A NoSQL basically belonging to the non-SQL or non-relational database that provides a mechanism for storage and retrieval of data. Here data is formed in medians other than the tabular relationships used in relational databases. NoSQL came into presence in the late 1960s but did not get the popularity until a wave of fame in the beginning twenty-first century. NoSQL databases are used in the real-time website and big data and their usage are rising over time. NoSQL methods are also seldom called Not only SQL to indicate the reality that they may hold SQL-like query languages.
A NoSQL database covers purity of design, easier horizontal scaling to clusters of machines and more elegant command over availability. The data structures used by NoSQL databases are distinct from those applied by default in relational databases which performs some actions faster in NoSQL. The appropriateness of a given NoSQL database depends on the difficulty it should resolve. Data structures used by NoSQL databases are seldom also seen as more manageable than relational database tables.
Various NoSQL stores yield proportion in favor of availability, agility and partition limit. Limits to the more comprehensive choice of NoSQL stores cover the use of low-level query languages, poverty of patterned interfaces, and enormous previous ventures in existing relational databases. Maximum NoSQL stores need true ACID(Atomicity, Consistency, Isolation, Durability) transactions but several databases, such as MarkLogic, Aerospike, Google Spanner (though technically a NewSQL database), Symas LMDB, and OrientDB have made them primary to their designs.
Maximum NoSQL databases propose an idea of inevitable consistency in which database modifications are propagated to all nodes so queries for data might not reflect renewed data quickly or might result in reading data that is not correct which is a problem known as stale reads. Also, some NoSQL systems may present lost writes and other types of data loss. Few NoSQL systems present thoughts such as write-ahead logging to evade data loss. To distributed transaction processing over various databases, data consistency is also a bigger difficulty. This is challenging for both NoSQL and relational databases. Even modern relational databases do not provide referential probity constraints to span databases. There are some systems that keep both X/Open XA models and ACID transactions for distributed transaction processing.
Advantages of NoSQL
There are various benefits of working with NoSQL databases such as MongoDB and Cassandra. The main benefits are high scalability and high availability.
NoSQL database support sharding for horizontal scaling. Partitioning of data and storing it on many machines in such a method that the order of the data is stored is sharding. Vertical scaling implies combining more resources to the existing machine whereas horizontal scaling implies joining more machines to control the data. Vertical scaling is not that simple to perform but horizontal scaling is easy to perform. Examples of horizontal scaling databases are MongoDB, Cassandra, etc. NoSQL can manage large amounts of data because of scalability, as the data grows NoSQL scale itself to manage that data in an efficient way.
Auto replication feature in NoSQL databases makes it highly available because in case of any failure data replicates itself to the previous consistent state.
Disadvantages of NoSQL
NoSQL has the following limitations.
NoSQL databases have extremely narrow focus as it is essentially designed for storage but it gives so small functionality. Relational databases are a more suitable choice in the domain of Transaction Management than NoSQL.
NoSQL is an open-source database. There is no certain standard for NoSQL still. In other words, two database systems are expected to be unequal.
The idea of big data tools is to make the management of a huge volume of data as easy as possible. Still, it is not so simple. Data management in NoSQL is extremely more complex than a relational database. NoSQL has a status for being challenging to establish and even more confused to handle on a regular basis.
GUI is not available
GUI tools to access the database are not flexibly available.
Backup is an important weak point for few NoSQL databases like MongoDB. MongoDB has no procedure for the backup of data in a uniform way.
Large document size
Most NoSQL database systems like MongoDB and CouchDB hold data in JSON format. This indicates that documents are very large (BigData, network bandwidth, speed), and having clear key names really hurts since they increase the document size.
Types of NoSQL database
Types of NoSQL databases and the name of the databases system are the following:
- Key-value store: Memcached, Redis, Coherence
- Tabular: Hbase, Big Table, Accumulo
- Document-based: MongoDB, CouchDB, Cloudant
When should NoSQL be used
- When a large amount of data needs to be saved and retrieved.
- The relationship between you and data is not that important
- The data changing over time and is not structured.
- Support of Constraints and Joins is not needed at the database level
- The data is increasing continuously and you want to scale the database regularly to manage the data.