MongoDB Online Training

Introduction to MongoDB

MongoDB is the open source NoSQL database product and has enabled developers to build new types of applications for cloud and social technologies. In this level of consistency can be chosen depending on the value of the data and allow faster access to data by utilizing internal memory for storing working set. Dynamic schema provides rich data model allowing maps to navigate programming language types.

Overview

MongoDB is a document database that provides high performance, high availability, and easy scalability.

§  Document Database

§  Documents (objects) map nicely to programming language data types.

§  Embedded documents and arrays reduce need for joins.

§  Dynamic schema makes polymorphism easier.

§  High Performance

§  Embedding makes reads and writes fast.

§  Indexes can include keys from embedded documents and arrays.

§  Optional streaming writes (no acknowledgments).

§  High Availability

§  Replicated servers with automatic master failover.

§  Easy Scalability

§  Automatic sharding distributes collection data across machines.

§  Eventually-consistent reads can be distributed over replicated servers.

§  Advanced Operations

§  With MongoDB Management Service (MMS) MongoDB supports a complete backup solution and full deployment monitoring.

MongoDB Data Model

A MongoDB deployment hosts a number of databases. A manual:database holds a set of collections. Amanual:collection holds a set of documents. A manual:document is a set of key-value pairs. Documents have dynamic schema. Dynamic schema means that documents in the same collection do not need to have the same set of fields or structure, and common fields in a collection’s documents may hold different types of data.

See Document Structure and Data Modeling for more information.

Deployment Architectures

Although MongoDB supports a “standalone” or single-instance operation, production MongoDB deployments are distributed by default. Replica sets provide high performance replication with automated failover, while sharded clusters make it possible to partition large data sets over many machines transparently to the users. MongoDB users combine replica sets and sharded clusters to provide high levels redundancy for large data sets transparently for applications.

MongoDB Queries

Queries in MongoDB provides a set of operators to define how the find()method selects documents from a collection based on a query specification document that uses a combination of exact equality matches and conditionals using a query operator.

MongoDB Design Philosophy

MongoDB wasn’t designed in a lab. We built MongoDB from our own experiences building large scale, high availability, robust systems. We didn’t start from scratch, we really tried to figure out what was broken, and tackle that. So the way I think about MongoDB is that if you take MySql, and change the data model from relational to document based, you get a lot of great features: embedded docs for speed, manageability, agile development with schema-less databases, easier horizontal scalability because joins aren’t as important. There are lots of things that work great in relational databases: indexes, dynamic queries and updates to name a few, and we haven’t changed much there. For example, the way you design your indexes in MongoDB should be exactly the way you do it in MySql or Oracle, you just have the option of indexing an embedded field.

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