Microsoft SQL Server vs MongoDB vs PostgreSQL

Overview

Microsoft SQL Server

Stacks21.3K

Followers15.5K

Votes540

PostgreSQL

Stacks103.0K

Followers83.9K

Votes3.6K

GitHub Stars19.0K

Forks5.2K

MongoDB

Stacks96.6K

Followers82.0K

Votes4.1K

GitHub Stars27.7K

Forks5.7K

Microsoft SQL Server vs MongoDB vs PostgreSQL: What are the differences?

Data Model: Microsoft SQL Server follows a relational data model, storing data in tables with rows and columns, enforcing ACID properties. MongoDB and PostgreSQL, on the other hand, follow a non-relational model, with MongoDB being a document-based database and PostgreSQL being an object-relational database.
Query Language: SQL Server uses Transact-SQL (T-SQL) for querying data using SQL statements. MongoDB uses its query language (based on JavaScript) to query documents in a collection. PostgreSQL also supports SQL for querying but provides support for additional features like JSON and XML data types.
Scalability: While SQL Server supports vertical scaling, allowing scaling by increasing server resources like CPU and RAM, MongoDB and PostgreSQL are designed for horizontal scaling, distributing data across multiple servers to handle large volumes of data and high traffic.
Consistency vs. Flexibility: SQL Server emphasizes data consistency and integrity through transactions, constraints, and referential integrity. MongoDB focuses on flexibility and scalability, allowing for schema-less data structures and dynamic schemas. PostgreSQL strikes a balance by supporting data consistency while also providing flexibility with features like JSONB data type.
Community and Ecosystem: SQL Server has a large community and ecosystem of tools and resources provided by Microsoft. MongoDB has a vibrant open-source community and a vast ecosystem of integrations and libraries. PostgreSQL has a strong community support with a wide range of extensions and tools developed by the community.
Licensing: SQL Server has a commercial license with different editions offering various features. MongoDB and PostgreSQL are open-source databases, free to use and distribute, with additional features available through enterprise versions or commercial support.

In Summary, Microsoft SQL Server is a relational database emphasizing data consistency, MongoDB offers flexibility and scalability with a document-based approach, and PostgreSQL provides a balance between consistency and flexibility with its object-relational design.

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Advice on Microsoft SQL Server, PostgreSQL, MongoDB

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

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	PostgreSQL	MongoDB
Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.	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.	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 19.0K	GitHub Stars 27.7K
GitHub Forks -	GitHub Forks 5.2K	GitHub Forks 5.7K
Stacks 21.3K	Stacks 103.0K	Stacks 96.6K
Followers 15.5K	Followers 83.9K	Followers 82.0K
Votes 540	Votes 3.6K	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 The maximum number of connections is only 14000 connect 1 Data pages is only 8k 1 Allwayon can loose data in asycronious mode	Pros 765 Relational database 511 High availability 439 Enterprise class database 383 Sql 304 Sql + nosql Cons 10 Table/index bloatings	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, PostgreSQL, 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.

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.

Oracle

Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.

Related Comparisons

Microsoft SQL Server vs MongoDB vs PostgreSQL: What are the differences?

Data Model: Microsoft SQL Server follows a relational data model, storing data in tables with rows and columns, enforcing ACID properties. MongoDB and PostgreSQL, on the other hand, follow a non-relational model, with MongoDB being a document-based database and PostgreSQL being an object-relational database.
Query Language: SQL Server uses Transact-SQL (T-SQL) for querying data using SQL statements. MongoDB uses its query language (based on JavaScript) to query documents in a collection. PostgreSQL also supports SQL for querying but provides support for additional features like JSON and XML data types.
Scalability: While SQL Server supports vertical scaling, allowing scaling by increasing server resources like CPU and RAM, MongoDB and PostgreSQL are designed for horizontal scaling, distributing data across multiple servers to handle large volumes of data and high traffic.
Consistency vs. Flexibility: SQL Server emphasizes data consistency and integrity through transactions, constraints, and referential integrity. MongoDB focuses on flexibility and scalability, allowing for schema-less data structures and dynamic schemas. PostgreSQL strikes a balance by supporting data consistency while also providing flexibility with features like JSONB data type.
Community and Ecosystem: SQL Server has a large community and ecosystem of tools and resources provided by Microsoft. MongoDB has a vibrant open-source community and a vast ecosystem of integrations and libraries. PostgreSQL has a strong community support with a wide range of extensions and tools developed by the community.
Licensing: SQL Server has a commercial license with different editions offering various features. MongoDB and PostgreSQL are open-source databases, free to use and distribute, with additional features available through enterprise versions or commercial support.

Microsoft SQL Server vs MongoDB vs PostgreSQL

Overview