![Compound Index
{
"_id": ObjectId(...),
"item": "Banana",
"category": ["food", "produce", "grocery"],
"location": "4th Street Store",
"stock": 4,
"type": "cases"
}
db.products.createIndex( { "item": 1, "stock": 1 } )
db.products.find( { item: "Banana" } )
db.products.find( { item: "Banana", stock: { $gt: 5 } } )](https://image.slidesharecdn.com/mongodbindexing-241218080703-7d852e8d/85/Sharing-about-MongoDB-Overview-and-Indexing-in-MongoDB-12-320.jpg)
![Multikey Index
{
_id: 1,
item: "abc",
stock: [
{ size: "S", color: "red", quantity: 25 },
{ size: "S", color: "blue", quantity: 10 },
{ size: "M", color: "blue", quantity: 50 }
]
}
db.inventory.createIndex( { "stock.size": 1, "stock.quantity": 1 } )
db.inventory.find( { "stock.size": "M" } )
db.inventory.find( { "stock.size": "M", "stock.quantity": { $lt: 20 } } )
db.inventory.find( { "stock": {"$elemMatch": {"size": "S", "quantity": { $gt: 20 } } } )](https://image.slidesharecdn.com/mongodbindexing-241218080703-7d852e8d/85/Sharing-about-MongoDB-Overview-and-Indexing-in-MongoDB-13-320.jpg)
![Wildcard Index
{ "userMetadata" : { "likes" : [ "dogs", "cats" ] } }
{ "userMetadata" : { "dislikes" : "pickles" } }
{ "userMetadata" : { "age" : 45 } }
{ "userMetadata" : "inactive" }
db.userData.createIndex( { "userMetadata.$**" : 1 } )
db.userData.find({ "userMetadata.likes" : "dogs" })
db.userData.find({ "userMetadata.dislikes" : "pickles" })
db.userData.find({ "userMetadata.age" : { $gt : 30 } })
db.userData.find({ "userMetadata" : "inactive" })](https://image.slidesharecdn.com/mongodbindexing-241218080703-7d852e8d/85/Sharing-about-MongoDB-Overview-and-Indexing-in-MongoDB-15-320.jpg)
![Text Index
{
_id: 1,
language: "portuguese",
original: "A sorte protege os audazes.",
translation:
[
{
language: "english",
quote: "Fortune favors the bold."
},
{
language: "spanish",
quote: "La suerte protege a los audaces."
}
]
}
db.quotes.createIndex( { original: "text", "translation.quote": "text" } )
db.quotes.find( { $text: { $search: "bold" } } )](https://image.slidesharecdn.com/mongodbindexing-241218080703-7d852e8d/85/Sharing-about-MongoDB-Overview-and-Indexing-in-MongoDB-16-320.jpg)
![2dsphere & 2d Index
db.places.createIndex( { loc : "2dsphere" } )
db.places.find( { loc : { $geoWithin : { $centerSphere : [ [ -88 , 30 ] , 10 / 3963.2 ] } } } )
db.places.createIndex( { loc : "2d" } )
db.places.find( { loc: { $geoWithin : { $center : [ [-74, 40.74 ] , 10 ] } } } )
● A 2dsphere index supports queries that calculate geometries on an earth-like sphere.
● A 2d index for data stored as points on a two-dimensional plane.
{
loc : { type: "Point", coordinates: [ -73.97, 40.77 ] },
name: "Central Park",
category : "Parks"
}](https://image.slidesharecdn.com/mongodbindexing-241218080703-7d852e8d/85/Sharing-about-MongoDB-Overview-and-Indexing-in-MongoDB-19-320.jpg)
Sharing about MongoDB Overview and Indexing in MongoDB
- 1. Indexing
- 2. Content ● History of MongoDB ● Overview ● Data Model Designs ● Indexing ● Q&A
- 3. History 2007 2009 2013 Founded by 10gen company - by Dwight Merriman, Eliot Horowitz and Kevin Ryan – the team behind DoubleClick. Officially launched 10gen changed company name to MongoDB, Inc
- 4. Overview ● MongoDB stores data in a document model, as compared to storing in rows in SQL.
- 5. Overview ● MongoDB stores data records as BSON documents. BSON is a binary representation of JSON documents. BSON contains more data types than JSON.
- 6. Data Model Designs ● Relation 1-1
- 7. Data Model Designs ● Relation 1-n
- 8. Data Model Designs ● Relation n-n
- 9. Indexing ● _id Index ● Single field Index ● Compound Index ● Multikey Index ● Wildcard Index ● Text Index ● Hashed Index ● 2dsphere & 2d Index
- 10. _id Index ● Default index in MongoDB, automatically generated when creating a new document. { "_id": ObjectId("570c04a4ad233577f97dc459"), "score": 1034, "location": { state: "NY", city: "New York" } }
- 11. Single Field Index { "_id": ObjectId("570c04a4ad233577f97dc459"), "score": 1034, "location": { state: "NY", city: "New York" } } db.records.createIndex( { score: 1 } ) db.records.find( { score: 2 } ) db.records.find( { score: { $gt: 10 } } )
- 12. Compound Index { "_id": ObjectId(...), "item": "Banana", "category": ["food", "produce", "grocery"], "location": "4th Street Store", "stock": 4, "type": "cases" } db.products.createIndex( { "item": 1, "stock": 1 } ) db.products.find( { item: "Banana" } ) db.products.find( { item: "Banana", stock: { $gt: 5 } } )
- 13. Multikey Index { _id: 1, item: "abc", stock: [ { size: "S", color: "red", quantity: 25 }, { size: "S", color: "blue", quantity: 10 }, { size: "M", color: "blue", quantity: 50 } ] } db.inventory.createIndex( { "stock.size": 1, "stock.quantity": 1 } ) db.inventory.find( { "stock.size": "M" } ) db.inventory.find( { "stock.size": "M", "stock.quantity": { $lt: 20 } } ) db.inventory.find( { "stock": {"$elemMatch": {"size": "S", "quantity": { $gt: 20 } } } )
- 14. Multikey Index
- 15. Wildcard Index { "userMetadata" : { "likes" : [ "dogs", "cats" ] } } { "userMetadata" : { "dislikes" : "pickles" } } { "userMetadata" : { "age" : 45 } } { "userMetadata" : "inactive" } db.userData.createIndex( { "userMetadata.$**" : 1 } ) db.userData.find({ "userMetadata.likes" : "dogs" }) db.userData.find({ "userMetadata.dislikes" : "pickles" }) db.userData.find({ "userMetadata.age" : { $gt : 30 } }) db.userData.find({ "userMetadata" : "inactive" })
- 16. Text Index { _id: 1, language: "portuguese", original: "A sorte protege os audazes.", translation: [ { language: "english", quote: "Fortune favors the bold." }, { language: "spanish", quote: "La suerte protege a los audaces." } ] } db.quotes.createIndex( { original: "text", "translation.quote": "text" } ) db.quotes.find( { $text: { $search: "bold" } } )
- 17. Text Index
- 18. Hash Index db.collection.createIndex( { _id: "hashed" } ) ● Hashed indexes maintain entries with hashes of the values of the indexed field. ● Support sharding using hashed shard keys. Hashed based sharding uses a hashed index of a field as the shard key to partition data across your sharded cluster.
- 19. 2dsphere & 2d Index db.places.createIndex( { loc : "2dsphere" } ) db.places.find( { loc : { $geoWithin : { $centerSphere : [ [ -88 , 30 ] , 10 / 3963.2 ] } } } ) db.places.createIndex( { loc : "2d" } ) db.places.find( { loc: { $geoWithin : { $center : [ [-74, 40.74 ] , 10 ] } } } ) ● A 2dsphere index supports queries that calculate geometries on an earth-like sphere. ● A 2d index for data stored as points on a two-dimensional plane. { loc : { type: "Point", coordinates: [ -73.97, 40.77 ] }, name: "Central Park", category : "Parks" }
- 20. Q&A
Editor's Notes
- #1: https://www.mongodb.com/docs/manual/ https://www.mongodb.com/blog/post/ebay-building-mission-critical-multi-data-center-applications-with-mongodb
- #3: Founded in 2007 by 10gen company - by Dwight Merriman, Eliot Horowitz and Kevin Ryan – the team behind DoubleClick. Officially launched in 2009 In 2013, 10gen changed company name to MongoDB, Inc Open-source by language C++
- #19: db.<collection>.find( { <location field> : { $geoWithin : { $centerSphere : [ [ <x>, <y> ] , <radius> ] } } } ) The following example queries grid coordinates and returns all documents within a 10 mile radius of longitude 88 W and latitude 30 N. The example converts the distance, 10 miles, to radians by dividing by the approximate equatorial radius of the earth, 3963.2 miles: db.places.find( { loc : { $geoWithin : { $centerSphere : [ [ -88 , 30 ] , 10 / 3963.2 ] } } } )