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Jul 12, 2015
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Choosing the Right NoSQL Database
NoSQL is picking up pace andjust in case you are planning anew application, choosing theright NoSQL database canlargely govern your budgetdistribution since a wrongmove at this point can end upcosting you lots of bucks in thecoming days.
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Choosing the Right NoSQL Database
Which is the right NoSQL Database?
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Choosing the Right NoSQL Database
NoSQL technology is relatively new undergoing innovations every now and then, it is safe to say there are no defined standards related to NoSQL database.
DataBase Launch Date
MongoDB 2009
Cassandra 2008
CouchDB 2008
Redis 2009
Neo4J 2007
Riak 2009
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Choosing the Right NoSQL Database
But we will categorize them
anyway.
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Choosing the Right NoSQL Database
NoSQL databases most popularly get categorized into four types
Key-Value Store Document-Based Store
Column-Based Store Graph-Based
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Key Value Store NoSQL Database
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Key-Value Store NoSQL Database
Key Value Store NoSQL databasesuses a hashtable to store unique key and pointers to each data value...... which in turn are stored in a schema less way......in other words data is stored in the form of keys and values......hence the name Key-Value-Store databases.
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Key-Value Store NoSQL Database
From the perspective of database, ‘value’ is just meaningless data that database just stores, without caring or knowing what’s inside.
There are no complex relations thus implementation is very easy, and since these databases only use a single primary key as access, they give great performance and can be very easily scaled.
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Key-Value Store NoSQL Database
Key-value databases are best utilized in the situations involving:
➔ Storing user session data➔ Maintaining schema-less user profiles➔ Storing user preferences➔ Storing shopping cart data
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Key-Value Store NoSQL Database
We generally avoid Key-Value Databases if
➔ we have to query the database by specific data value,➔ we need relationships between data values➔ we need to operate on multiple unique keys
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Key-Value Store NoSQL Database
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Document Store NoSQL Database
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Document Store NoSQL Database
Imagine a Key-value Store database in which instead of storing ‘value’ we are storing entire documents.
If you can imagine it then you have grasped the crux of Document Store database.
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Document Store NoSQL Database
These documents are mainly stored in common notation formats like XML, JSON, BSON etc, therefore they give greater flexibility in querying .
Document Store database embeds attribute metadata related to the stored content with each document because of which you can search data by primary key and the value in documents
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Document Store NoSQL Database
Document Store databases are useful when you have to implement
➔ Content management systems➔ Blogging platforms➔ Analytics platforms➔ E-commerce platforms
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Document Store NoSQL Database
App developers avoid Document Store if
➔ They have to run complex search queries➔ Their application requires complex multiple-
operation transactions.➔ The information stored inside the document need to
queries and matched very frequently.
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Document Store NoSQL Database
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Column Store NoSQL Database
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Column Store NoSQL Database
Column-family databases are row-based databases. In this type of database data is stored in rows that have a unique row id, and the data is stored in form of flexible columns.
Because of its similarity to SQL databases, column store are easier to query. But they are not as flexible in storing random information.
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Column Store NoSQL Database
The key difference betweenColumn Store and SQLdatabase is that in Column-store you don’t have tomaintain consistent columnnumbers. You can add a newcolumn to any row withouthaving to add them in all therows of the database.
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Column Store NoSQL Database
Developers mainly use Column Store Databases in
➔ Content management systems➔ Blogging platforms➔ Systems that maintain counters➔ Services that have expiring usage➔ Systems that require heavy write requests
(like log aggregators)
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Column Store NoSQL Database
We generally avoid Column Store databases if
➔ We have to use complex querying➔ Our querying patterns have to be changed regularly➔ You don’t have established database requirements➔ Need to store arbitrary data very fast.
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Column Store NoSQL Database
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Graph Base NoSQL Database
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Graph Base NoSQL Database
Graph Base databases are all about edge-nodes and graph relations. You store data entities and allow users to add relations to these database entities.
However the database neither knows nor care what is stored in data entities or how it is stored.
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Document Store NoSQL Database
The database uses complex graph structures complete with the ability to define edges, nodes and node properties.
This not only gives greater abilities in querying relational data but also provide index-free searches, a problem that plagues previously mentioned NoSQL types.
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Graph Base NoSQL Database
Graph based databases are enormously useful in applications that have connected data, such as ➔ social networks, ➔ routing infocenters, ➔ recommendation engine applications, spatial data
and mapping applications➔ and other applications requiring unique key
relations.
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Graph Base NoSQL Database
App developers avoid document store if
➔ They have to run complex search queries on values➔ Some data have to be stored that have no defines
relations➔ Require careful handling so that database does not
become inconsistent on scaling
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Graph Base NoSQL Database
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The Combination Solutions
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The Combination Solutions
Though there are various types of databases, it is not advisable nor is it the norm to opt for a single database. More popularly, and as much sensibly, it’s the idea of using more than one database which does the trick.
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The Combination Solutions
For example in a social network appit is best to use a Graph Database tomanage connections and DocumentDatabase for managing profile andpost data.
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The Combination Solution EXPERTS
To know more which combination is the best for your
application, let the database experts study your case and
come up with the best suggestions for your model.
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Snehesh Mitra
AuthorDirector
Mobile Technologies
Snehesh is leading the mobility business
development function, mobility strategy and
consulting practice at Algoworks. He is an expert
of all mobile technologies and has experience in
managing teams involved in the development of
custom iPhone/iPad/Android apps. He has
special interest in mobile industry and cross
platform technologies. In the past, Snehesh
founded reality technology company to connect
buyers and real estate builders and brokers. He
holds a graduate degree in Electronics and
Electrical Engineering from the Indian Institute of
Technology, Kharagpur.
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