SQL vs NoSQL:
A Performance Comparison
By Laurent Mauer · November 8, 2022 · 7 min read
SQL stands for Structured Query Language, invented as a standard high-level interface for most databases, usually used as DDL and DML for the management of relational database management systems (RDBMS).
Databases based on the relational model include MySQL, MS-SQL Server, Oracle database and so on, each of them supports SQL as the query language.
NoSQL, which stands for Not Only SQL, however is a non-relational database management system. Leading NoSQL databases include MongoDB, Cassandra, CouchDB, HBase, etc. With the current popularity of “Big Data”, NoSQL databases were pioneered and improved a lot by top internet companies like Amazon, Google and LinkedIn.
The main difference between the non-relational data model and the traditional one is, the non-relational model is designed for processing huge amounts of data in a second, with relatively low consistency requirements. As a consequence, it relaxes the ACID constraints provided by many relational database systems, in exchange for the improvement of performance.
SQL is a decades-old method for accessing relational databases, and most who work with databases are familiar with it. As unstructured data, amounts of storage and processing power and types of analytics have changed over the years, however, we’ve seen different database technologies become available that are a better fit for newer types of use cases. These databases are commonly called NoSQL.
SQL and NoSQL differ in whether they are relational (SQL) or non-relational (NoSQL), whether their schemas are predefined or dynamic, how they scale, the type of data they include and whether they are more fit for multi-row transactions or unstructured data.
How does SQL work?
SQL databases are valuable in handling structured data, or data that has relationships between its variables and entities.
In general, SQL databases can scale vertically, meaning you can increase the load on a server by migrating to a larger server that adds more CPU, RAM or SSD capability. While vertical scalability is used most frequently, SQL databases can also scale horizontally through sharding or partitioning logic, although that’s not well-supported.
SQL database schema organizes data in relational, tabular ways, using tables with columns or attributes and rows of records. Because SQL works with such a strictly predefined schema, it requires organizing and structuring data before starting with the SQL database.
RDBMS, which uses SQL, must exhibit four properties, known by the acronym ACID. These ensure that transactions are processed successfully and that the SQL database has a high level of reliability:
All transactions must succeed or fail completely and cannot be left partially complete, even in the case of system failure.
The database must follow rules that validate and prevent corruption at every step.
Concurrent transactions cannot affect each other.
Transactions are final, and even system failure cannot “roll back” a complete transaction.
Because SQL databases have a long history now, they have huge communities, and many examples of their stable codebases online. There are many experts available to support SQL and programming relational data.
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How does NoSQL work?
Unlike SQL, NoSQL systems allow you to work with different data structures within a database. Because they allow a dynamic schema for unstructured data, there’s less need to pre-plan and pre-organize data, and it’s easier to make modifications. NoSQL databases allow you to add new attributes and fields, as well as use varied syntax across databases.
NoSQL databases scale better horizontally, which means one can add additional servers or nodes as needed to increase load.
NoSQL databases are not relational, so they don’t solely store data in rows and tables. Instead, they generally fall into one of four types of structures:
Column-oriented, where data is stored in cells grouped in a virtually unlimited number of columns rather than rows.
Key-value stores, which use an associative array (also known as a dictionary or map) as their data model. This model represents data as a collection of key-value pairs.
Graph databases, which represent data on a graph that shows how different sets of data relate to each other. Neo4j, RedisGraph (a graph module built into Redis) and OrientDB are examples of graph databases.
While SQL calls for ACID properties, NoSQL follows the CAP theory (although some NoSQL databases — such as IBM’s DB2, MongoDB, AWS’s DynamoDB and Apache’s CouchDB — can also integrate and follow ACID rules).
The CAP theorem says that distributed data systems allow a trade-off that can guarantee only two of the following three properties (which form the acronym CAP) at any one time.
Every request receives either the most recent result or an error. MongoDB is an example of a strongly consistent system, whereas others such as Cassandra offer eventual consistency.
Every request has a non-error result.
Partition tolerance: Any delays or losses between nodes do not interrupt the system operation.
While NoSQL has quickly been adopted, it has smaller user communities and, therefore, less support. NoSQL users do benefit from open-source systems, as opposed to the many SQL languages that are proprietary.
SQL vs NoSQL Comparison:
NoSQL is much faster than traditional SQL databases in terms of read and write speed, especially in key-value storage like Berkeley DB, which means less waiting time in scenarios such as online transactions. In terms of writing speed, MySQL is slower than MongoDB in the beginning, but gradually becomes faster than MongoDB when data collection becomes huge. This may be because of the pre-heat of the DBMS system, but more experiments still needed to conclude a reason.
Based on the results, we can conclude that in NoSQL database different types of operation will lead to various performance. Although not all NoSQL databases perform better than SQL databases, we can conclude that NoSQL databases are generally faster than SQL databases.
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