按类别和date按date分组数据

我需要执行以下操作：

1. 按date进行分组，然后logging总数和logging总数
2. 按类别分组，然后聚合必须由所有类别的upvote总数和每天upvote和所有date的倒数投票所有类别的downvote。

这是我的代码：

db.collection.aggregate([{ $unwind: '$votes' }, { $match: { 'category_id': array[i] } }, { $group: { _id:'$votes.date', "category_id": { $first: "$category_id" }, up_vote: { $sum: { $cond: [{ '$gt': ['$votes.score', 0] }, "$votes.score", 0] } }, down_vote: { $sum: { $cond: [{ '$lt': ['$votes.score', 0] }, "$votes.score", 0] } } } }, { "$group": { "_id": "$_id", "categories": { "$push": { "category_id": "$category_id", "up_vote ": "$up_vote", "down_vote": "$down_vote" } }, "total_up_vote": { $sum: { $cond: [{ '$lt': ['$votes.score', 0] }, "$votes.score", 0] } }, "total_down_vote": { "$sum": "$down_vote" } } }{ "$unwind": "$categories" }, { "$project": { "category_id": "$categories.category_id", "down_vote": "$categories.down_vote", "down_vote_Percentage": { "$multiply": [{ "$divide": [ "$categories.down_vote", "$total_down_vote" ] }, 100 ] }, "up_vote": "$categories.up_vote", "up_vote_Percentage": { "$multiply": [{ "$divide": [ "$categories.down_vote", "$total_total_up_vote" ] }, 100 ] } } } ], function(err, results) { res.send(result) }) 

这是我的数据库结构：

  "_id" : ObjectId("590f1ab8a45e6eb418be32cd"), "category_id" : "singer", "celebrity_id" : ObjectId("591e71884e743d8015fd1ae0"), "user_id" : "591e81277bd0b65c141e64be", "votes" : [ { "date" : "2017/4/7", "score" : -1 }, { "date" : "2017/4/19", "score" : -1 } ] } { "_id" : ObjectId("59204135dab356f410d1b8a6"), "category_id" : "actor", "celebrity_id" : ObjectId("591e80e47bd0b65c141e64bc"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : 1 } ] } { "_id" : ObjectId("5920415cdab356f410d1b8a7"), "category_id" : "actor", "celebrity_id" : ObjectId("591e81177bd0b65c141e64bd"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : 1 } ] } 

Answer 1

你想要这样的东西：

 db.collection.aggregate([ { "$unwind": "$votes" }, { "$group": { "_id": { "date": "$votes.date", "category_id": "$category_id", }, "upvote": { "$sum": { "$cond": [ { "$gt": [ "$votes.score", 0 ] }, 1, 0 ] } }, "downvote": { "$sum": { "$cond": [ { "$lt": [ "$votes.score", 0 ] }, 1, 0 ] } } }}, { "$group": { "_id": "$_id.date", "categories": { "$push": { "category": "$_id.category_id", "upvote": "$upvote", "downvote": "$downvote" } }, "total_upvote": { "$sum": "$upvote" }, "total_downvote": { "$sum": "$downvote" } }}, { "$unwind": "$categories" }, { "$project": { "category": "$categories.category", "upvote": "$categories.upvote", "upvote_percent": { "$multiply": [ { "$divide": [ "$categories.upvote", { "$cond": [{ "$eq": [ "$total_upvote", 0 ]}, 1, "$total_upvote" ] } ]}, 100 ] }, "downvote": "$categories.downvote", "downvote_percent": { "$multiply": [ { "$divide": [ "$categories.downvote", { "$cond": [{ "$eq": [ "$total_downvote", 0 ]}, 1, "$total_downvote" ] } ]}, 100 ] } }} ]) 

记住，作为一个“stream水线”，每个阶段文件的观点等于文件如何从前一阶段输出。

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源数据

 { "_id" : ObjectId("590f1ab8a45e6eb418be32cd"), "category_id" : "singer", "celebrity_id" : ObjectId("591e71884e743d8015fd1ae0"), "user_id" : "591e81277bd0b65c141e64be", "votes" : [ { "date" : "2017/4/7", "score" : -1 }, { "date" : "2017/4/19", "score" : -1 } ] } { "_id" : ObjectId("59204135dab356f410d1b8a6"), "category_id" : "actor", "celebrity_id" : ObjectId("591e80e47bd0b65c141e64bc"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : 1 } ] } { "_id" : ObjectId("5920415cdab356f410d1b8a7"), "category_id" : "actor", "celebrity_id" : ObjectId("591e81177bd0b65c141e64bd"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : 1 } ] } { "_id" : ObjectId("5923c7fdbcc8728a67bcc653"), "category_id" : "actor", "celebrity_id" : ObjectId("591e81177bd0b65c141e64bd"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : -11 } ] } { "_id" : ObjectId("5923d1b9bcc8728a67bcc655"), "category_id" : "blip", "celebrity_id" : ObjectId("591e81177bd0b65c141e64bd"), "user_id" : "591974b64abd73701dc7c4aa", "votes" : [ { "date" : "2017/4/20", "score" : -11 } ] } 

产量

 { "_id" : "2017/4/19", "category" : "singer", "upvote" : 0, "upvote_percent" : 0, "downvote" : 1, "downvote_percent" : 100 } { "_id" : "2017/4/7", "category" : "singer", "upvote" : 0, "upvote_percent" : 0, "downvote" : 1, "downvote_percent" : 100 } { "_id" : "2017/4/20", "category" : "blip", "upvote" : 0, "upvote_percent" : 0, "downvote" : 1, "downvote_percent" : 50 } { "_id" : "2017/4/20", "category" : "actor", "upvote" : 2, "upvote_percent" : 100, "downvote" : 1, "downvote_percent" : 50 }