MongoDB

[true-matrix]

MongoDB (Database -> Collection -> Document)

---

1. Create DB use mydb

2. Show DBs show dbs

3. Current DB db

4. Drop DB db.dropDatabase()

5. Create Collection Method-1 : db.createCollection("myCollection") Method-2 : db.myCollection2.insertOne({"name":"Rajesh"})

6. Show Collection show collections

7. Drop Collection db.myCollection.drop()

8.Insert Data in Document db.students.insertOne({ "StudentNo" : "1", "FirstName" : "Max", "LastName" : "Joe", "Age" : "10" })

9.Insert Many Data in Document db.students.insertMany([{ "StudentNo" : "2", "FirstName" : "Tax", "LastName" : "Koe", "Age" : "9" },{ "StudentNo" : "3", "FirstName" : "Fax", "LastName" : "Loe", "Age" : "10.4" }])

10. Show data from document db.students.find()

[true-matrix]

MongoDB (Database -> Collection -> Document)

    "_id" : ObjectId("65d5964c1172d5d9824698fd"),
    "StudentNo" : "1",
    "FirstName" : "Max",
    "LastName" : "Gole",
    "Age" : "10"
}
{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "StudentNo" : "2",
    "FirstName" : "Tax",
    "LastName" : "Koe",
    "Age" : "9"
}
{
    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "StudentNo" : "3",
    "FirstName" : "Fax",
    "LastName" : "Gole",
    "Age" : "10"
}

---

11. Query Document - AND db.students.find({ "FirstName" : "Tax", "Age" :"9" })

12. Query Document - OR db.students.find({ $or : [{"FirstName" : "Max"},{"Age" : "9"}] })

13. Query Document -AND OR db.students.find({ "FirstName" : "Max", $or : [{"Age" : "10"},{"Age" : "9"}] })

14. Update Single Document db.students.update( {"_id" : ObjectId("65d597481172d5d9824698ff")}, {$set : {"Age" : "12"}} )

⏺update the document by inserting a new nested field like: Address and inside address there is city and pincode

db.students.updateOne(
    { "StudentNo": "1" },
    {
        $set: {
            "Address.City": "Mohali",
            "Address.Pincode": "12345"
        }
    }
); 

⏺Insert nested array of objects

db.students.updateOne(
    { "StudentNo": "2" },
    {
        $set: {
            "Address": [
                {
                    "City": "Salt Lake - 1",
                    "Pincode": "700092"
                },
                 {
                    "City": "Salt Lake - 2",
                    "Pincode": "700093"
                },
            ]
        }
    }
);

15. Update Many fields in Document db.students.update( {"Age" : "10"}, {$set : {"LastName" : "Gole"}}, {multi : true} )

16. Remove all Document db.students.remove()

17. Remove all Document db.students.remove()

18. Remove a specific Document db.students.remove({ "_id" : ObjectId("65d5964c1172d5d9824698fd"), })

19. Remove one Document of Age=16 (althouth multiple document matches this conditions) db.students.remove({ "Age" : "16", },1)

[true-matrix]

MongoDB (Database -> Collection -> Document)

✅MongoDB - Projection -> projection means selecting only necessary data rather than selecting whole of the data of a document

db.COLLECTION_NAME.find({},{KEY:1})`

**20. Input : `db.students.find({},{"FirstName" : 1})`**
Output : `{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "FirstName" : "Tax"
}
{
    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "FirstName" : "Fax"
}

21. Input : db.students.find({},{"FirstName" : 1, "_id":0}) Output : { "FirstName" : "Tax" } { "FirstName" : "Fax" }

22. limit using limit how many data we want to show Input : db.students.find({},{"StudentNo": 1, "FirstName" : 1, "_id" : 0}).limit(1) Output : { "StudentNo" : "2", "FirstName" : "Tax" }

23. skip *using skip how many data we want to skip from first Input : db.students.find({},{"StudentNo": 1, "FirstName" : 1, "_id" : 0}).skip(1) Output : { "StudentNo" : "3", "FirstName" : "Fax" }

24. skip *using skip how many data we want to skip from first Input : db.students.find({},{"StudentNo": 1, "FirstName" : 1, "_id" : 0}).skip(1) Output : { "StudentNo" : "3", "FirstName" : "Fax" }

25. skip *using skip and limit together if we want to skip first 1 result and show 2 result after skip in this example

Input : db.students.find({},{"StudentNo": 1, "FirstName" : 1, "_id" : 0}).skip(1).limit(2)

Output : { "StudentNo" : "3", "FirstName" : "Fax" }, { "StudentNo" : "4", "FirstName" : "Rax" }

26. sort Sort in ascending order use 1, for descending order -1 Input : db.students.find({},{"StudentNo": 1, "FirstName" : 1, "_id" : 0}).sort({"FirstName" : 1})

Output : { "StudentNo" : "3", "FirstName" : "Fax" } { "StudentNo" : "2", "FirstName" : "Tax" }

[true-matrix]

MongoDB (Database -> Collection -> Document) ✅ Indexing

🔵What are Indexes/Indexing in MongoDB? Indexes are the special data structure that stores the small portion of data set in the easy to pass way

🟠Why use Indexes in MongoDB? Indexes are data structures that support the efficient execution of queries in MongoDB. They contain copies of parts of the data in documents to make queries more efficient. Without indexes, MongoDB must scan every document in a collection to find the documents that match each query

[true-matrix]

🔵Distinct in MongoDB? Distinct is used to find the unique values

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "StudentNo" : "2",
    "FirstName" : "Tax",
    "LastName" : "Koe",
    "Age" : "9"
}
{
    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "StudentNo" : "3",
    "FirstName" : "Fax",
    "LastName" : "Gole",
    "Age" : "10"
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ee"),
    "StudentNo" : "1",
    "FirstName" : "Lyca",
    "LastName" : "Vela",
    "Age" : "8"
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ef"),
    "StudentNo" : "4",
    "FirstName" : "Aham",
    "LastName" : "Bill",
    "Age" : "10"
}

Input : db.students.distinct('Age') Output : [ "10", "8", "9" ]

Input : db.students.distinct('Age', {"StudentNo" : {"$gte" : "2"}}) Output : [ "10", "9" ]

🟣Count() db.students.find().count()

[true-matrix]

🔵elemMatch in MongoDB? To find the document by using the conditions which data is in nested array of objects

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "StudentNo" : "2",
    "FirstName" : "Tax",
    "LastName" : "Koe",
    "Age" : "9",
    "Address" : [
        {
            "City" : "Salt Lake - 1",
            "Pincode" : "700092"
        },
        {
            "City" : "Salt Lake - 2",
            "Pincode" : "700093"
        }
    ]
}

✅db.students.find({"Address" : {$elemMatch: {"Pincode" : "700092"}}})

[true-matrix]

✅ Pipeline Stages

$match: Filters the documents based on specified criteria. $project: Shapes the documents, selecting specific fields, adding new fields, or renaming existing ones. $group: Groups documents by a specified key and performs aggregation operations on the grouped data. $sort: Sorts the documents based on specified criteria. $skip: Skips a specified number of documents in the pipeline. $limit: Limits the number of documents passed to the next stage in the pipeline. $unwind: Deconstructs an array field, producing a separate document for each element in the array. $lookup: Performs a left outer join to another collection in the same database. $addFields: Adds new fields to documents. $replaceRoot: Replaces the input document with a specified document. $facet: Enables the execution of multiple pipelines within a single stage.

[true-matrix]

✅ $match

db.collection.aggregate([ { $match: { field1: value1, field2: value2 // Add more conditions as needed } }, // Additional stages in the pipeline ]);

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "StudentNo" : "2",
    "FirstName" : "Tax",
    "LastName" : "Koe",
    "Age" : "9",
    "Address" : [
        {
            "City" : "Salt Lake - 1",
            "Pincode" : "700092"
        },
        {
            "City" : "Salt Lake - 2",
            "Pincode" : "700093"
        }
    ],
    "Status" : "Active"
}
{
    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "StudentNo" : "3",
    "FirstName" : "Fax",
    "LastName" : "Gole",
    "Age" : "10",
    "Status" : "Inactive"
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ee"),
    "StudentNo" : "1",
    "FirstName" : "Lyca",
    "LastName" : "Vela",
    "Age" : "8",
    "Address" : {
        "City" : "Mohali",
        "Pincode" : "12345"
    },
    "Status" : "Active"
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ef"),
    "StudentNo" : "4",
    "FirstName" : "Aham",
    "LastName" : "Bill",
    "Age" : "10",
    "Address" : {
        "City" : "Mumbai",
        "Pincode" : "400236"
    },
    "Status" : "Active"
}

👍 Input : db.students.aggregate([{$match : {'Status' : "Inactive"}}]) ☑Output :

    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "StudentNo" : "3",
    "FirstName" : "Fax",
    "LastName" : "Gole",
    "Age" : "10",
    "Status" : "Inactive"
}

[true-matrix]

✅ $group

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "StudentNo" : "2",
    "FirstName" : "Tax",
    "LastName" : "Koe",
    "Age" : "9",
    "Address" : [
        {
            "City" : "Salt Lake - 1",
            "Pincode" : "700092"
        },
        {
            "City" : "Salt Lake - 2",
            "Pincode" : "700093"
        }
    ],
    "Status" : "Active",
    "Department" : "Science",
    "Marks" : NumberInt(60)
}
{
    "_id" : ObjectId("65d597481172d5d9824698ff"),
    "StudentNo" : "3",
    "FirstName" : "Fax",
    "LastName" : "Gole",
    "Age" : "10",
    "Status" : "Inactive",
    "Department" : "Commerce",
    "Marks" : NumberInt(88)
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ee"),
    "StudentNo" : "1",
    "FirstName" : "Lyca",
    "LastName" : "Vela",
    "Age" : "8",
    "Address" : {
        "City" : "Mohali",
        "Pincode" : "12345"
    },
    "Status" : "Active",
    "Department" : "Arts",
    "Marks" : NumberInt(90)
}
{
    "_id" : ObjectId("65d5bd5b768e0e2b126763ef"),
    "StudentNo" : "4",
    "FirstName" : "Aham",
    "LastName" : "Bill",
    "Age" : "10",
    "Address" : {
        "City" : "Mumbai",
        "Pincode" : "400236"
    },
    "Status" : "Active",
    "Department" : "Science",
    "Marks" : NumberInt(67)
}

Input :

    {
        $group: {
            _id: "$Department",
            totMarks: { $sum: "$Marks" }
        }
    }
]);

Output :

{
    "_id" : "Commerce",
    "totMarks" : NumberInt(88)
}
{
    "_id" : "Arts",
    "totMarks" : NumberInt(90)
}
{
    "_id" : "Science",
    "totMarks" : NumberInt(127)
}

[true-matrix]

✅ $project

Input :

db.students.aggregate([{$match : {Status : "Active"}},{$project : {"StudentNo":1, "FirstName":1, "_id":0}}])

Output :

{
    "StudentNo" : "2",
    "FirstName" : "Tax"
}
{
    "StudentNo" : "1",
    "FirstName" : "Lyca"
}
{
    "StudentNo" : "4",
    "FirstName" : "Aham"
}

✅ $project and $type

Input :

db.students.aggregate([{$match : {Status : "Active"}},{$project : {"StudentNo":{$type:"$StudentNo"}, "Marks":{$type:"$Marks"}, "_id":{$type : "$_id"}}}])

Output :

{
    "StudentNo" : "string",
    "Marks" : "int",
    "_id" : "objectId"
}
{
    "StudentNo" : "string",
    "Marks" : "int",
    "_id" : "objectId"
}
{
    "StudentNo" : "string",
    "Marks" : "int",
    "_id" : "objectId"
}

[true-matrix]

✅ $unwind unwind : we can seperate the array {$unwind : "$arrayFieldName"}

👉Input : (Without unwind)

db.students.aggregate([{$match : {StudentNo: "2"}}, {$project: {"Address":1, "FirstName":1}}])

👉Output : (Without unwind)

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "FirstName" : "Tax",
    "Address" : [
        {
            "City" : "Salt Lake - 1",
            "Pincode" : "700092"
        },
        {
            "City" : "Salt Lake - 2",
            "Pincode" : "700093"
        }
    ]
}

🟩Input : ($unwind)

db.students.aggregate([{$unwind : "$Address"},{$match : {StudentNo: "2"}}, {$project: {"Address":1, "FirstName":1}}])

🟩Output : ($unwind)

{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "FirstName" : "Tax",
    "Address" : {
        "City" : "Salt Lake - 1",
        "Pincode" : "700092"
    }
}
{
    "_id" : ObjectId("65d597481172d5d9824698fe"),
    "FirstName" : "Tax",
    "Address" : {
        "City" : "Salt Lake - 2",
        "Pincode" : "700093"
    }
}

[true-matrix]

✅$lookup() in mongoDB✳

🔴$lookup is an aggregation pipeline stage that allows you to perform a left outer join between two collections

users collection

{
    "_id" : ObjectId("65d5e1ae768e0e2b126763f9"),
    "name" : "Rahul",
    "dept" : "Tech"
}
{
    "_id" : ObjectId("65d5e1ae768e0e2b126763fa"),
    "name" : "Nisha",
    "dept" : "HR"
}
{
    "_id" : ObjectId("65d5e1ae768e0e2b126763fb"),
    "name" : "Ram",
    "dept" : "Tech"
}

department collection

{
    "_id" : ObjectId("65d5e118768e0e2b126763f0"),
    "name" : "Tech",
    "code" : "T9021"
}
{
    "_id" : ObjectId("65d5e118768e0e2b126763f1"),
    "name" : "HR",
    "code" : "H9075"
}
{
    "_id" : ObjectId("65d5e118768e0e2b126763f2"),
    "name" : "DevOps",
    "code" : "D9080"
}

👇👇query using lookup(LEFT OUTER JOIN)👇👇

Note: using lookup we can achieve left outer join. Here users(left collection) and department(right collection), the common field between this two table is dept(users) and name(department). So it will fetch all the data from the users table/collection and the common field between users and department table and show the data in the field name "anything"

db.users.aggregate({
    $lookup: {
        from: "department",
        localField: "dept",
        foreignField: "name",
        as: "anything",
    }
})

🟩Output :

{
    "_id" : ObjectId("65d5e1ae768e0e2b126763f9"),
    "name" : "Rahul",
    "dept" : "Tech",
    "anything" : [
        {
            "_id" : ObjectId("65d5e118768e0e2b126763f0"),
            "name" : "Tech",
            "code" : "T9021"
        }
    ]
}
{
    "_id" : ObjectId("65d5e1ae768e0e2b126763fa"),
    "name" : "Nisha",
    "dept" : "HR",
    "anything" : [
        {
            "_id" : ObjectId("65d5e118768e0e2b126763f1"),
            "name" : "HR",
            "code" : "H9075"
        }
    ]
}
{
    "_id" : ObjectId("65d5e1ae768e0e2b126763fb"),
    "name" : "Ram",
    "dept" : "Tech",
    "anything" : [
        {
            "_id" : ObjectId("65d5e118768e0e2b126763f0"),
            "name" : "Tech",
            "code" : "T9021"
        }
    ]
}

▶Right Outer Join :

db.department.aggregate({
    $lookup: {
        from: "users",
        localField: "name",
        foreignField: "dept",
        as: "anything",
    }
})

🟧Output :

{
    "_id" : ObjectId("65d5e118768e0e2b126763f0"),
    "name" : "Tech",
    "code" : "T9021",
    "anything" : [
        {
            "_id" : ObjectId("65d5e1ae768e0e2b126763f9"),
            "name" : "Rahul",
            "dept" : "Tech"
        },
        {
            "_id" : ObjectId("65d5e1ae768e0e2b126763fb"),
            "name" : "Ram",
            "dept" : "Tech"
        }
    ]
}
{
    "_id" : ObjectId("65d5e118768e0e2b126763f1"),
    "name" : "HR",
    "code" : "H9075",
    "anything" : [
        {
            "_id" : ObjectId("65d5e1ae768e0e2b126763fa"),
            "name" : "Nisha",
            "dept" : "HR"
        }
    ]
}
{
    "_id" : ObjectId("65d5e118768e0e2b126763f2"),
    "name" : "DevOps",
    "code" : "D9080",
    "anything" : [

    ]
}

▶Inner Join :

db.users.aggregate([
    {
        $lookup: {
            from: "department",
            localField: "dept",
            foreignField: "name",
            as: "departmentInfo"
        }
    },
    {
        $project: {
            "name": 1,
            "dept": 1,
            "departmentInfo.code": 1,
            "_id": 0
        }
    }
]);

➡Output :

{
    "name" : "Rahul",
    "dept" : "Tech",
    "departmentInfo" : [
        {
            "code" : "T9021"
        }
    ]
}
{
    "name" : "Nisha",
    "dept" : "HR",
    "departmentInfo" : [
        {
            "code" : "H9075"
        }
    ]
}
{
    "name" : "Ram",
    "dept" : "Tech",
    "departmentInfo" : [
        {
            "code" : "T9021"
        }
    ]
}