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Searching value in 2 different fields mongodb + node.js

Posted by: admin November 30, 2017 Leave a comment

Questions:

I am newbie. But I try to learn the most logical ways to write the queries.

Assume I have collection which is as;

{ 
    "id" : NumberInt(1), 
    "school" : [
        {
            "name" : "george", 
            "code" : "01"
        }, 
        {
            "name" : "michelangelo", 
            "code" : "01"
        }
    ], 
    "enrolledStudents" : [
        {
            "userName" : "elisabeth", 
            "code" : NumberInt(21)
        }
    ]
}
{ 
    "id" : NumberInt(2), 
    "school" : [
        {
            "name" : "leonarda da vinci", 
            "code" : "01"
        }
    ], 
    "enrolledStudents" : [
        {
            "userName" : "michelangelo", 
            "code" : NumberInt(25)
        }
    ]
}

I want to list occurence of a key with their corresponding code values.

As an example key : michelangelo

To find the occurence of the key, I wrote two differen aggregation queries as;

db.test.aggregate([
    {$unwind: "$school"},
    {$match : {"school.name" : "michelangelo"}},
    {$project: {_id: "$id", "key" : "$school.name", "code" : "$school.code"}}
])

and

db.test.aggregate([
    {$unwind: "$enrolledStudents"},
    {$match : {"enrolledStudents.userName" : "michelangelo"}},
    {$project: {_id: "$id", "key" : "$enrolledStudents.userName", "code" : "$enrolledStudents.code"}}
])

the result of these 2 queries return what I want as;

{ "_id" : 1, "key" : "michelangelo", "code" : "01" }
{ "_id" : 2, "key" : "michelangelo", "code" : 25 }

One of them to search in enrolledStudents, the other one is searching in school field.

Can these 2 queries reduced into more logical query? Or is this the only way to do it?

ps: I am aware that database structure is not logical, but I tried to simulate.

edit
I try to write a query with find.

db.test.find({$or: [{"enrolledStudents.userName" : "michelangelo"} , {"school.name" : "michelangelo"}]}).pretty()

but this returns the whole documents as;

{
    "id" : 1,
    "school" : [
        {
            "name" : "george",
            "code" : "01"
        },
        {
            "name" : "michelangelo",
            "code" : "01"
        }
    ],
    "enrolledStudents" : [
        {
            "userName" : "elisabeth",
            "code" : 21
        }
    ]
}
{
    "id" : 2,
    "school" : [
        {
            "name" : "leonarda da vinci",
            "code" : "01"
        }
    ],
    "enrolledStudents" : [
        {
            "userName" : "michelangelo",
            "code" : 25
        }
    ]
}
Answers:

Mongo 3.4

$match – This stage will keep all the school array and enrolledStudents where there is atleast one embedded document matching both the query condition

$group – This stage will combine all the school and enrolledStudents array to 2d array for each _id in a group.

$project – This stage will $filter the merge array for matching query condition and $map the array to with new labels values array.

$unwind – This stage will flatten the array.

$addFields & $replaceRoot – This stages will add the id field and promote the values array to the top.

db.collection.aggregate([
    {$match : {$or: [{"enrolledStudents.userName" : "michelangelo"} , {"school.name" : "michelangelo"}]}},
    {$group: {_id: "$id", merge : {$push:{$setUnion:["$school", "$enrolledStudents"]}}}},
    {$project: {
        values: {
              $map:
                 {
                   input: {
                            $filter: {
                                input: {"$arrayElemAt":["$merge",0]},
                                as: "onef",
                                cond: {
                                    $or: [{
                                        $eq: ["$$onef.userName", "michelangelo"]
                                    }, {
                                        $eq: ["$$onef.name", "michelangelo"]
                                    }]
                                }
                            }
                        },
                   as: "onem",
                   in: { 
                         key : { $ifNull: [ "$$onem.userName", "$$onem.name" ] },
                         code : "$$onem.code"}
                 }
            }
        }
    },
    {$unwind: "$values"},
    {$addFields:{"values.id":"$_id"}},
    {$replaceRoot: { newRoot:"$values"}}
])

Sample Response

{ "_id" : 2, "key" : "michelangelo", "code" : 25 }
{ "_id" : 1, "key" : "michelangelo", "code" : "01" }

Mongo <= 3.2

Replace last two stages of above aggregation with $project to format the response.

{$project: {"_id": 0 , id:"$_id", key:"$values.key", code:"$values.code"}}

Sample Response

{ "_id" : 2, "key" : "michelangelo", "code" : 25 }
{ "_id" : 1, "key" : "michelangelo", "code" : "01" }

You can use $redact instead of $group & match and add $project with $map to format the response.

$redact to go through a document level at a time and perform $$DESCEND and $$PRUNE on the matching criteria.

The only thing to note is usage of $ifNull in the first document level for id so that you can $$DESCEND to embedded document level for further processing.

db.collection.aggregate([
    {
        $redact: {
            $cond: [{
                $or: [{
                    $eq: ["$userName", "michelangelo"]
                }, {
                    $eq: ["$name", "michelangelo"]
                }, {
                    $ifNull: ["$id", false]
                }]
            }, "$$DESCEND", "$$PRUNE"]
        }
    },
    {
        $project: {
            id:1,
            values: {
              $map:
                 {
                   input: {$setUnion:["$school", "$enrolledStudents"]},
                   as: "onem",
                   in: { 
                         key : { $ifNull: [ "$$onem.userName", "$$onem.name" ] },
                         code : "$$onem.code"}
                 }
            }
        }
    },
    {$unwind: "$values"},
    {$project: {_id:0,id:"$id", key:"$values.key", code:"$values.code"}}
])