利用Java多线程技术导入数据到Elasticsearch的方法步

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(); 

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持自由互联。