SQL or structured query language have been in existence for more than four decades. Its usages were noticed with the rise of web applications and other databases, especially open source MySQL, PostgreSQL, and SQLite.
On the other hand, NoSQL is in existence since 1960 but recently they have become popular after the launch of MongoDB, Redis, Apache Cassandra and CouchDB like open source software.
These two database handling techniques are different from each other and this article discusses their difference and their characteristics. The detailed difference will help you to choose one over the other smartly. Following discussion covers these points:
SQL or structured query language is associated with relational databases and is used to process structured database, while NoSQL databases are not relational. NoSQL databases are mostly document-oriented, non-structured and distributed, unlike SQL databases. Non-relational databases are more distributed and scattered in comparison to structured databases.
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SQL databases are one of the widely used databases and if we talk some other most used database then it can be only MySQL and ORACLE. Recently, NoSQL database like MongoDB is also gaining popularity. Both SAL and NoSQL are used for the same purpose, which is to store and organize the data.
Relational databases are structured like phone books having a list of addresses and contact number, while in non-structured databases files are stored which may hole much information except just phone number and address.
1). Relational and non-Relational Databases
The user has to use SQL or structured query language for manipulating and defining data and so it uses a pre-defined and similar schema for analyzing data. SQL is a great choice for complex queries. But as it is a powerful query language so it uses a predefined schema to determine the structure of data and needs up-front preparation for defining schemas. In SQL even, a minute change in the database can affect the whole system.
In case of NoSQL, dynamic schema's are used to manage unstructured data. Here, data need not be stored in tables, it can be stored in graphs, columns, documents or any other form and it can be extracted easily from there. So, it has following benefits:
SQL databases are mostly vertically scalable, it does mean that a load of a single server can only be increased just by adding more CPU, RAM or SSD. On the other hand, NoSQL databases are also horizontal scalable, which means to handle large databases more servers can be added.
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You can take above concepts like the addition of more floors in the same building or addition of more adjacent building in the above listed respective cases. As Addition of more servers makes the process more powerful, so NoSQL is considered as the ever-changing and preferred choice to manage large datasets.
SQL databases use tables while NoSQL databases use documents and use key-value pairs or graph databases or column-based databases. So, SQL uses multi-row transactions and is considered as a better option for applications, especially for any accounting or legacy system.
Examples of SQL databases are Oracle, MySQL, PostgreSQL and Microsoft Server. While for NoSQL the examples are BigTable, MongoDB, RavenDB, Redis, HBase, Neo4j, CouchDB, and Cassandra.
Now, as we have seen that what are the key differences between structured and non-structured query languages, now let's see that what are the functional similarities and differences between both by taking the example of MySQL and MongoDB as SQL and NoSQL databases:
MySQL as a SQL database offers following benefits, which are considered ad strength of this database:
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MongoDB as a NoSQL database offers following benefits and strength to the users:
|SQL Database||NoSQL Database|
|Another Name||Also known as RDBMS or Relational Database||Is non-relational and distributed by nature|
|Basis||Use SQL to define and manipulate data, based on tables||Document-level queries are used and graphs and wide-columns can be handled|
|Data Storage||A Non-hierarchical database is used||Data is stored in hierarchical order|
|Types of Data Used||Good for those data sets stored in a structured manner||It isgood for semi-structured, nested and complex data|
|New Data||New data addition may require schema alteration||Without any alteration, new data fields can be added|
|Scalability||Are vertical scalable and with increasing hardware horsepower can be scales||Are horizontal scalable and just by increasing database servers and pool of resources the load can be distributed|
|Normalization||Often relationships are handled in a normalized manner and join are used to resolve table references||By denormalization, the data is often captured. All data for a single record is captured in an object|
|Complex Query Handling||Intensive environment to handle complex queries||Are not good to handle complex queries|
|Data Set Size||Are not good at handling large data sets||Mostly preferred for large datasets|
|Support and Adoption||It adopts widely and wide support is also available||Is not adopted widely only local community support is available|
|ACID Properties||Emphasize is on ACID property maintenance||Only a few databases follow ACID properties|
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Today NoSQL databases are becoming major choices of the organizations and can even be a real game changer in coming days. Properties like low cost, scalability feature makes it quite appealing and suitable option. Though it does not offer as many features as MySQL. To choose any one of the developers must know that which feature they require and important for their database. Some drawbacks like the absence of ACID property support and standardization absence makes it disadvantageous.
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