MongoDB聽vs聽MySQL聽vs聽PostgreSQL

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MongoDB

51.8K
41.5K
+ 1
4K
MySQL

68.6K
53K
+ 1
3.7K
PostgreSQL

51.8K
40K
+ 1
3.5K
Decisions about MongoDB, MySQL, and PostgreSQL

Backend:

  • Considering that our main app functionality involves data processing, we chose Python as the programming language because it offers many powerful math libraries for data-related tasks. We will use Flask for the server due to its good integration with Python. We will use a relational database because it has good performance and we are mostly dealing with CSV files that have a fixed structure. We originally chose SQLite, but after realizing the limitations of file-based databases, we decided to switch to PostgreSQL, which has better compatibility with our hosting service, Heroku.
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Anthony Simon
Lead Engineer at Stylight | 20 upvotes 路 29.1K views

I try to follow an 80/20 distribution when it comes to my choice of tools. This means my stack consists of about 80% software I already know well, but I do allow myself 20% of the stack to explore tech I have less experience with.

The exact ratio is not what鈥檚 important here, it鈥檚 more the fact that you should lean towards using proven technologies.

I wrote more about this on my blog post on Choosing Boring Technology: https://panelbear.com/blog/boring-tech/

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Sergey Rodovinsky

We were looking at several alternative databases that would support following architectural requirements: - very quick prototyping for an unknown domain - ability to support large amounts of data - native ability to replicate and fail over - full stack approach for Node.js development After careful consideration MongoDB came on top, and 3 years later we are still very happy with that decision. Currently we keep almost 2TB of data in our cluster, and start thinking about sharding.

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Pros of MongoDB
Pros of MySQL
Pros of PostgreSQL
  • 822
    Document-oriented storage
  • 585
    No sql
  • 544
    Ease of use
  • 462
    Fast
  • 404
    High performance
  • 251
    Free
  • 212
    Open source
  • 177
    Flexible
  • 139
    Replication & high availability
  • 107
    Easy to maintain
  • 39
    Querying
  • 35
    Easy scalability
  • 34
    Auto-sharding
  • 33
    High availability
  • 29
    Map/reduce
  • 26
    Document database
  • 24
    Easy setup
  • 24
    Full index support
  • 15
    Reliable
  • 14
    Fast in-place updates
  • 13
    Agile programming, flexible, fast
  • 11
    No database migrations
  • 7
    Enterprise
  • 7
    Easy integration with Node.Js
  • 5
    Enterprise Support
  • 4
    Great NoSQL DB
  • 3
    Aggregation Framework
  • 3
    Drivers support is good
  • 3
    Support for many languages through different drivers
  • 2
    Schemaless
  • 2
    Managed service
  • 2
    Easy to Scale
  • 2
    Fast
  • 2
    Awesome
  • 1
    Consistent
  • 790
    Sql
  • 674
    Free
  • 557
    Easy
  • 527
    Widely used
  • 485
    Open source
  • 180
    High availability
  • 158
    Cross-platform support
  • 103
    Great community
  • 77
    Secure
  • 75
    Full-text indexing and searching
  • 25
    Fast, open, available
  • 14
    SSL support
  • 13
    Robust
  • 13
    Reliable
  • 8
    Enterprise Version
  • 7
    Easy to set up on all platforms
  • 1
    Easy, light, scalable
  • 1
    Relational database
  • 1
    NoSQL access to JSON data type
  • 1
    Sequel Pro (best SQL GUI)
  • 1
    Replica Support
  • 754
    Relational database
  • 506
    High availability
  • 437
    Enterprise class database
  • 379
    Sql
  • 299
    Sql + nosql
  • 171
    Great community
  • 145
    Easy to setup
  • 129
    Heroku
  • 128
    Secure by default
  • 111
    Postgis
  • 48
    Supports Key-Value
  • 46
    Great JSON support
  • 32
    Cross platform
  • 29
    Extensible
  • 25
    Replication
  • 24
    Triggers
  • 22
    Rollback
  • 21
    Multiversion concurrency control
  • 20
    Open source
  • 17
    Heroku Add-on
  • 14
    Stable, Simple and Good Performance
  • 13
    Powerful
  • 12
    Lets be serious, what other SQL DB would you go for?
  • 9
    Good documentation
  • 7
    Scalable
  • 7
    Intelligent optimizer
  • 6
    Transactional DDL
  • 6
    Modern
  • 6
    Reliable
  • 5
    One stop solution for all things sql no matter the os
  • 5
    Free
  • 4
    Relational database with MVCC
  • 3
    Full-Text Search
  • 3
    Developer friendly
  • 3
    Faster Development
  • 2
    Excellent source code
  • 2
    Great DB for Transactional system or Application
  • 1
    Free version
  • 1
    Text
  • 1
    Open-source
  • 1
    search
  • 1
    Full-text

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Cons of MongoDB
Cons of MySQL
Cons of PostgreSQL
  • 5
    Very slowly for connected models that require joins
  • 3
    Not acid compliant
  • 1
    Proprietary query language
  • 13
    Owned by a company with their own agenda
  • 1
    Can't roll back schema changes
  • 9
    Table/index bloatings

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What is MongoDB?

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.

What is 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.

What is 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.

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What companies use MongoDB?
What companies use MySQL?
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What tools integrate with MongoDB?
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Blog Posts

Dec 8 2020 at 5:50PM
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DigitalOcean

GitHubMySQLMongoDB+11
2
1515
MySQLKafkaApache Spark+6
2
1351
Nov 20 2019 at 3:38AM
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OneSignal

PostgreSQLRedisRuby+8
7
3634
Jul 9 2019 at 7:22PM
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Blue Medora

DockerPostgreSQLNew Relic+8
11
1624
What are some alternatives to MongoDB, MySQL, and PostgreSQL?
Amazon DynamoDB
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
Couchbase
Developed as an alternative to traditionally inflexible SQL databases, the Couchbase NoSQL database is built on an open source foundation and architected to help developers solve real-world problems and meet high scalability demands.
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.
Elasticsearch
Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
Neo4j
Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
See all alternatives
Interest over time
How much does MongoDB cost?
How much does MySQL cost?
How much does PostgreSQL cost?
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