MongoDB的使用学习之（七）MongoDB的聚合查询（两种方式）附项目源码

　　<!-- Default bean name is ‘mongo‘ -->
    <!-- 定义mongo对象，对应的是mongodb官方jar包中的Mongo，replica-set设置集群副本的ip地址和端口 -->
    <mongo:mongo id="mongo" host="localhost" port="47017"  />
    
    <mongo:db-factory id="mongoDbFactory" dbname="mongoTest" mongo-ref="mongo" username="root1" password="root1"  />

    <!-- 自动扫描以下包的类 -->
     <!-- 映射转换器，扫描back-package目录下的文件，根据注释，把它们作为mongodb的一个collection的映射 -->
    <mongo:mapping-converter base-package="com.ontheroad.**.po" />

    <bean id="mappingContext" class="org.springframework.data.mongodb.core.mapping.MongoMappingContext" />
    <!-- 配置mongodb映射类型 -->
    <bean id="mappingMongoConverter" class="org.springframework.data.mongodb.core.convert.MappingMongoConverter">
        <constructor-arg name="mongoDbFactory" ref="mongoDbFactory" />
        <constructor-arg name="mappingContext" ref="mappingContext" />
        <property name="typeMapper" ref="defaultMongoTypeMapper" />
    </bean>

    <!-- 默认Mongodb类型映射 -->
    <bean id="defaultMongoTypeMapper" class="org.springframework.data.mongodb.core.convert.DefaultMongoTypeMapper">
        <constructor-arg name="typeKey">
            <null /><!-- 这里设置为空,可以把 spring data mongodb 多余保存的_class字段去掉 -->
        </constructor-arg>
    </bean>
    <!-- 操作mongodb -->
    <!-- mongodb的主要操作对象mongoTemplate(Spring Data提供)，所有对mongodb的增删改查的操作都是通过它完成 -->
    <bean id="mongoTemplate" class="org.springframework.data.mongodb.core.MongoTemplate">
        <constructor-arg name="mongoDbFactory" ref="mongoDbFactory" />
        <constructor-arg name="mongoConverter" ref="mappingMongoConverter" />
    </bean>

@Test
    public  void save() {
        News n = null;
        for (int i = 0; i < 10000; i++) {
            n = new News();
            n.setTitle("title_" + i);
            n.setUrl("url_" + i);
            
            //2014-01-01到2014-01-01之间的随机时间
            Date randomDate=DateUtil.randomDate("2014-01-01","2014-05-11");
            //MongoDB里如果时间类型存的是Date，那么会差8个小时的时区，因为MongoDB使用的格林威治时间，中国所处的是+8区，so……
            //比如我保存的是2014-05-01 00:00:00，那么保存到MongoDB里则是2014-05-01 08:00:00，所以为了统一方面，那就保存字符串类型，底下保存的long类型
            n.setPublishTimeStr(DateUtil.formatDateTimeByDate(randomDate));
            //long类型在查询速度中肯定会比较快
            n.setPublishTime(randomDate.getTime());
            n.setPublishDate(randomDate);
            
            n.setPublishMedia("publishMedia_" + i);
            
            String[] areaArr = {"1024", "102401", "102402", "102403", "102404", "102405", "102406", "102407", "102408"
                    , "10240101", "10240102", "10240201", "10240202", "10240301", "10240302", "10240401", "10240402"
                    , "10240501", "10240502", "10240601", "10240602", "10240701", "10240702", "10240801", "10240802"};
            int areaNum=(int)(Math.random() * areaArr.length);//产生0-strs.length的整数随机数
            String area = areaArr[areaNum];
            n.setArea(area);
            
            String[] ckeyArr = {"A101", "A102", "A201", "A202", "A203"
                    , "B101", "B102", "B103", "C201", "C202", "C203", "22", "23", "24", "25", "26"};
            int ckeyNum=(int)(Math.random() * ckeyArr.length);//产生0-strs.length的整数随机数
            List<String> list = new ArrayList<String>();
            for (int j = 0; j < ckeyNum; j ++) {
                int ckeyNum1=(int)(Math.random() * ckeyArr.length);//产生0-strs.length的整数随机数
                list.add(ckeyArr[ckeyNum1]);
            }
            n.setClassKey(list);
            
            Integer[] evalArr = {1, 0};
            int evalNum=(int)(Math.random() * evalArr.length);//产生0-strs.length的整数随机数
            n.setEvaluate(evalArr[evalNum]);
            
            Integer[] mproArr = {1, 2, 100};
            int mproNum=(int)(Math.random() * mproArr.length);//产生0-strs.length的整数随机数
            n.setMediaProperty(mproArr[mproNum]);

            Integer[] mtypeArr = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11};
            int mtypeNum=(int)(Math.random() * mtypeArr.length);//产生0-strs.length的整数随机数
            n.setMediaType(mtypeArr[mtypeNum]);
            
            Integer[] levelArr = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12};
            int levelNum=(int)(Math.random() * levelArr.length);//产生0-strs.length的整数随机数
            n.setLevel(levelArr[levelNum]);
            
            newsService.save(n);
        }
        System.out.println("OK");
    }

/**
     * 
     * 功能：使用Mongo本身提供的AggregationOutput进行分组查询
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testGroup1 () {
        //按照eval字段进行分组，注意$eval必须是存在mongodb里面的字段，不能写$evaluate(此字段是News类中定义的，和存入mongodb中的有区别)
        //{$group:{_id:{‘AAA‘:‘$BBB‘},CCC:{$sum:1}}}固定格式：把要分组的字段放在_id:{}里面，BBB是mongodb里面的某个字段，AAA是BBB的重命名，CCC是$sum:1的重命名
        //此查询语句== select eval as eval, count(*) as docsNum from news group by eval having docsNum>=85 order by docsNum desc
        //具体的mongodb和sql的对照可以参考：http://docs.mongodb.org/manual/reference/sql-aggregation-comparison/
        String groupStr = "{$group:{_id:{‘eval‘:‘$eval‘},docsNum:{$sum:1}}}";
        DBObject group = (DBObject) JSON.parse(groupStr);
        String matchStr = "{$match:{docsNum:{$gte:85}}}";
        DBObject match = (DBObject) JSON.parse(matchStr);
        String sortStr = "{$sort:{_id.docsNum:-1}}";
        DBObject sort = (DBObject) JSON.parse(sortStr);
        AggregationOutput output = mongoTemplate.getCollection("news").aggregate(group, match, sort);
        System.out.println(output.getCommand());
        //转换为执行原生的mongodb查询语句
        //{ "aggregate" : "news" , "pipeline" : [ { "$group" : { "_id" : { "eval" : "$eval"} , "docsNum" : { "$sum" : 1}}} , { "$match" : { "docsNum" : { "$gte" : 85}}} , { "$sort" : { "_id.docsNum" : -1}}]}
        System.out.println(output.getCommandResult());
        //查询结果
        //{ "serverUsed" : "localhost/127.0.0.1:47017" , "result" : [ { "_id" : { "evaluate" : 1} , "docsNum" : 9955} , { "_id" : { "evaluate" : 0} , "docsNum" : 10047}] , "ok" : 1.0}
        
        //也可以把查询结果封装到NewsNumDTO，这样以一个dto对象返回前台操作就更容易了
        NewsNumDTO dto = new NewsNumDTO();
        for( Iterator< DBObject > it = output.results().iterator(); it.hasNext(); ){
            BasicDBObject dbo = ( BasicDBObject ) it.next();
            BasicDBObject keyValus = (BasicDBObject)dbo.get("_id");
            int eval = keyValus.getInt("eval");
            long docsNum = ((Integer)dbo.get("docsNum")).longValue();
            if(eval == 1){
                dto.setPositiveNum(docsNum);
            }else {
                dto.setNegativeNum(docsNum);
            }
        }
        
    }

    /**
     * 
     * 功能：获取和testGroup1方法同样结果的另一种写法，Spring Data MongoDB隆重登场，语法更加简洁易懂
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testAggregation1() {
        TypedAggregation<News> agg = Aggregation.newAggregation(
                News.class,
                project("evaluate")
                ,group("evaluate").count().as("totalNum")
                ,match(Criteria.where("totalNum").gte(85))
                ,sort(Sort.Direction.DESC, "totalNum")
            );

            AggregationResults<BasicDBObject> result = mongoTemplate.aggregate(agg, BasicDBObject.class);
            System.out.println(agg.toString());
            //执行语句差不多
            //{ "aggregate" : "__collection__" , "pipeline" : [ { "$project" : { "evaluate" : 1}} , { "$group" : { "_id" : "$evaluate" , "totalNum" : { "$sum" : 1}}} , { "$match" : { "totalNum" : { "$gte" : 85}}} , { "$sort" : { "totalNum" : -1}}]}
            System.out.println(result.getMappedResults());
            //查询结果简洁明了
            //[{ "_id" : 0 , "totalNum" : 10047}, { "_id" : 1 , "totalNum" : 9955}]
            
            //使用此方法，如果封装好了某一个类，类里面的属性和结果集的属性一一对应，那么，Spring是可以直接把结果集给封装进去的
            //就是AggregationResults<BasicDBObject> result = mongoTemplate.aggregate(agg, BasicDBObject);中的BasicDBObject改为自己封装的类
            //但是感觉这样做有点不灵活，其实吧，应该是自己现在火候还不到，还看不到他的灵活性，好处在哪里；等火候旺了再说呗
            //所以，就用这个万能的BasicDBObject类来封装返回结果
            List<BasicDBObject> resultList = result.getMappedResults();
            NewsNumDTO dto = new NewsNumDTO();
            for(BasicDBObject dbo : resultList){
                int eval = dbo.getInt("_id");
                long num = dbo.getLong("totalNum");
                if(eval == 1){
                    dto.setPositiveNum(num);
                }else {
                    dto.setNegativeNum(num);
                }
            }
            System.out.println(dto.getPositiveNum());
    }

/**
     * 
     * 功能：previousOperation的简单使用--为分组的字段（_id）建立别名
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testAggregation2() {
        TypedAggregation<News> agg = Aggregation.newAggregation(
                News.class,
//                match(Criteria.where("mediaType").is(100))
                project("evaluate")
                ,group("evaluate").count().as("totalNum")
                ,project("evaluate", "totalNum")
                    .and("eval").previousOperation()
                    //为分组的字段（_id）建立别名
            );
            AggregationResults<BasicDBObject> result = mongoTemplate.aggregate(agg, BasicDBObject.class);
            System.out.println(agg.toString());
//            { "aggregate" : "__collection__" , "pipeline" : [ { "$project" : { "evaluate" : 1}} , { "$group" : { "_id" : "$evaluate" , "totalNum" : { "$sum" : 1}}} , { "$project" : { "evaluate" : "$_id.evaluate" , "totalNum" : 1 , "_id" : 0 , "eval" : "$_id"}}]}
            System.out.println(result.getMappedResults());
//            [{ "totalNum" : 10047 , "eval" : 0}, { "totalNum" : 9955 , "eval" : 1}]
    }

/**
     * 
     * 功能：unwind()的使用，通过Spring Data MongoDB
     *         unwind()就是$unwind这个命令的转换，
     *         $unwind - 可以将一个包含数组的文档切分成多个, 比如你的文档有 中有个数组字段 A, A中有10个元素, 那么                                 
     *         经过 $unwind处理后会产生10个文档，这些文档只有 字段 A不同
     *         详见：http://my.oschina.net/GivingOnenessDestiny/blog/88006
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testAggregation3() {
        TypedAggregation<News> agg = Aggregation.newAggregation(
                News.class,
                unwind("classKey")
                ,project("evaluate", "classKey")
//                这里说明一点就是如果group>=2个字段，那么结果集的分组字段就没有_id了，取而代之的是具体的字段名（和testAggregation()最对比）
                ,group("evaluate", "classKey").count().as("totalNum")
                ,sort(Sort.Direction.DESC, "totalNum")
            );

            AggregationResults<BasicDBObject> result = mongoTemplate.aggregate(agg, BasicDBObject.class);
            System.out.println(agg.toString());
//            { "aggregate" : "__collection__" , "pipeline" : [ { "$unwind" : "$classKey"} , { "$project" : { "evaluate" : 1 , "classKey" : 1}} , { "$group" : { "_id" : { "evaluate" : "$evaluate" , "classKey" : "$classKey"} , "totalNum" : { "$sum" : 1}}} , { "$sort" : { "totalNum" : -1}}]}
            System.out.println(result.getMappedResults());
//            结果就是酱紫，一目了然，怎么操作，就交给你自己了
//            [{ "evaluate" : 0 , "classKey" : "A201" , "totalNum" : 4857}, { "evaluate" : 0 , "classKey" : "23" , "totalNum" : 4857}, { "evaluate" : 0 , "classKey" : "A101" , "totalNum" : 4842}, { "evaluate" : 0 , "classKey" : "24" , "totalNum" : 4806}, { "evaluate" : 1 , "classKey" : "A101" , "totalNum" : 4787}, { "evaluate" : 0 , "classKey" : "C201" , "totalNum" : 4787}, { "evaluate" : 0 , "classKey" : "A102" , "totalNum" : 4783},……]
    }

/**
     * 
     * 功能：Spring Data MongoDB，按照时间（字符串）分组
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testAggregation4() {
        TypedAggregation<News> agg = Aggregation.newAggregation(
                News.class,
                //project().andExpression()里面是一个表达式
//                详见api：http://docs.spring.io/spring-data/data-mongodb/docs/current/reference/htmlsingle/#mongo.aggregation
//                搜索  .andExpression 定位到具体的方法模块
                project("evaluate")
                    .andExpression("substr(publishTimeStr,0,10)").as("publishDate")
                ,group("evaluate", "publishDate").count().as("totalNum")
                ,sort(Sort.Direction.DESC, "totalNum")
            );

            AggregationResults<BasicDBObject> result = mongoTemplate.aggregate(agg, BasicDBObject.class);
            System.out.println(agg.toString());
//            { "aggregate" : "__collection__" , "pipeline" : [ { "$project" : { "evaluate" : 1 , "publishDate" : { "$substr" : [ "$publishTimeStr" , 0 , 10]}}} , { "$group" : { "_id" : { "evaluate" : "$evaluate" , "publishDate" : "$publishDate"} , "totalNum" : { "$sum" : 1}}} , { "$sort" : { "totalNum" : -1}}]}
            System.out.println(result.getMappedResults());
//            [{ "evaluate" : 0 , "publishDate" : "2014-03-09" , "totalNum" : 101}, { "evaluate" : 1 , "publishDate" : "2014-02-14" , "totalNum" : 100}, { "evaluate" : 1 , "publishDate" : "2014-02-11" , "totalNum" : 99}, { "evaluate" : 0 , "publishDate" : "2014-03-17" , "totalNum" : 98}, { "evaluate" : 1 , "publishDate" : "2014-03-26" , "totalNum" : 98},  ……]
//            这个查询结果貌似不是我们想要的效果，理想，一目了然的效果应该是，以日期为单位，日期底下正面多少，负面多少：
//            [
//            { "publishDate" : "2014-03-09" , "evalInfo" : [{"evaluate" : 0 , "totalNum" : 101}, {"evaluate" : 1 , "totalNum" : 44}]}
//            { "publishDate" : "2014-03-12" , "evalInfo" : [{"evaluate" : 0 , "totalNum" : 11}, {"evaluate" : 1 , "totalNum" : 32}]},
//            ……
//            ]
//            无奈本人功力尚浅，查了N多资料，各种论坛，中文的，英文的都查了，就是找不到Spring Data MongoDB 分组方法
//            ，所以就引出了testAggregation5
    }

/**
     * 
     * 功能：使用原生态mongodb语法，按照时间（字符串）分组，整合查询结果，使用$push命令
     * 参数：   
     * 创建人：OnTheRoad_Lee 
     * 修改人：OnTheRoad_Lee 
     * 最后修改时间：2014-5-26
     */
    public void testAggregation5() {
                     
        /* Group操作*/
        String groupStr = "{$group:{_id:{‘evaluate‘:‘$eval‘,‘ptimes‘:{$substr:[‘$ptimes‘,0,10]}},totalNum:{$sum:1}}}";
        DBObject group = (DBObject) JSON.parse(groupStr);
                     
        /* Reshape Group Result*/
        DBObject projectFields = new BasicDBObject();
        projectFields.put("ptimes", "$_id.ptimes");
        projectFields.put("evalInfo", new BasicDBObject("evaluate","$_id.evaluate").append("totalNum", "$totalNum"));
        DBObject project = new BasicDBObject("$project", projectFields);
                     
        /* 将结果push到一起*/
        DBObject groupAgainFields = new BasicDBObject("_id", "$ptimes");
        groupAgainFields.put("evalInfo", new BasicDBObject("$push", "$evalInfo"));
        DBObject reshapeGroup = new BasicDBObject("$group", groupAgainFields);
         
        /* 查看Group结果 */
        AggregationOutput output = mongoTemplate.getCollection("news").aggregate(group, project, reshapeGroup);
        System.out.println(output.getCommand());
//        { "aggregate" : "news" , "pipeline" : [ { "$group" : { "_id" : { "evaluate" : "$eval" , "ptimes" : { "$substr" : [ "$ptimes" , 0 , 10]}} , "totalNum" : { "$sum" : 1}}} , { "$project" : { "ptimes" : "$_id.ptimes" , "evalInfo" : { "evaluate" : "$_id.evaluate" , "totalNum" : "$totalNum"}}} , { "$group" : { "_id" : "$ptimes" , "evalInfo" : { "$push" : "$evalInfo"}}}]}
        System.out.println(output.getCommandResult());
//        { "serverUsed" : "localhost/127.0.0.1:47017" , "result" : [ 
//          { "_id" : "2014-04-24" , "evalInfo" : [ { "evaluate" : 1 , "totalNum" : 67} , { "evaluate" : 0 , "totalNum" : 76}]} 
//        , { "_id" : "2014-02-05" , "evalInfo" : [ { "evaluate" : 1 , "totalNum" : 70} , { "evaluate" : 0 , "totalNum" : 84}]}
//        , { "_id" : "2014-03-21" , "evalInfo" : [ { "evaluate" : 0 , "totalNum" : 82} , { "evaluate" : 1 , "totalNum" : 89}]}}……] 
//        , "ok" : 1.0}
    }