MongoDB聚合查询以获取记录中每个实例的唯一元素列表和计数

Question

我有 2 个收藏，如下所示。

数据1：

{ "_id" : , "timestamp" : ISODate("2016-01-05T07:42:37.312Z"), "Prof_Name" : "Jack ", "SUBJECT" : "Maths, Chemistry, Machinery1, Ele1" }
{ "_id" : , "timestamp" : ISODate("2016-01-05T07:42:37.312Z"), "Prof_Name" : "Mac", "SUBJECT" : "Chemistry, CS, German" }

数据2：

{ "_id" : ObjectId(""), timestamp" : ISODate("2016-08-05T07:42:37.312Z", "SUBJECT_ID" : "Maths", "ID" : "OI-12", "Rating" : 6, "UUID" : 8123 }
{ "_id" : ObjectId(""), timestamp" : ISODate("2017-09-05T07:42:37.312Z", "SUBJECT_ID" : "Maths, Machinery1, German", "ID" : "OI-134", "Rating" : 6, "UUID" : 8123 }
{ "_id" : ObjectId(""), timestamp" : ISODate("2016-01-05T07:42:37.312Z", "SUBJECT_ID" : "Machinery1, Maths, French, German", "ID" : "OI-32", "Rating" : 3, "UUID" : 8123 }
{ "_id" : ObjectId(""), timestamp" : ISODate("2016-01-05T07:42:37.312Z", "SUBJECT_ID" : "CS, Chemistry", "ID" : "OI-36", "Rating" : , "UUID" : 8124 }

我想在时间戳 2016 年 1 月到 2106 年 11 月之间获得 3 个集合，其中对于来自“data1”的每个 Prof_Name 和 SUBJECT 中的主题，检查它是否存在于“data2”中并将 UUID 和 UUID 计数为 1，如果在下一条记录中找到相同的主题，使 UUID 计数 =2，依此类推。这就是我的收藏的样子..

数据3：

{ "_id" : ,
"Prof_Name" : "Jack", 
"Subjects_list" : [ "Maths", "Chemistry", "Machinery1"], 
"UUID_list" : [8123, 8124 ], 
"UUID_count" : 3,   // Because UUID 8123 has present in 2 records which comes under 2016 timestamp
"subject_count" : 3 } // Ele1 is not mentioned because it has not been seen in any of the data2 record
{ "_id" : , 
"Prof_Name" : "Mac", 
"Subjects_list" : [ "CS"], 
"UUID_list" : [8124 ],  
"UUID_count" : 1,   // Because UUID 8123 has present in 2 records which comes under 2016 timestamp
"subject_count" : 1 }

我的汇总查询是：

db.data1.aggregate([
  {
    "$addFields": {
      "SUBJECT": {
        "$split": [
          "$SUBJECT",
          ", "
        ]
      }
    }
  },
  {
    "$unwind": "$SUBJECT"
  },
  {
    "$lookup": {
      "from": "data2",
      "let": {
        "subject": "$SUBJECT"
      },
      "pipeline": [
        {
          "$addFields": {
            "SUBJECT_ID": {
              "$split": [
                "$SUBJECT_ID",
                ", "
              ]
            }
          }
        },
        {
          "$match": {
            "$expr": {
              "$in": [
                "$$subject",
                "$SUBJECT_ID"
              ]
            }
          }
        },
        {
          "$project": {
            "UUID": 1,
            "_id": 0
          }
        }
      ],
      "as": "ref_data"
    }
  },
  {
    "$unwind": {
      "path": "$ref_data",
      "preserveNullAndEmptyArrays": true
    }
  },
  {
    "$group": {
      "_id": "$Prof_Name",
      "subjects_list": {
        "$addToSet": "$SUBJECT"
      },
      "UUID_list": {
        "$addToSet": "$ref_data.UUID"
      }
    }
  },
  {
    "$addFields": {
      "Prof_Name": "$_id",
      "UUID_count": {
        "$size": "$UUID_list"
      },
      "subject_count": {
        "$size": "$subjects_list"
      }
    }
  },
  {
    "$project": {
      "_id": 0
    }
  },
  {
    "$out": "data3"
  }
])

这个查询需要什么修改才能得到上面提到的集合数据3，主要是UUID_list和UUID-count和Subject_list。

还想知道如何在下面的查询聚合查询中匹配给定月份和年份但不是 iso 的记录的时间戳。

试过这个：

    { "$project": {"year":{"$year":"$timestamp"},"month":{"$month":"$timestamp"}}},{ "$match":{"year" :"2016","month": "01"}}  

但确实有效。

Answer 1

您可以通过将主题从逗号分隔值更改为数据库中的数组来简化聚合。

前"SUBJECT" : ["Maths", "", "Chemistry", "Machinery1", "Ele1"]

您可以使用以下聚合。

db.data1.aggregate([
{"$lookup":{
  "from":"data2",
  "localField":"SUBJECT",
  "foreignField":"SUBJECT_ID",
  "as":"ref_data"
}}, // outputs all the input documents where there is any match between two subjects array.
{"$unwind":{"path":"$ref_data","preserveNullAndEmptyArrays":true}},
{"$match":{"ref_data.timestamp":{"$gte":ISODate("2016-01-01T00:00:00.000Z"), "$lte":ISODate("2016-11-31T11:59:59.999Z")}}},
{"$addFields":{"SUBJECT":{"$setIntersection":["$SUBJECT","$ref_data.SUBJECT_ID"]}}}, // outputs the common subjects (matching) between two subjects array
{"$unwind":"$SUBJECT"},
{"$group":{
  "_id":{
    "Prof_Name":"$Prof_Name",
    "UUID":"$ref_data.UUID",
    "SUBJECT":"$SUBJECT"
  }
}},// outputs all the distinct combination of UUID and Subject
{"$group":{
  "_id":"$_id.Prof_Name",
  "UUID_count":{"$sum":1},
  "subjects_list":{"$push":"$_id.SUBJECT"},
  "UUID_distinct_list":{"$addToSet":"$_id.UUID"}
}}, // outputs the distinct uuid list, count the uuids & subjects list 
{"$addFields": {
  "Prof_Name": "$_id",
  "UUID_distinct_count": {
    "$size": "$UUID_distinct_list"
  },
  "subject_count": {
    "$size": "$subjects_list"
  }
}}, // Adds the subject list size
{"$project": {"_id": 0}},// excludes the id from final output
{"$out":"data3"}])

无需修改架构，您就可以使用以下聚合查询。

db.data1.aggregate([
  {"$lookup":{
    "from":"data2",
    "let":{"subject":{"$split":["$SUBJECT",", "]}},
    "pipeline":[
      {"$match": {"expr":{"$and":[{"$eq":[{"$year":"$timestamp"}, 2016]}, {"$eq":[{"$month":"$timestamp"}, 1]}]}}},
      {"$addFields":{"SUBJECT_ID":{"$split":["$SUBJECT_ID",", "]},"SUBJECT":"$$subject"}},
      {"$unwind":"$SUBJECT"},
      {"$match":{"$expr":{"$in":["$SUBJECT","$SUBJECT_ID"]}}},
      {"$facet":{
        "UUID":[{"$group":{"_id":{"id":"$_id","UUID":"$UUID"}}},{"$count":"UUID_Count"}],
        "REST":[
          {"$group":{"_id":null,"subjects_list":{"$addToSet":"$SUBJECT"},"UUID_distinct_list":{"$addToSet":"$UUID"}}},
          {"$addFields":{"subject_count":{"$size":"$subjects_list"},"UUID_distinct_count":{"$size":"$UUID_distinct_list"}}},
          {"$project":{"_id":0}}
         ]
      }},
      {"$replaceRoot":{"newRoot":{"$mergeObjects":[{"$arrayElemAt":["$UUID",0]},{"$arrayElemAt":["$REST",0]}]}}}
    ],
    "as":"ref_data"
  }},
  {"$unwind":{"path":"$ref_data","preserveNullAndEmptyArrays":true}},
  {"$addFields":{"ref_data.Prof_Name":"$Prof_Name"}},
  {"$replaceRoot":{"newRoot":"$ref_data"}},
  {"$out":"data3"}
])