近期接到一个任务,需要改造现有从mysql往Elasticsearch导入数据MTE(mysqlToEs)小工具,由于之前采用单线程导入,千亿数据需要两周左右的时间才能导入完成,导入效率非常低。所以楼主花了3天的时间,利用java线程池框架Executors中的FixedThreadPool线程池重写了MTE导入工具,单台服务器导入效率提高十几倍(合理调整线程数据,效率更高)。

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maven依赖
mysql mysql-connector-java ${mysql.version} org.elasticsearch elasticsearch ${elasticsearch.version} org.elasticsearch.client transport ${elasticsearch.version} org.projectlombok lombok ${lombok.version} com.alibaba fastjson ${fastjson.version} 
java线程池设置
默认线程池大小为21个,可调整。其中POR为处理流程已办数据线程池,ROR为处理流程已阅数据线程池。
- private static int THREADS = 21;
 - public static ExecutorService POR = Executors.newFixedThreadPool(THREADS);
 - public static ExecutorService ROR = Executors.newFixedThreadPool(THREADS);
 
定义已办生产者线程/已阅生产者线程:ZlPendProducer/ZlReadProducer
- public class ZlPendProducer implements Runnable {
 - ...
 - @Override
 - public void run() {
 - System.out.println(threadName + "::启动...");
 - for (int j = 0; j < Const.TBL.TBL_PEND_COUNT; j++)
 - try {
 - ....
 - int size = 1000;
 - for (int i = 0; i < count; i += size) {
 - if (i + size > count) {
 - //作用为size***没有100条数据则剩余几条newList中就装几条
 - size = count - i;
 - }
 - String sql = "select * from " + tableName + " limit " + i + ", " + size;
 - System.out.println(tableName + "::sql::" + sql);
 - rs = statement.executeQuery(sql);
 - List
 lst = new ArrayList<>(); - while (rs.next()) {
 - HistPendingEntity p = PendUtils.getHistPendingEntity(rs);
 - lst.add(p);
 - }
 - MteExecutor.POR.submit(new ZlPendConsumer(lst));
 - Thread.sleep(2000);
 - }
 - ....
 - } catch (Exception e) {
 - e.printStackTrace();
 - }
 - }
 - }
 - public class ZlReadProducer implements Runnable {
 - ...已阅生产者处理逻辑同已办生产者
 - }
 
定义已办消费者线程/已阅生产者线程:ZlPendConsumer/ZlReadConsumer
- public class ZlPendConsumer implements Runnable {
 - private String threadName;
 - private List
 lst; - public ZlPendConsumer(List
 lst) { - this.lst = lst;
 - }
 - @Override
 - public void run() {
 - ...
 - lst.forEach(v -> {
 - try {
 - String json = new Gson().toJson(v);
 - EsClient.addDataInJSON(json, Const.ES.HistPendDB_Index, Const.ES.HistPendDB_type, v.getPendingId(), null);
 - Const.COUNTER.LD_P.incrementAndGet();
 - } catch (Exception e) {
 - e.printStackTrace();
 - System.out.println("err::PendingId::" + v.getPendingId());
 - }
 - });
 - ...
 - }
 - }
 - public class ZlReadConsumer implements Runnable {
 - //已阅消费者处理逻辑同已办消费者
 - }
 
定义导入Elasticsearch数据监控线程:Monitor
监控线程-Monitor为了计算每分钟导入Elasticsearch的数据总条数,利用监控线程,可以调整线程池的线程数的大小,以便利用多线程更快速的导入数据。
- public void monitorToES() {
 - new Thread(() -> {
 - while (true) {
 - StringBuilder sb = new StringBuilder();
 - sb.append("已办表数::").append(Const.TBL.TBL_PEND_COUNT)
 - .append("::已办总数::").append(Const.COUNTER.LD_P_TOTAL)
 - .append("::已办入库总数::").append(Const.COUNTER.LD_P);
 - sb.append("~~~~已阅表数::").append(Const.TBL.TBL_READ_COUNT);
 - sb.append("::已阅总数::").append(Const.COUNTER.LD_R_TOTAL)
 - .append("::已阅入库总数::").append(Const.COUNTER.LD_R);
 - if (ldPrevPendCount == 0 && ldPrevReadCount == 0) {
 - ldPrevPendCount = Const.COUNTER.LD_P.get();
 - ldPrevReadCount = Const.COUNTER.LD_R.get();
 - start = System.currentTimeMillis();
 - } else {
 - long end = System.currentTimeMillis();
 - if ((end - start) / 1000 >= 60) {
 - start = end;
 - sb.append("\n#########################################\n");
 - sb.append("已办每分钟TPS::" + (Const.COUNTER.LD_P.get() - ldPrevPendCount) + "条");
 - sb.append("::已阅每分钟TPS::" + (Const.COUNTER.LD_R.get() - ldPrevReadCount) + "条");
 - ldPrevPendCount = Const.COUNTER.LD_P.get();
 - ldPrevReadCount = Const.COUNTER.LD_R.get();
 - }
 - }
 - System.out.println(sb.toString());
 - try {
 - Thread.sleep(3000);
 - } catch (InterruptedException e) {
 - e.printStackTrace();
 - }
 - }
 - }).start();
 - }
 
初始化Elasticsearch:EsClient
- String cName = meta.get("cName");//es集群名字
 - String esNodes = meta.get("esNodes");//es集群ip节点
 - Settings esSetting = Settings.builder()
 - .put("cluster.name", cName)
 - .put("client.transport.sniff", true)//增加嗅探机制,找到ES集群
 - .put("thread_pool.search.size", 5)//增加线程池个数,暂时设为5
 - .build();
 - String[] nodes = esNodes.split(",");
 - client = new PreBuiltTransportClient(esSetting);
 - for (String node : nodes) {
 - if (node.length() > 0) {
 - String[] hostPort = node.split(":");
 - client.addTransportAddress(new TransportAddress(InetAddress.getByName(hostPort[0]), Integer.parseInt(hostPort[1])));
 - }
 - }
 
初始化数据库连接
- conn = DriverManager.getConnection(url, user, password);
 
启动参数
- nohup java -jar mte.jar ES-Cluster2019 node1:9300,node2:9300,node3:9300 root 123456! jdbc:mysql://ip:3306/mte 130 130 >> ./mte.log 2>&1 &
 
参数说明
ES-Cluster2019 为Elasticsearch集群名字
node1:9300,node2:9300,node3:9300为es的节点IP
130 130为已办已阅分表的数据
程序入口:MteMain
- // 监控线程
 - Monitor monitorService = new Monitor();
 - monitorService.monitorToES();
 - // 已办生产者线程
 - Thread pendProducerThread = new Thread(new ZlPendProducer(conn, "ZlPendProducer"));
 - pendProducerThread.start();
 - // 已阅生产者线程
 - Thread readProducerThread = new Thread(new ZlReadProducer(conn, "ZlReadProducer"));
 - readProducerThread.start();
 
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