MSSQL vs MongoDB

Overview

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

MSSQL

Stacks1.0K

Followers417

Votes3

MSSQL vs MongoDB: What are the differences?

Data Structure: MSSQL is a relational database management system that stores data in tables with predefined schemas, ensuring data consistency and integrity. MongoDB is a NoSQL database that stores data in collections of JSON-like documents, allowing for flexible and dynamic schemas.
Query Language: MSSQL uses SQL (Structured Query Language) for querying data, which is powerful and widely used in the industry. MongoDB uses a query language based on JSON-like queries, allowing for complex querying capabilities, especially when dealing with nested data.
Scaling: MSSQL traditionally relies on vertical scaling, where more powerful hardware is used to handle data growth. MongoDB excels at horizontal scaling, allowing for distributed databases across multiple commodity servers, which can handle larger amounts of data more efficiently.
ACID Compliance: MSSQL is known for its strict ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring that transactions are processed reliably. MongoDB sacrifices strict ACID compliance for better performance and scalability, offering eventual consistency where data may be temporarily inconsistent.
Indexing: In MSSQL, indexes are crucial for optimizing query performance, but they need to be carefully managed to maintain performance. MongoDB also utilizes indexes but offers more flexibility, allowing for different types of indexes to be used depending on the data and queries.
Schema Flexibility: MSSQL requires a predefined schema that enforces data structure, making it suitable for applications with stable data models. On the other hand, MongoDB's schema-less design allows for agile development, making it ideal for applications that require frequent schema changes or deal with unstructured data.

In Summary, SQL and NoSQL databases like MSSQL and MongoDB differ in data structure, query language, scaling capabilities, ACID compliance, indexing mechanisms, and schema flexibility.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on MongoDB, MSSQL

Kyle

Web Application Developer at Redacted DevWorks

Dec 3, 2019

Decidedon

PostGIS

While there's been some very clever techniques that has allowed non-natively supported geo querying to be performed, it is incredibly slow in the long game and error prone at best.

MySQL finally introduced it's own GEO functions and special indexing operations for GIS type data. I prototyped with this, as MySQL is the most familiar database to me. But no matter what I did with it, how much tuning i'd give it, how much I played with it, the results would come back inconsistent.

It was very disappointing.

I figured, at this point, that SQL Server, being an enterprise solution authored by one of the biggest worldwide software developers in the world, Microsoft, might contain some decent GIS in it.

I was very disappointed.

Postgres is a Database solution i'm still getting familiar with, but I noticed it had no built in support for GIS. So I hilariously didn't pay it too much attention. That was until I stumbled upon PostGIS and my world changed forever.

449k views449k

Comments

George

Student

Mar 18, 2020

Needs adviceon

PostgreSQL

Python

Django

Hello everyone,

Well, I want to build a large-scale project, but I do not know which ORDBMS to choose. The app should handle real-time operations, not chatting, but things like future scheduling or reminders. It should be also really secure, fast and easy to use. And last but not least, should I use them both. I mean PostgreSQL with Python / Django and MongoDB with Node.js? Or would it be better to use PostgreSQL with Node.js?

*The project is going to use React for the front-end and GraphQL is going to be used for the API.

Thank you all. Any answer or advice would be really helpful!

620k views620k

Comments

Ido

Mar 6, 2020

Decided

My data was inherently hierarchical, but there was not enough content in each level of the hierarchy to justify a relational DB (SQL) with a one-to-many approach. It was also far easier to share data between the frontend (Angular), backend (Node.js) and DB (MongoDB) as they all pass around JSON natively. This allowed me to skip the translation layer from relational to hierarchical. You do need to think about correct indexes in MongoDB, and make sure the objects have finite size. For instance, an object in your DB shouldn't have a property which is an array that grows over time, without limit. In addition, I did use MySQL for other types of data, such as a catalog of products which (a) has a lot of data, (b) flat and not hierarchical, (c) needed very fast queries.

575k views575k

Comments

Detailed Comparison

MongoDB	MSSQL
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.	It is capable of storing any type of data that you want. It will let you quickly store and retrieve information and multiple web site visitors can use it at one time.
Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS	Resumable online index rebuild; SQL Server machine learning services; Query processing improvements; Automatic database tuning; TempDB file size improvements; Smart differential backup; Smart transaction log backup.
Statistics
GitHub Stars 27.7K	GitHub Stars -
GitHub Forks 5.7K	GitHub Forks -
Stacks 96.6K	Stacks 1.0K
Followers 82.0K	Followers 417
Votes 4.1K	Votes 3
Pros & Cons
Pros 829 Document-oriented storage 594 No sql 554 Ease of use 465 Fast 410 High performance Cons 6 Very slowly for connected models that require joins 3 Not acid compliant 2 Proprietary query language	Pros 3 Easy of use Cons 1 Vendor lock-in 1 License Cost
Integrations
No integrations available	MySQL PostgreSQL Oracle SQLite

What are some alternatives to MongoDB, MSSQL?

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

InfluxDB

InfluxDB is a scalable datastore for metrics, events, and real-time analytics. It has a built-in HTTP API so you don't have to write any server side code to get up and running. InfluxDB is designed to be scalable, simple to install and manage, and fast to get data in and out.

Related Comparisons

MSSQL vs MongoDB: What are the differences?

Data Structure: MSSQL is a relational database management system that stores data in tables with predefined schemas, ensuring data consistency and integrity. MongoDB is a NoSQL database that stores data in collections of JSON-like documents, allowing for flexible and dynamic schemas.
Query Language: MSSQL uses SQL (Structured Query Language) for querying data, which is powerful and widely used in the industry. MongoDB uses a query language based on JSON-like queries, allowing for complex querying capabilities, especially when dealing with nested data.
Scaling: MSSQL traditionally relies on vertical scaling, where more powerful hardware is used to handle data growth. MongoDB excels at horizontal scaling, allowing for distributed databases across multiple commodity servers, which can handle larger amounts of data more efficiently.
ACID Compliance: MSSQL is known for its strict ACID (Atomicity, Consistency, Isolation, Durability) compliance, ensuring that transactions are processed reliably. MongoDB sacrifices strict ACID compliance for better performance and scalability, offering eventual consistency where data may be temporarily inconsistent.
Indexing: In MSSQL, indexes are crucial for optimizing query performance, but they need to be carefully managed to maintain performance. MongoDB also utilizes indexes but offers more flexibility, allowing for different types of indexes to be used depending on the data and queries.
Schema Flexibility: MSSQL requires a predefined schema that enforces data structure, making it suitable for applications with stable data models. On the other hand, MongoDB's schema-less design allows for agile development, making it ideal for applications that require frequent schema changes or deal with unstructured data.

In Summary, SQL and NoSQL databases like MSSQL and MongoDB differ in data structure, query language, scaling capabilities, ACID compliance, indexing mechanisms, and schema flexibility.

MSSQL vs MongoDB

Overview