您好,登錄后才能下訂單哦!
本文小編為大家詳細介紹“Java如何實現多線程大批量同步數據”,內容詳細,步驟清晰,細節處理妥當,希望這篇“Java如何實現多線程大批量同步數據”文章能幫助大家解決疑惑,下面跟著小編的思路慢慢深入,一起來學習新知識吧。
最近遇到個功能,兩個月有300w+的數據,之后還在累加,因一開始該數據就全部存儲在mysql表,現需要展示在頁面,還需要關聯另一張表的數據,而且產品要求頁面的查詢條件多達20個條件,最終,這個功能卡的要死,基本查不出來數據。
最后是打算把這兩張表的數據同時存儲到MongoDB中去,以提高查詢效率。
一開始同步的時候,采用單線程,循環以分頁的模式去同步這兩張表數據,結果是…一晚上,只同步了30w數據,特慢!!!
最后,改造了一番,2小時,就成功同步了300w+數據。
以下是主要邏輯。
線程的個數請根據你自己的服務器性能酌情設置。
先通過count查出結果集的總條數,設置每個線程分頁查詢的條數,通過總條數和單次條數得到線程數量,通過改變limit的下標實現分批查詢。
package com.github.admin.controller.loans; import com.baomidou.mybatisplus.mapper.EntityWrapper; import com.github.admin.model.entity.CaseCheckCallRecord; import com.github.admin.model.entity.duyan.DuyanCallRecordDetail; import com.github.admin.model.entity.loans.CaseCallRemarkRecord; import com.github.admin.service.duyan.DuyanCallRecordDetailService; import com.github.admin.service.loans.CaseCallRemarkRecordService; import com.github.common.constant.MongodbConstant; import com.github.common.util.DingDingMsgSendUtils; import com.github.common.util.ListUtils; import com.github.common.util.Response; import com.github.common.util.concurrent.Executors; import lombok.extern.slf4j.Slf4j; import org.apache.commons.collections.CollectionUtils; import org.springframework.beans.BeanUtils; import org.springframework.beans.factory.DisposableBean; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.beans.factory.annotation.Value; import org.springframework.data.mongodb.core.MongoTemplate; import org.springframework.data.mongodb.core.query.Criteria; import org.springframework.web.bind.annotation.GetMapping; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RestController; import java.util.ArrayList; import java.util.List; import java.util.Map; import java.util.concurrent.Callable; import java.util.concurrent.ExecutorService; import java.util.concurrent.Future; /** * 多線程同步歷史數據 * @author songfayuan * @date 2019-09-26 15:38 */ @Slf4j @RestController @RequestMapping("/demo") public class SynchronizeHistoricalDataController implements DisposableBean { private ExecutorService executor = Executors.newFixedThreadPool(10, "SynchronizeHistoricalDataController"); //newFixedThreadPool 創建一個定長線程池,可控制線程最大并發數,超出的線程會在隊列中等待。 @Value("${spring.profiles.active}") private String profile; @Autowired private DuyanCallRecordDetailService duyanCallRecordDetailService; @Autowired private MongoTemplate mongoTemplate; @Autowired private CaseCallRemarkRecordService caseCallRemarkRecordService; /** * 多線程同步通話記錄歷史數據 * @param params * @return * @throws Exception */ @GetMapping("/syncHistoryData") public Response syncHistoryData(Map<String, Object> params) throws Exception { executor.execute(new Runnable() { @Override public void run() { try { logicHandler(params); } catch (Exception e) { log.warn("多線程同步稽查通話記錄歷史數據才處理異常,errMsg = {}", e); DingDingMsgSendUtils.sendDingDingGroupMsg("【系統消息】" + profile + "環境,多線程同步稽查通話記錄歷史數據才處理異常,errMsg = "+e); } } }); return Response.success("請求成功"); } /** * 處理數據邏輯 * @param params * @throws Exception */ private void logicHandler(Map<String, Object> params) throws Exception { /******返回結果:多線程處理完的最終數據******/ List<DuyanCallRecordDetail> result = new ArrayList<>(); /******查詢數據庫總的數據條數******/ int count = this.duyanCallRecordDetailService.selectCount(new EntityWrapper<DuyanCallRecordDetail>() .eq("is_delete", 0) .eq("platform_type", 1)); DingDingMsgSendUtils.sendDingDingGroupMsg("【系統消息】" + profile + "環境,本次需要同步" + count + "條歷史稽查通話記錄數據。"); // int count = 2620266; /******限制每次查詢的條數******/ int num = 1000; /******計算需要查詢的次數******/ int times = count / num; if (count % num != 0) { times = times + 1; } /******每個線程開始查詢的行數******/ int offset = 0; /******添加任務******/ List<Callable<List<DuyanCallRecordDetail>>> tasks = new ArrayList<>(); for (int i = 0; i < times; i++) { Callable<List<DuyanCallRecordDetail>> qfe = new ThredQuery(duyanCallRecordDetailService, params, offset, num); tasks.add(qfe); offset = offset + num; } /******為避免太多任務的最終數據全部存在list導致內存溢出,故將任務再次拆分單獨處理******/ List<List<Callable<List<DuyanCallRecordDetail>>>> smallList = ListUtils.partition(tasks, 10); for (List<Callable<List<DuyanCallRecordDetail>>> callableList : smallList) { if (CollectionUtils.isNotEmpty(callableList)) { // executor.execute(new Runnable() { // @Override // public void run() { // log.info("任務拆分執行開始:線程{}拆分處理開始...", Thread.currentThread().getName()); // // log.info("任務拆分執行結束:線程{}拆分處理開始...", Thread.currentThread().getName()); // } // }); try { List<Future<List<DuyanCallRecordDetail>>> futures = executor.invokeAll(callableList); /******處理線程返回結果******/ if (futures != null && futures.size() > 0) { for (Future<List<DuyanCallRecordDetail>> future : futures) { List<DuyanCallRecordDetail> duyanCallRecordDetailList = future.get(); if (CollectionUtils.isNotEmpty(duyanCallRecordDetailList)){ executor.execute(new Runnable() { @Override public void run() { /******異步存儲******/ log.info("異步存儲MongoDB開始:線程{}拆分處理開始...", Thread.currentThread().getName()); saveMongoDB(duyanCallRecordDetailList); log.info("異步存儲MongoDB結束:線程{}拆分處理開始...", Thread.currentThread().getName()); } }); } //result.addAll(future.get()); } } } catch (Exception e) { log.warn("任務拆分執行異常,errMsg = {}", e); DingDingMsgSendUtils.sendDingDingGroupMsg("【系統消息】" + profile + "環境,任務拆分執行異常,errMsg = "+e); } } } } /** * 數據存儲MongoDB * @param duyanCallRecordDetailList */ private void saveMongoDB(List<DuyanCallRecordDetail> duyanCallRecordDetailList) { for (DuyanCallRecordDetail duyanCallRecordDetail : duyanCallRecordDetailList) { /******重復數據不同步MongoDB******/ org.springframework.data.mongodb.core.query.Query query = new org.springframework.data.mongodb.core.query.Query(); query.addCriteria(Criteria.where("callUuid").is(duyanCallRecordDetail.getCallUuid())); List<CaseCheckCallRecord> caseCheckCallRecordList = mongoTemplate.find(query, CaseCheckCallRecord.class, MongodbConstant.CASE_CHECK_CALL_RECORD); if (CollectionUtils.isNotEmpty(caseCheckCallRecordList)) { log.warn("call_uuid = {}在MongoDB已經存在數據,后面數據將被舍棄...", duyanCallRecordDetail.getCallUuid()); continue; } /******關聯填寫的記錄******/ CaseCallRemarkRecord caseCallRemarkRecord = this.caseCallRemarkRecordService.selectOne(new EntityWrapper<CaseCallRemarkRecord>() .eq("is_delete", 0) .eq("call_uuid", duyanCallRecordDetail.getCallUuid())); CaseCheckCallRecord caseCheckCallRecord = new CaseCheckCallRecord(); BeanUtils.copyProperties(duyanCallRecordDetail, caseCheckCallRecord); //補充 caseCheckCallRecord.setCollectorUserId(duyanCallRecordDetail.getUserId()); if (caseCallRemarkRecord != null) { //補充 caseCheckCallRecord.setCalleeName(caseCallRemarkRecord.getContactName()); } log.info("正在存儲數據到MongoDB:{}", caseCheckCallRecord.toString()); this.mongoTemplate.save(caseCheckCallRecord, MongodbConstant.CASE_CHECK_CALL_RECORD); } } @Override public void destroy() throws Exception { executor.shutdown(); } } class ThredQuery implements Callable<List<DuyanCallRecordDetail>> { /******需要通過構造方法把對應的業務service傳進來 實際用的時候把類型變為對應的類型******/ private DuyanCallRecordDetailService myService; /******查詢條件 根據條件來定義該類的屬性******/ private Map<String, Object> params; /******分頁index******/ private int offset; /******數量******/ private int num; public ThredQuery(DuyanCallRecordDetailService myService, Map<String, Object> params, int offset, int num) { this.myService = myService; this.params = params; this.offset = offset; this.num = num; } @Override public List<DuyanCallRecordDetail> call() throws Exception { /******通過service查詢得到對應結果******/ List<DuyanCallRecordDetail> duyanCallRecordDetailList = myService.selectList(new EntityWrapper<DuyanCallRecordDetail>() .eq("is_delete", 0) .eq("platform_type", 1) .last("limit "+offset+", "+num)); return duyanCallRecordDetailList; } }
ListUtils工具
package com.github.common.util; import com.google.common.collect.Lists; import lombok.extern.slf4j.Slf4j; import java.io.*; import java.util.ArrayList; import java.util.List; /** * 描述:List工具類 * @author songfayuan * 2018年7月22日下午2:23:22 */ @Slf4j public class ListUtils { /** * 描述:list集合深拷貝 * @param src * @return * @throws IOException * @throws ClassNotFoundException * @author songfayuan * 2018年7月22日下午2:35:23 */ public static <T> List<T> deepCopy(List<T> src) { try { ByteArrayOutputStream byteout = new ByteArrayOutputStream(); ObjectOutputStream out = new ObjectOutputStream(byteout); out.writeObject(src); ByteArrayInputStream bytein = new ByteArrayInputStream(byteout.toByteArray()); ObjectInputStream in = new ObjectInputStream(bytein); @SuppressWarnings("unchecked") List<T> dest = (List<T>) in.readObject(); return dest; } catch (ClassNotFoundException e) { e.printStackTrace(); return null; } catch (IOException e) { e.printStackTrace(); return null; } } /** * 描述:對象深拷貝 * @param src * @return * @throws IOException * @throws ClassNotFoundException * @author songfayuan * 2018年12月14日 */ public static <T> T objDeepCopy(T src) { try { ByteArrayOutputStream byteout = new ByteArrayOutputStream(); ObjectOutputStream out = new ObjectOutputStream(byteout); out.writeObject(src); ByteArrayInputStream bytein = new ByteArrayInputStream(byteout.toByteArray()); ObjectInputStream in = new ObjectInputStream(bytein); @SuppressWarnings("unchecked") T dest = (T) in.readObject(); return dest; } catch (ClassNotFoundException e) { log.error("errMsg = {}", e); return null; } catch (IOException e) { log.error("errMsg = {}", e); return null; } } /** * 將一個list均分成n個list,主要通過偏移量來實現的 * @author songfayuan * 2018年12月14日 */ public static <T> List<List<T>> averageAssign(List<T> source, int n) { List<List<T>> result = new ArrayList<List<T>>(); int remaider = source.size() % n; //(先計算出余數) int number = source.size() / n; //然后是商 int offset = 0;//偏移量 for (int i = 0; i < n; i++) { List<T> value = null; if (remaider > 0) { value = source.subList(i * number + offset, (i + 1) * number + offset + 1); remaider--; offset++; } else { value = source.subList(i * number + offset, (i + 1) * number + offset); } result.add(value); } return result; } /** * List按指定長度分割 * @param list the list to return consecutive sublists of (需要分隔的list) * @param size the desired size of each sublist (the last may be smaller) (分隔的長度) * @author songfayuan * @date 2019-07-07 21:37 */ public static <T> List<List<T>> partition(List<T> list, int size){ return Lists.partition(list, size); // 使用guava } /** * 測試 * @param args */ public static void main(String[] args) { List<Integer> bigList = new ArrayList<>(); for (int i = 0; i < 101; i++){ bigList.add(i); } log.info("bigList長度為:{}", bigList.size()); log.info("bigList為:{}", bigList); List<List<Integer>> smallists = partition(bigList, 20); log.info("smallists長度為:{}", smallists.size()); for (List<Integer> smallist : smallists) { log.info("拆分結果:{},長度為:{}", smallist, smallist.size()); } } }
讀到這里,這篇“Java如何實現多線程大批量同步數據”文章已經介紹完畢,想要掌握這篇文章的知識點還需要大家自己動手實踐使用過才能領會,如果想了解更多相關內容的文章,歡迎關注億速云行業資訊頻道。
免責聲明:本站發布的內容(圖片、視頻和文字)以原創、轉載和分享為主,文章觀點不代表本網站立場,如果涉及侵權請聯系站長郵箱:is@yisu.com進行舉報,并提供相關證據,一經查實,將立刻刪除涉嫌侵權內容。