mongdb高级操作（group by ）

首先介绍哈方法

/** * 利用java驱动自带函数分组查询 *

@param key 用来分组文档的字段 【group by key】 *

@param cond 执行过滤的条件 【where name=? and age=?】 *

@param initial reduce中使用变量的初始化 * @param reduce reduce（参数：当前文档和累加器文档.） *

@param fn 结束后执行函数 *

@return */

参考例子1：

group(DBObject key,DBObject cond,DBObject initial,String reduce,String fn ){

//key：用来分组文档的字段。和keyf两者必须有一个 [类似于group by]

BasicDBObject key = new BasicDBObject(); key.put("optCode", true);

//执行过滤的条件 [类似于where]

BasicDBObject[] array={ new BasicDBObject("startTimeLong",

new BasicDBObject("$gte", startTime)),

new BasicDBObject("startTimeLong", new BasicDBObject("$lte", endTime)) };

BasicDBObject cond = new BasicDBObject(); //cond.put("$and",array);

//initial：reduce中使用变量的初始化

BasicDBObject initial = new BasicDBObject();

initial.append("count", 0);

//reduce（当前文档和累加器文档.）

String reduce = "function(doc, aggr){" + " aggr.count += 1;" + " }";

String fn = null;

dao.group(key, cond, initial, reduce, fn);

}

参考例子2：

//求总数和平局数

public Double findAverage(String sumField, String groupField, BasicDBObject where)

{

// 分组项字段 【group by groupField】

DBObject key = new BasicDBObject(groupField, null)

// 结果数据计数器 【select avg,rsdata.sum,rsdata.count 】

BasicDBObject counter = new BasicDBObject();

DBObject index = new BasicDBObject();

index.put("count", 0);

index.put("sum", 0);

counter.put("rsdata", index);counter.put("avg", 0);}

// reduce处理函数

String procFunction = "function(doc,aggr){" + "aggr.rsdata.sum+=parseFloat(doc." + sumField + ");" + "aggr.rsdata.count+=1;" + "}";

// 结果处理函数

String finallyFunction = "function(doc){" + "doc.avg=doc.rsdata.sum/doc.rsdata.count;" + "}";

BasicDBList rs = (BasicDBList) getCollection().group(key, where, counter, procFunction, finallyFunction);

if (null != rs && rs.size() > 0)

{BasicDBObject data = (BasicDBObject) rs.get(0);

return Double.parseDouble(data.get("avg").toString());} return 0.0;

}