Common MongoDB Use Cases
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MongoDB is a NoSQL database that is well-suited for applications with certain characteristics. It uses a flexible document data model that allows for variable data in objects without sparse tables or complex joins. MongoDB provides low latency access through memory mapping and data locality. It can also scale to handle high read and write throughput through sharding and replication across multiple servers. MongoDB is a good fit for applications that need to store large numbers of objects or be deployed in the cloud.
![Behavioral Profiles
Rich profiles
collecting multiple
complex actions
1 See Ad
Scale out to support { cookie_id: “1234512413243”,
high throughput of advertiser:{
apple: {
activities tracked actions: [
2 See Ad { impression: ‘ad1’, time: 123 },
{ impression: ‘ad2’, time: 232 },
{ click: ‘ad2’, time: 235 },
{ add_to_cart: ‘laptop’,
sku: ‘asdf23f’,
time: 254 },
Click { purchase: ‘laptop’, time: 354 }
3 ]
}
}
}
Dynamic schemas
make it easy to track
Indexing and
4 Convert vendor specific
querying to support
attributes
matching, frequency
capping](https://image.slidesharecdn.com/mongodbusecases20121127-dataversity-121129224159-phpapp01/85/Common-MongoDB-Use-Cases-11-320.jpg)
![Content Management
Geo spatial indexing
Flexible data model for location based
GridFS for large
for similar, but searches
object storage
different objects
{ camera: “Nikon d4”,
location: [ -122.418333, 37.775 ]
}
{ camera: “Canon 5d mkII”,
people: [ “Jim”, “Carol” ],
taken_on: ISODate("2012-03-07T18:32:35.002Z")
}
{ origin: “facebook.com/photos/xwdf23fsdf”,
license: “Creative Commons CC0”,
size: {
dimensions: [ 124, 52 ],
units: “pixels”
Horizontal scalability }
for large data sets }](https://image.slidesharecdn.com/mongodbusecases20121127-dataversity-121129224159-phpapp01/85/Common-MongoDB-Use-Cases-14-320.jpg)
Common MongoDB Use Cases
- 1. MongoDB Common Use Cases
- 2. Emerging NoSQL Space RDBMS RDBMS RDBMS Data Data Warehou Warehou NoSQL se se The beginning Last 10 years Today
- 3. Qualities of NoSQL Workloads Flexible data models High Throughput Large Data Sizes • Lists, Nested Objects • Lots of reads • Aggregate data size • Sparse schemas • Lots of writes • Number of objects • Semi-structured data • Agile Development Low Latency Cloud Computing Commodity • Both reads and writes • Run anywhere Hardware • Millisecond latency • No assumptions about • Ethernet hardware • Local disks • No / Few Knobs
- 4. MongoDB was designed for this Flexible data models High Throughput Large Data Sizes • Lists, Nested Objects • Lots of reads • Aggregate data size • schemas • SparseJSON based • writes • Lots of Replica Sets to • Number of objects shards • 1000’s of • Semi-structuredmodel object data scale reads in a single DB • Dynamic • Agile Development • Sharding to • Partitioning of schemas scale writes data Low Latency Cloud Computing Commodity • Both reads and writes • Run anywhere Hardware • In-memory • Millisecond latency • No • Scale-out to assumptions about • Ethernet • Designed for cache overcome hardware • Local disks • No / Few Knobs “typical” OS and • Scale-out hardware local file system working set limitations
- 5. Example customers Content Management Operational Intelligence Product Data Management w User Data Management High Volume Data Feeds
- 6. USE CASES THAT LEVERAGE NOSQL
- 7. High Volume Data Feeds Machine • More machines, more sensors, more Generated data Data • Variably structured Stock Market • High frequency trading Data Social Media • Multiple sources of data Firehose • Each changes their format constantly
- 8. High Volume Data Feed Flexible document model can adapt to changes in sensor format Asynchronous writes Data Data Sources Data Sources Data Write to memory with Sources periodic disk flush Sources Scale writes over multiple shards
- 9. Operational Intelligence • Large volume of state about users Ad Targeting • Very strict latency requirements • Expose report data to millions of customers Real time • Report on large volumes of data dashboards • Reports that update in real time Social Media • What are people talking about? Monitoring
- 10. Operational Intelligence Parallelize queries Low latency reads across replicas and shards API In database aggregation Dashboards Flexible schema adapts to changing input data Can use same cluster to collect, store, and report on data
- 11. Behavioral Profiles Rich profiles collecting multiple complex actions 1 See Ad Scale out to support { cookie_id: “1234512413243”, high throughput of advertiser:{ apple: { activities tracked actions: [ 2 See Ad { impression: ‘ad1’, time: 123 }, { impression: ‘ad2’, time: 232 }, { click: ‘ad2’, time: 235 }, { add_to_cart: ‘laptop’, sku: ‘asdf23f’, time: 254 }, Click { purchase: ‘laptop’, time: 354 } 3 ] } } } Dynamic schemas make it easy to track Indexing and 4 Convert vendor specific querying to support attributes matching, frequency capping
- 12. Product Data Management E-Commerce • Diverse product portfolio Product • Complex querying and filtering Catalog • Scale for short bursts of high- volume traffic Flash Sales • Scalable but consistent view of inventory
- 13. Content Management • Comments and user generated News Site content • Personalization of content, layout Multi-Device • Generate layout on the fly for each rendering device that connects • No need to cache static pages • Store large objects Sharing • Simple modeling of metadata
- 14. Content Management Geo spatial indexing Flexible data model for location based GridFS for large for similar, but searches object storage different objects { camera: “Nikon d4”, location: [ -122.418333, 37.775 ] } { camera: “Canon 5d mkII”, people: [ “Jim”, “Carol” ], taken_on: ISODate("2012-03-07T18:32:35.002Z") } { origin: “facebook.com/photos/xwdf23fsdf”, license: “Creative Commons CC0”, size: { dimensions: [ 124, 52 ], units: “pixels” Horizontal scalability } for large data sets }
- 15. User Data Management Video • User state and session Games management Social • Scale out to large graphs Graphs • Easy to search and process Identity • Authentication, Authorization, Management and Accounting
- 16. Social Graphs Native support for Arrays makes it easy to store connections inside user profile Sharding partitions user profiles across Documents enable Social Graph available servers disk locality of all profile data for a user
- 17. IS MY USE CASE A GOOD FIT FOR MONGODB?
- 18. Good fits for MongoDB Application Characteristic Why MongoDB might be a good fit Variable data in objects Dynamic schema and JSON data model enable flexible data storage without sparse tables or complex joins Low Latency Access Memory Mapped storage engine caches documents in RAM, enabling in-memory performance. Data locality of documents can significantly improve latency over join based approaches High write or read throughput Sharding + Replication lets you scale read and write traffic across multiple servers Large number of objects to Sharding lets you split objects across multiple store servers Cloud based deployment Sharding and replication let you work around hardware limitations in clouds.
- 19. THANK YOU! Free online training available for MongoDB at: http://education.10gen.com