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***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();