利用Java多线程技术导入数据到Elasticsearch的方法步骤
前言
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)。
关键技术栈
- Elasticsearch
- jdbc
- ExecutorService\Thread
- sql
工具说明
maven依赖
<dependency> <groupId>mysql</groupId> <artifactId>mysql-connector-java</artifactId> <version>${mysql.version}</version> </dependency> <dependency> <groupId>org.elasticsearch</groupId> <artifactId>elasticsearch</artifactId> <version>${elasticsearch.version}</version> </dependency> <dependency> <groupId>org.elasticsearch.client</groupId> <artifactId>transport</artifactId> <version>${elasticsearch.version}</version> </dependency> <dependency> <groupId>org.projectlombok</groupId> <artifactId>lombok</artifactId> <version>${lombok.version}</version> </dependency> <dependency> <groupId>com.alibaba</groupId> <artifactId>fastjson</artifactId> <version>${fastjson.version}</version> </dependency>
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最后没有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<HistPendingEntity> 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<HistPendingEntity> lst; public ZlPendConsumer(List<HistPendingEntity> 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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