()) {
        if (!ignoreClusterName && !clusterName.equals(entry.getValue().getClusterName())) {
            logger.warn("node {} not part of the cluster {}, ignoring...", entry.getValue().getState().nodes().localNode(), clusterName);
            newFilteredNodes.add(entry.getKey());
            continue;
        }
//接下来在这个地方拿到所有的data nodes 写入到nodes节点里边
        for (ObjectCursor<DiscoveryNode> cursor : entry.getValue().getState().nodes().dataNodes().values()) {
            newNodes.add(cursor.value);
        }
    }
 
    nodes = validateNewNodes(newNodes);
    filteredNodes = Collections.unmodifiableList(new ArrayList<>(newFilteredNodes));
}

View Code

其中调用时机分为两部分：

1 client.addTransportAddress(transportAddress);

2 ScheduledNodeSampler，默认每隔5s会进行一次对各个节点的请求操作；

连接是何时创建的呢

也是在doSampler调用，最终由NettryTransport创建

这个时候发现，如果是light则创建轻连接，也就是，否则创建fully connect，其中包括

recovery：做数据恢复recovery，默认个数2个；
bulk：用于bulk请求，默认个数3个；
med/reg：典型的搜索和单doc索引，默认个数6个；
high:如集群state的发送等，默认个数1个；
ping：就是node之间的ping咯。默认个数1个；

对应的代码为：

public void start() {
    List<Channel> newAllChannels = new ArrayList<>();
    newAllChannels.addAll(Arrays.asList(recovery));
    newAllChannels.addAll(Arrays.asList(bulk));
    newAllChannels.addAll(Arrays.asList(reg));
    newAllChannels.addAll(Arrays.asList(state));
    newAllChannels.addAll(Arrays.asList(ping));
    this.allChannels = Collections.unmodifiableList(newAllChannels);
}

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原文地址：https://www.cnblogs.com/zhangxiaoguang/p/5824749.html