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MONGODB
WHAT IS MONGODB???
MongoDB (from humongous ) is a cross-platform document-oriented database.
Classified as a NoSQL database, MongoDB eschews the traditional table-based
relational database structure in favor of JSON-like documents with dynamic schemas
(MongoDB calls the format BSON), making the integration of data in certain types of
applications easier and faster. Released under a combination of the GNU Affero
General Public License and the Apache License, MongoDB is free and open-source
software.
First developed by the software company 10gen (now MongoDB Inc.) in October 2007
as a component of a planned platform as a service product, the company shifted to an
open source development model in 2009, with 10gen offering commercial support and
other services. [1]^ Since then, MongoDB has been adopted as backend software by a
number of major websites and services, including Craigslist, eBay, Foursquare
, SourceForge, Viacom, and The New York Times among others. [ citation needed ]^ As of 2014,
MongoDB was the most popular NoSQL database system. [2]
MongoDB is one of several database types to arise in the mid-2000s under the
NoSQL banner. Instead of using tables and rows as in relational databases,
MongoDB is built on an architecture of collections and documents. Documents
comprise sets of key-value pairs and are the basic unit of data in MongoDB.
KEY FEATURES OF MONGODB.
MongoDB stores data in JSON documents (which we serialize to
BSON). JSON provides a rich data model that seamlessly maps to
native programming language types, and the dynamic schema
makes it easier to evolve your data model than with a system with
enforced schemas such as a RDBMS.
MongoDB provides a lot of the features of a traditional RDBMS
such as secondary indexes, dynamic queries, sorting, rich updates,
upserts (update if document exists, insert if it doesn’t), and easy
aggregation. This gives you the breadth of functionality that you
are used to from an RDBMS, with the flexibility and scaling
capability that the non-relational model allows.
By keeping related data together in documents, queries can be
much faster than in a relational database where related data is
separated into multiple tables and then needs to be joined later.
MongoDB also makes it easy to scale out your database.
Autosharding allows you to scale your cluster linearly by adding
more machines. It is possible to increase capacity without any
downtime, which is very important on the web when load can
increase suddenly and bringing down the website for extended
maintenance can cost your business large amounts of revenue.
MongoDB works hard to be very easy to install, configure,
maintain, and use. To this end, MongoDB provides few
configuration options, and instead tries to automatically do the
“right thing” whenever possible. This means that MongoDB works
right out of the box, and you can dive right into developing your
application, instead of spending a lot of time fine-tuning obscure
database configurations.
Why should use MongoDB?
- Document Oriented Storage : Data is stored in the form of JSON style documents
- Index on any attribute
- Replication & High Availability
- Auto-Sharding
- Rich Queries
SQL Schema Statements MongoDB Schema Statements
CREATE TABLE users ( id MEDIUMINT NOT NULL AUTO_INCREMENT, user_id Varchar(30), age Number, status char(1), PRIMARY KEY (id) )
Implicitly created on first insert() operation. The primary key
_id is automatically added if _id field is not specified.
db.users.insert( { user_id: "abc123", age: 55, status: "A" } )
However, you can also explicitly create a collection:
db.createCollection("users") ALTER TABLE users ADD join_date DATETIME
Collections do not describe or enforce the structure of its
documents; i.e. there is no structural alteration at the collection
level.
However, at the document level, update() operations can add
fields to existing documents using the $set operator.
db.users.update( { }, { $set: { join_date: new Date() } }, { multi: true } ) ALTER TABLE users DROP COLUMN join_date
Collections do not describe or enforce the structure of its
documents; i.e. there is no structural alteration at the collection
level.
However, at the document level, update() operations can
remove fields from documents using the $unset operator.
db.users.update( { }, { $unset: { join_date: "" } }, { multi: true } ) CREATE INDEX idx_user_id_asc ON users(user_id)
db.users.createIndex( { user_id: 1 } )
CREATE INDEX
idx_user_id_asc_age_desc ON users(user_id, age DESC)
db.users.createIndex( { user_id: 1, age: -1 } )
DROP TABLE users db.users.drop()
Insert
The following table presents the various SQL statements related to inserting records into tables
and the corresponding MongoDB statements.
SQL INSERT Statements MongoDB insert() Statements
INSERT INTO users(user_id, age,
db.users.insert( { user_id: "bcd001", age: 45, status: "A" }
status) VALUES ("bcd001", 45, "A")
Select
The following table presents the various SQL statements related to reading records from tables
and the corresponding MongoDB statements.
SQL SELECT Statements MongoDB find() Statements
SELECT *
FROM users
db.users.find()
SELECT id, user_id, status FROM users
db.users.find( { }, { user_id: 1, status: 1 } ) SELECT user_id, status FROM users
db.users.find( { }, { user_id: 1, status: 1, _id: 0 } ) SELECT * FROM users WHERE status = "A"
db.users.find( { status: "A" } ) SELECT user_id, status FROM users WHERE status = "A"
db.users.find( { status: "A" }, { user_id: 1, status: 1, _id: 0 } ) SELECT * FROM users WHERE status != "A"
db.users.find( { status: { $ne: "A" } } ) SELECT * FROM users WHERE status = "A" AND age = 50
db.users.find( { status: "A", age: 50 } )
SELECT * FROM users WHERE status = "A" OR age = 50
db.users.find( { $or: [ { status: "A" } , { age: 50 } ] } )
SELECT * FROM users WHERE age > 25
db.users.find( { age: { $gt: 25 } } ) SELECT * FROM users WHERE age < 25
db.users.find( { age: { $lt: 25 } } ) SELECT * FROM users WHERE age > 25 AND age <= 50
db.users.find( { age: { $gt: 25, $lte: 50 } } )
SELECT *
FROM users WHERE user_id like "%bc
db.users.find( { user_id: /bc/ } )
UPDATE users SET age = age + 3 WHERE status = "A"
db.users.update( { status: "A" } , { $inc: { age: 3 } }, { multi: true } )
Delete Records
The following table presents the various SQL statements related to deleting records from tables
and the corresponding MongoDB statements.
SQL Delete Statements MongoDB remove() Statements
DELETE FROM users WHERE status = "D"
db.users.remove( { status: "D" } )
DELETE FROM users db.users.remove({})
BIGDATA
Big data is an evolving term that describes any voluminous amount of structured, semi-
structured and unstructured data that has the potential to be mined for information.
Big data is a broad term for data sets so large or complex that traditional data
processing applications are inadequate. Challenges include analysis, capture, data
curation, search, sharing, storage, transfer, visualization, and information privacy. The
term often refers simply to the use of predictive analytics or other certain advanced
methods to extract value from data, and seldom to a particular size of data set.
Accuracy in big data may lead to more confident decision making. And better decisions
can mean greater operational efficiency, cost reductions and reduced risk.
