MongoDb - Details on the POCPresentation Transcript
Goodbye rows and tables, hello documents and collections
Lots of pretty pictures to fool you.
Introduction M ongoDB bridges the gap between key-value stores (which are fast and highly scalable) and traditional RDBMS systems (which provide rich queries and deep functionality). MongoDB is document-oriented , schema-free , scalable , high-performance , open source. Written in C++ Mongo is not a relational database like MySQL Goodbye rows and tables, hello documents and collections
Documents (objects) map nicely to programming language data types
Embedded documents and arrays reduce need for joins
No joins and no multi-document transactions for high performance and easy scalability
No joins and embedding makes reads and writes fast
Indexes including indexing of keys from embedded documents and arrays
Replicated servers with automatic master failover
Automatic sharding (auto-partitioning of data across servers)
Reads and writes are distributed over shards
No joins or multi-document transactions make distributed queries easy and fast
Eventually-consistent reads can be distributed over replicated servers
Cost - MongoDB is free
MongoDb is easily installable.
MongoDB is blazingly fast
MongoDB is schemaless
Ease of scale-out
If load increases it can be distributed to other nodes across computer networks.
It's trivially easy to add more fields -- even complex fields -- to your objects.
So as requirements change, you can adapt code quickly.
MongoDB is a stand-alone server
Development time is faster, too, since there are no schemas to manage.
Which allows a developer to use a single programming language for both client and server side code
Mongo is limited to a total data size of 2GB for all databases in 32-bit mode.
No referential integrity
Data size in MongoDB is typically higher.
At the moment Map/Reduce (e.g. to do aggregations/data analysis) is OK,
but not blisteringly fast.
Group By : less than 10,000 keys.
For larger grouping operations without limits, please use map/reduce .
Lack of predefined schema is a double-edged sword
No support for Joins & transactions
Benchmarking (MongoDB Vs. MySQL) Test Machine configuration: CPU : Intel Xeon 1.6 GHz - Quad Core, 64 Bit Memory : 8 GB RAM OS : Centos 5.2 - Kernel 2.6.18 64 bit Record Structure Field1 -> String, Indexed Field2 -> String, Indexed Filed3 -> Date, Not Indexed Filed4 -> Integer, Indexed
Mongo data model
A Mongo system (see deployment above) holds a set of databases
A database holds a set of collections
A collection holds a set of documents
A document is a set of fields
A field is a key-value pair
A key is a name (string)
A value is a
basic type like string, integer, float, timestamp, binary, etc.,
a document, or
an array of values
MySQL Term Mongo Term database database table collection index index row BSON document column BSON field Primary key _id field
SQL to Mongo Mapping Chart
Continued ... SQL Statement Mongo Statement
Replication / Sharding
Distribute read load
(compared to "normal" master-slave)
Disaster recovery from user error
Automatic balancing for changes in
load and data distribution
Easy addition of new machines
Scaling out to one thousand nodes
No single points of failure
These slides are online: http://amardeep.in/intro_to_mongodb.ppt