Overview

Microsoft SQL Server

Stacks20.2K

Followers15.5K

Votes540

MongoDB

Stacks95.2K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Microsoft SQL Server vs MongoDB: What are the differences?

# Introduction
This comparison highlights key differences between Microsoft SQL Server and MongoDB.

1. **Data Modeling Approach**: In Microsoft SQL Server, data is structured into tables with a clearly defined schema, enforce relationships, and support transactions. On the other hand, MongoDB is a NoSQL database that follows a document-based model, offering flexibility in data structures with schemaless collections and no predefined relationships between documents.
  
2. **Language Support**: SQL Server primarily uses Transact-SQL (T-SQL) for querying and managing data, following a relational database management system approach. MongoDB, on the contrary, supports a rich query language that allows for ad-hoc queries and manipulates data using JavaScript-based methods.

3. **Scalability and Performance**: SQL Server is known for its vertical scalability, which involves upgrading hardware resources for performance enhancement. MongoDB, on the other hand, is designed for horizontal scalability, allowing the distribution of data across multiple servers to handle large volumes of unstructured data efficiently.

4. **Data Consistency and Transactions**: SQL Server guarantees ACID (Atomicity, Consistency, Isolation, Durability) properties making it suitable for applications that require strict data consistency and transaction support. In contrast, MongoDB sacrifices ACID properties for better scalability and performance, offering eventual consistency with support for multi-document transactions in recent versions.

5. **Data Integrity and Security**: SQL Server provides robust data validation and integrity constraints through advanced features like constraints, triggers, and stored procedures, ensuring data quality and security. While MongoDB offers basic data validation and security mechanisms, it lacks the advanced features of SQL Server for ensuring data integrity.

6. **Community and Ecosystem**: SQL Server has a vast community and ecosystem, providing comprehensive support, documentation, and third-party tools for development and management. MongoDB also has a growing community, but it may have fewer resources and tools compared to SQL Server due to its relatively newer presence in the market.

In Summary, the key differences between Microsoft SQL Server and MongoDB lie in their data modeling approach, language support, scalability strategies, data consistency mechanisms, data integrity features, and community support.

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Advice on Microsoft SQL Server, MongoDB

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

Mike

Mar 20, 2020

Needs advice

We Have thousands of .pdf docs generated from the same form but with lots of variability. We need to extract data from open text and more important - from tables inside the docs. The output of Couchbase/Mongo will be one row per document for backend processing. ADOBE renders the tables in an unusable form.

241k views241k

Comments

Detailed Comparison

Microsoft SQL Server	MongoDB
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.
-	Flexible data model, expressive query language, secondary indexes, replication, auto-sharding, in-place updates, aggregation, GridFS
Statistics
GitHub Stars -	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.7K
Stacks 20.2K	Stacks 95.2K
Followers 15.5K	Followers 82.0K
Votes 540	Votes 4.1K
Pros & Cons
Pros 139 Reliable and easy to use 101 High performance 95 Great with .net 65 Works well with .net 56 Easy to maintain Cons 4 Expensive Licensing 2 Microsoft 1 Replication can loose the data 1 The maximum number of connections is only 14000 connect 1 Data pages is only 8k	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

What are some alternatives to Microsoft SQL Server, MongoDB?

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.

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.

CouchDB

Apache CouchDB is a database that uses JSON for documents, JavaScript for MapReduce indexes, and regular HTTP for its API. CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP. Index, combine, and transform your documents with JavaScript.