HTML5: Download Attribute with Value
1 2 3 | <a href="Picture1.jpg" download="book.jpg">
Download Book Cover
</a> |
Download Attribute: HTML5
Full Free Code: HTML5 Download Attribute with Value
1 2 3 4 5 6 7 8 9 10 11 12 13 | < !doctype html>
<html>
<head>
<title>Download Attribute</title>
</head>
<body>
<a href="Picture1.jpg" download="book.jpg">
Download Book Cover
</a>
</body>
</html> |
temp: database name
name: collection name
Inserting 1000 Documents
1 2 3 4 5 6 7 8 | 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 }) |
Basic Cursor
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | > 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
} |
explain() method: MongoDB
Creating Index on fields “a” and “b”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | > 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
} |
indexOnly: true
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | > 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
} |
temp: database name
stu: collection name
Inserting 10 Million Documents
1 2 3 4 5 | use temp
switched to db temp
for(i=1; i < = 10000000; i++)
db.no.insert({"student_id": i}); |
index creation in the foreground
1 2 3 4 5 6 7 | > db.stu.ensureIndex({student_id: 1});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
} |
Background Index Creation: MongoDB
index creation in the background
1 2 3 4 5 6 7 | > db.stu.ensureIndex({student_id: 1}, {background: true});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
} |
It takes time to complete the process, as there are 10 Million documents in the collection.
example: database name
company: collection name
Insert documents
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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" } |
Sparse Index – Unique Key: MongoDB
Duplicate key error
1 2 3 4 5 6 7 8 9 | > 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
} |
Sparse Index
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1 2 3 4 5 6 7 | > 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | > 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
} |
sort() method
1 2 3 4 5 6 7 8 9 10 11 12 13 | > 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"
} |
Basic Cursor
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | > 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
} |
hint() method
1 2 3 4 5 6 7 8 9 10 11 | > 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"
} |
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | Sexe free sex amature teen tube free video mature amateur streaming photo sexe amateur ado Pics free young amater movies teen xxx sex party movies amateur libertin sexe xxx ffmm sexyMovies sexe video x amateur gratuit sexe amateur wife mature pussy bondage > 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
},
{
"$maxElement" : 1
}
]
]
},
"server" : "Satish-PC:27017",
"filterSet" : false
} |
Insert documents
1 2 3 4 5 6 7 8 9 10 11 12 | > db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.test" }
> db.test.insert({name: "Satish", age: 27});
WriteResult({ "nInserted" : 1 })
> db.test.insert({name: "Kiran", age: 28});
WriteResult({ "nInserted" : 1 })
> db.test.insert({name: "Satish", age: 27});
WriteResult({ "nInserted" : 1 }) |
dropDups() To Remove Duplicate Documents: MongoDB
Creating unique key on field “name”
1 2 3 4 5 6 7 8 9 | > db.test.ensureIndex({name: 1}, {unique: true});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"ok" : 0,
"errmsg" : "E11000 duplicate key error index: foo.test.$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
1 2 3 4 5 6 7 8 9 10 11 | > 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 } |
foo: database name
name: collection name
Primary Key in MongoDB: _id
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | > 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.$_id_ dup key: { : 1.0 }"
}
}) |
Creating Unique Key/index: MongoDB
Creating Key/Index
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | > db.name.insert({a: 1});
WriteResult({ "nInserted" : 1 })
> db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
> db.name.ensureIndex({a: 1});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
}
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.name" }
{ "v" : 1, "key" : { "a" : 1 }, "name" : "a_1", "ns" : "foo.name" } |
Here we create index on field “a”.
Inserting duplicate values into key field
1 2 3 4 5 6 7 | > db.name.insert({a: 1});
WriteResult({ "nInserted" : 1 })
> db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
{ "_id" : ObjectId("53d8cb4dbbfe6d81d0bcc365"), "a" : 1 } |
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Removing documents and Key/Index on field “a”
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | > db.name.find()
{ "_id" : ObjectId("53d8cadbbbfe6d81d0bcc364"), "a" : 1 }
{ "_id" : 1, "a" : 1 }
{ "_id" : ObjectId("53d8cb4dbbfe6d81d0bcc365"), "a" : 1 }
> db.name.remove({a: 1});
WriteResult({ "nRemoved" : 3 })
> db.name.find()
> db.name.dropIndex({a: 1});
{ "nIndexesWas" : 2, "ok" : 1 }
> db.system.indexes.find()
{ "v" : 1, "key" : { "_id" : 1 }, "name" : "_id_", "ns" : "foo.name" } |
Creating Unique key/index on field “a”
1 2 3 4 5 6 7 | > db.name.ensureIndex({a: 1}, {unique: true});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
} |
Duplicate key error on our Unique Key!
1 2 3 4 5 6 7 8 9 10 11 12 | > db.name.find()
{ "_id" : ObjectId("53d8cb85bbfe6d81d0bcc366"), "a" : 1 }
> db.name.insert({a: 1});
WriteResult({
"nInserted" : 0,
"writeError" : {
"code" : 11000,
"errmsg" : "insertDocument :: caused by :: 11000 E11000
duplicate key error index: foo.name.$a_1 dup key: { : 1.0 }"
}
}) |
Now if we try to insert duplicate values into field “a” it throws duplicate key error.
foo: database name
name: collection name
Insert a document
1 2 3 4 5 6 7 8 | 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
Basic Cursor
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | > 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
} |
Lets create index on a and b
1 2 3 4 5 6 7 | > db.name.ensureIndex({a: 1, b: 1});
{
"createdCollectionAutomatically" : false,
"numIndexesBefore" : 1,
"numIndexesAfter" : 2,
"ok" : 1
} |
Btree Cursor with multi-key as false
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | > 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
} |
Lets insert another document
1 2 | > db.name.insert({a: [0, 1, 2], b: 2, c: 3});
WriteResult({ "nInserted" : 1 }) |
Btree Cursor with Multi-key true
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 | > 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
} |
Multi-Key Condition in MongoDB
1 2 3 4 5 6 7 8 9 | > 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]"
}
}) |
Sample document
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | > 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
We shall take a look at “system.indexes” collection
1 2 3 4 5 | > 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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | > 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" } |
To get index on individual collection
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | > 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"
}
] |
Removing / deleting / dropping – index / key
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | > db.no.dropIndex({"student_id": 1});
{ "nIndexesWas" : 2, "ok" : 1 }
> db.no.getIndexes()
[
{
"v" : 1,
"key" : {
"_id" : 1
},
"name" : "_id_",
"ns" : "temp.no"
}
] |
Lets learn to create index and to optimize the database in MongoDB.
1 2 3 4 5 | use temp
switched to db temp
for(i=0; i< = 10000000; i++)
db.no.insert({"student_id": i, "name": "Satish"}); |
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 | 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 |
index creation: MongoDB
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | > 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"
} |
Creating index
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | > 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" } |
1 2 3 4 5 6 7 8 9 10 11 12 13 | > 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.
Lets look at some basics of indexes in MongoDB.
indexes / keys: MongoDB