面向高稳定，高性能之-Hbase数据实时同步到ElasticSearch(之二)

我在这篇focus在两个主题：如何支持多表同步共用一个jar包，如何持续稳定的与ES交互写入数据。在《使用Hbase协作器(Coprocessor)同步数据到ElasticSearch 》中作者把两个关键组件中的属性和方法都声明为static，这意味什么？类方法和属性在所有的线程中共享，源代码请参考该博客。问题出来了，当你用如下传参数的方式绑定到多个表：

alter 'test_record', METHOD => 'table_att', 'coprocessor' => 'hdfs:///hbase_es/hbase-observer-elasticsearch-1.0-SNAPSHOT-zcestestrecord.jar|org.eminem.hbase.observer.HbaseDataSyncEsObserver|1001|es_cluster=zcits,es_type=zcestestrecord,es_index=zcestestrecord,es_port=9100,es_host=master'

Hbase中的多个表同步到ES会串数据，什么意思？比如说，同步Hbase中的A、B表到ES中A`、B`，A表的数据都到B`中了。造成这种错误的原因就是上述两个构件使用了静态的方法和属性。如何改正，就是都改为非静态的方法和类，用到该构件的时候实例化。代码如下：

EsClient构件：

public class EsClient {

	// ElasticSearch的集群名称
	private String clusterName;
	// ElasticSearch的host
	private String[] nodeHost;
	// ElasticSearch的端口（Java API用的是Transport端口，也就是TCP）
	private int nodePort;
	private TransportClient client = null;

	private static final Log LOG = LogFactory.getLog(EsClient.class);

	/**
	 * get Es config
	 * 
	 * @return
	 */
	public EsClient(String clusterName, String nodeHost, int nodePort) {
		this.clusterName = clusterName;
		this.nodeHost = nodeHost.split("-");
		this.nodePort = nodePort;
		this.client = initEsClient();

	}

	public String getInfo() {
		List<String> fields = new ArrayList<String>();
		try {
			for (Field f : EsClient.class.getDeclaredFields()) {
				fields.add(f.getName() + "=" + f.get(this));
			}
		} catch (IllegalAccessException ex) {
			ex.printStackTrace();
		}
		return StringUtils.join(fields, ", ");
	}

	public String getOneNodeHost() {
		if (this.nodeHost == null || this.nodeHost.length == 0) {
			return "";
		}
		Random rand = new Random();
		return nodeHost[rand.nextInt(this.nodeHost.length)];

	}

	/**
	 * init ES client
	 */
	public TransportClient initEsClient() {
		LOG.info("---------- Init ES Client " + this.clusterName + " -----------");
		TransportClient client = null;
		Settings settings = Settings.builder().put("cluster.name", this.clusterName).put("client.transport.sniff", true).build();

		try {
			client = new PreBuiltTransportClient(settings).addTransportAddress(new InetSocketTransportAddress(InetAddress.getByName(getOneNodeHost()), this.nodePort));
		} catch (UnknownHostException e) {
			e.printStackTrace();
		}
		return client;
	}

	public void repeatInitEsClient() {
		this.client = initEsClient();
	}

	/**
	 * @return the clusterName
	 */
	public String getClusterName() {
		return clusterName;
	}

	/**
	 * @param clusterName the clusterName to set
	 */
	public void setClusterName(String clusterName) {
		this.clusterName = clusterName;
	}

	/**
	 * @return the nodePort
	 */
	public int getNodePort() {
		return nodePort;
	}

	/**
	 * @param nodePort the nodePort to set
	 */
	public void setNodePort(int nodePort) {
		this.nodePort = nodePort;
	}

	/**
	 * @return the client
	 */
	public TransportClient getClient() {
		return client;
	}

	/**
	 * @param client the client to set
	 */
	public void setClient(TransportClient client) {
		this.client = client;
	}

}

ElasticSearchBulkOperator构件：

public class ElasticSearchBulkOperator {

	private static final Log LOG = LogFactory.getLog(ElasticSearchBulkOperator.class);

	private static final int MAX_BULK_COUNT = 5000;

	private BulkRequestBuilder bulkRequestBuilder = null;

	private Lock commitLock = new ReentrantLock();

	private ScheduledExecutorService scheduledExecutorService = null;
	private EsClient esClient = null;

	public ElasticSearchBulkOperator(final EsClient esClient) {
		LOG.info("----------------- Init Bulk Operator for Table: " + " ----------------");
		this.esClient = esClient;
		// init es bulkRequestBuilder
		this.bulkRequestBuilder = esClient.getClient().prepareBulk();
		// init thread pool and set size 1
		this.scheduledExecutorService = Executors.newScheduledThreadPool(1);

		// create beeper thread( it will be sync data to ES cluster)use a commitLock to protected bulk es as thread-save
		Runnable beeper = new Runnable() {
			@Override
			public void run() {
				commitLock.lock();
				try {
					LOG.info("Scheduled Thread start run for ");
					bulkRequest(0);
				} catch (Exception ex) {
					LOG.error("Time Bulk " + " index error : " + ex.getMessage());
				} finally {
					commitLock.unlock();
				}
			}
		};

		// set beeper thread(15 second to delay first execution , 25 second period between successive executions)
		scheduledExecutorService.scheduleAtFixedRate(beeper, 15, 25, TimeUnit.SECONDS);

	}

	/**
	 * shutdown time task immediately
	 */
	public void shutdownScheduEx() {
		if (null != scheduledExecutorService && !scheduledExecutorService.isShutdown()) {
			scheduledExecutorService.shutdown();
		}
	}

	/**
	 * bulk request when number of builders is grate then threshold
	 * 
	 * @param threshold
	 */
	public void bulkRequest(int threshold) {
		int count = bulkRequestBuilder.numberOfActions();
		if (bulkRequestBuilder.numberOfActions() > threshold) {
			try {
				LOG.info("Bulk Request Run " + ", the row count is: " + count);
				BulkResponse bulkItemResponse = bulkRequestBuilder.execute().actionGet();
				if (bulkItemResponse.hasFailures()) {
					LOG.error("------------- Begin: Error Response Items of Bulk Requests to ES ------------");
					LOG.error(bulkItemResponse.buildFailureMessage());
					LOG.error("------------- End: Error Response Items of Bulk Requests to ES ------------");
				}
				bulkRequestBuilder = esClient.getClient().prepareBulk();
			} catch (Exception e) {// two cause: 1. transport client is closed 2. None of the configured nodes are available
				LOG.error(" Bulk Request " + " index error : " + e.getMessage());
				LOG.error("Reconnect the ES server...");
				List<DocWriteRequest> tempRequests = bulkRequestBuilder.request().requests();
				esClient.getClient().close();
				esClient.repeatInitEsClient();
				bulkRequestBuilder = esClient.getClient().prepareBulk();
				bulkRequestBuilder.request().add(tempRequests);
			}
		}
	}

	/**
	 * add update builder to bulk use commitLock to protected bulk as
	 * thread-save
	 * 
	 * @param builder
	 */
	public void addUpdateBuilderToBulk(UpdateRequestBuilder builder) {
		commitLock.lock();
		try {
			bulkRequestBuilder.add(builder);
			bulkRequest(MAX_BULK_COUNT);
		} catch (Exception ex) {
			LOG.error(" Add Bulk index error : " + ex.getMessage());
		} finally {
			commitLock.unlock();
		}
	}

	/**
	 * add delete builder to bulk use commitLock to protected bulk as
	 * thread-save
	 * 
	 * @param builder
	 */
	public void addDeleteBuilderToBulk(DeleteRequestBuilder builder) {
		commitLock.lock();
		try {
			bulkRequestBuilder.add(builder);
			bulkRequest(MAX_BULK_COUNT);
		} catch (Exception ex) {
			LOG.error(" delete Bulk index error : " + ex.getMessage());
		} finally {
			commitLock.unlock();
		}
	}
}

注意：我在TransportClient的setting中用了 "client.transport.sniff"=true，这对持续同步的稳定性至关重要，前提ES是多台机器的集群。这样就可以实现多个表同时绑定一个jar包传入不同参数时，不发生串表的奇怪现象。 ElasticSearchBulkOperator构件的bulkRequest方法至关重要，写不好轻则同步数据丢失，重则Hbase挂掉。比如，导致Hbase中RegionServer的堆积的RPC过多，导致数据不能写入Hbase，如下图所示：

面向高稳定，高性能之-Hbase数据实时同步到ElasticSearch(之二)

为什么会出现上图这种情况，我建议研究一下Coprocessor运行机制，以及RegionServer与Master的交互机制。在这里就不多说了。我们有必要花点时间研究一下bulkRequest方法：我采取定时定量的方式来执行一次BulkRequest方法， BulkResponse bulkItemResponse = bulkRequestBuilder.execute().actionGet(); 该次交互有序的请求多条Requests，返回对应的多依次次对应的Responses结果，可能某些条数据没有成功，很大原因是ES的Mapping类型抛出异常，导致数据插入失败，题外话就是一定要进行数据的校验和帅选。言归正传，这些没有成功的数据你如何处理这要看你的处理机制——直接舍弃，还是记在某些地方。千万不要像《使用Hbase协作器(Coprocessor)同步数据到ElasticSearch 》中那样： if (!bulkItemResponse.hasFailures()) { bulkRequestBuilder = ESClient.client.prepareBulk(); } 这样你会死的很惨。 esclient不能保证一直连接不失败吧，所以要有重连机制，这对单台的ES服务器至关重要。上述代码列出了两种esclient连接断掉的原因：1. transport client is closed 2. None of the configured nodes are available。为了不丢失上次请求失败的数据，我们要把这些数据加入到新建的esclient中的bulkRequestBuilder，重新发送，逻辑如下：

LOG.error("Reconnect the ES server...");
List<DocWriteRequest> tempRequests = bulkRequestBuilder.request().requests();
esClient.getClient().close();
esClient.repeatInitEsClient();
bulkRequestBuilder = esClient.getClient().prepareBulk();
bulkRequestBuilder.request().add(tempRequests);

上述都是必须要做的，你也可以扩展，比如设计重连机制。第二篇就讲到这吧，下一篇写一些如何把Coprocessor的侵入性给Hbase带来的宕机风险降到最低，如何高效的实现同步，以及心得。

面向高稳定，高性能之-Hbase数据实时同步到ElasticSearch(之二)

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