Mongodb Aggregation Framework总结

2.2版本新特性，比group, MapReduce简单强大，reference里有很多操作和[运算]可用
pipeline operators:
• $project (page 285)
• $match (page 281)
• $limit (page 280)
• $skip (page 287)
• $unwind (page 290)// 用于嵌套文档，可对数组拆解
• $group (page 278)
• $sort (page 287)
• $geoNear (page 276) wang chaoqun

和SQL的对应关系：
WHERE $match (page 281)
GROUP BY $group (page 278)
HAVING $match (page 281)
SELECT $project (page 285)
ORDER BY $sort (page 287)
LIMIT $limit (page 280)
SUM() $sum
COUNT() $sum
join No direct corresponding operator; however, the $unwind (page 290) operator allows for somewhat similar functionality, but with fields embedded within the document.

场景: 比如按省份城市统计发帖数目，比如按人统计发帖排名：

db.topic.aggregate(
{ $match: { createtime : { $gt:1370000000000 } } },
{ $group : {
  _id : { id:"$creatorId", name:"$creator" },
  upCount : {$sum : "$upCount"},
  downCount : {$sum : "$downCount"}
  }},
{ $project : {
  _id : 0,
  name : "$_id.name",
  total : { $add : ["$upCount", "$downCount"]}
  }},
{ $sort: { total : -1 } },
{ $skip: 0 },
{ $limit: 3 }
);
{
  "result" : [
      {
              "name" : "俞章数",
              "total" : 108
      },
      {
              "name" : "马晴",
              "total" : 87
      },
      {
              "name" : "苏妙玲",
              "total" : 58
      }
  ],
  "ok" : 1
}

标签：mongodb
java driver 官方示例：

// create our pipeline operations, first with the $match
DBObject match = new BasicDBObject("$match", new BasicDBObject("type", "airfare") );

// build the $projection operation
DBObject fields = new BasicDBObject("department", 1);
fields.put("amount", 1);
fields.put("_id", 0);
DBObject project = new BasicDBObject("$project", fields );

// Now the $group operation
DBObject groupFields = new BasicDBObject( "_id", "$department");
groupFields.put("average", new BasicDBObject( "$avg", "$amount"));
DBObject group = new BasicDBObject("$group", groupFields);

// run aggregation
AggregationOutput output = collection.aggregate( match, project, group );

spring data mongodb，@since 1.3，注意升级
官方例子是对城市人口做统计，group两次（对第一次group的结果再group）
好处在于对结果进行了封装，返回List，注意ZipInfoStats嵌套City，使用nested和bind:

class ZipInfo {
  String id;
  String city;
  String state;
  @Field("pop") int population;
  @Field("loc") double[] location;
}
class City {
  String name;
  int population;
}
class ZipInfoStats {
  String id;
  String state;
  City biggestCity;
  City smallestCity;
}

import static org.springframework.data.mongodb.core.aggregation.Aggregation.*;
TypedAggregation<ZipInfo> aggregation = newAggregation(ZipInfo.class,
  group("state", "city")
      .sum("population").as("pop"),
  sort(ASC, "pop", "state", "city"),
  group("state")
      .last("city").as("biggestCity")
      .last("pop").as("biggestPop")
      .first("city").as("smallestCity")
      .first("pop").as("smallestPop"),
  project()
      .and("state").previousOperation()
      .and("biggestCity")
      .nested(bind("name", "biggestCity").and("population", "biggestPop"))
      .and("smallestCity")
      .nested(bind("name", "smallestCity").and("population", "smallestPop")),
  sort(ASC, "state")
);
AggregationResults<ZipInfoStats> result = mongoTemplate.aggregate(aggregation, ZipInfoStats.class);
ZipInfoStats firstZipInfoStats = result.getMappedResults().get(0);

对应的shell即是：

db.zipcodes.aggregate(
{ $group:
  {    _id: { state: "$state", city: "$city" },
      pop: { $sum: "$pop" } }
},
{ $sort: { pop: 1 } },
{ $group:
  {    _id : "$_id.state",
      biggestCity: { $last: "$_id.city" },
      biggestPop: { $last: "$pop" },
      smallestCity: { $first: "$_id.city" },
      smallestPop: { $first: "$pop" } }
},
// the following $project is optional, and
// modifies the output format.
{ $project:
  {    _id: 0,
      state: "$_id",
      biggestCity: { name: "$biggestCity", pop: "$biggestPop" },
      smallestCity: { name: "$smallestCity", pop: "$smallestPop" } }
} )