接口性能优化十倍，来了解一下Arthas的强大！
1背景
helios 系统要处理的数据量比较大，尤其是查询所有服务一天的评分数据时要返回每日 1440 分钟的所有应用的评分，总计有几十万个数据点，接口有时延迟会达到数秒。
本文记录如何利用 arthas ，将接口从几百几千 ms，优化到几十 ms。
从链路上看，线上获取一整天的数据时大概 300 多 ms，而查询数据库只有 11ms，说明大部分时间都是程序组装数据时消耗的，于是动起了优化代码的念头。
基于 spring boot + mybatis plus + vue & element 实现的后台管理系统 + 用户小程序，支持 rbac 动态权限、多租户、数据权限、工作流、三方登录、支付、短信、商城等功能
项目地址：https://github.com/yunaiv/ruoyi-vue-pro 视频教程：https://doc.iocoder.cn/video/ 2优化过程 代码业务可以不用去理解，最主要的是看利用trace优化的过程
基于 spring cloud alibaba + gateway + nacos + rocketmq + vue & element 实现的后台管理系统 + 用户小程序，支持 rbac 动态权限、多租户、数据权限、工作流、三方登录、支付、短信、商城等功能
项目地址：https://github.com/yunaiv/yudao-cloud 视频教程：https://doc.iocoder.cn/video/ 3初始未优化版本 代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscores = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscores)) { return response; } set dateset = new hashset(); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list value : groupbyappidheliosscores.values()) { value.sort(comparator.comparing(heliosscore::gettimefrom)); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(value.get(0).getnamespace()); score.setappid(value.get(0).getappid()); for (heliosscore heliosscore : value) { list splitheliosscores = heliosscore.split(); for (heliosscore splitheliosscore : splitheliosscores) { if (splitheliosscore.gettimefrom().compareto(request.getstarttime()) 0) { break; } dateset.add(dateutils.yyyymmddhhmm.formatdate(splitheliosscore.gettimefrom())); if (splitheliosscore.getscores() == null) { splitheliosscore.setscores(100); log.error(查询时发现数据缺失: {}, heliosscore); } score.add(math.max(0, integer.parseint(splitheliosscore.getscores())), null); } } response.getvalues().add(score); } response.setdates(new arraylist(dateset).stream().sorted().collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 16:28:00;thread_name=http-nio-8080-exec-10;id=81;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@20864cd1 `---[4046.398447ms] xxxservice.controller.helioscontroller:queryscores() +---[0.022259ms] xxxservice.model.helios.heliosgetscoreresponse:() #147 +---[0.007132ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #149 +---[0.006985ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #149 +---[0.008704ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #149 +---[19.284658ms] xxxservice.service.heliosservice:queryscorestimebetween() #149 +---[0.017468ms] org.apache.commons.collections.collectionutils:isempty() #150 +---[0.008054ms] java.util.hashset:() #154 +---[0.027591ms] java.util.list:stream() #156 +---[0.044229ms] java.util.stream.collectors:groupingby() #156 +---[0.155582ms] java.util.stream.stream:collect() #156 +---[0.018318ms] java.util.map:values() #157 +---[0.019199ms] java.util.collection:iterator() #157 +---[min=3.51e-4ms,max=0.014266ms,total=0.125003ms,count=123] java.util.iterator:hasnext() #157 +---[min=5.11e-4ms,max=0.010188ms,total=0.145693ms,count=122] java.util.iterator:next() #157 +---[min=4.89e-4ms,max=0.045356ms,total=0.321978ms,count=122] java.util.comparator:comparing() #158 +---[min=0.003637ms,max=0.033049ms,total=0.928795ms,count=122] java.util.list:sort() #158 +---[min=5.94e-4ms,max=0.010442ms,total=0.1485ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:() #159 +---[min=4.5e-4ms,max=0.010857ms,total=0.12773ms,count=122] java.util.list:get() #160 +---[min=5.01e-4ms,max=0.007849ms,total=0.123696ms,count=122] xxxservice.helios.entity.heliosscore:getnamespace() #160 +---[min=6.5e-4ms,max=0.007324ms,total=0.135906ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #160 +---[min=3.72e-4ms,max=0.010288ms,total=0.086703ms,count=122] java.util.list:get() #161 +---[min=5.1e-4ms,max=0.00627ms,total=0.103871ms,count=122] xxxservice.helios.entity.heliosscore:getappid() #161 +---[min=5.97e-4ms,max=0.006531ms,total=0.126184ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #161 +---[min=4.45e-4ms,max=0.020198ms,total=0.138299ms,count=122] java.util.list:iterator() #162 +---[min=3.42e-4ms,max=0.014615ms,total=0.256056ms,count=366] java.util.iterator:hasnext() #162 +---[min=3.59e-4ms,max=0.014974ms,total=0.174396ms,count=244] java.util.iterator:next() #162 +---[min=0.071035ms,max=0.148132ms,total=19.444179ms,count=244] xxxservice.helios.entity.heliosscore:split() #163 +---[min=4.06e-4ms,max=0.022364ms,total=0.210152ms,count=244] java.util.list:iterator() #164 +---[min=3.07e-4ms,max=0.199649ms,total=143.267893ms,count=351604] java.util.iterator:hasnext() #164 +---[min=3.25e-4ms,max=24.863976ms,total=177.15363ms,count=351360] java.util.iterator:next() #164 +---[min=3.93e-4ms,max=0.096771ms,total=176.843018ms,count=351360] xxxservice.helios.entity.heliosscore:gettimefrom() #165 +---[min=4.07e-4ms,max=18.772715ms,total=205.632183ms,count=351360] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #165 +---[min=3.33e-4ms,max=0.045589ms,total=149.24486ms,count=351360] java.util.date:compareto() #165 +---[min=3.93e-4ms,max=0.032972ms,total=86.466793ms,count=175680] xxxservice.helios.entity.heliosscore:gettimefrom() #168 +---[min=4.12e-4ms,max=0.061003ms,total=94.294061ms,count=175680] xxxservice.model.helios.heliosgetscorerequest:getendtime() #168 +---[min=3.37e-4ms,max=0.038792ms,total=74.505056ms,count=175680] java.util.date:compareto() #168 +---[min=3.97e-4ms,max=0.036548ms,total=87.693935ms,count=175680] xxxservice.helios.entity.heliosscore:gettimefrom() #171 1 +---[min=0.001952ms,max=0.068413ms,total=391.739063ms,count=175680] xxxservice.utils.dateutils$yyyymmddhhmm:formatdate() #171 +---[min=4.07e-4ms,max=0.037904ms,total=108.107714ms,count=175680] java.util.set:add() #171 +---[min=3.95e-4ms,max=0.031555ms,total=88.173857ms,count=175680] xxxservice.helios.entity.heliosscore:getscores() #172 +---[min=3.88e-4ms,max=0.033584ms,total=84.689466ms,count=175680] xxxservice.helios.entity.heliosscore:getscores() #176 +---[min=3.11e-4ms,max=0.038121ms,total=69.708752ms,count=175680] java.lang.math:max() #176 +---[min=4.66e-4ms,max=0.03391ms,total=104.476576ms,count=175680] xxxservice.model.helios.heliosgetscoreresponse$score:add() #176 +---[min=6.17e-4ms,max=0.01503ms,total=0.159826ms,count=122] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #179 +---[min=6.44e-4ms,max=0.03742ms,total=0.21068ms,count=122] java.util.list:add() #179 +---[0.108961ms] java.util.arraylist:() #182 +---[0.017455ms] java.util.arraylist:stream() #182 +---[0.011099ms] java.util.stream.stream:sorted() #182 +---[0.013699ms] java.util.stream.collectors:tolist() #182 +---[0.38178ms] java.util.stream.stream:collect() #182 `---[0.004627ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #182 分析 arthas 显示总共花了 4 秒，但实际上在链路上看大概是 350~450ms 左右。其他多出来的时间是 arthas 每一次执行统计的消耗，因为方法里的循环比较多。这也告诉我们，不要用 trace 去看循环很多的方法。会对性能有非常严重的影响。
可以看出整个函数有 3 个循环，第一层循环的数量为 appid 的数量约为 140，第二层是查出来的数据条数，一天的数据已经归并了所以这里应该是 1，第三层是时间区间的分钟数，一天的话就是 1440 个。
trace 中可以看到消耗最多的是封装的一个 simpledateformat.formatdate()。
4第一次优化 优化方向 遍历每个时间点的思路改变，把合并过的大对象拆分成一个个小对象直接遍历，改成先合并起来，通过时间点逻辑上遍历。这样会减少创建几十万个对象。
将时间点集合 set dateset 改为 set，这样减少反复 formatdate() 的开销。
优化字符串转数字的过程，减少 integer.parseint方法调用，改为用 map 提前创建出 0~100 的字符串数字字典。（后来经过 jmh 测试，还是 integer.parseint 最快）
代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist()); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 calendar indexdate = dateutils.rounddownminute(request.getstarttime().gettime()); int index = 0; // 如果 timefrom 0) { heliosscore.gettimefrom().settime(heliosscore.gettimefrom().gettime() + 60_000); index++; } while (indexdate.gettime().compareto(request.getendtime()) <= 0 && indexdate.gettime().compareto(heliosscore.gettimeto()) <= 0 && index < scoreintlist.size()) { integer scoreint = scoreintlist.get(index++); score.getscores().add(scoreint); dateset.add(indexdate.gettime()); indexdate.add(calendar.minute, 1); } } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 14:44:11;thread_name=http-nio-8080-exec-10;id=ab;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@16ea0f22 `---[6997.005629ms] xxxservice.controller.helioscontroller:queryscores() +---[0.020032ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.007451ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[min=0.001054ms,max=7.458198ms,total=213.19538ms,count=170754] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.007267ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 +---[15.255919ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.020045ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.015161ms] java.util.hashset:() #156 +---[20.06713ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #158 +---[0.043042ms] java.util.list:stream() #160 +---[0.028232ms] java.util.stream.collectors:groupingby() #57 +---[min=0.087087ms,max=1.931641ms,total=2.018728ms,count=2] java.util.stream.stream:collect() #57 +---[0.0151ms] java.util.map:values() #162 +---[0.019611ms] java.util.collection:iterator() #57 +---[min=7.55e-4ms,max=0.015165ms,total=0.201221ms,count=121] java.util.iterator:hasnext() #57 +---[min=0.001178ms,max=0.02477ms,total=0.220931ms,count=120] java.util.iterator:next() #57 +---[min=8.14e-4ms,max=0.01101ms,total=0.155044ms,count=120] java.util.list:get() #163 +---[min=0.001049ms,max=0.009425ms,total=0.231297ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #164 +---[min=0.001167ms,max=0.009721ms,total=0.194502ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #165 +---[min=0.001222ms,max=0.020409ms,total=0.264791ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001097ms,max=0.006475ms,total=0.169987ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #166 +---[min=0.00121ms,max=0.007106ms,total=0.207877ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=8.63e-4ms,max=0.008981ms,total=0.176195ms,count=120] java.util.arraylist:() #167 +---[min=0.001225ms,max=0.021948ms,total=0.340375ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.00112ms,max=0.008984ms,total=0.196212ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #168 +---[min=7.64e-4ms,max=0.027237ms,total=154.660479ms,count=170753] java.util.list:add() #57 +---[min=0.028779ms,max=0.237608ms,total=20.049731ms,count=120] xxxservice.helios.helioshelper:splitscores() #170 +---[min=0.001178ms,max=0.008102ms,total=0.199087ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #173 +---[min=6.89e-4ms,max=0.048069ms,total=140.74298ms,count=170040] java.util.date:gettime() #57 +---[min=0.004686ms,max=0.03805ms,total=0.775394ms,count=120] xxxservice.utils.dateutils:rounddownminute() #57 +---[min=7.84e-4ms,max=7.562581ms,total=162.855553ms,count=170040] java.util.calendar:gettime() #176 2 +---[min=9.94e-4ms,max=0.029962ms,total=385.371864ms,count=339960] xxxservice.helios.entity.heliosscore:gettimefrom() #57 1 +---[min=7.76e-4ms,max=7.936578ms,total=483.361269ms,count=511428] java.util.date:compareto() #57 +---[min=9.95e-4ms,max=0.077109ms,total=192.749805ms,count=169920] xxxservice.helios.entity.heliosscore:gettimefrom() #177 +---[min=6.94e-4ms,max=7.358942ms,total=151.184751ms,count=169920] java.util.date:settime() #57 +---[min=7.67e-4ms,max=0.029244ms,total=152.500401ms,count=170753] java.util.calendar:gettime() #181 +---[min=7.65e-4ms,max=0.016336ms,total=151.879643ms,count=170635] java.util.calendar:gettime() #182 +---[min=0.001011ms,max=0.028133ms,total=196.192946ms,count=170635] xxxservice.helios.entity.heliosscore:gettimeto() #57 +---[min=6.93e-4ms,max=0.836104ms,total=141.443001ms,count=170635] java.util.list:size() #57 +---[min=7.63e-4ms,max=7.940119ms,total=162.285955ms,count=170633] java.util.list:get() #183 3 +---[min=0.001068ms,max=0.973964ms,total=209.721ms,count=170633] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #184 +---[min=7.71e-4ms,max=0.028856ms,total=154.918574ms,count=170633] java.util.calendar:gettime() #185 +---[min=8.07e-4ms,max=8.030316ms,total=186.971072ms,count=170633] java.util.set:add() #57 +---[min=7.82e-4ms,max=0.034732ms,total=156.2645ms,count=170633] java.util.calendar:add() #186 +---[0.050615ms] java.util.arraylist:() #190 +---[0.019114ms] java.util.arraylist:stream() #57 +---[0.029096ms] java.util.stream.stream:sorted() #57 +---[0.018823ms] java.util.stream.stream:map() #57 +---[0.009092ms] java.util.stream.collectors:tolist() #57 `---[0.006768ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步实际上执行时间优化了 50ms 左右。
从 trace 中看耗时时间最长的是 date 的 compareto，也就是代码中的 if (splitheliosscore.gettimefrom().compareto(request.getstarttime()) < 0)
而比较意外的是从对象中 get 属性居然也是有开销的。
5第二次优化 优化方向 结合上一次 arthas trace 的结果，在以下几个方向进行优化：
将 date 对象的换成 long 型时间戳进行比较 将 date 对象反复 gettime、settime，改为 long 型时间戳 += 60_000 实现，得到结果后只 settime 一次。 每次填充数据都往 set dateset 放入数据，改为通过标识判断只放入一次。 存放分数的 arraylist 在第一次循环之后，可以确认大小，之后循环创建 arraylist 时直接填入固定的大小，减少内存创建。 代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); boolean isdatesetinitial = false; int scoresize = 16; list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist(scoresize)); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 long indexdatemills = request.getstarttime().gettime(); int index = 0; // 如果 timefrom heliosscoretimefrommills) { heliosscoretimefrommills += 60_000; index++; } heliosscore.gettimefrom().settime(heliosscoretimefrommills); long requestendtimemills = request.getendtime().gettime(); long heliosscoretimetomills = heliosscore.gettimeto().gettime(); // 循环条件为 (当前时间 <= 请求最大时间) && (当前时间 <= 数据最大时间) && (index < 数据条数) while (indexdatemills <= requestendtimemills && indexdatemills <= heliosscoretimetomills && index < scoreintlist.size()) { score.getscores().add(scoreintlist.get(index++)); if (!isdatesetinitial) { dateset.add(new date(indexdatemills)); } indexdatemills += 60_000; } // 性能优化，减少重复放入的次数 isdatesetinitial = true; // 性能优化，初始化足够的 size 减少扩容次数。x1.1 为了万一数据数量不一致，留出一点 buffer。 scoresize = (int) (score.getscores().size() * 1.1); } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 15:20:41;thread_name=http-nio-8080-exec-7;id=aa;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@14be750c `---[1411.395123ms] xxxservice.controller.helioscontroller:queryscores() +---[0.016102ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.019084ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.007879ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.006808ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 +---[27.494178ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.02087ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.007694ms] java.util.hashset:() #156 +---[19.990512ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.044161ms] java.util.list:stream() #162 +---[0.025737ms] java.util.stream.collectors:groupingby() #57 +---[min=0.079651ms,max=2.007048ms,total=2.086699ms,count=2] java.util.stream.stream:collect() #57 +---[0.018405ms] java.util.map:values() #164 +---[0.021408ms] java.util.collection:iterator() #57 +---[min=7.4e-4ms,max=0.015625ms,total=0.177657ms,count=121] java.util.iterator:hasnext() #57 +---[min=0.001193ms,max=0.026712ms,total=0.258491ms,count=120] java.util.iterator:next() #57 +---[min=7.69e-4ms,max=0.011855ms,total=0.158671ms,count=120] java.util.list:get() #165 +---[min=0.001045ms,max=0.019788ms,total=0.232004ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001072ms,max=0.007958ms,total=0.193652ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001164ms,max=0.007796ms,total=0.201584ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001048ms,max=0.007456ms,total=0.178323ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.001137ms,max=0.010225ms,total=0.201887ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.001627ms,max=0.010431ms,total=0.291395ms,count=120] java.util.arraylist:() #169 +---[min=0.00116ms,max=0.0088ms,total=0.20171ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001076ms,max=0.010293ms,total=0.199407ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=7.54e-4ms,max=0.086952ms,total=150.86682ms,count=170753] java.util.list:add() #57 +---[min=0.020428ms,max=0.269554ms,total=19.477128ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001092ms,max=0.005258ms,total=0.202045ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=7.09e-4ms,max=0.021027ms,total=0.630747ms,count=480] java.util.date:gettime() #57 +---[min=0.00106ms,max=0.015055ms,total=0.188439ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001025ms,max=0.009712ms,total=0.171506ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=7.4e-4ms,max=0.092253ms,total=0.251068ms,count=120] java.util.date:settime() #57 +---[min=0.001086ms,max=0.006234ms,total=0.184256ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001036ms,max=0.012332ms,total=0.176491ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 3 +---[min=6.73e-4ms,max=0.066785ms,total=135.009239ms,count=170635] java.util.list:size() #188 1 +---[min=0.001085ms,max=0.089243ms,total=208.003309ms,count=170633] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #189 2 +---[min=7.31e-4ms,max=0.070823ms,total=145.488732ms,count=170633] java.util.list:get() #57 +---[min=0.001177ms,max=0.143546ms,total=2.319379ms,count=1440] java.util.date:() #191 +---[min=0.001346ms,max=0.064411ms,total=2.839878ms,count=1440] java.util.set:add() #57 +---[min=0.001096ms,max=0.009059ms,total=0.190336ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #198 +---[min=6.92e-4ms,max=0.016223ms,total=0.141751ms,count=120] java.util.list:size() #57 +---[0.069753ms] java.util.arraylist:() #201 +---[0.021066ms] java.util.arraylist:stream() #57 +---[0.029498ms] java.util.stream.stream:sorted() #57 +---[0.014089ms] java.util.stream.stream:map() #57 +---[0.013053ms] java.util.stream.collectors:tolist() #57 `---[0.009818ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步将执行时间又优化了 80ms 左右。现在还剩是 160ms 了。
从 trace 中看耗时时间最长的是三个方法：
getscores 直接 get 了属性啥也没干，但是积少成多 list.size() list.get(index) 也就是说虽然这几个函数里也没干什么东西，但是函数调用、指针寻址本身也是有开销的。
6第三次优化 优化方向 减少 list 属性的调用 一次次 list.add 方法改成 sublist 一次性放入 也就是说循环中不做任何耗时操作，不做任何指针/引用。
代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); boolean isdatesetinitial = false; int scoresize = 16; list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist(scoresize)); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 long indexdatemills = request.getstarttime().gettime(); int index = 0; // 如果 timefrom heliosscoretimefrommills) { heliosscoretimefrommills += 60_000; index++; } heliosscore.gettimefrom().settime(heliosscoretimefrommills); long requestendtimemills = request.getendtime().gettime(); long heliosscoretimetomills = heliosscore.gettimeto().gettime(); // 循环条件为 (当前时间 <= 请求最大时间) && (当前时间 <= 数据最大时间) && (index < 数据条数) int scoreintlistsize = scoreintlist.size(); int indexstart = index; while (indexdatemills <= requestendtimemills && indexdatemills <= heliosscoretimetomills && index++ < scoreintlistsize) { if (!isdatesetinitial) { dateset.add(new date(indexdatemills)); } indexdatemills += 60_000; } score.getscores().addall(scoreintlist.sublist(indexstart, index - 1)); // 性能优化，减少重复放入的次数 isdatesetinitial = true; // 性能优化，初始化足够的 size 减少扩容次数。x1.1 为了万一数据数量不一致，留出一点 buffer。 scoresize = (int) (score.getscores().size() * 1.1); } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 15:33:40;thread_name=http-nio-8080-exec-11;id=f1;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@d1c5cf2 `---[138.624811ms] xxxservice.controller.helioscontroller:queryscores() +---[0.021852ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.00746ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.005838ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.006341ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 2 +---[15.227453ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.02168ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.008923ms] java.util.hashset:() #156 1 +---[22.703926ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.047118ms] java.util.list:stream() #162 +---[0.043183ms] java.util.stream.collectors:groupingby() #57 +---[min=0.095654ms,max=2.183288ms,total=2.278942ms,count=2] java.util.stream.stream:collect() #57 +---[0.022906ms] java.util.map:values() #164 +---[0.025777ms] java.util.collection:iterator() #57 +---[min=9.28e-4ms,max=0.017187ms,total=0.261862ms,count=121] java.util.iterator:hasnext() #57 +---[min=9.88e-4ms,max=0.018901ms,total=0.280889ms,count=120] java.util.iterator:next() #57 +---[min=9.65e-4ms,max=0.014741ms,total=0.262695ms,count=120] java.util.list:get() #165 +---[min=0.001215ms,max=0.013928ms,total=0.347762ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001253ms,max=0.010855ms,total=0.328842ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001316ms,max=0.014714ms,total=0.372553ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001211ms,max=0.010511ms,total=0.322723ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.00132ms,max=0.010201ms,total=0.334627ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.00116ms,max=0.014504ms,total=0.386879ms,count=120] java.util.arraylist:() #169 +---[min=0.00131ms,max=0.014072ms,total=0.344922ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001261ms,max=0.017312ms,total=0.356444ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=9.73e-4ms,max=0.016531ms,total=0.275794ms,count=120] java.util.list:add() #57 3 +---[min=0.023208ms,max=19.808819ms,total=47.196601ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001289ms,max=0.009578ms,total=0.36878ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=8.85e-4ms,max=0.016405ms,total=0.994157ms,count=480] java.util.date:gettime() #57 +---[min=0.001238ms,max=0.016801ms,total=0.34399ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001217ms,max=0.008931ms,total=0.316197ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=9.14e-4ms,max=0.015929ms,total=0.277078ms,count=120] java.util.date:settime() #57 +---[min=0.001238ms,max=0.01061ms,total=0.3375ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001225ms,max=0.018059ms,total=0.315198ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 +---[min=8.79e-4ms,max=0.022669ms,total=0.272356ms,count=120] java.util.list:size() #189 +---[min=0.002001ms,max=0.056977ms,total=4.32853ms,count=1440] java.util.date:() #193 +---[min=0.002174ms,max=0.040594ms,total=4.594415ms,count=1440] java.util.set:add() #57 +---[min=0.001302ms,max=0.012925ms,total=0.353165ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #197 +---[min=0.001004ms,max=0.033424ms,total=0.338294ms,count=120] java.util.list:sublist() #57 +---[min=0.004871ms,max=0.051046ms,total=2.945263ms,count=120] java.util.list:addall() #57 +---[min=0.001291ms,max=0.009831ms,total=0.314292ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #201 +---[min=8.84e-4ms,max=0.018168ms,total=0.249321ms,count=120] java.util.list:size() #57 +---[0.054305ms] java.util.arraylist:() #204 +---[0.024481ms] java.util.arraylist:stream() #57 +---[0.028717ms] java.util.stream.stream:sorted() #57 +---[0.013725ms] java.util.stream.stream:map() #57 +---[0.0128ms] java.util.stream.collectors:tolist() #57 `---[0.007166ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步又优化了 100ms 左右，现在还剩 60ms。
现在从 trace 上看耗时操作只有三个了：
查数据库 合并数据 拆分得分字符串 100,100,100 为 int 数组 [100,100,100] 7第四次优化 优化方向 查数据库发现由于 sql 判断不准确，每次会多查出来一条数据，在后边循环的时候会多循环一倍
合并数据时发现可以针对单条数据的情况直接过滤，减少开销。
代码 改了 sql 并验证，减少查询出来的数据量 单条数据时不再处理合并逻辑 arthas trace ---ts=2021-08-17 16:03:24;thread_name=http-nio-8080-exec-13;id=f1;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@69e2fe3b `---[38.171379ms] xxxservice.controller.helioscontroller:queryscores() +---[0.009463ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.00348ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.003233ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.003395ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 1 +---[10.157226ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.009989ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.003394ms] java.util.hashset:() #156 +---[0.083535ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.017819ms] java.util.list:stream() #162 +---[0.011787ms] java.util.stream.collectors:groupingby() #57 +---[min=0.047561ms,max=2.02786ms,total=2.075421ms,count=2] java.util.stream.stream:collect() #57 +---[0.015525ms] java.util.map:values() #164 +---[0.021965ms] java.util.collection:iterator() #57 +---[min=7.25e-4ms,max=0.009733ms,total=0.115783ms,count=121] java.util.iterator:hasnext() #57 +---[min=8.43e-4ms,max=0.011422ms,total=0.142771ms,count=120] java.util.iterator:next() #57 +---[min=7.81e-4ms,max=0.010883ms,total=0.128809ms,count=120] java.util.list:get() #165 +---[min=0.001023ms,max=0.004301ms,total=0.150165ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001066ms,max=0.004648ms,total=0.154698ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001137ms,max=0.005607ms,total=0.170279ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001023ms,max=0.004292ms,total=0.151767ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.001105ms,max=0.004701ms,total=0.164955ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.001359ms,max=0.007931ms,total=0.233665ms,count=120] java.util.arraylist:() #169 +---[min=0.001117ms,max=0.00785ms,total=0.168539ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001073ms,max=0.004488ms,total=0.156654ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=7.98e-4ms,max=0.00977ms,total=0.125818ms,count=120] java.util.list:add() #57 +---[min=0.022304ms,max=0.12093ms,total=8.88628ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001092ms,max=0.004967ms,total=0.161288ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=7.02e-4ms,max=0.012136ms,total=0.467786ms,count=480] java.util.date:gettime() #57 +---[min=0.001022ms,max=0.004944ms,total=0.151353ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001018ms,max=0.004731ms,total=0.148025ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=7.3e-4ms,max=0.009359ms,total=0.120588ms,count=120] java.util.date:settime() #57 +---[min=0.00107ms,max=0.008948ms,total=0.162848ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001034ms,max=0.014003ms,total=0.158614ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 +---[min=6.99e-4ms,max=0.009995ms,total=0.11179ms,count=120] java.util.list:size() #189 +---[min=6.95e-4ms,max=0.005468ms,total=1.116308ms,count=1440] java.util.date:() #193 +---[min=7.79e-4ms,max=0.029909ms,total=1.407528ms,count=1440] java.util.set:add() #57 +---[min=0.001097ms,max=0.008616ms,total=0.160597ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #197 +---[min=8.23e-4ms,max=0.0294ms,total=0.153353ms,count=120] java.util.list:sublist() #57 +---[min=0.005771ms,max=0.04465ms,total=1.992151ms,count=120] java.util.list:addall() #57 +---[min=0.001098ms,max=0.007013ms,total=0.169555ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #201 +---[min=7.04e-4ms,max=0.01315ms,total=0.120998ms,count=120] java.util.list:size() #57 +---[0.197732ms] java.util.arraylist:() #204 +---[0.018589ms] java.util.arraylist:stream() #57 +---[0.025192ms] java.util.stream.stream:sorted() #57 +---[0.012544ms] java.util.stream.stream:map() #57 +---[0.012188ms] java.util.stream.collectors:tolist() #57 `---[0.0067ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 可以看到现在最大耗时的地方终于是数据库查询了。现在查询一整天的数据，也只需要 25~40ms 左右。
8结果 链路 链路上看程序代码还是要处理个十几 ms，主要是字符串转 int[] 时的开销，这一步可以再想办法继续优化。 结论
从这次优化我们可以得到一些结论：
1、尽量少创建对象
2、simpledateformat的开销很大
3、date.compare 的开销不低
4、哪怕最简单的操作如 list.size() list.add次数多了开销也很可观
5、对于性能分析和优化一定要有合适工具，才能得出有用的结论并针对性优化。一开始我以为减少对象创建就万事大吉，但实际上性能消耗的大头并不在这里。还是得借助 arthas 的 trace 才能真正针对性地优化
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helios 系统要处理的数据量比较大，尤其是查询所有服务一天的评分数据时要返回每日 1440 分钟的所有应用的评分，总计有几十万个数据点，接口有时延迟会达到数秒。
本文记录如何利用 arthas ，将接口从几百几千 ms，优化到几十 ms。
从链路上看，线上获取一整天的数据时大概 300 多 ms，而查询数据库只有 11ms，说明大部分时间都是程序组装数据时消耗的，于是动起了优化代码的念头。
基于 spring boot + mybatis plus + vue & element 实现的后台管理系统 + 用户小程序，支持 rbac 动态权限、多租户、数据权限、工作流、三方登录、支付、短信、商城等功能
项目地址：https://github.com/yunaiv/ruoyi-vue-pro 视频教程：https://doc.iocoder.cn/video/ 2优化过程 代码业务可以不用去理解，最主要的是看利用trace优化的过程
基于 spring cloud alibaba + gateway + nacos + rocketmq + vue & element 实现的后台管理系统 + 用户小程序，支持 rbac 动态权限、多租户、数据权限、工作流、三方登录、支付、短信、商城等功能
项目地址：https://github.com/yunaiv/yudao-cloud 视频教程：https://doc.iocoder.cn/video/ 3初始未优化版本 代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscores = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscores)) { return response; } set dateset = new hashset(); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list value : groupbyappidheliosscores.values()) { value.sort(comparator.comparing(heliosscore::gettimefrom)); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(value.get(0).getnamespace()); score.setappid(value.get(0).getappid()); for (heliosscore heliosscore : value) { list splitheliosscores = heliosscore.split(); for (heliosscore splitheliosscore : splitheliosscores) { if (splitheliosscore.gettimefrom().compareto(request.getstarttime()) 0) { break; } dateset.add(dateutils.yyyymmddhhmm.formatdate(splitheliosscore.gettimefrom())); if (splitheliosscore.getscores() == null) { splitheliosscore.setscores(100); log.error(查询时发现数据缺失: {}, heliosscore); } score.add(math.max(0, integer.parseint(splitheliosscore.getscores())), null); } } response.getvalues().add(score); } response.setdates(new arraylist(dateset).stream().sorted().collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 16:28:00;thread_name=http-nio-8080-exec-10;id=81;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@20864cd1 `---[4046.398447ms] xxxservice.controller.helioscontroller:queryscores() +---[0.022259ms] xxxservice.model.helios.heliosgetscoreresponse:() #147 +---[0.007132ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #149 +---[0.006985ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #149 +---[0.008704ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #149 +---[19.284658ms] xxxservice.service.heliosservice:queryscorestimebetween() #149 +---[0.017468ms] org.apache.commons.collections.collectionutils:isempty() #150 +---[0.008054ms] java.util.hashset:() #154 +---[0.027591ms] java.util.list:stream() #156 +---[0.044229ms] java.util.stream.collectors:groupingby() #156 +---[0.155582ms] java.util.stream.stream:collect() #156 +---[0.018318ms] java.util.map:values() #157 +---[0.019199ms] java.util.collection:iterator() #157 +---[min=3.51e-4ms,max=0.014266ms,total=0.125003ms,count=123] java.util.iterator:hasnext() #157 +---[min=5.11e-4ms,max=0.010188ms,total=0.145693ms,count=122] java.util.iterator:next() #157 +---[min=4.89e-4ms,max=0.045356ms,total=0.321978ms,count=122] java.util.comparator:comparing() #158 +---[min=0.003637ms,max=0.033049ms,total=0.928795ms,count=122] java.util.list:sort() #158 +---[min=5.94e-4ms,max=0.010442ms,total=0.1485ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:() #159 +---[min=4.5e-4ms,max=0.010857ms,total=0.12773ms,count=122] java.util.list:get() #160 +---[min=5.01e-4ms,max=0.007849ms,total=0.123696ms,count=122] xxxservice.helios.entity.heliosscore:getnamespace() #160 +---[min=6.5e-4ms,max=0.007324ms,total=0.135906ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #160 +---[min=3.72e-4ms,max=0.010288ms,total=0.086703ms,count=122] java.util.list:get() #161 +---[min=5.1e-4ms,max=0.00627ms,total=0.103871ms,count=122] xxxservice.helios.entity.heliosscore:getappid() #161 +---[min=5.97e-4ms,max=0.006531ms,total=0.126184ms,count=122] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #161 +---[min=4.45e-4ms,max=0.020198ms,total=0.138299ms,count=122] java.util.list:iterator() #162 +---[min=3.42e-4ms,max=0.014615ms,total=0.256056ms,count=366] java.util.iterator:hasnext() #162 +---[min=3.59e-4ms,max=0.014974ms,total=0.174396ms,count=244] java.util.iterator:next() #162 +---[min=0.071035ms,max=0.148132ms,total=19.444179ms,count=244] xxxservice.helios.entity.heliosscore:split() #163 +---[min=4.06e-4ms,max=0.022364ms,total=0.210152ms,count=244] java.util.list:iterator() #164 +---[min=3.07e-4ms,max=0.199649ms,total=143.267893ms,count=351604] java.util.iterator:hasnext() #164 +---[min=3.25e-4ms,max=24.863976ms,total=177.15363ms,count=351360] java.util.iterator:next() #164 +---[min=3.93e-4ms,max=0.096771ms,total=176.843018ms,count=351360] xxxservice.helios.entity.heliosscore:gettimefrom() #165 +---[min=4.07e-4ms,max=18.772715ms,total=205.632183ms,count=351360] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #165 +---[min=3.33e-4ms,max=0.045589ms,total=149.24486ms,count=351360] java.util.date:compareto() #165 +---[min=3.93e-4ms,max=0.032972ms,total=86.466793ms,count=175680] xxxservice.helios.entity.heliosscore:gettimefrom() #168 +---[min=4.12e-4ms,max=0.061003ms,total=94.294061ms,count=175680] xxxservice.model.helios.heliosgetscorerequest:getendtime() #168 +---[min=3.37e-4ms,max=0.038792ms,total=74.505056ms,count=175680] java.util.date:compareto() #168 +---[min=3.97e-4ms,max=0.036548ms,total=87.693935ms,count=175680] xxxservice.helios.entity.heliosscore:gettimefrom() #171 1 +---[min=0.001952ms,max=0.068413ms,total=391.739063ms,count=175680] xxxservice.utils.dateutils$yyyymmddhhmm:formatdate() #171 +---[min=4.07e-4ms,max=0.037904ms,total=108.107714ms,count=175680] java.util.set:add() #171 +---[min=3.95e-4ms,max=0.031555ms,total=88.173857ms,count=175680] xxxservice.helios.entity.heliosscore:getscores() #172 +---[min=3.88e-4ms,max=0.033584ms,total=84.689466ms,count=175680] xxxservice.helios.entity.heliosscore:getscores() #176 +---[min=3.11e-4ms,max=0.038121ms,total=69.708752ms,count=175680] java.lang.math:max() #176 +---[min=4.66e-4ms,max=0.03391ms,total=104.476576ms,count=175680] xxxservice.model.helios.heliosgetscoreresponse$score:add() #176 +---[min=6.17e-4ms,max=0.01503ms,total=0.159826ms,count=122] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #179 +---[min=6.44e-4ms,max=0.03742ms,total=0.21068ms,count=122] java.util.list:add() #179 +---[0.108961ms] java.util.arraylist:() #182 +---[0.017455ms] java.util.arraylist:stream() #182 +---[0.011099ms] java.util.stream.stream:sorted() #182 +---[0.013699ms] java.util.stream.collectors:tolist() #182 +---[0.38178ms] java.util.stream.stream:collect() #182 `---[0.004627ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #182 分析 arthas 显示总共花了 4 秒，但实际上在链路上看大概是 350~450ms 左右。其他多出来的时间是 arthas 每一次执行统计的消耗，因为方法里的循环比较多。这也告诉我们，不要用 trace 去看循环很多的方法。会对性能有非常严重的影响。
可以看出整个函数有 3 个循环，第一层循环的数量为 appid 的数量约为 140，第二层是查出来的数据条数，一天的数据已经归并了所以这里应该是 1，第三层是时间区间的分钟数，一天的话就是 1440 个。
trace 中可以看到消耗最多的是封装的一个 simpledateformat.formatdate()。
4第一次优化 优化方向 遍历每个时间点的思路改变，把合并过的大对象拆分成一个个小对象直接遍历，改成先合并起来，通过时间点逻辑上遍历。这样会减少创建几十万个对象。
将时间点集合 set dateset 改为 set，这样减少反复 formatdate() 的开销。
优化字符串转数字的过程，减少 integer.parseint方法调用，改为用 map 提前创建出 0~100 的字符串数字字典。（后来经过 jmh 测试，还是 integer.parseint 最快）
代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist()); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 calendar indexdate = dateutils.rounddownminute(request.getstarttime().gettime()); int index = 0; // 如果 timefrom 0) { heliosscore.gettimefrom().settime(heliosscore.gettimefrom().gettime() + 60_000); index++; } while (indexdate.gettime().compareto(request.getendtime()) <= 0 && indexdate.gettime().compareto(heliosscore.gettimeto()) <= 0 && index < scoreintlist.size()) { integer scoreint = scoreintlist.get(index++); score.getscores().add(scoreint); dateset.add(indexdate.gettime()); indexdate.add(calendar.minute, 1); } } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 14:44:11;thread_name=http-nio-8080-exec-10;id=ab;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@16ea0f22 `---[6997.005629ms] xxxservice.controller.helioscontroller:queryscores() +---[0.020032ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.007451ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[min=0.001054ms,max=7.458198ms,total=213.19538ms,count=170754] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.007267ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 +---[15.255919ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.020045ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.015161ms] java.util.hashset:() #156 +---[20.06713ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #158 +---[0.043042ms] java.util.list:stream() #160 +---[0.028232ms] java.util.stream.collectors:groupingby() #57 +---[min=0.087087ms,max=1.931641ms,total=2.018728ms,count=2] java.util.stream.stream:collect() #57 +---[0.0151ms] java.util.map:values() #162 +---[0.019611ms] java.util.collection:iterator() #57 +---[min=7.55e-4ms,max=0.015165ms,total=0.201221ms,count=121] java.util.iterator:hasnext() #57 +---[min=0.001178ms,max=0.02477ms,total=0.220931ms,count=120] java.util.iterator:next() #57 +---[min=8.14e-4ms,max=0.01101ms,total=0.155044ms,count=120] java.util.list:get() #163 +---[min=0.001049ms,max=0.009425ms,total=0.231297ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #164 +---[min=0.001167ms,max=0.009721ms,total=0.194502ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #165 +---[min=0.001222ms,max=0.020409ms,total=0.264791ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001097ms,max=0.006475ms,total=0.169987ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #166 +---[min=0.00121ms,max=0.007106ms,total=0.207877ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=8.63e-4ms,max=0.008981ms,total=0.176195ms,count=120] java.util.arraylist:() #167 +---[min=0.001225ms,max=0.021948ms,total=0.340375ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.00112ms,max=0.008984ms,total=0.196212ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #168 +---[min=7.64e-4ms,max=0.027237ms,total=154.660479ms,count=170753] java.util.list:add() #57 +---[min=0.028779ms,max=0.237608ms,total=20.049731ms,count=120] xxxservice.helios.helioshelper:splitscores() #170 +---[min=0.001178ms,max=0.008102ms,total=0.199087ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #173 +---[min=6.89e-4ms,max=0.048069ms,total=140.74298ms,count=170040] java.util.date:gettime() #57 +---[min=0.004686ms,max=0.03805ms,total=0.775394ms,count=120] xxxservice.utils.dateutils:rounddownminute() #57 +---[min=7.84e-4ms,max=7.562581ms,total=162.855553ms,count=170040] java.util.calendar:gettime() #176 2 +---[min=9.94e-4ms,max=0.029962ms,total=385.371864ms,count=339960] xxxservice.helios.entity.heliosscore:gettimefrom() #57 1 +---[min=7.76e-4ms,max=7.936578ms,total=483.361269ms,count=511428] java.util.date:compareto() #57 +---[min=9.95e-4ms,max=0.077109ms,total=192.749805ms,count=169920] xxxservice.helios.entity.heliosscore:gettimefrom() #177 +---[min=6.94e-4ms,max=7.358942ms,total=151.184751ms,count=169920] java.util.date:settime() #57 +---[min=7.67e-4ms,max=0.029244ms,total=152.500401ms,count=170753] java.util.calendar:gettime() #181 +---[min=7.65e-4ms,max=0.016336ms,total=151.879643ms,count=170635] java.util.calendar:gettime() #182 +---[min=0.001011ms,max=0.028133ms,total=196.192946ms,count=170635] xxxservice.helios.entity.heliosscore:gettimeto() #57 +---[min=6.93e-4ms,max=0.836104ms,total=141.443001ms,count=170635] java.util.list:size() #57 +---[min=7.63e-4ms,max=7.940119ms,total=162.285955ms,count=170633] java.util.list:get() #183 3 +---[min=0.001068ms,max=0.973964ms,total=209.721ms,count=170633] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #184 +---[min=7.71e-4ms,max=0.028856ms,total=154.918574ms,count=170633] java.util.calendar:gettime() #185 +---[min=8.07e-4ms,max=8.030316ms,total=186.971072ms,count=170633] java.util.set:add() #57 +---[min=7.82e-4ms,max=0.034732ms,total=156.2645ms,count=170633] java.util.calendar:add() #186 +---[0.050615ms] java.util.arraylist:() #190 +---[0.019114ms] java.util.arraylist:stream() #57 +---[0.029096ms] java.util.stream.stream:sorted() #57 +---[0.018823ms] java.util.stream.stream:map() #57 +---[0.009092ms] java.util.stream.collectors:tolist() #57 `---[0.006768ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步实际上执行时间优化了 50ms 左右。
从 trace 中看耗时时间最长的是 date 的 compareto，也就是代码中的 if (splitheliosscore.gettimefrom().compareto(request.getstarttime()) < 0)
而比较意外的是从对象中 get 属性居然也是有开销的。
5第二次优化 优化方向 结合上一次 arthas trace 的结果，在以下几个方向进行优化：
将 date 对象的换成 long 型时间戳进行比较 将 date 对象反复 gettime、settime，改为 long 型时间戳 += 60_000 实现，得到结果后只 settime 一次。 每次填充数据都往 set dateset 放入数据，改为通过标识判断只放入一次。 存放分数的 arraylist 在第一次循环之后，可以确认大小，之后循环创建 arraylist 时直接填入固定的大小，减少内存创建。 代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); boolean isdatesetinitial = false; int scoresize = 16; list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist(scoresize)); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 long indexdatemills = request.getstarttime().gettime(); int index = 0; // 如果 timefrom heliosscoretimefrommills) { heliosscoretimefrommills += 60_000; index++; } heliosscore.gettimefrom().settime(heliosscoretimefrommills); long requestendtimemills = request.getendtime().gettime(); long heliosscoretimetomills = heliosscore.gettimeto().gettime(); // 循环条件为 (当前时间 <= 请求最大时间) && (当前时间 <= 数据最大时间) && (index < 数据条数) while (indexdatemills <= requestendtimemills && indexdatemills <= heliosscoretimetomills && index < scoreintlist.size()) { score.getscores().add(scoreintlist.get(index++)); if (!isdatesetinitial) { dateset.add(new date(indexdatemills)); } indexdatemills += 60_000; } // 性能优化，减少重复放入的次数 isdatesetinitial = true; // 性能优化，初始化足够的 size 减少扩容次数。x1.1 为了万一数据数量不一致，留出一点 buffer。 scoresize = (int) (score.getscores().size() * 1.1); } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 15:20:41;thread_name=http-nio-8080-exec-7;id=aa;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@14be750c `---[1411.395123ms] xxxservice.controller.helioscontroller:queryscores() +---[0.016102ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.019084ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.007879ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.006808ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 +---[27.494178ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.02087ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.007694ms] java.util.hashset:() #156 +---[19.990512ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.044161ms] java.util.list:stream() #162 +---[0.025737ms] java.util.stream.collectors:groupingby() #57 +---[min=0.079651ms,max=2.007048ms,total=2.086699ms,count=2] java.util.stream.stream:collect() #57 +---[0.018405ms] java.util.map:values() #164 +---[0.021408ms] java.util.collection:iterator() #57 +---[min=7.4e-4ms,max=0.015625ms,total=0.177657ms,count=121] java.util.iterator:hasnext() #57 +---[min=0.001193ms,max=0.026712ms,total=0.258491ms,count=120] java.util.iterator:next() #57 +---[min=7.69e-4ms,max=0.011855ms,total=0.158671ms,count=120] java.util.list:get() #165 +---[min=0.001045ms,max=0.019788ms,total=0.232004ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001072ms,max=0.007958ms,total=0.193652ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001164ms,max=0.007796ms,total=0.201584ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001048ms,max=0.007456ms,total=0.178323ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.001137ms,max=0.010225ms,total=0.201887ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.001627ms,max=0.010431ms,total=0.291395ms,count=120] java.util.arraylist:() #169 +---[min=0.00116ms,max=0.0088ms,total=0.20171ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001076ms,max=0.010293ms,total=0.199407ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=7.54e-4ms,max=0.086952ms,total=150.86682ms,count=170753] java.util.list:add() #57 +---[min=0.020428ms,max=0.269554ms,total=19.477128ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001092ms,max=0.005258ms,total=0.202045ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=7.09e-4ms,max=0.021027ms,total=0.630747ms,count=480] java.util.date:gettime() #57 +---[min=0.00106ms,max=0.015055ms,total=0.188439ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001025ms,max=0.009712ms,total=0.171506ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=7.4e-4ms,max=0.092253ms,total=0.251068ms,count=120] java.util.date:settime() #57 +---[min=0.001086ms,max=0.006234ms,total=0.184256ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001036ms,max=0.012332ms,total=0.176491ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 3 +---[min=6.73e-4ms,max=0.066785ms,total=135.009239ms,count=170635] java.util.list:size() #188 1 +---[min=0.001085ms,max=0.089243ms,total=208.003309ms,count=170633] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #189 2 +---[min=7.31e-4ms,max=0.070823ms,total=145.488732ms,count=170633] java.util.list:get() #57 +---[min=0.001177ms,max=0.143546ms,total=2.319379ms,count=1440] java.util.date:() #191 +---[min=0.001346ms,max=0.064411ms,total=2.839878ms,count=1440] java.util.set:add() #57 +---[min=0.001096ms,max=0.009059ms,total=0.190336ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #198 +---[min=6.92e-4ms,max=0.016223ms,total=0.141751ms,count=120] java.util.list:size() #57 +---[0.069753ms] java.util.arraylist:() #201 +---[0.021066ms] java.util.arraylist:stream() #57 +---[0.029498ms] java.util.stream.stream:sorted() #57 +---[0.014089ms] java.util.stream.stream:map() #57 +---[0.013053ms] java.util.stream.collectors:tolist() #57 `---[0.009818ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步将执行时间又优化了 80ms 左右。现在还剩是 160ms 了。
从 trace 中看耗时时间最长的是三个方法：
getscores 直接 get 了属性啥也没干，但是积少成多 list.size() list.get(index) 也就是说虽然这几个函数里也没干什么东西，但是函数调用、指针寻址本身也是有开销的。
6第三次优化 优化方向 减少 list 属性的调用 一次次 list.add 方法改成 sublist 一次性放入 也就是说循环中不做任何耗时操作，不做任何指针/引用。
代码 private heliosgetscoreresponse queryscores(heliosgetscorerequest request) { heliosgetscoreresponse response = new heliosgetscoreresponse(); list heliosscoresrecord = heliosservice.queryscorestimebetween(request.getstarttime(), request.getendtime(), request.getfilterbyappid()); if (collectionutils.isempty(heliosscoresrecord)) { return response; } set dateset = new hashset(); boolean isdatesetinitial = false; int scoresize = 16; list heliosscores = heliosdatamergejob.mergedata(heliosscoresrecord); map groupbyappidheliosscores = heliosscores.stream().collect(collectors.groupingby(heliosscore::getappid)); for (list scores : groupbyappidheliosscores.values()) { heliosscore heliosscore = scores.get(0); heliosgetscoreresponse.score score = new heliosgetscoreresponse.score(); score.setnamespace(heliosscore.getnamespace()); score.setappid(heliosscore.getappid()); score.setscores(new arraylist(scoresize)); response.getvalues().add(score); list scoreintlist = helioshelper.splitscores(heliosscore); // 以 requesttime 为准 long indexdatemills = request.getstarttime().gettime(); int index = 0; // 如果 timefrom heliosscoretimefrommills) { heliosscoretimefrommills += 60_000; index++; } heliosscore.gettimefrom().settime(heliosscoretimefrommills); long requestendtimemills = request.getendtime().gettime(); long heliosscoretimetomills = heliosscore.gettimeto().gettime(); // 循环条件为 (当前时间 <= 请求最大时间) && (当前时间 <= 数据最大时间) && (index < 数据条数) int scoreintlistsize = scoreintlist.size(); int indexstart = index; while (indexdatemills <= requestendtimemills && indexdatemills <= heliosscoretimetomills && index++ < scoreintlistsize) { if (!isdatesetinitial) { dateset.add(new date(indexdatemills)); } indexdatemills += 60_000; } score.getscores().addall(scoreintlist.sublist(indexstart, index - 1)); // 性能优化，减少重复放入的次数 isdatesetinitial = true; // 性能优化，初始化足够的 size 减少扩容次数。x1.1 为了万一数据数量不一致，留出一点 buffer。 scoresize = (int) (score.getscores().size() * 1.1); } response.setdates(new arraylist(dateset).stream().sorted().map(dateutils.yyyymmddhhmm::formatdate).collect(collectors.tolist())); return response;} arthas trace ---ts=2021-08-17 15:33:40;thread_name=http-nio-8080-exec-11;id=f1;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@d1c5cf2 `---[138.624811ms] xxxservice.controller.helioscontroller:queryscores() +---[0.021852ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.00746ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.005838ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.006341ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 2 +---[15.227453ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.02168ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.008923ms] java.util.hashset:() #156 1 +---[22.703926ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.047118ms] java.util.list:stream() #162 +---[0.043183ms] java.util.stream.collectors:groupingby() #57 +---[min=0.095654ms,max=2.183288ms,total=2.278942ms,count=2] java.util.stream.stream:collect() #57 +---[0.022906ms] java.util.map:values() #164 +---[0.025777ms] java.util.collection:iterator() #57 +---[min=9.28e-4ms,max=0.017187ms,total=0.261862ms,count=121] java.util.iterator:hasnext() #57 +---[min=9.88e-4ms,max=0.018901ms,total=0.280889ms,count=120] java.util.iterator:next() #57 +---[min=9.65e-4ms,max=0.014741ms,total=0.262695ms,count=120] java.util.list:get() #165 +---[min=0.001215ms,max=0.013928ms,total=0.347762ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001253ms,max=0.010855ms,total=0.328842ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001316ms,max=0.014714ms,total=0.372553ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001211ms,max=0.010511ms,total=0.322723ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.00132ms,max=0.010201ms,total=0.334627ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.00116ms,max=0.014504ms,total=0.386879ms,count=120] java.util.arraylist:() #169 +---[min=0.00131ms,max=0.014072ms,total=0.344922ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001261ms,max=0.017312ms,total=0.356444ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=9.73e-4ms,max=0.016531ms,total=0.275794ms,count=120] java.util.list:add() #57 3 +---[min=0.023208ms,max=19.808819ms,total=47.196601ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001289ms,max=0.009578ms,total=0.36878ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=8.85e-4ms,max=0.016405ms,total=0.994157ms,count=480] java.util.date:gettime() #57 +---[min=0.001238ms,max=0.016801ms,total=0.34399ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001217ms,max=0.008931ms,total=0.316197ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=9.14e-4ms,max=0.015929ms,total=0.277078ms,count=120] java.util.date:settime() #57 +---[min=0.001238ms,max=0.01061ms,total=0.3375ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001225ms,max=0.018059ms,total=0.315198ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 +---[min=8.79e-4ms,max=0.022669ms,total=0.272356ms,count=120] java.util.list:size() #189 +---[min=0.002001ms,max=0.056977ms,total=4.32853ms,count=1440] java.util.date:() #193 +---[min=0.002174ms,max=0.040594ms,total=4.594415ms,count=1440] java.util.set:add() #57 +---[min=0.001302ms,max=0.012925ms,total=0.353165ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #197 +---[min=0.001004ms,max=0.033424ms,total=0.338294ms,count=120] java.util.list:sublist() #57 +---[min=0.004871ms,max=0.051046ms,total=2.945263ms,count=120] java.util.list:addall() #57 +---[min=0.001291ms,max=0.009831ms,total=0.314292ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #201 +---[min=8.84e-4ms,max=0.018168ms,total=0.249321ms,count=120] java.util.list:size() #57 +---[0.054305ms] java.util.arraylist:() #204 +---[0.024481ms] java.util.arraylist:stream() #57 +---[0.028717ms] java.util.stream.stream:sorted() #57 +---[0.013725ms] java.util.stream.stream:map() #57 +---[0.0128ms] java.util.stream.collectors:tolist() #57 `---[0.007166ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 这一步又优化了 100ms 左右，现在还剩 60ms。
现在从 trace 上看耗时操作只有三个了：
查数据库 合并数据 拆分得分字符串 100,100,100 为 int 数组 [100,100,100] 7第四次优化 优化方向 查数据库发现由于 sql 判断不准确，每次会多查出来一条数据，在后边循环的时候会多循环一倍
合并数据时发现可以针对单条数据的情况直接过滤，减少开销。
代码 改了 sql 并验证，减少查询出来的数据量 单条数据时不再处理合并逻辑 arthas trace ---ts=2021-08-17 16:03:24;thread_name=http-nio-8080-exec-13;id=f1;is_daemon=true;priority=5;tccl=org.springframework.boot.web.embedded.tomcat.tomcatembeddedwebappclassloader@69e2fe3b `---[38.171379ms] xxxservice.controller.helioscontroller:queryscores() +---[0.009463ms] xxxservice.model.helios.heliosgetscoreresponse:() #149 +---[0.00348ms] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #151 +---[0.003233ms] xxxservice.model.helios.heliosgetscorerequest:getendtime() #57 +---[0.003395ms] xxxservice.model.helios.heliosgetscorerequest:getfilterbyappid() #57 1 +---[10.157226ms] xxxservice.service.heliosservice:queryscorestimebetween() #57 +---[0.009989ms] org.apache.commons.collections.collectionutils:isempty() #152 +---[0.003394ms] java.util.hashset:() #156 +---[0.083535ms] xxxservice.helios.jobs.heliosdatamergejob:mergedata() #160 +---[0.017819ms] java.util.list:stream() #162 +---[0.011787ms] java.util.stream.collectors:groupingby() #57 +---[min=0.047561ms,max=2.02786ms,total=2.075421ms,count=2] java.util.stream.stream:collect() #57 +---[0.015525ms] java.util.map:values() #164 +---[0.021965ms] java.util.collection:iterator() #57 +---[min=7.25e-4ms,max=0.009733ms,total=0.115783ms,count=121] java.util.iterator:hasnext() #57 +---[min=8.43e-4ms,max=0.011422ms,total=0.142771ms,count=120] java.util.iterator:next() #57 +---[min=7.81e-4ms,max=0.010883ms,total=0.128809ms,count=120] java.util.list:get() #165 +---[min=0.001023ms,max=0.004301ms,total=0.150165ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:() #166 +---[min=0.001066ms,max=0.004648ms,total=0.154698ms,count=120] xxxservice.helios.entity.heliosscore:getnamespace() #167 +---[min=0.001137ms,max=0.005607ms,total=0.170279ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setnamespace() #57 +---[min=0.001023ms,max=0.004292ms,total=0.151767ms,count=120] xxxservice.helios.entity.heliosscore:getappid() #168 +---[min=0.001105ms,max=0.004701ms,total=0.164955ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setappid() #57 +---[min=0.001359ms,max=0.007931ms,total=0.233665ms,count=120] java.util.arraylist:() #169 +---[min=0.001117ms,max=0.00785ms,total=0.168539ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:setscores() #57 +---[min=0.001073ms,max=0.004488ms,total=0.156654ms,count=120] xxxservice.model.helios.heliosgetscoreresponse:getvalues() #170 +---[min=7.98e-4ms,max=0.00977ms,total=0.125818ms,count=120] java.util.list:add() #57 +---[min=0.022304ms,max=0.12093ms,total=8.88628ms,count=120] xxxservice.helios.helioshelper:splitscores() #172 +---[min=0.001092ms,max=0.004967ms,total=0.161288ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getstarttime() #175 +---[min=7.02e-4ms,max=0.012136ms,total=0.467786ms,count=480] java.util.date:gettime() #57 +---[min=0.001022ms,max=0.004944ms,total=0.151353ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #178 +---[min=0.001018ms,max=0.004731ms,total=0.148025ms,count=120] xxxservice.helios.entity.heliosscore:gettimefrom() #183 +---[min=7.3e-4ms,max=0.009359ms,total=0.120588ms,count=120] java.util.date:settime() #57 +---[min=0.00107ms,max=0.008948ms,total=0.162848ms,count=120] xxxservice.model.helios.heliosgetscorerequest:getendtime() #185 +---[min=0.001034ms,max=0.014003ms,total=0.158614ms,count=120] xxxservice.helios.entity.heliosscore:gettimeto() #186 +---[min=6.99e-4ms,max=0.009995ms,total=0.11179ms,count=120] java.util.list:size() #189 +---[min=6.95e-4ms,max=0.005468ms,total=1.116308ms,count=1440] java.util.date:() #193 +---[min=7.79e-4ms,max=0.029909ms,total=1.407528ms,count=1440] java.util.set:add() #57 +---[min=0.001097ms,max=0.008616ms,total=0.160597ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #197 +---[min=8.23e-4ms,max=0.0294ms,total=0.153353ms,count=120] java.util.list:sublist() #57 +---[min=0.005771ms,max=0.04465ms,total=1.992151ms,count=120] java.util.list:addall() #57 +---[min=0.001098ms,max=0.007013ms,total=0.169555ms,count=120] xxxservice.model.helios.heliosgetscoreresponse$score:getscores() #201 +---[min=7.04e-4ms,max=0.01315ms,total=0.120998ms,count=120] java.util.list:size() #57 +---[0.197732ms] java.util.arraylist:() #204 +---[0.018589ms] java.util.arraylist:stream() #57 +---[0.025192ms] java.util.stream.stream:sorted() #57 +---[0.012544ms] java.util.stream.stream:map() #57 +---[0.012188ms] java.util.stream.collectors:tolist() #57 `---[0.0067ms] xxxservice.model.helios.heliosgetscoreresponse:setdates() #57 分析 可以看到现在最大耗时的地方终于是数据库查询了。现在查询一整天的数据，也只需要 25~40ms 左右。
8结果 链路 链路上看程序代码还是要处理个十几 ms，主要是字符串转 int[] 时的开销，这一步可以再想办法继续优化。 结论
从这次优化我们可以得到一些结论：
1、尽量少创建对象
2、simpledateformat的开销很大
3、date.compare 的开销不低
4、哪怕最简单的操作如 list.size() list.add次数多了开销也很可观
5、对于性能分析和优化一定要有合适工具，才能得出有用的结论并针对性优化。一开始我以为减少对象创建就万事大吉，但实际上性能消耗的大头并不在这里。还是得借助 arthas 的 trace 才能真正针对性地优化
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