Non-SQL, MongoDB

What is NoSQL?

A NoSQL database environment is, simply put, a non-relational and largely distributed database system that enables rapid, ad-hoc organization and analysis of extremely high-volume, disparate data types. NoSQL databases are sometimes referred to as cloud databases, non-relational databases, Big Data databases and a myriad of other terms and were developed in response to the sheer volume of data being generated, stored and analyzed by modern users (user-generated data) and their applications (machine-generated data).

In general, NoSQL databases have become the first alternative to relational databases, with scalability, availability, and fault tolerance being key deciding factors. They go well beyond the more widely understood legacy, relational databases (such as Oracle, SQL Server and DB2 databases) in satisfying the needs of today’s modern business applications. A very flexible and schema-less data model, horizontal scalability, distributed architectures, and the use of languages and interfaces that are “not only” SQL typically characterize this technology.

From a business standpoint, considering a NoSQL or ‘Big Data’ environment has been shown to provide a clear competitive advantage in numerous industries. In the ‘age of data’, this is compelling information as a great saying about the importance of data is summed up with the following “if your data isn’t growing then neither is your business”.

Types of NoSQL Databases

There are four general types of NoSQL databases, each with their own specific attributes:

• Graph database – Based on graph theory, these databases are designed for data whose relations are well represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Examples include: Neo4j and Titan.
• Key-Value store – we start with this type of database because these are some of the least complex NoSQL options. These databases are designed for storing data in a schema-less way. In a key-value store, all of the data within consists of an indexed key and a value, hence the name. Examples of this type of database include: Cassandra, DyanmoDB, Azure Table Storage (ATS), Riak, BerkeleyDB.
• Column store – (also known as wide-column stores) instead of storing data in rows, these databases are designed for storing data tables as sections of columns of data, rather than as rows of data. While this simple description sounds like the inverse of a standard database, wide-column stores offer very high performance and a highly scalable architecture. Examples include: HBase, BigTable and HyperTable.
• Document database – expands on the basic idea of key-value stores where “documents” contain more complex in that they contain data and each document is assigned a unique key, which is used to retrieve the document. These are designed for storing, retrieving, and managing document-oriented information, also known as semi-structured data. Examples include: MongoDB and CouchDB.

What is MongoDb?

MongoDb is a Open Source database written in C++.

Drivers and client libraries are typically written in their respective languages, although some drivers use C extensions for better performance.

If the load increases, by adding more nodes (such as a computer), the performance can be retained.

It can be used to store data for very high performance applications (for example Foursquare is using it in production).

MongoDB does not support SQL It supports a rich, ad-hoc query language of its own.

MongoDb stores data as documents. So it is a document oriented database.

FirstName="Arun", Address="St. Xavier's Road", Spouse=[{Name:"Kiran"}], Children=[{Name:"Rihit", Age:8}].

FirstName="Sameer",Address="8 Gandhi Road".

Notice there are two different documents (separated by "."). Storing data in this fashion is called as document oriented database. MongoDb is a document oriented database.

MongoDB : Databases, Schemas and Tables

Databases : MongoDB is a document-oriented DBMS, with JSON-like objects comprising the data model, rather than RDBMS tables. MongoDB does not support joins nor transactions. However, it features secondary indexes, an expressive query language, atomic writes on a per-document level, and fully-consistent reads. MongoDB uses BSON, a binary object format similar to, but more expressive than JSON.

Schemas : MongoDB uses dynamic schemas. We can create collections without defining the structure, i.e. the fields or the types of their values, of the documents. You can change the structure of documents simply by adding new fields or deleting existing ones. Documents in a collection need unique set of fields.

Tables : MongoDB database stores its data in collections not in tables The collections are the rough equivalent of RDBMS tables. A collection holds one or more documents, which corresponds to a record or a row in a relational database table, and each document has one or more fields, which corresponds to a column in a relational database table.

MongoDB and ACID transactions

MongoDB does not support multi-document transactions, but provides atomic operations on a single document. Often these document-level atomic operations are sufficient to solve problems that would require ACID transactions in a relational database.

In MongoDB, you can embed related data in nested arrays or nested documents within a single document and update the entire document in a single atomic operation. Relational databases might represent the same kind of data with multiple tables and rows, which would require transaction support to update the data atomically.

CRUD operations create, read, update, and delete documents.

Create Operations

Create or insert operations add new documents to a collection. If the collection does not currently exist, insert operations will create the collection.

MongoDB provides the following methods to insert documents into a collection:

• db.collection.insert()
• db.collection.insertOne() New in version 3.2
• db.collection.insertMany() New in version 3.2

Ex: db.users.insert({name:”sue”, age:26, status: “A”})

Read Operations

Read operations retrieves documents from a collection; i.e. queries a collection for documents. MongoDB provides the following methods to read documents from a collection:

• db.collection.find()

Ex: db.users.find({age: {$gt:18}}, {name:1, addess:1}).limit(5)

More about filter & criteria

Update Operations

Update operations modify existing documents in a collection. MongoDB provides the following methods to update documents of a collection:

• db.collection.update()
• db.collection.updateOne() New in version 3.2
• db.collection.updateMany() New in version 3.2
• db.collection.replaceOne() New in version 3.2

In MongoDB, update operations target a single collection. All write operations in MongoDB are atomic on the level of a single document.

You can specify criteria, or filters, that identify the documents to update. These filters use the same syntax as read operations.

Ex: db.users.update({age: {$gt:18 }}, {$set: {status: “D”}}, {multi:true})

More about Update

Delete Operations

Delete operations remove documents from a collection. MongoDB provides the following methods to delete documents of a collection:

• db.collection.remove()
• db.collection.deleteOne() New in version 3.2
• db.collection.deleteMany() New in version 3.2

In MongoDB, delete operations target a single collection. All write operations in MongoDB are atomic on the level of a single document.

You can specify criteria, or filters, that identify the documents to remove. These filters use the same syntax as read operations.

Ex: db.users.delete({status: “D”})

More about Delete