audioTrack来进行播放

C++层通过aar提供了回调方法给我大概如下，通过了通道和还有码流等，但是我播出来的就是有噪音，不知道是不是我的用法有个问题，decodeFrame.data是音频的

权限已设置，代码运行不报错，我感觉是播放数据没设置过去，或者获取音频的网址没有加入网络安全配置文件，就像之前获取数据和专辑图的图片都要分开加入进去，想跳转过去时看是否有数据，但是不知道怎么调试。

在B站看到作者大大的项目，准备动手尝试，运行源码时，发现点击音频播放按钮时，没有反应，不会播放。想问一下，这是哪方面的原因，我是一个软件工程专业的学生，希望得到作者大大的回答。感谢！

问个问题，音频文件跟图像文件的base64编码方法一样吗，还是说音频和图片的base64不是同一个编码方法

而当时的解释是：NestedScrollView是通过NestedScrollingParent3实现的相关接口进行通信的，RecyclerView实现了一个NestedScrollingChild3的接口

领券联盟-定义首页相关接口前面我们编写了首页的布局领券联盟-编写首页布局但是呢，分类这些东西还没有数据我们需要去获取分类数据回来。

什么是ffmpegFFmpeg是一款强大的开源跨平台音视频处理工具集，它能够完成录制、转换、流化以及复用/解复用等多种音频和视频相关的任务。

通过文章标签计算相关文章的方式在我们写博客系统的时候，如果我们没有引入其他的框架，直接自己写代码，能不能找到比较相似的文章推荐给用户呢？我们来试试吧！

项目的github地址一个具备远程访问功能的音频分享软件，软件应具备在线听歌，在线听故事，在线分享等功能的音频分享appDailyListen项目简介整个项目分为7个模块：用户模块、订阅模块，推荐模块，

p>1.多Activity管理

2.http协议实现多线程

3.http协议实现断点续传

4.android手机硬件

5.自定义控件

跟这些相关的

自定义控件手表相关的素材和图解求刻度的图解这个应该没什么难度吧，也就是三角函数注意坐标的原点位置是左上角还Y轴的正方向。表盘背景okay在线课程地址，请参考这里阳光沙滩程序猿拉大锯

上传个音频文件，服务器那边要求是application/octet-stream然后请求头里面包含了json，请问要怎么样才能让请求头包含json呢（post上传）附个接口文档：https://aidoc.jd.com

不管我是静态注册权限还是动态申请权限我的模拟器sd卡上的音频文件就是读取不到??有谁遇到过这个坑么

AndroidStudio奇淫技巧-项目相关上一篇文章，我们学习了如何设置IDE，相当于磨刀，对吧！但这个系列下来，不全是奇淫技巧的，最基本的还是要让同学们学会androidstudio的使用。

（不合并MP4），使用MediaRecorder不能获取到系统声音，MediaMuxer只能合并成MP4，是否有其他方法能够合并音频与视频并推流 合并成h264或者ts或其他？

在android中使用webView去加载后台富文本框数据，其中包含音频、视频。请问能否监听或者获取webView中音视频的播放状态？

Android开发基础之把数据存储到SD卡上以及SD卡相关的API前面我们把数据保存到应用的内部：/data/data/com.sunofbeaches.qqlogindemo/files那么现在我们就开始学习怎么把这个数据保存到

3，应用功能：保存文字、图片、音频到云存储。查询了很多，有很多说要用java来写会稍微复杂一点，但效率高，用php写也可以但性能没有Java好。

--MP3音频处理--><dependency><groupId>javazoom</groupId><artifactId>jlayer</

模式如：AudioManager.STREAM_MUSIC参数durationHint：参考如下durationHint参数AudioManager.AUDIOFOCUS_NONE用于指示未获得或丢失音频焦点或请求音频焦点

喜马拉雅项目p35集测试播放中有一个play方法，就是用来播放列表音乐的那个，为什么我的手机没有播放出声音？是我代码有问题吗？目前没有报错，debug也成功运行到play方法那一行

富文本编辑器中，要读取html内容，想要获得输入文字的个数，html中有各种标签，有图片，有视频音频等，怎么提取纯文字呢。

[TOC]一.ElasticSearch介绍1.1引言在海量数据中执行搜索功能时，如果使用MySQL，效率太低。如果关键字输入的不准确，一样可以搜索到想要的数据。将关键字，以红色的样式展示。1.2ES的介绍ES是基于Java语言并且基于Lucene编写的搜索引擎框架，他提供了分布式的全文搜索功能，提供了一个统一的基于RESTful风格的WEB接口，官方客户端也对多种语言都提供了相应的API。Lucene：是一个搜索引擎的底层。全文搜索：将一段词语进行分词，并且将分出的单个词语统一的放到一个分词库中，在搜索时，根据关键字去分词库中检索，找到匹配的内容。二.ElasticSearch安装2.1安装ES&Kibana图形化界面version:"3.1"services:elasticsearch:image:daocloud.io/library/elasticsearch:6.5.4restart:alwayscontainer_name:elasticsearchports:-9200:9200kibana:image:daocloud.io/library/kibana:6.5.4restart:alwayscontainer_name:kibanaports:-5601:5601environment:-elasticsearch_url=http://192.168.0.17:9200depends_on:-elasticsearch2.2安装IK分词器下载地址：https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.5.4/elasticsearch-analysis-ik-6.5.4.zip进入ES容器内部，并跳转到bin目录下，执行bin目录下的脚本文件：./elasticsearch-plugininstallhttps://github.com/medcl/elasticsearch-analysis-ik/releases/download/v6.5.4/elasticsearch-analysis-ik-6.5.4.zip安装IK之后一定要重启ES。三.ES的基本操作3.1ES的结构３.１.１索引Index，分片和备份ES的服务中，可以创建多个索引。每个索引默认会被分成5片存储。每一个分片都会存在至少一个备份分片。备份分片默认不会帮助检索数据，当ES检索压力特别大的时候，备份分片才会帮助检索数据。备份分片必须放在不同的服务器中。3.1.2类型Type一个索引下，可以创建多个类型。Ps：根据版本不同，类型的创建也不同3.1.3文档Doc一个类型下，可以有多个文档。这个文档就类似于MySQL表中的多行数据。3.1.4属性field一个文档下，可以有多个属性。这个文档就类似于MySQL表中的一行数据存在多个列。3.2操作ES的RESTful语法GET请求：http://ip:port/index：查询索引信息​http://ip:port/index/type/doc_id：查询指定的文档信息POST请求：​http://ip:port/index/type/_search：查询文档，可以在请求体中添加json字符串来代表查询条件​http://ip:port/index/type/doc_id/_update：修改文档，可以在请求体中指定json字符串代表修改的具体信息PUT请求：​http://ip:port/index：创建一个索引，需要在请求体中指定索引的信息、类型和结构​http://ip:port/index/type/_mappings：代表创建索引时，指定索引文档存储的属性信息DELETE请求：​http://ip:port/index：删除跑路​http://ip:port/index/type/doc_id：删除指定的文档3.3索引的操作3.3.1创建索引#创建索引PUT/person{"settings":{"number_of_shards":5,"number_of_replicas":1}}3.3.2查看索引信息#查看索引信息GET/person3.3.3删除索引#删除索引DELETE/person3.4ES中Field可以指定的类型官方类型字符串类型text：一般被用于全文检索，将当前Field进行分词。keyword：当前Field不会被分词。数值类型：long:integer：short：byte：double：float：half_float：精度比float小一半。scaled_float：根据一个long和scaled来表达一个浮点型。时间类型：date类型：针对时间类型指定具体的格式。布尔类型：boolean：表达true和false。二进制类型：binary类型暂时支持Base64encodestring范围类型：https://www.elastic.co/guide/en/elasticsearch/reference/6.5/range.html经纬度类型：https://www.elastic.co/guide/en/elasticsearch/reference/6.5/geo-point.html3.5创建索引并指定数据结构#创建索引，指定数据结构PUT/book#索引名{"settings":{"number_of_shards":5,#分片数"number_of_replicas":1#备份数},#指定数据结构"mappings":{"novel":{#类型type#文档存储的Field"properties":{#Field属性名"name":{"type":"text",#指定类型"analyzer":"ik_max_word",#指定类型"index":true,#指定当前的Field可以作为查询的条件"store":false#是否需要额外存储},"author":{"type":"keyword"},"count":{"type":"long"},"on-sale":{"type":"date","format":"yyyy-MM-ddHH:mm:ss||yyyy-MM-dd||epoch_millis"},"descr":{"type":"text","analyzer":"ik_max_word"}}}}}3.6文档的操作文档在ES服务中的唯一标识，_index，_type，_id三个个内容为组合，锁定一个文档。3.6.1新建文档自动生成_id#添加文档，自动生成idPOST/book/novel{"name":"盘龙","author":"whoami","count":100000,"on-sale":"2000-01-01","descr":"哈哈哈哈哈哈"}手动指定id#添加文档，手动指定idPUT/book/novel/1{"name":"红楼梦","author":"曹雪芹","count":10000000,"on-sale":"1985-01-01","descr":"哈哈哈哈哈哈嘻嘻嘻嘻嘻"}3.6.2修改文档覆盖式修改PUT/book/novel/1{"name":"红楼梦","author":"曹雪芹","count":34344554,"on-sale":"1985-01-01","descr":"哈哈哈哈哈哈嘻嘻嘻嘻嘻"}doc修改方式#修改文档，基于doc方式POST/book/novel/1/_update{"doc":{#指定需要修改的field和对应的值即可"count":1234567}}3.6.3删除文档#根据id删除文档DELETE/book/novel/_id四.Java操作elasticsearch4.1测试连接ES创建Maven工程导入依赖<dependencies><dependency><groupId>org.elasticsearch</groupId><artifactId>elasticsearch</artifactId><version>6.5.4</version></dependency><dependency><groupId>org.elasticsearch.client</groupId><artifactId>elasticsearch-rest-high-level-client</artifactId><version>6.5.4</version></dependency><dependency><groupId>org.projectlombok</groupId><artifactId>lombok</artifactId><version>1.16.22</version></dependency></dependencies>创建测试类，连接ESpublicclassESClient{publicstaticRestHighLevelClientgetClient(){//创建HttpHost对象HttpHosthttpHost=newHttpHost("192.168.0.17",9200);//创建RestClientBuilderRestClientBuilderclientBuilder=RestClient.builder(httpHost);//创建RestHighLevelClientRestHighLevelClientclient=newRestHighLevelClient(clientBuilder);//返回returnclient;}}publicclassDemo1{publicstaticvoidmain(String[]args){RestHighLevelClientclient=ESClient.getClient();System.out.println("OK");}}4.2Java创建索引publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";Stringtype="man";//1.准备关于索引的settingsSettings.Buildersettings=Settings.builder().put("number_of_shards",3).put("number_of_replicas",1);//2.准备关于索引的结构mappingsXContentBuildermappings=JsonXContent.contentBuilder().startObject().startObject("properties").startObject("name").field("type","text").endObject().startObject("age").field("type","integer").endObject().startObject("birthday").field("type","date").field("format","yyyy-MM-dd").endObject().endObject().endObject();//3.将settings和mappings封装到一个Request对象CreateIndexRequestrequest=newCreateIndexRequest(index).settings(settings).mapping(type,mappings);//4.通过client对象去连接ES并执行创建索引CreateIndexResponseresponse=client.indices().create(request,RequestOptions.DEFAULT);//5.输出测试System.out.println(request.toString());}4.2.1检查索引是否存在,删除索引检查索引是否存在publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";//1.准备request对象GetIndexRequestrequest=newGetIndexRequest();request.indices(index);//2.通过client去操作booleanexists=client.indices().exists(request,RequestOptions.DEFAULT);//3.输出System.out.println(exists);}删除索引publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";//1.准备request对象DeleteIndexRequestrequest=newDeleteIndexRequest();request.indices(index);//2.通过client去操作AcknowledgedResponsedelete=client.indices().delete(request,RequestOptions.DEFAULT);//3.输出System.out.println(delete.isAcknowledged());}4.3Java操作文档4.3.1添加文档publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="person";Stringtype="man";//1.准备一个json数据Personperson=newPerson(1,"张三",23,newDate());Stringjson=mapper.writeValueAsString(person);//2.准备一个request对象（手动指定id）IndexRequestrequest=newIndexRequest(index,type,person.getId().toString());request.source(json,XContentType.JSON);//3.通过client对象执行添加IndexResponseresp=client.index(request,RequestOptions.DEFAULT);//4.输出返回结果System.out.println(resp.getResult().toString());}4.3.2修改文档publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";Stringtype="man";StringdocId="1";//1.创建一个Map，指定需要修改的内容Map<String,Object>doc=newHashMap<String,Object>();doc.put("name","张大帅");//2.创建热区对象，封装数据UpdateRequestrequest=newUpdateRequest(index,type,docId);request.doc(doc);//3.通过client对象执行操作UpdateResponseupdate=client.update(request,RequestOptions.DEFAULT);//4.输出返回结果System.out.println(update.getResult().toString());}4.3.3删除文档publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";Stringtype="man";//1.封装Request对象DeleteRequestrequest=newDeleteRequest(index,type,"1");//2.client执行DeleteResponseresponse=client.delete(request,RequestOptions.DEFAULT);//3。输出结果System.out.println(response.getResult().toString());}4.4Java批量操作文档4.4.1批量添加文档publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="person";Stringtype="man";//1.准备多个json数据Personp1=newPerson(1,"张三",23,newDate());Personp2=newPerson(2,"李四",24,newDate());Personp3=newPerson(3,"王五",25,newDate());Stringjson1=mapper.writeValueAsString(p1);Stringjson2=mapper.writeValueAsString(p2);Stringjson3=mapper.writeValueAsString(p3);//2.创建Request，将准备好的数据封装进去BulkRequestrequest=newBulkRequest();request.add(newIndexRequest(index,type,p1.getId().toString()).source(json1,XContentType.JSON));request.add(newIndexRequest(index,type,p2.getId().toString()).source(json2,XContentType.JSON));request.add(newIndexRequest(index,type,p3.getId().toString()).source(json3,XContentType.JSON));//3.用client执行BulkResponseresp=client.bulk(request,RequestOptions.DEFAULT);//4.输出结果System.out.println(resp.toString());}4.4.2批量删除文档publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="person";Stringtype="man";//1.封装request对象BulkRequestrequest=newBulkRequest();request.add(newDeleteRequest(index,type,"1"));request.add(newDeleteRequest(index,type,"2"));request.add(newDeleteRequest(index,type,"3"));//2.执行client操作BulkResponseresp=client.bulk(request,RequestOptions.DEFAULT);//3.输出结果System.out.println(resp);}五.ES练习索引：sms-logs-index类型：sms-logs-type字段名称备注createDate创建时间sendDate发送时间longCode发送长号码，如"1069886622"mobile如：13800000000corpName发送公司名称，需要分词检索smsContent下发短信内容，需要分词检索state短信下发状态0成功1失败operatorId运营商编号1移动2联通3电信proince省份IpAddr下发服务器IP地址replyTotal短信状态报告返回时长（秒）fee扣费（分）六.ES的各种查询@Data@NoArgsConstructor@AllArgsConstructorpublicclassSmsLogs{privateStringid;//唯一IDprivateDatecreateDate;//创建时间privateDatesendDate;//发送时间privateStringlongCode;//发送的长号码privateStringmobile;//下发手机号privateStringcorpName;//发送公司名称privateStringsmsContent;//下发短信内容privateIntegerstate;//短信下发状态0成功1失败privateIntegeroperatorId;//运营商编号1移动2联通3电信privateStringprovince;//省份privateStringipAddr;//下发服务器IP地址privateIntegerreplyTotal;//短信状态报告返回时长privateIntegerfee;//费用}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.准备关于索引的settingsSettings.Buildersettings=Settings.builder().put("number_of_shards",3).put("number_of_replicas",1);//2.准备关于索引的结构mappingsXContentBuildermappings=JsonXContent.contentBuilder().startObject().startObject("properties").startObject("corpName").field("type","keyword").endObject().startObject("createDate").field("type","date").endObject().startObject("fee").field("type","long").endObject().startObject("ipAddr").field("type","ip").endObject().startObject("longCode").field("type","keyword").endObject().startObject("mobile").field("type","keyword").endObject().startObject("operatorId").field("type","integer").endObject().startObject("province").field("type","keyword").endObject().startObject("replyTotal").field("type","integer").endObject().startObject("sendDate").field("type","date").endObject().startObject("smsContent").field("type","text").field("analyzer","ik_max_word").endObject().startObject("state").field("type","integer").endObject().endObject().endObject();//3.将settings和mappings封装到一个Request对象CreateIndexRequestrequest=newCreateIndexRequest(index).settings(settings).mapping(type,mappings);//4.通过client对象去连接ES并执行创建索引CreateIndexResponseresponse=client.indices().create(request,RequestOptions.DEFAULT);//5.输出测试System.out.println(request.toString());}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.准备多个json数据SmsLogss1=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","盒马鲜生","盒马鲜生盒马鲜生盒马鲜生盒马鲜生",0,2,"上海","10.126.2.9",15,5);SmsLogss2=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","嘀嘀打车","嘀嘀打车嘀嘀打车嘀嘀打车嘀嘀打车",0,3,"北京","10.126.2.10",15,5);SmsLogss3=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","中国移动","中国移动中国移动中国移动中国移动",0,1,"沈阳","10.126.2.11",14,5);SmsLogss4=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","招商银行","招商银行招商银行招商银行招商银行",0,2,"大连","10.126.2.9",15,5);SmsLogss5=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","途虎养车","途虎养车途虎养车途虎养车途虎养车",0,2,"重庆","10.126.2.9",13,5);SmsLogss6=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","建设银行","建设银行建设银行建设银行建设银行",0,2,"鄂尔多斯","10.126.2.9",13,5);SmsLogss7=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","中国移动","中国移动中国移动中国移动中国移动",0,2,"赤峰","10.126.2.9",15,5);SmsLogss8=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","盒马鲜生","盒马鲜生盒马鲜生盒马鲜生盒马鲜生",0,2,"武汉","10.126.2.9",17,5);SmsLogss9=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","嘀嘀打车","嘀嘀打车嘀嘀打车嘀嘀打车嘀嘀打车",0,2,"上海","10.126.2.9",12,5);SmsLogss10=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","途虎养车","途虎养车途虎养车途虎养车途虎养车",0,2,"沈阳","10.126.2.9",11,5);SmsLogss11=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","建设银行","建设银行建设银行建设银行建设银行",0,2,"大连","10.126.2.9",10,5);SmsLogss12=newSmsLogs("",newDate(),newDate(),"10660000988","13800000000","嘀嘀打车","嘀嘀打车嘀嘀打车嘀嘀打车嘀嘀打车",0,2,"鄂尔多斯","10.126.2.9",19,5);Stringjson1=mapper.writeValueAsString(s1);Stringjson2=mapper.writeValueAsString(s2);Stringjson3=mapper.writeValueAsString(s3);Stringjson4=mapper.writeValueAsString(s4);Stringjson5=mapper.writeValueAsString(s5);Stringjson6=mapper.writeValueAsString(s6);Stringjson7=mapper.writeValueAsString(s7);Stringjson8=mapper.writeValueAsString(s8);Stringjson9=mapper.writeValueAsString(s9);Stringjson10=mapper.writeValueAsString(s10);Stringjson11=mapper.writeValueAsString(s11);Stringjson12=mapper.writeValueAsString(s12);//2.创建Request，将准备好的数据封装进去BulkRequestrequest=newBulkRequest();request.add(newIndexRequest(index,type,"1").source(json1,XContentType.JSON));request.add(newIndexRequest(index,type,"2").source(json2,XContentType.JSON));request.add(newIndexRequest(index,type,"3").source(json3,XContentType.JSON));request.add(newIndexRequest(index,type,"4").source(json4,XContentType.JSON));request.add(newIndexRequest(index,type,"5").source(json5,XContentType.JSON));request.add(newIndexRequest(index,type,"6").source(json6,XContentType.JSON));request.add(newIndexRequest(index,type,"7").source(json7,XContentType.JSON));request.add(newIndexRequest(index,type,"8").source(json8,XContentType.JSON));request.add(newIndexRequest(index,type,"9").source(json9,XContentType.JSON));request.add(newIndexRequest(index,type,"10").source(json10,XContentType.JSON));request.add(newIndexRequest(index,type,"11").source(json11,XContentType.JSON));request.add(newIndexRequest(index,type,"12").source(json12,XContentType.JSON));//3.用client执行BulkResponseresp=client.bulk(request,RequestOptions.DEFAULT);//4.输出结果System.out.println(resp.toString());}6.1term&terms查询6.1.1term查询javaterm查询是代表完全匹配，搜索之前不会对你的搜索关键字进行分词，直接去文档分词库中去匹配内容。#term查询POST/sms-logs-index/sms-logs-type/_search{"from":0,"size":5,"query":{"term":{"province":{"value":"北京"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.from(0);builder.size(5);builder.query(QueryBuilders.termQuery("province","北京"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.1.2terms查询terms查询的机制跟term是一样的。terms是针对一个字段包含多个值的时候用。term：whereprovince=北京；terms：whereprovince=北京orprovince=？orprovince=？#terms查询POST/sms-logs-index/sms-logs-type/_search{"query":{"terms":{"province":["北京","上海"]}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.封装查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.termsQuery("province","北京","上海"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.2match查询match查询属于高层查询，他会根据你查询的字段类型不一样，采用不同的查询方式。查询的是日期或者是数值的话，它会将你基于的字符串查询内容转换为日期或者数值对待。查询的内容是一个不能被分词的内容（keyword），match查询不会对你指定的查询关键字进行分词。如果是可以被分词的内容（text），match会将你指定的查询内容根据一定的方式分词，去分词库中匹配指定的内容。match查询，实际底层是多个term查询，将多个term查询的结果给你封装到一起。6.2.1match_all查询查询全部内容。不指定任何查询条件。#match_all查询POST/sms-logs-index/sms-logs-type/_search{"query":{"match_all":{}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.matchAllQuery());builder.size(20);//ES默认查10条数据request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.2.2match查询指定一个Field作为筛选的条件#match查询POST/sms-logs-index/sms-logs-type/_search{"query":{"match":{"smsContent":"嘀嘀打车建设银行"}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.matchQuery("smsContent","嘀嘀打车建设银行"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.2.3布尔match查询基于一个Field匹配的内容，采用and或者or的方式连接#布尔match查询POST/sms-logs-index/sms-logs-type/_search{"query":{"match":{"smsContent":{"query":"嘀嘀打车","operator":"and"}}}}#布尔match查询POST/sms-logs-index/sms-logs-type/_search{"query":{"match":{"smsContent":{"query":"嘀嘀建设","operator":"or"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.matchQuery("smsContent","嘀嘀建设").operator(Operator.OR));//Operator.ANDrequest.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.2.4multi_match查询match针对一个field做检索，multi_match针对多个field进行检索，多个field对应一个text。#multi_match查询POST/sms-logs-index/sms-logs-type/_search{"query":{"multi_match":{"query":"银行",#指定text"fields":["smsContent","province"]#指定field们}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.multiMatchQuery("银行","province","smsContent"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3其他查询6.3.1id查询#id查询GET/sms-logs-index/sms-logs-type/_id#最后面添加要查询的idpublicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象GetRequestrequest=newGetRequest(index,type,"3");//2.执行查询GetResponseresp=client.get(request,RequestOptions.DEFAULT);//3.获取数据System.out.println(resp.getSourceAsMap());}6.3.2ids查询根据多个id查询，类似于MySQL中的whereidin(id1,id2,....)#ids查询POST/sms-logs-index/sms-logs-type/_search{"query":{"ids":{"values":["1","2","3"]}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.idsQuery().addIds("1","2","3"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3.3prefix查询前缀查询，可以通过一个关键字去指定一个Field的前缀，从而查询到指定的文档。#prefix查询POST/sms-logs-index/sms-logs-type/_search{"query":{"prefix":{"corpName":{"value":"中国"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.prefixQuery("corpName","中国"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3.4fuzzy查询模糊查询，我们输入字符的大概，ES就可以去根据输入的内容大概去匹配一下结果。#fuzzy查询POST/sms-logs-index/sms-logs-type/_search{"query":{"fuzzy":{"corpName":{"value":"建设银航","prefix_length":2　　#指定前面几个字符是不允许出现错误}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.fuzzyQuery("corpName","建设银航").prefixLength(2));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3.5wildcard查询通配查询,和MySQL中的like是一个套路,可以在查询时,在字符串中指定通配符*和占位符?#wildcard查询POST/sms-logs-index/sms-logs-type/_search{"query":{"wildcard":{"corpName":{"value":"嘀嘀*"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.wildcardQuery("corpName","嘀嘀*"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3.6range查询范围查询,只针对数值类型,对某一个Field进行大于或者小于的范围#range查询POST/sms-logs-index/sms-logs-type/_search{"query":{"range":{"fee":{"gte":5,"lte":10}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.rangeQuery("fee").gte(5).lte(10));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.3.7regexp查询正则查询,通过你编写的正则表达式去匹配内容.Ps:prefix,fuzzy,wildcard和regexp查询效率相对比较低,要求效率高时,避免使用#regexp查询POST/sms-logs-index/sms-logs-type/_search{"query":{"regexp":{"mobile":"180[0-9]{8}"}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.regexpQuery("mobile","180[0-9]{8}"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.4深分页ScrollES对from+size是有限制的,from和size二者之和不能超过1W。原理：form+size在ES查询数据的方式：第一步，将用户指定的关键字进行分词。第二步，将词汇去分词库中进行检索，得到多个文档的id。第三步，去各个分片中拉取指定的数据，耗时较长。第四步，将数据根据score进行排序，耗时较长。第五步，根据from的值，将查询到的数据舍弃一部分。第六步，返回结果。Scroll+size在ES查询数据的方式：第一步，将用户指定的关键字进行分词。第二步，将词汇去分词库中检索，得到多个文档的id。第三步，将文档的id存放在ES的上下文中。第四步，根据指定的size去ES中检索指定的数据，拿完数据文档id，会从上下文中移除。第五步，如果需要下一页数据，直接去ES的上下文中找后续的内容。Scroll的弊端：不适合做实时查询。#执行Scroll查询，返回第一页数据，并且将文档id信息存放在ES上下文中，指定生存时间1分钟POST/sms-logs-index/sms-logs-type/_search?scroll=1m{"query":{"match_all":{}},"size":2,"sort":[{"fee":{"order":"desc"}}]}#根据scroll查询下一页数据POST/_search/scroll{"scroll_id":"根据第一步得到的scroll_id去指定","scroll":"1m"}#删除scroll在ES上下文中的数据DELETE/_search/scroll/scroll_idpublicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定scroll信息request.scroll(TimeValue.timeValueMinutes(1L));//3.指定查询的条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.size(4);builder.sort("fee",SortOrder.DESC);builder.query(QueryBuilders.matchAllQuery());request.source(builder);//4.获取数据scroll_id，sourceSearchResponseresp=client.search(request,RequestOptions.DEFAULT);StringscrollId=resp.getScrollId();System.out.println("----------首页-----------");for(SearchHithit:resp.getHits().getHits()){System.out.println(hit.getSourceAsMap());}while(true){//5.循环-创建SearchScrollRequestSearchScrollRequestscrollRequest=newSearchScrollRequest(scrollId);//6.指定scrollId的生存时间scrollRequest.scroll(TimeValue.timeValueMinutes(1L));//7.执行查询获取返回结果SearchResponsescrollResp=client.scroll(scrollRequest,RequestOptions.DEFAULT);//8.判断是否查询到了数据SearchHit[]hits=scrollResp.getHits().getHits();if(hits!=null&&hits.length>0){System.out.println("----------下一页-----------");for(SearchHithit:hits){System.out.println(hit.getSourceAsMap());}}else{//9.判断没有查询到数据-退出循环System.out.println("----------结束-----------");break;}}//10.创建ClearScrollRequestClearScrollRequestclearScrollRequest=newClearScrollRequest();//11.指定scrollIdclearScrollRequest.addScrollId(scrollId);//12.删除ScrollIdClearScrollResponseclearScrollResponse=client.clearScroll(clearScrollRequest,RequestOptions.DEFAULT);//13.输出结果System.out.println("删除scroll："+clearScrollResponse.isSucceeded());}6.5delete-by-query根据term、match等查询方式去删除大量文档Ps：如果需要删除的内容是index下的大部分数据，不推荐此方法，推荐创建一个全新的index，将保留的文档内容，添加到全新的索引中#delete-by-queryPOST/sms-logs-index/sms-logs-type/_delete_by_query{"query":{"range":{"fee":{"lt":5}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象DeleteByQueryRequestrequest=newDeleteByQueryRequest(index);request.types(type);//2.指定查询条件request.setQuery(QueryBuilders.rangeQuery("fee").lt(5));//3.执行查询BulkByScrollResponseresp=client.deleteByQuery(request,RequestOptions.DEFAULT);//4.获取数据System.out.println(resp.toString());}6.6复合查询6.6.1bool查询复合过滤器，将你的多个查询条件，以一定的逻辑组合在一起。must：所有的条件用must组合在一起，表示And的意思。must_not：将must_not中的条件，全部都不匹配，表示Not的意思。should：所有的条件用should组合在一起，表示or的意思。#查询省份为武汉或北京#运营商不是联通#smsContent中包含嘀嘀和打车#bool查询POST/sms-logs-index/sms-logs-type/_search{"query":{"bool":{"should":[{"term":{"province":{"value":"北京"}}},{"term":{"province":{"value":"武汉"}}}],"must_not":[{"term":{"operatorId":{"value":"2"}}}],"must":[{"match":{"smsContent":"嘀嘀"}},{"match":{"smsContent":"打车"}}]}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();BoolQueryBuilderboolQuery=QueryBuilders.boolQuery();//查询省份为武汉或者北京boolQuery.should(QueryBuilders.termQuery("province","北京"));boolQuery.should(QueryBuilders.termQuery("province","武汉"));//运营商不是联通boolQuery.mustNot(QueryBuilders.termQuery("operatorId",2));//smsContent中包含嘀嘀和打车boolQuery.must(QueryBuilders.matchQuery("smsContent","嘀嘀"));boolQuery.must(QueryBuilders.matchQuery("smsContent","打车"));builder.query(boolQuery);request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){System.out.println(hit.getSourceAsMap());}}6.6.2boosting查询boosting查询可以帮助我们去影响查询后的score。positive：只有匹配上positive的查询的内容，才会被放到返回的结果集中。negative：如果匹配上positive并且也匹配上了negative，就可以降低这样文档的score。negative_boost：指定系数，必须小于1.0。关于查询时，分数是如何计算的：搜索的关键字在文档中出现的频次越高，分数就越高。指定的文档内容越短，分数越高。在搜索时，指定的关键字也会被分词，这个被分词的内容，在分词库中匹配的个数越多，分数越高。#boosting查询POST/sms-logs-index/sms-logs-type/_search{"query":{"boosting":{"positive":{"match":{"smsContent":"嘀嘀打车"}},"negative":{"match":{"smsContent":"打车"#在positive匹配的基础上再去匹配positive匹配得到的内容}},"negative_boost":0.5}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();BoostingQueryBuilderboostingQuery=QueryBuilders.boostingQuery(QueryBuilders.matchQuery("smsContent","嘀嘀打车"),QueryBuilders.matchQuery("smsContent","打车")).negativeBoost(0.5f);builder.query(boostingQuery);request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.7filter查询query查询，根据查询的条件，去计算文档的匹配度得到一个分数，并且根据分数进行排序，不会做缓存。filter查询，根据查询的条件去查询文档，但不会去计算分数，而且filter会对经常被过滤的数据进行缓存。#filter查询POST/sms-logs-index/sms-logs-type/_search{"query":{"bool":{"filter":[{"term":{"corpName":"途虎养车"}},{"range":{"fee":{"lte":5}}}]}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();BoolQueryBuilderboolQuery=QueryBuilders.boolQuery();boolQuery.filter(QueryBuilders.termQuery("corpName","途虎养车"));boolQuery.filter(QueryBuilders.rangeQuery("fee").lte(5));builder.query(boolQuery);request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){Map<String,Object>result=hit.getSourceAsMap();System.out.println(result);}}6.8高亮查询高亮查询就是用户输入的关键字，以一定的特殊样式展示给用户。ES提供了一个highlight属性，和query同级别。fragment_size：指定高亮数据展示多少个字符。pre_tags：指定前缀标签，例如：post_tags：指定后缀标签，例如：fields：指定哪几个Fields一高亮的形式返回publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.query(QueryBuilders.matchQuery("smsContent","嘀嘀"));HighlightBuilderhighlightBuilder=newHighlightBuilder();highlightBuilder.field("smsContent",6).preTags("<fontcolor='red'>").postTags("</font>");builder.highlighter(highlightBuilder);request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){System.out.println(hit.getHighlightFields().get("smsContent"));}}6.9聚合查询ES的聚合查询和MySQL的聚合查询类似，ES的聚合查询相比MySQL强大的多，ES提供的统计数据的方式多得多。#ES的聚合查询的RESTful语法POST/index/type/_search{"aggs":{"名字(agg)":{"agg_type":{"属性":"值"}}}}6.9.1去重计数查询去重计数，即Cardinality，将返回的文档中的一个指定的field进行去重，统计一共有多少条。POST/sms-logs-index/sms-logs-type/_search{"aggs":{"agg":{"cardinality":{"field":"province"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.aggregation(AggregationBuilders.cardinality("agg").field("province"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据Cardinalityagg=resp.getAggregations().get("agg");System.out.println(agg.getValue());}6.9.2范围统计统计一定范围内出现的文档个数。范围统计可以针对普通的数值、时间类型、IP类型都可以统计。数值统计POST/sms-logs-index/sms-logs-type/_search{"aggs":{"a":{"range":{"field":"fee","ranges":[{"to":5},{"from":5,#from包含等于"to":10},{"from":10}]}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.aggregation(AggregationBuilders.range("agg").field("fee").addUnboundedTo(5).addRange(5,10).addUnboundedFrom(10));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据Rangeagg=resp.getAggregations().get("agg");for(Range.Bucketbucket:agg.getBuckets()){Stringkey=bucket.getKeyAsString();Objectfrom=bucket.getFrom();Objectto=bucket.getTo();longdocCount=bucket.getDocCount();System.out.println(String.format("key：%s，form：%s，to：%s，docCount：%s",key,from,to,docCount));}}时间范围统计POST/sms-logs-index/sms-logs-type/_search{"aggs":{"agg":{"date_range":{"field":"createDate","format":"yyyy","ranges":[{"to":2000},{"from":2000}]}}}}IP范围统计POST/sms-logs-index/sms-logs-type/_search{"aggs":{"agg":{"ip_range":{"field":"ipAddr","ranges":[{"to":"10.126.2.10"},{"from":"10.126.2.10"}]}}}}6.9.3统计聚合查询它可以帮你指定Field的最大值、最小值、平均值。。。。使用：extended_statsPOST/sms-logs-index/sms-logs-type/_search{"aggs":{"agg":{"extended_stats":{"field":"fee"}}}}publicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="sms-logs-index";Stringtype="sms-logs-type";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();builder.aggregation(AggregationBuilders.extendedStats("agg").field("fee"));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据ExtendedStatsagg=resp.getAggregations().get("agg");System.out.println(agg.getCount());System.out.println(agg.getMin());System.out.println(agg.getMax());System.out.println(agg.getAvg());System.out.println(agg.getSum());}6.10地图经纬度搜索ES中提供了一个数据类型geo_point,这个类型就是用来存储经纬度的。先创建测试数据PUT/map{"settings":{"number_of_shards":5,"number_of_replicas":1},"mappings":{"map":{"properties":{"name":{"type":"text"},"location":{"type":"geo_point"}}}}}PUT/map/map/1{"name":"天安门","location":{"lon":116.403981,"lat":39.914492}}PUT/map/map/2{"name":"海淀公园","location":{"lon":116.302509,"lat":39.991152}}PUT/map/map/3{"name":"北京动物园","location":{"lon":116.343184,"lat":39.947468}}6.10.1ES的地图检索方式geo_distance：直线距离检索方式geo_bounding_box：以两个点确定一个矩形，获取在矩形内的全部数据geo_polygon：以多个点，确定一个多边形，获取多边形内的全部数据6.10.2基于RESTful实现地图检索geo_distancePOST/map/map/_search{"query":{"geo_distance":{"location":{#确定一个点"lon":116.433733,"lat":39.908404},"distance":3000,#确定半径"distance_type":"arc"#指定形状为圆形}}}geo_bounding_boxPOST/map/map/_search{"query":{"geo_bounding_box":{"location":{"top_left":{#左上角坐标点"lon":116.326943,"lat":39.95499},"bottom_right":{#右下角坐标点"lon":116.347783,"lat":39.939281}}}}}geo_polygonPOST/map/map/_search{"query":{"geo_polygon":{"location":{"points":[#指定多个点确定一个多边形{"lon":116.298772,"lat":39.998227},{"lon":116.295394,"lat":39.975672},{"lon":116.323206,"lat":39.989936}]}}}}6.10.3Java实现geo_polygonpublicstaticvoidmain(String[]args)throwsIOException{RestHighLevelClientclient=ESClient.getClient();ObjectMappermapper=newObjectMapper();Stringindex="map";Stringtype="map";//1.创建request对象SearchRequestrequest=newSearchRequest(index);request.types(type);//2.指定查询条件SearchSourceBuilderbuilder=newSearchSourceBuilder();List<GeoPoint>points=newArrayList<GeoPoint>();points.add(newGeoPoint(39.998227,116.298772));points.add(newGeoPoint(39.975672,116.295394));points.add(newGeoPoint(39.989936,116.323206));builder.query(QueryBuilders.geoPolygonQuery("location",points));request.source(builder);//3.执行查询SearchResponseresp=client.search(request,RequestOptions.DEFAULT);//4.获取数据for(SearchHithit:resp.getHits().getHits()){System.out.println(hit.getSourceAsMap());}}