Overview of Mongo DB 3.4 : New Features

Mongo DB has been wildly popular ever since its introduction for plenty of reasons. The biggest one was because it got rid of the Object-Relation Mapping to a large extent, which had been the source of trouble of programmers for years. Even today, it is the 5th most popular database. However, the graph of popularity of Mongo DB decreased somewhat over the years, due to introduction of more advanced and simplified NoSQL databases. This might change with the release of Mongo DB 3.4, released late last year. According to the company, they seek to attain a "digital transformation" with this release.

The clear message that the company gave with this release was that it is aiming to simplify the life of large enterprises that have depended upon Mongo DB for long now. Like Python, Mongo DB is aiming to evolve so that it alone suffices for tasks that earlier required multiple technologies. Since we have seen this formula succeeding more than once, we have to admit this is a very smart move from the company.

Graph support was the need of the hour in Mongo DB for quite some time now. Taking more than 3 years to become a reality, it is arguably the biggest addition in the new version. While it does not seem to pose any threat to established graph databases like Neo4J, the graph support is sure to simplify things for its existing users. This feature is sure to have large impact, as it will facilitate companies to explore hitherto doubted avenues like Deep Analytics, Internet of Things and Artificial Intelligence. This would be further aided by Atlas, Mongo DB's database cloud service released earlier last year.

Ecommerce websites working upon Mongo DB had toiled hard for long to provide decent search functionality to its customers. This ends with the faceted navigation feature, which uses filters to narrow down the query results. This ensures faster and more relevant search results. Also, a read-only mode was introduced that could expose the information of an application while preventing any modification. Another huge feature was the creation of Geo-distributed Mongo DB zones, which deals with the problem of data sovereignty and solves it by providing tagging via a higher abstraction of “zones”.

The release also had few things in store for the regular users. The new SQL interface is sure to greatly ease things for the users who have struggled for long to import their SQL code into Mongo. Mongo DB also introduced the ($switch) operator, which greatly simplifies complex branching, while making it more readable. Like the popular "switch" expression, it tests a number of cases, executing only the one that turns out to be true. Another addition was the ($reduce) operator, that could reduce the results of multiple arrays into a single expression.

Apart from this, there has been a whole array of other additions, whose actual importance would only be realized in the long run. This includes elastic clustering, tunable consistency and enhanced DBA.

Overall, this release has been quite impressive and an instant success. Mongo DB has made its intention very clear: It is here to stay and win. With this, other NoSQL providers like Redis and Cassandra as well as established SQL players like MySQL and Oracle will have to up their game.

How and Why To Bridge between SQL and NoSQL

SQL have for long now been the synonym of "database" for us. For any sort of data management, SQL had been our instinctive choice. However, the past decade saw the emergence of NoSQL which gave rise to a fierce competition of preferences.

  

What haunts the mind of every aspiring database developer today is the question of choice: To SQL or NoSQL. We want to keep in touch with the latest trends in the technology, but don't want the established technologies to slip away either. However, the most basic point that most people seem to miss is this: SQL and NoSQL are not competitors, and most certainly not antonyms of each other.

SQL or Structured Query Language is the most standard concept of database management systems today. SQL considers data to be stored in the form of tables called Relations, that consist of tuples and attributes. While this concept had been a hugely successful improvement over the data-storage systems present at that time, like flat files, things have changed today.

NoSQL came as a breath of fresh air in an industry that was rapidly changing. The world is going digital, and the digital world is messy. We can never predict the volume, variety or velocity of incoming data. The data, apart from being unpredictable, is also unstructured. Since relational databases are not inherently adept to handle them, something else was required. At the same time, distributed computing is all the rage today, because most businesses are moving towards the cloud. The expansion of relational databases cannot keep up with the pace; thus, NoSQL entered into the scene.

Why to migrate from SQL to NoSQL

Strictly speaking, NoSQL aims to do what SQL cannot. It is not based on relations and it may sometimes even fail to follow the ACID properties! But unlike what you have been taught, ACID properties, though really useful, are not the ultimate necessity. The ultimate necessity is fault tolerance, and NoSQL manages to achieve that anyway.

NoSQL cannot be defined in a single line, as there is no single definition. While all SQL-based databases follow strict guidelines that adhere to SQL-standards, NoSQL gives the databases a free rein. With so many lacks of standards, one might wonder: Are the reasons enough to migrate to NoSQL?

Yes, because we have only touched the crux of the importance of NoSQL in modern world. The two biggest reasons why NoSQL trumps over SQL are agility and scalability.

With the rapid changes that occur daily in the industry, being agile is the only way to survive. However, Relational databases couldn't ever hope to achieve that, with their rigid schemas and complex development. The aforementioned rapid changes are also met by growing size, which require rapid scalability. However, scalability was one aspect that was blatantly ignored in SQL (as it was made in a time when web and internet were non-existent). To cope up with these issues, NoSQL seems like our best bet.

Why to Bridge SQL and NoSQL

"Now that we know how NoSQL differs from SQL, the question arises: Why to bridge them? Why not adopt NoSQL altogether?   "

Simply, because NoSQL doesn't have the same penetration as SQL. A huge number of companies have their entire existing architecture based on relational databases, which would be quite a headache to change. But that doesn't mean that one has to remain stuck with SQL forever. The best option in such scenarios is to bridge the existing SQL framework with a NoSQL database. The benefit? To put it simple, it will bring out "the best of both worlds".

As far the "bridging" goes, there is no one, simple way to do that. The easiest way would be to use third-party drivers like easysoft, which provides ODBC-like bridging capabilities. However, as it comes from a third-party vendor, it might have its own security and licensing issues.

An alternative approach would be to develop languages that could extend SQL functionality to NoSQL databases. One example would be the N1QL, introduced by Couchbase Server, which extends SQL to JSON.

The ways to bridge the gap between these two technologies may differ and evolve; but we can all agree that co-existence of the two is best for the progress of industry.

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Read more on NO SQL- NOT ONLY SQL here - WhatisNoSQL

Introduction To NewSQL

NewSQL is a new class of Databases products that offers high performance and scalability like NoSQL for online transaction processing and also incorporates the relational database properties like SQL and ACID (atomicity, consistency, isolation and durability) transactions.

The NewSQL architecture (distributed systems architecture) significantly reduces overhead and is proficient of running on a large number of nodes without suffering bottlenecks. Also they conserve the high-level language query potential of SQL. NewSQL is now considered as an alternative to NoSQL. 

NO SQL- NOT ONLY SQL

“NoSQL  just means non-relational and not SQL”

NoSQL is a class of database management system that doesn’t use structured query language for data processing. This set of database is useful while working with huge volume of data. NoSQL databases are known for being able to handle high volume data that doesn't fit the relational model, i.e. why companies like Amazon, Facebook, and Google go for it. NoSQL was an answer for all the shortcomings of relational databases. MongoDB (Document Database) is one of examples of NoSQL database.

Relational database where introduced in the late 70s to store data in an organized manner. With the arrival of social networks and more web products the amount of data has become out of control. Relational database were not designed to handle the huge amount of data

Big Data, Big Users, and Cloud Computing are taking the option of NoSQL technology as an alternative to relational databases.Examples are Apache Cassandra,CouchDB,Hadoop & Hbase,MongoDB,StupidDB,BigTable Etc.

Comparison with SQL Systems

1. More flexible, faster and high performance

2. Schema Free, No predefined Schema

3. ACID transaction properties are not needed. ACID (atomic, consistent, integrity, durable) is a set of properties that guarantees that database transactions are processed reliably.

TYPE OF NO SQL DATABASES

1.Column: Each storage block contains data from only one colum

Eg:HBase, Accumulo

2.Document: Stores documents made up of tagged elements

Eg: MongoDB, Couchbase 

3.Key-value : Allow the application to store its data in a schema-less (key, value) pairs

Eg: Dynamo, Riak, Redis, Cache, Project Voldemort 

4.Graph: Example : Neo4J, Allegro, Virtuoso