Elasticsearch2.3.3 java client s
2016-11-18 本文已影响479人
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问题
-
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？如下配置了一个节点：
单个node配置
- es client如何做到负载均衡？
- 一个es node挂掉之后，es client如何摘掉该节点？
- es client node检测分为两种模式，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 : 节点嗅探类
Client初始化过程
初始化代码
Settings.Builder builder = Settings.settingsBuilder()
.put("cluster.name", clusterName)
.put("client.transport.sniff", true);
Settings settings = builder.build();
TransportClient client = TransportClient.builder().settings(settings).build();
for (TransportAddress transportAddress : transportAddresses) {
client.addTransportAddress(transportAddress);
}
- ES 通过builder模式构造了基础的配置参数；
- 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；
- 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
build code
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），具体可参考如下：
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：
TransportClient code
Guice通过注解进行注入
Guice 注解注入code
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 volatileListlistedNodes= Collections.emptyList();
2 private volatileListnodes= Collections.emptyList();
3 private volatileListfilteredNodes= Collections.emptyList();
- 代表配置文件中主动加入的节点；
- 代表参与请求的节点；
- 过滤掉的不能进行请求处理的节点；
Client如何做到负载均衡
负载均衡code
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());
}
}
}
其中最重要的就是1和2
- 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
- nodes的数据是何时写入的？
- 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
SetnodesToPing = Sets.newHashSet();
for (DiscoveryNode node : listedNodes) {
nodesToPing.add(node);
}
for (DiscoveryNode node : nodes) {
nodesToPing.add(node);
}
final CountDownLatch latch = new CountDownLatch(nodesToPing.size());
final ConcurrentMapclusterStateResponses = 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.STAE).withTimeout(pingTimeout).build(),
new BaseTransportResponseHandler() {
@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" : { "poxubF0LTVue84GMrZ7rwA" : { "name" : "node1", "transport_address" : "1.1.1.1:8888", "attributes" : { "data" : "false", "master" : "true" } }, "9Cz8m3GkTza7vgmpf3L65Q" : { "name" : "node2", "transport_address" : "1.1.1.2:8889", "attributes" : { "master" : "false" } } }, "metadata" : { "cluster_uuid" : "_na_", "templates" : { }, "indices" : { } }, "routing_table" : { "indices" : { } }, "routing_nodes" : { "unassigned" : [ ], "nodes" : { "lZqD-WExRu-gaSUiCXaJcg" : [ ], "hR6PbFrgQVSY0MHajNDmgA" : [ ], } }}*/
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;
}
HashSetnewNodes = new HashSet<>(); HashSetnewFilteredNodes = new HashSet<>();
for (Map.Entryentry : 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 (ObjectCursorcursor : entry.getValue().getState().nodes().dataNodes().values()){
newNodes.add(cursor.value);}}
nodes = validateNewNodes(newNodes);
filteredNodes = Collections.unmodifiableList(new ArrayList<(newFilteredNodes));
}
其中调用时机分为两部分：
- client.addTransportAddress(transportAddress);
- 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
创建连接code
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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