Hello guys, if you are wondering whether to choose a NoSQL database like MongoDB, Cassandara, or MySQL for your application then you have come to the right place. In an earlier article, MySQL vs PostgreSQL I had shared the pros and cons of choosing MySQL over PostgreSQL for your application, and in this article, I am going to show the benefits and drawbacks of choosing MySQL over NoSQL or vice-versa.
What is NoSQL?
A
non-relational database is one that does not employ the relational
database's tabular schema of rows and columns. Its storage strategy, on
the other hand, is tailored to the sort of data it stores. Databases
that aren't relational are referred to as NoSQL databases, which stands
for "Not Only SQL." Non-relational databases, unlike relational
databases, may employ a variety of query languages.
NoSQL databases are divided into four categories.
- Document-oriented
databases, also known as document stores, are databases that are used
to store, retrieve, and manage document-oriented data. Each key in a
document database is frequently paired with a sophisticated data
structure (called a document).
- Key-Value Stores — A key-value
store is a database that employs several keys, each of which is linked
to just one value in a collection. Consider it a dictionary. Among the
NoSQL database types, this is one of the most basic.
- Wide-Column
Stores — unlike a relational database, this database employs tables,
rows, and columns, but the names and formats of the columns can change
from row to row in the same table.
- Graph Stores - A graph database represents and stores data using graph structures for semantic searches, such as nodes, edges, and attributes.
As
more firms turn to big data for research and reporting, non-relational
databases are becoming increasingly popular. NoSQL databases provide
additional flexibility because vital data may not necessarily fit into a
pre-defined structure.
When to use SQL vs NoSQL?
Are you considering switching from SQL to NoSQL but aren't sure if it's the appropriate move?
The
distinction between SQL and NoSQL databases boils down to the
distinction between relational and non-relational databases. When to use
SQL and when to utilize NoSQL relies on the type of data you're keeping
and the best manner to store it. Both categories store data, although
they do it in distinct ways.
The true answer
for SQL vs NoSQL is that it depends on what you're building, the limits
imposed by who you're developing for, and the desired outcome.
While
NoSQL is gaining popularity and adoption, it is not a replacement for
SQL. It's only a different alternative. Although it is sometimes
necessary to choose one over the other, many development teams choose to
employ both.
Now, let's compare each of them to the factors that matter.
SQL vs NoSQL in terms of scalability
One
of the most serious problems with SQL is its lack of scalability. SQL
was created with scalability in mind. The term "scaling up" or "scaling
vertically" refers to the addition of additional hardware, RAM,
computing power, and other resources in order to enhance capacity.
With
SQL we’re constrained since we will certainly max out on capacity and
scaling up is costly. Scaling out SQL is conceivable, but it takes a lot
of time and money (partitioning, sharding, clustering, and so on).
When
it comes to scalability, the main distinction between SQL and NoSQL is
this: NoSQL databases are built to scale out and make use of cloud
computing. We are adding resources to a single node whether expanding
out or horizontally (a computer or server). A single database can be
used by several nodes. We can easily add (or delete) nodes when we scale out (or back in).
This makes NoSQL a
fantastic match for the cloud. Because it can scale out, you will be
leveraging the scalability benefits of the cloud. You can run SQL on
Azure, for example, but you will be constrained in your capacity to
scale.
SQL vs NoSQL in terms of speed
NoSQL
is easier to design because of its capacity to store large volumes of
data in a flexible manner. A database can be created without a formal
database model. Without identifying the type of data in advance, store a
wide range of data.
Without having to rewrite the schema, you may add
new data types. Fast-paced, agile development teams work well with
NoSQL. As the scope and needs change, it enables quick adjustments to
the database structure.
This isn't to say that
SQL is sluggish. There's probably no need to adopt NoSQL if your data is
highly organized and you expect minimal modification. Your engineers
won't stumble into difficulties they can't address since SQL is mature
and backed by a large technical community.
You're more likely to come
across difficult difficulties using NoSQL that don't have published
answers, which might cause delays.
Standardization
is also lacking in NoSQL databases. MongoDB and Cassandra DB, for
example, are both ideal NoSQL databases to learn for an engineer new to
NoSQL. However, because NoSQL lacks standards, an engineer who first
learns MongoDB may struggle with Cassandra DB.
SQL
is also recognized for its powerful capabilities and tools. Tools for
monitoring, backing up, and maintaining NoSQL databases are provided by
NoSQL frameworks. SQL is still more powerful nowadays. However, as NoSQL
develops, more functionalities become accessible. Some instances are as
follows:
While both SQL and NoSQL databases
offer high availability and auto-replication (automatically interacting
with another instance when one fails), SQL needs configuration, whereas
many NoSQL databases come with these capabilities pre-installed.
You'll
require sharding and partitioning if you're dealing with a multi-tenant
application (separating very large databases into smaller, faster, more
easily managed parts). Additional code is required to do this with SQL
databases. These functionalities are built-in to NoSQL databases (such
as CosmosDB).
Conclusion
That's all about MySQL vs NoSQL. There's
a lot to think about and navigate. We started by looking at the
questions we ask. We then get into greater detail about which type of
NoSQL database to use, which NoSQL framework is the best and why, how
and when to migrate old SQL to NoSQL, how to perform a cost comparison,
how to help developers, and how to avoid common problems.
My Other SQL Articles and Tutorials you may like
- 15 SQL Interview Questions with Answers
- How to use LEFT, RIGHT, and SELF JOIN
- How to find Nth Highest salary in SQL
- How to remove duplicate rows in SQL
- Difference between DDL and DML commands in SQL
- How to concatenate columns in SQL
- How to use Cross Join in SQL
- How to use ORDER BY in SQL with example
- How to use row_number function in SQL
- What is a Virtual column in MySQL? Example
- How to do pagination in SQL with Examples
- How to transpose data in SQL with examples
Thanks
for reading this article so far. If you like this MySQL vs NoSQL comparison and my analysis of the pros and cons of choosing MySQL vs Non-Relationional DB then please share it with
your friends and colleagues. If you have any questions or feedback then
please drop a note.
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