An Introduction to MongoDB
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MongoDB is a free and open-source document-oriented NoSQL database created by Dwight Merriman, Eliot Horowitz and Kevin Ryan in 2007. It uses a schema-less structure that stores data in flexible, JSON-like documents, allowing for easy scaling and fast querying. Key features include replication for high availability, auto-sharding for horizontal scaling, and aggregation capabilities for complex queries. Common use cases include storing high volumes of unstructured or semi-structured data like blog posts, user comments, and digital content.
An Introduction to MongoDB
- 1. Presented by Chamodi Adikaram (179333J)
- 2. content introduction cap theorem terminology structure key features crud operations best use cases
- 3. introduction mongoDB was founded in 2007 by Dwight Merriman, Eliot Horowitz and Kevin Ryan (10gen) mongo comes from the term humongous. it is a free and open-source cross-platform document-oriented NoSQL database program
- 4. cap theorem availability consistency partition tolerance ca cp ap
- 5. cap theorem cntd consistency: all replicas contain the same version of the data partition tolerance: multiple entry points system remains operational on system split single master architecture to have consistency over availability
- 6. terminology RDBMS mongoDB database database table collection index index row document column field join embedding & reference & $lookup
- 7. structure
- 8. structure documents in BSON (binary JSON): JSON like structure
- 9. key features 1. schema less application tracks the schema and mapping you can have different fields in every document no Data Definition Language keys are a basic data type but in reality stored as strings document Identifiers (_id) will be created for each document, field name reserved by system results in lot of flexibility
- 10. key features cntd 2. replication sets single master architecture maintaining backup copies of your database instance secondaries can elect a new primary within seconds if your primary goes down
- 11. key features cntd 3. auto- sharding for big data distributes a single logical database system across a cluster of machines ranges of some indexed value you specify are assigned to different replica sets can be turned on off per collection facilitates built in horizontal scaling
- 12. key features cntd 4. querying mongo shell you can write scripts for the mongo shell in javascript that manipulate data in mongoDB or perform administrative operation alternative to shell robomongo
- 13. key features cntd 5. fast in-place updates the database does not have to allocate and write a full new copy of the object this can be highly performant for frequent update use cases mongoDB disk writes are lazy
- 14. key features cntd 6. aggregated functionality provides SQL-like aggregation functionality expressions produce output documents based on calculations performed on input documents db.parts.aggregate ( {$group : {_id: type, totalquantity : {$sum:quanity}} })
- 15. key features cntd 7. map/reduce functionality can perform map reduce operations without hadoop file system: gridFS performs complex aggregator functions given a collection of keys, value pairs db.collection.mapReduce( <mapfunction>, <reducefunction>, { out: <collection>, query: <document>, sort: <document>, limit: <number>, finalize: <function>, scope: <document>, jsMode: <boolean>, verbose: <boolean> } )
- 16. crud operations create db.restaurants.insert({borough : 'Colombo',cuisine : 'Chinese',name: 'Fab',restaurant_id : '41704627'}); read db.restaurants.find({cuisine : 'Chinese'}); update db.restaurants.update({restaurant_id : '41704627'},{$set: {name:'Fabulous'}}); delete db.restaurants.remove({restaurant_id : '41704627'});
- 17. best use cases
- 18. best use cases cntd suitable for high volume and scaling data such as: blog posts comments digital content management not suitable for highly transactional, business intelligence related instances
- 19. Q&A Thank you!