The peak of NoSQL (NotOnlySQL) databases beginning in the late 2000s overlapped with multiple upgrades. While multi-core processors and virtualization were becoming unremarkable, the cloud was taking off, and lots of users around the globe were going online for the first time using their smartphones. Everything had to grow, and the most beneficial way to achieve this scale is horizontal scaling. Relational Database Management Systems (RBDMS) using SQL have been used to store application information for a long time period. Modern SQL databases such as MySQL and PostgreSQL are the most prevalent databases nowadays. But when is SQL not enough?
SQL databases can easily scale vertically by adding more RAM and computing to a single node, but spreading your dataset across multiple nodes is more difficult. You can do this using the method named sharding. When performing with extensive data sets and increased throughput, sharding helps reduce the load on a single server and enables scaling via the addition or reduction of servers.
MySQL Sharding and Limitations
Sharding allows SQL databases to scale horizontally. MySQL uses the NDB storage engine. MySQL supports NDB groupings that can divide a single, big table into multiple smaller tables. This process is known as partitioning. When stored across multiple servers, the smaller tables create the shards. The databases in your cluster each store one of the shards. All of the databases together form your full data set.
Using sharding in SQL databases can provide a significantly increased scaling of dataset size, but it can also complicate your application. You have to carefully configure how your data is partitioned into multiple shards because this affects overall database performance.
The most widespread NoSQL database is MongoDB. MongoDB is document-oriented. Data is stored in documents analogous to JSON entities and each document includes pairs of fields and values. This is the opposite of SQL databases which use tables and rows to format data. NoSQL databases like MongoDB are commonly nicely fitted for horizontal scaling.
MongoDB especially uses a format called BSON, which is derived from JSON, but this will vary with each database.
Schemas and Shards
MongoDB is schemaless (or schema-free), this means it doesn’t require a specified organizational structure. The schema is built into your code at the application level and this gives us a lot of flexibility to modify the structure later. While they lack the rigorously executed texture of ACID-compliant SQL databases, MongoDB and other NoSQL databases excel at availability and division toleration.
MongoDB and other NoSQL databases are scheduled to adapt sharding at a structural level. MongoDB lets us horizontally scale by deploying shards as replica sets. Replica sets are groupings of nodes with duplicative copies. They provide availability and redundancy when applied in big domains and are not limited by a predetermined scheme.
NoSQL databases usually use quite more storage than SQL databases because of the quantity of duplicative data needed to achieve availability across massive horizontal deployments. NoSQL write speeds tend to exceed SQL databases but queries are slow. NoSQL databases are not ACID compliant. This makes them less practical for applications handling increased volumes of financial transactions.
But when is SQL not enough? The answer is not explicit but there are some procedures we can follow when creating our applications. What does our app ought to do and how large does it need to be? From there, we can decide our number one priority. Saying “SQL scales vertically and NoSQL scales horizontally” is not true, but we can say “most SQL databases were designed with consistency in mind while most NoSQL databases were designed to accommodate scaling.”
Deploying Databases on HostRooster
Get more information about HostRooster Managed Databases or sign up to receive updates on your desired database engine. You can also deploy database-managed systems like MongoDB from the HostRooster Marketplace or follow our guides to install a database on a mixture of Linux distros, like Installing MongoDB on CentOS 7.
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