Download Attribute: HTML5

In this video tutorial we’ll show you how we can add download attribute and make the UX(User Experience) little better!

Related Read: HTML5 and CSS3 Video Tutorial List

We’ve seen so many times that a PDF file available for download opens up in the browser once the link is clicked. Same way the rar files, zip files, image files etc. We could add download attribute and fix this simple issue.

HTML5: Download Attribute with Value

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 <a href="Picture1.jpg" download="book.jpg">
    Download Book Cover
 </a>

Once we add download attribute to the anchor tag, once someone clicks on the link, the linked resource starts to download, instead of opening in the browser.

The value, if present, to download attribute will be used to replace the original name of the resource file linked in the anchor tag.

Download Attribute: HTML5


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YouTube Link: http://www.youtube.com/watch?v=Wg6bMQ9leMQ [Watch the Video In Full Screen.]



Full Free Code: HTML5 Download Attribute with Value

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< !doctype html>
<html>
<head>
<title>Download Attribute</title>
</head>
<body>
 
 <a href="Picture1.jpg" download="book.jpg">
    Download Book Cover
 </a>
 
</body>
</html>

Make sure you have the doctype of html5(First line in above code).

explain() method: MongoDB

We’ve seen the working of explain() method briefly in previous video tutorials – in this video tutorial lets dive in to learn more about this useful method, which helps us analyze and optimize our MongoDB commands.

explain method mongodb explain() method: MongoDB

Related Read:
Create and Insert Documents: MongoDB
index creation: MongoDB

temp: database name
name: collection name

Inserting 1000 Documents

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MongoDB shell version: 2.6.1
connecting to: test
 
> use temp
switched to db temp
 
> for(i = 1; i < = 1000; i++) db.name.insert({a: i, b: i, c: i});
WriteResult({ "nInserted" : 1 })

Here we inserted 1000 documents using for loop.

Basic Cursor

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> db.name.find({a: 5})
{ "_id" : ObjectId("53dcd070340ac74e061d7215"), "a" : 5, "b" : 5, "c" : 5 }
 
> db.name.find({a: 5}).explain()
{
        "cursor" : "BasicCursor",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 1000,
        "nscanned" : 1000,
        "nscannedObjectsAllPlans" : 1000,
        "nscannedAllPlans" : 1000,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 7,
        "nChunkSkips" : 0,
        "millis" : 1,
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

Here MongoDB server is returning a basic cursor, as we do not have index on field “a”. Also note that, it’s scanning 1000 documents in the “name” collection and scanning 1000 index keys in “system.indexes” collection, where the default key is “_id”.

explain() method: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=q59nF4ziW-w [Watch the Video In Full Screen.]



Creating Index on fields “a” and “b”

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> db.name.ensureIndex({a: 1, b: 1});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}
 
> db.name.find({a: 5}).explain()
{
        "cursor" : "BtreeCursor a_1_b_1",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 1,
        "nscanned" : 1,
        "nscannedObjectsAllPlans" : 1,
        "nscannedAllPlans" : 1,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "a" : [
                        [
                                5,
                                5
                        ]
                ],
                "b" : [
                        [
                                {
                                        "$minElement" : 1
                                },
                                {
                                        "$maxElement" : 1
                                }
                        ]
                ]
        },
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

Here we create index on key “a” and “b” – it’s called compound key – after which the explain() method shows that MongoDB server is returning a Btree cursor, meaning it’s making use of index key to execute the query/command.

Also not the number of objects scanned in the “name” collection as well as in the “system.indexes” collection.

indexOnly: true

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> db.name.find({a: 5})
{ "_id" : ObjectId("53dcd070340ac74e061d7215"), "a" : 5, "b" : 5, "c" : 5 }
 
> db.name.find({a: 5}, {a: 1, b: 1, _id: 0})
{ "a" : 5, "b" : 5 }
 
> db.name.find({a: 5}, {a: 1, b: 1, _id: 0}).explain()
{
        "cursor" : "BtreeCursor a_1_b_1",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 0,
        "nscanned" : 1,
        "nscannedObjectsAllPlans" : 0,
        "nscannedAllPlans" : 1,
        "scanAndOrder" : false,
        "indexOnly" : true,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "a" : [
                        [
                                5,
                                5
                        ]
                ],
                "b" : [
                        [
                                {
                                        "$minElement" : 1
                                },
                                {
                                        "$maxElement" : 1
                                }
                        ]
                ]
        },
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

Covered Index or indexOnly
If our command is requesting for values present inside the index, mongo engine will fetch that information from the “system.indexes” collection itself and does not go to the collection where the entire document is present – in our case “name” collection. Since index is on keys “a” and “b” in our case, if we query for “a” and “b” values only, it fetches those values directly from the index information and will not go to “name” collection. Hence the speed of execution of the command is faster, and this type of commands are very efficient in creating optimized web applications.

Note from above explain() result, the number of objects scanned in the “name” collection is 0, and the number of scans made on indexes in only 1.

Index Creation for Production Server: MongoDB

We know the uses of index, how to create indexes, how to delete indexes – now its time to learn the right way of creating index/key in MongoDB for its optimum usage.

background index creation mongodb Index Creation for Production Server: MongoDB

temp: database name
stu: collection name

Inserting 10 Million Documents

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use temp
switched to db temp
 
for(i=1; i < = 10000000; i++)
db.no.insert({"student_id": i});

This inserts 10 Million documents inside “stu” collection. It takes atleast a minute or so to complete the insertion – patience is key!

index creation in the foreground

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> db.stu.ensureIndex({student_id: 1});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}

Since there are 10 Million documents inside “stu” collection, it takes some time to create index on “student_id” key.

Things to note:
1. By default, index creation in MongoDB takes place in the foreground.
2. It’s faster than background index creation.
3. It blocks other write operation
4. Ideal to use when in development system – as you’re the only one writing to the server(database). Also ideal to use when you’ve replica sets – where you’ll have multiple set of mongod with same data set to operate on, in which case you can have index information on a separate replica set which helps in faster and efficient index creation.

Background Index Creation: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=PIyBA3LvVDs [Watch the Video In Full Screen.]



index creation in the background

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> db.stu.ensureIndex({student_id: 1}, {background: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}

Simply pass in the option in second parameter to ensureIndex() method – {background: true}

It takes time to complete the process, as there are 10 Million documents in the collection.

Things to note:
1. It’s slower than foreground index creation method.
2. It does not block any other write operation.
3. Ideal in production server/system. As there’ll be other people who’ll be performing read and write operation on the same database server.
4. Ideal when you’re not making use of replica sets yet.

Sparse Index: MongoDB

All sparse indexes are unique index, but not all unique indexes are sparse! There are situations where we want to create unique index on key/field which is not present in all documents. Wherever the key is not present, it’s value will be treated as NULL. If more than 1 document has NULL value to the key we want to make as unique, then it violates unique key rule. In such situations we can make use of Sparse index and create unique key on only those documents which has the key in it.

sparse index unique key mongodb Sparse Index: MongoDB

example: database name
company: collection name

Insert documents

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MongoDB shell version: 2.6.1
connecting to: test
> use example
switched to db example
 
> db.company.insert({name: "Apple", product: "iPhone"});
WriteResult({ "nInserted" : 1 })
 
> db.company.insert({name: "Motorola", product: "Smart Watch"});
WriteResult({ "nInserted" : 1 })
 
> db.company.insert({name: "Technotip"});
WriteResult({ "nInserted" : 1 })
 
> db.company.insert({name: "Google"});
WriteResult({ "nInserted" : 1 })
 
 
> db.company.find().pretty()
{
        "_id" : ObjectId("53d9f5125d1942042b4e092b"),
        "name" : "Apple",
        "product" : "iPhone"
}
{
        "_id" : ObjectId("53d9f52d5d1942042b4e092c"),
        "name" : "Motorola",
        "product" : "Smart Watch"
}
{ "_id" : ObjectId("53d9f5385d1942042b4e092d"), "name" : "Technotip" }
{ "_id" : ObjectId("53d9f53f5d1942042b4e092e"), "name" : "Google" }

We have 4 documents in “company” collection. First 2 documents has “name” and “product” keys. Last 2 documents has only “name” key.

Sparse Index – Unique Key: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=6pxI5j6TZRU [Watch the Video In Full Screen.]



Duplicate key error

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> db.company.ensureIndex({product: 1}, {unique: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "ok" : 0,
        "errmsg" : "E11000 duplicate key error index: example.company.$product_1
  dup key: { : null }",
        "code" : 11000
}

Since last 2 documents do not have “product” key, its value will be treated as “NULL”. Since both these documents have value of “product” as “NULL”, trying to create unique key on “product” throws duplicate key error.

Sparse Index

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> db.company.ensureIndex({product: 1}, {unique: true, sparse: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}

This creates a sparse index on “product” key/field.

system.indexes collection

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> db.system.indexes.find().pretty()
{
        "v" : 1,
        "key" : {
                "_id" : 1
        },
        "name" : "_id_",
        "ns" : "example.company"
}
{
        "v" : 1,
        "unique" : true,
        "key" : {
                "product" : 1
        },
        "name" : "product_1",
        "ns" : "example.company",
        "sparse" : true
}

The “system.indexes” collection shows that the key “product” is a unique key and is a sparse index.

sort() method

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> db.company.find().sort({product: 1}).pretty();
{ "_id" : ObjectId("53d9f5385d1942042b4e092d"), "name" : "Technotip" }
{ "_id" : ObjectId("53d9f53f5d1942042b4e092e"), "name" : "Google" }
{
        "_id" : ObjectId("53d9f52d5d1942042b4e092c"),
        "name" : "Motorola",
        "product" : "Smart Watch"
}
{
        "_id" : ObjectId("53d9f5125d1942042b4e092b"),
        "name" : "Apple",
        "product" : "iPhone"
}

This command sorts all the documents in lexicographical order. The output includes all the documents. Those documents which do not have “product” key in them are listed first.

Basic Cursor

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> db.company.find().sort({product: 1}).explain()
{
        "cursor" : "BasicCursor",
        "isMultiKey" : false,
        "n" : 4,
        "nscannedObjects" : 4,
        "nscanned" : 4,
        "nscannedObjectsAllPlans" : 4,
        "nscannedAllPlans" : 4,
        "scanAndOrder" : true,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

Since the command is even listing/sorting the documents which do not have “product” key in them, it’s simply making use of Basic Cursor – which do not help in optimizing the command/query performance.

hint() method

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> db.company.find().sort({product: 1}).hint({product: 1}).pretty();
{
        "_id" : ObjectId("53d9f52d5d1942042b4e092c"),
        "name" : "Motorola",
        "product" : "Smart Watch"
}
{
        "_id" : ObjectId("53d9f5125d1942042b4e092b"),
        "name" : "Apple",
        "product" : "iPhone"
}

hint() method tells the mongoDB server to operate only on the sparse key.. hence only retrieving and operating on the documents which has the sparse key in them.

Btree Cursor

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> db.company.find().sort({product: 1}).hint({product: 1}).explain()
{
        "cursor" : "BtreeCursor product_1",
        "isMultiKey" : false,
        "n" : 2,
        "nscannedObjects" : 2,
        "nscanned" : 2,
        "nscannedObjectsAllPlans" : 2,
        "nscannedAllPlans" : 2,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "product" : [
                        [
                                {
                                        "$minElement" : 1
                                },
                                {
                                        "$maxElement" : 1
                                }
                        ]
                ]
        },
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

After using hint() method, mongoDB server is only looking for documents which has the sparse index key in them, so it can directly look for the sparse index key inside “system.indexes” collection, hence it’s using Btree Cursor – and is efficient.

Remove Duplicate Documents: MongoDB

We learnt how to create unique key/index using {unique: true} option with ensureIndex() method. Now lets see how we can create unique key when there are duplicate entries/documents already present inside the collection.

dropDups unique key index mongodb Remove Duplicate Documents: MongoDB

Insert documents

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> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.test" }
 
 
> db.test.insert({name: "Satish", age: 27});
WriteResult({ "nInserted" : 1 })
 
> db.test.insert({name: "Kiran", age: 28});
WriteResult({ "nInserted" : 1 })
 
> db.test.insert({name: "Satish", age: 27});
WriteResult({ "nInserted" : 1 })

Here we have 3 documents. First and the last document has same value for “name” and “age” fields.

dropDups() To Remove Duplicate Documents: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=aQXdtDWKBiU [Watch the Video In Full Screen.]



Creating unique key on field “name”

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> db.test.ensureIndex({name: 1}, {unique: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "ok" : 0,
        "errmsg" : "E11000 duplicate key error index: foo.test.$name_1  dup key:
 { : \"Satish\" }",
        "code" : 11000
}

This creates error, as the collection “test” already has duplicate entries/documents.

Create Unique Key by dropping random duplicate entries

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> db.test.ensureIndex({name: 1}, {unique: true, dropDups: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}
 
> db.test.find();
{ "_id" : ObjectId("53d8f1268019dce2ce61eb86"), "name" : "Satish", "age" : 27 }
{ "_id" : ObjectId("53d8f12f8019dce2ce61eb87"), "name" : "Kiran", "age" : 28 }

dropDups() method retains only 1 document randomly and deletes/removes/drops all other duplicate entries/documents permanently.

Note: Since the documents are deleted randomly and can not be restored, you need to be very careful while making use of dropDup() method.

Creating Unique Key/index: MongoDB

We have learnt how to create a key/index so far – today lets learn how to create unique key/index in MongoDB.

creating unique key index mongodb Creating Unique Key/index: MongoDB

Related Read:
ObjectId ( _id ) as Primary Key: MongoDB
index creation: MongoDB

foo: database name
name: collection name

Primary Key in MongoDB: _id

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> db.name.insert({_id: 1, a: 1});
WriteResult({ "nInserted" : 1 })
 
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.name" }
 
> db.name.insert({_id: 1, a: 2});
WriteResult({
        "nInserted" : 0,
        "writeError" : {
                "code" : 11000,
                "errmsg" : "insertDocument :: caused by :: 11000 E11000 
                            duplicate key error index: foo.name.$_id_  dup key: { : 1.0 }"
        }
})

Since “_id” is treated as primary key in mongoDB, we can’t insert duplicate values to it. In above case, we are trying to insert value of “_id” as 1 twice – the second time around it threw an error stating the entered value as duplicate.

Related Read:
DBMS Basics: Getting Started Guide
Primary Foreign Unique Keys, AUTO_INCREMENT: MySQL
Primary Key & Foreign Key Implementation: MySQL

Creating Unique Key/index: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=QEy1IctH99w [Watch the Video In Full Screen.]



Creating Key/Index

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> db.name.insert({a: 1});
WriteResult({ "nInserted" : 1 })
 
> db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
 
> db.name.ensureIndex({a: 1});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.name" }
{ "v" : 1, "key" : { "a" : 1 }, "name" : "a_1", "ns" : "foo.name" }

Here we create index on field “a”.

Inserting duplicate values into key field

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> db.name.insert({a: 1});
WriteResult({ "nInserted" : 1 })
 
> db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
{ "_id" : ObjectId("53d8cb4dbbfe6d81d0bcc365"), "a" : 1 }

insert operation simply inserts the duplicate value to field “a” even though its made as a key/index.

Removing documents and Key/Index on field “a”

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> db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
{ "_id" : ObjectId("53d8cb4dbbfe6d81d0bcc365"), "a" : 1 }
 
> db.name.remove({a: 1});
WriteResult({ "nRemoved" : 3 })
 
> db.name.find()
 
> db.name.dropIndex({a: 1});
{ "nIndexesWas" : 2, "ok" : 1 }
 
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.name" }

Before implementing unique key on field “a” we need to first remove the duplicate entries present inside our collection orelse it’ll through errors. Here we also remove the index/key on “a”, so that we can create unique key/index on “a”.

Creating Unique key/index on field “a”

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> db.name.ensureIndex({a: 1}, {unique: true});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}

To create unique key/index, we need to make use of ensureIndex() method – first parameter being the field name to be made as unique key along with it’s value 1 or -1. 1 signifies ascending order, -1 signifies descending order. The second parameter {unique: true}, specifies that the key/index must be unique key/index, like that of “_id”.

Duplicate key error on our Unique Key!

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> db.name.find()
{ "_id" : ObjectId("53d8cb85bbfe6d81d0bcc366"), "a" : 1 }
 
> db.name.insert({a: 1});
WriteResult({
        "nInserted" : 0,
        "writeError" : {
                "code" : 11000,
                "errmsg" : "insertDocument :: caused by :: 11000 E11000 
                            duplicate key error index: foo.name.$a_1  dup key: { : 1.0 }"
        }
})

Now if we try to insert duplicate values into field “a” it throws duplicate key error.

Multi-key Indexes and Arrays: MongoDB

We have learnt the basics of multi-key indexes in MongoDB. Lets look at an example to demonstrate the multi-key indexing on arrays.

arrays multi key index mongodb Multi key Indexes and Arrays: MongoDB

foo: database name
name: collection name

Insert a document

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MongoDB shell version: 2.6.1
connecting to: test
 
> use foo
switched to db foo
 
> db.name.insert({a: 1, b: 2, c: 3});
WriteResult({ "nInserted" : 1 })

Here we insert {a: 1, b: 2, c: 3} into “name” collection.

Multi-key Indexes and Arrays: MongoDB


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Basic Cursor

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> db.name.find({a: 1, b: 2})
{ "_id" : ObjectId("53d8982b79142c385cddc607"), "a" : 1, "b" : 2, "c" : 3 }
 
> db.name.find({a: 1, b: 2}).explain()
{
        "cursor" : "BasicCursor",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 1,
        "nscanned" : 1,
        "nscannedObjectsAllPlans" : 1,
        "nscannedAllPlans" : 1,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

We find() the document using fields “a” and “b” and the query/command returns a basic cursor, as we do not have indexing on them.

Related Read: index creation: MongoDB

Lets create index on a and b

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> db.name.ensureIndex({a: 1, b: 1});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}

Previous there was only 1 index i.e., on “_id” Now there are 2 indexes – “_id” and “{a: 1, b: 1}”

Btree Cursor with multi-key as false

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> db.name.find({a: 1, b: 2}).explain()
{
        "cursor" : "BtreeCursor a_1_b_1",
        "isMultiKey" : false,
        "n" : 1,
        "nscannedObjects" : 1,
        "nscanned" : 1,
        "nscannedObjectsAllPlans" : 1,
        "nscannedAllPlans" : 1,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "a" : [
                        [
                                1,
                                1
                        ]
                ],
                "b" : [
                        [
                                2,
                                2
                        ]
                ]
        },
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

After creating the index on “a” and “b”, chain explain() method on the same command, and it shows you that, now it returns a Btree Cursor.

Lets insert another document

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> db.name.insert({a: [0, 1, 2], b: 2, c: 3});
WriteResult({ "nInserted" : 1 })

Lets insert an array as value to field “a” and scalar values to “b” and “c”.

Btree Cursor with Multi-key true

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> db.name.find({a: 1, b: 2})
{ "_id" : ObjectId("53d8982b79142c385cddc607"), 
  "a" : 1, "b" : 2, "c" : 3 }
{ "_id" : ObjectId("53d8986f79142c385cddc608"), 
  "a" : [ 0, 1, 2 ], "b" : 2, "c": 3 }
 
> db.name.find({a: 1, b: 2}).explain()
{
        "cursor" : "BtreeCursor a_1_b_1",
        "isMultiKey" : true,
        "n" : 2,
        "nscannedObjects" : 2,
        "nscanned" : 2,
        "nscannedObjectsAllPlans" : 2,
        "nscannedAllPlans" : 2,
        "scanAndOrder" : false,
        "indexOnly" : false,
        "nYields" : 0,
        "nChunkSkips" : 0,
        "millis" : 0,
        "indexBounds" : {
                "a" : [
                        [
                                1,
                                1
                        ]
                ],
                "b" : [
                        [
                                2,
                                2
                        ]
                ]
        },
        "server" : "Satish-PC:27017",
        "filterSet" : false
}

Now append explain() method to our command, it shows us that it returns a Btree Cursor and multi-key as true. MongoDB engine need to match every element of the array present in field “a” with the scalar value of field “b”. Hence it uses Multi-Key indexing.

Multi-Key Condition in MongoDB

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> db.name.insert({a: [0, 1, 2], b: [3, 4], c: 3});
WriteResult({
        "nInserted" : 0,
        "writeError" : {
                "code" : 10088,
                "errmsg" : "insertDocument :: caused by :: 10088 cannot 
                            index parallel arrays [b] [a]"
        }
})

It’s difficult to match every combination of the array elements present inside both “a” and “b” fields. If both keys/indexes has its value as an array, then it gets complicated. Thus, mongoDB doesn’t allow both keys to be arrays. Either one of them must be a scalar value.

Get Index and Delete Index: MongoDB

We learnt the uses of having an index/key on our collection and how to create the index. Now, in this video tutorial lets learn how to get index on individual collection and how to drop / remove / delete the index we’ve created.

getIndex dropIndex mongodb Get Index and Delete Index: MongoDB

Related Read: index creation: MongoDB

temp: Database name
no, another: collection names
We’ve 10 Million documents inside “no” collection.

Sample document

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> use temp
switched to db temp
> show collections
no
system.indexes
 
> db.no.find({"student_id": {$lt: 3}}).pretty()
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cda"),
        "student_id" : 0,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdb"),
        "student_id" : 1,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdc"),
        "student_id" : 2,
        "name" : "Satish"
}

Fetch Index and Drop / remove Index: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=qYHIRWHS_5I [Watch the Video In Full Screen.]



We shall take a look at “system.indexes” collection

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> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, 
           "name" : "_id_", "ns" : "temp.no" }
{ "v" : 1, "key" : { "student_id" : 1 }, 
           "name" : "student_id_1", "ns" : "temp.no" }

Create “another” collection

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> db.another.insert({"name": "Satish", "age": 27});
WriteResult({ "nInserted" : 1 })
 
> show collections
another
no
system.indexes
 
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, 
           "name" : "_id_", "ns" : "temp.no" }
{ "v" : 1, "key" : { "student_id" : 1 }, 
           "name" : "student_id_1", "ns" : "temp.no" }
{ "v" : 1, "key" : { "_id" : 1 }, 
           "name" : "_id_", "ns" : "temp.another" }

After creating “another” collection, mongoDB engine generates default key on its “_id” field. And “system.indexes” shows all the keys present inside the database for all the collections it has. This can get messy if we have large number of collections – which we do in even slightly bigger projects.

To get index on individual collection

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> db.another.getIndexes()
[
        {
                "v" : 1,
                "key" : {
                        "_id" : 1
                },
                "name" : "_id_",
                "ns" : "temp.another"
        }
]
 
> db.no.getIndexes()
[
        {
                "v" : 1,
                "key" : {
                        "_id" : 1
                },
                "name" : "_id_",
                "ns" : "temp.no"
        },
        {
                "v" : 1,
                "key" : {
                        "student_id" : 1
                },
                "name" : "student_id_1",
                "ns" : "temp.no"
        }
]

We can make use of getIndex() method to fetch or get indexes / keys present on individual collection.

Removing / deleting / dropping – index / key

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> db.no.dropIndex({"student_id": 1});
{ "nIndexesWas" : 2, "ok" : 1 }
 
> db.no.getIndexes()
[
        {
                "v" : 1,
                "key" : {
                        "_id" : 1
                },
                "name" : "_id_",
                "ns" : "temp.no"
        }
]

make use of dropIndex() method and pass-in the index object similar to that used while creating the index. This shall drop the index.

index creation: MongoDB

Lets learn to create index and to optimize the database in MongoDB.

Creating “Database”: “temp”, “Collection”: “no”, and inserting 10 Million documents inside it

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use temp
switched to db temp
 
for(i=0; i< = 10000000; i++)
db.no.insert({"student_id": i, "name": "Satish"});

Since Mongo Shell is built out of JavaScript, you can pass in any valid Javascript code to it. So we write a for loop and insert 10 Million documents inside “no” collection.

creating index mongodb index creation: MongoDB

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MongoDB shell version: 2.6.1
connecting to: test
> show dbs
admin    (empty)
daily    0.078GB
local    0.078GB
nesting  0.078GB
school   0.078GB
temp     3.952GB
test     0.078GB
> use temp
switched to db temp
> show collections
no
system.indexes
> db.no.find().pretty()
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cda"),
        "student_id" : 0,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdb"),
        "student_id" : 1,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdc"),
        "student_id" : 2,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdd"),
        "student_id" : 3,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cde"),
        "student_id" : 4,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdf"),
        "student_id" : 5,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce0"),
        "student_id" : 6,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce1"),
        "student_id" : 7,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce2"),
        "student_id" : 8,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce3"),
        "student_id" : 9,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce4"),
        "student_id" : 10,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce5"),
        "student_id" : 11,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce6"),
        "student_id" : 12,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce7"),
        "student_id" : 13,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce8"),
        "student_id" : 14,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ce9"),
        "student_id" : 15,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cea"),
        "student_id" : 16,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ceb"),
        "student_id" : 17,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cec"),
        "student_id" : 18,
        "name" : "Satish"
}
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833ced"),
        "student_id" : 19,
        "name" : "Satish"
}
Type "it" for more
 
> it

“no” collection has 10 Million record, but it won’t fetch you all records at once, as it would take a lot of time and resources of your computer! So it only fetches 20 records at a time. You can iterate through next 20 documents by using command “it“.

index creation: MongoDB


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YouTube Link: http://www.youtube.com/watch?v=zK_mRyiNs-I [Watch the Video In Full Screen.]



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> db.no.find({"student_id": 5}).pretty()
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdf"),
        "student_id" : 5,
        "name" : "Satish"
}
 
 
> db.no.findOne({"student_id": 5});
{
        "_id" : ObjectId("53c9020abcdd1ea7fb833cdf"),
        "student_id" : 5,
        "name" : "Satish"
}
> db.no.find({"student_id": 5000000}).pretty()
{
        "_id" : ObjectId("53c90ca6bcdd1ea7fbcf881a"),
        "student_id" : 5000000,
        "name" : "Satish"
}

find() method scans through all the documents present in the collection to find multiple matches for the condition. So in above case, find() method scans through 10 Million documents, hence returns the result slowly. Where as findOne() method stops scanning the collection as soon as it finds the first matching document, so findOne() returns result faster than find() method.

Related Read:
Multi-key Index: MongoDB
index / key: MongoDB

Creating index

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> show collections
no
system.indexes
 
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "temp.no" }
 
> db.no.ensureIndex({"student_id": 1});
{
        "createdCollectionAutomatically" : false,
        "numIndexesBefore" : 1,
        "numIndexesAfter" : 2,
        "ok" : 1
}
 
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, 
                     "name" : "_id_", "ns" : "temp.no" }
{ "v" : 1, "key" : { "student_id" : 1 }, 
                     "name" : "student_id_1", "ns" : "temp.no" }

We create index on “student_id”. It takes little time to create the index, as we have 10 Million documents inside “no” collection.

After creating index on “student_id”, run the same command and you’ll get the results instantly – maybe it takes 0.01 ms, but the delay can’t be noticed.
Why does it return results faster after creating index on “student_id”? Watch this short video lesson to know it: index / key: MongoDB

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> db.no.find({"student_id": 5000000}).pretty()
{
        "_id" : ObjectId("53c90ca6bcdd1ea7fbcf881a"),
        "student_id" : 5000000,
        "name" : "Satish"
}
> db.no.find({"student_id": 10000000}).pretty()
{
        "_id" : ObjectId("53c914adbcdd1ea7fb1bd35a"),
        "student_id" : 10000000,
        "name" : "Satish"
}
>

So the querys/commands can be optimized by creating indexes on frequently accessed fields.

index / key: MongoDB

Lets look at some basics of indexes in MongoDB.

If we have 3 fields in a document – name, age, sex
We could make name or age or sex or (name, age) or (name, age, sex) as index.

index key mongodb index / key: MongoDB

Assume that we make a index out of (name, age, sex)
In this case, we need to use the keys from left to right.

If we use “name” in our command, it makes use of the index.
If we use (“name”, “age”) in our command, it makes use of the index.
If we use (“name”, “age”, “sex”) in our command, it makes use of the index.

If we use “age” in our command, it can’t use the index for its operation.
If we use (“age”, “sex”) in our command, it can’t use the index for its operation.

If we use (“name”, “sex”) in our command, it simply uses “name” field and ignores “sex” field.

indexes / keys: MongoDB


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For convenience, mongoDB adds “_id” field to each document inserted. “_id” is unique across the collection. And index is automatically created on “_id”.

Since index information is stored in “system.indexes” collection – it consumes disk too. So we need to make sure to add indexes to only those fields which we access frequently. Also note that, each time a document is inserted, “system.indexes” collection must be updated with the new index information, which takes time, bandwidth and disk space. So we need to be careful while creating indexes.