Alternatives to SQLAlchemy logo

Alternatives to SQLAlchemy

Django, Pandas, Entity Framework, peewee, and MySQL are the most popular alternatives and competitors to SQLAlchemy.
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What is SQLAlchemy and what are its top alternatives?

SQLAlchemy is the Python SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL.
SQLAlchemy is a tool in the Object Relational Mapper (ORM) category of a tech stack.
SQLAlchemy is an open source tool with 3.4K GitHub stars and 884 GitHub forks. Here鈥檚 a link to SQLAlchemy's open source repository on GitHub

Top Alternatives to SQLAlchemy

  • Django

    Django

    Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...

  • Pandas

    Pandas

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...

  • Entity Framework

    Entity Framework

    It is an object-relational mapper that enables .NET developers to work with relational data using domain-specific objects. It eliminates the need for most of the data-access code that developers usually need to write. ...

  • peewee

    peewee

    A small, expressive orm, written in python (2.6+, 3.2+), with built-in support for sqlite, mysql and postgresql and special extensions like hstore. ...

  • MySQL

    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. ...

  • Hibernate

    Hibernate

    Hibernate is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper. ...

  • Sequelize

    Sequelize

    Sequelize is a promise-based ORM for Node.js and io.js. It supports the dialects PostgreSQL, MySQL, MariaDB, SQLite and MSSQL and features solid transaction support, relations, read replication and more. ...

  • Doctrine 2

    Doctrine 2

    Doctrine 2 sits on top of a powerful database abstraction layer (DBAL). One of its key features is the option to write database queries in a proprietary object oriented SQL dialect called Doctrine Query Language (DQL), inspired by Hibernates HQL. ...

SQLAlchemy alternatives & related posts

Django logo

Django

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The Web framework for perfectionists with deadlines
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PROS OF DJANGO
  • 604
    Rapid development
  • 448
    Open source
  • 388
    Great community
  • 338
    Easy to learn
  • 250
    Mvc
  • 203
    Elegant
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    Beautiful code
  • 181
    Free
  • 180
    Great packages
  • 168
    Great libraries
  • 56
    Restful
  • 53
    Comes with auth and crud admin panel
  • 52
    Powerful
  • 49
    Great documentation
  • 47
    Great for web
  • 37
    Python
  • 32
    Great orm
  • 28
    Great for api
  • 22
    All included
  • 18
    Web Apps
  • 16
    Fast
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    Used by top startups
  • 12
    Clean
  • 11
    Easy setup
  • 10
    Sexy
  • 8
    Convention over configuration
  • 5
    ORM
  • 5
    Allows for very rapid development with great libraries
  • 5
    The Django community
  • 3
    Mvt
  • 3
    Its elegant and practical
  • 3
    Great MVC and templating engine
  • 2
    Easy to use
  • 2
    Easy
  • 2
    Easy to develop end to end AI Models
  • 2
    Cross-Platform
  • 2
    Fast prototyping
  • 2
    Full stack
  • 2
    Batteries included
  • 2
    Easy Structure , useful inbuilt library
  • 1
    Great peformance
  • 1
    Many libraries
  • 1
    Zero code burden to change databases
  • 1
    Have not found anything that it can't do
  • 1
    Map
  • 1
    Scaffold
  • 1
    Modular
  • 1
    Very quick to get something up and running
  • 1
    Just the right level of abstraction
  • 1
    Python community
  • 1
    Full-Text Search
  • 1
    King of backend world
CONS OF DJANGO
  • 24
    Underpowered templating
  • 19
    Underpowered ORM
  • 18
    Autoreload restarts whole server
  • 15
    URL dispatcher ignores HTTP method
  • 10
    Internal subcomponents coupling
  • 7
    Not nodejs
  • 6
    Admin
  • 4
    Configuration hell
  • 3
    Not as clean and nice documentation like Laravel
  • 2
    Python
  • 2
    Overwhelming folder structure
  • 2
    Not typed
  • 2
    Bloated admin panel included
  • 1
    InEffective Multithreading

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Dmitry Mukhin

Simple controls over complex technologies, as we put it, wouldn't be possible without neat UIs for our user areas including start page, dashboard, settings, and docs.

Initially, there was Django. Back in 2011, considering our Python-centric approach, that was the best choice. Later, we realized we needed to iterate on our website more quickly. And this led us to detaching Django from our front end. That was when we decided to build an SPA.

For building user interfaces, we're currently using React as it provided the fastest rendering back when we were building our toolkit. It鈥檚 worth mentioning Uploadcare is not a front-end-focused SPA: we aren鈥檛 running at high levels of complexity. If it were, we鈥檇 go with Ember.js.

However, there's a chance we will shift to the faster Preact, with its motto of using as little code as possible, and because it makes more use of browser APIs. One of our future tasks for our front end is to configure our Webpack bundler to split up the code for different site sections. For styles, we use PostCSS along with its plugins such as cssnano which minifies all the code.

All that allows us to provide a great user experience and quickly implement changes where they are needed with as little code as possible.

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Hey, so I developed a basic application with Python. But to use it, you need a python interpreter. I want to add a GUI to make it more appealing. What should I choose to develop a GUI? I have very basic skills in front end development (CSS, JavaScript). I am fluent in python. I'm looking for a tool that is easy to use and doesn't require too much code knowledge. I have recently tried out Flask, but it is kinda complicated. Should I stick with it, move to Django, or is there another nice framework to use?

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Pandas logo

Pandas

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High-performance, easy-to-use data structures and data analysis tools for the Python programming language
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PROS OF PANDAS
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    Easy data frame management
  • 1
    Extensive file format compatibility
CONS OF PANDAS
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    Server side

    We decided to use Python for our backend because it is one of the industry standard languages for data analysis and machine learning. It also has a lot of support due to its large user base.

    • Web Server: We chose Flask because we want to keep our machine learning / data analysis and the web server in the same language. Flask is easy to use and we all have experience with it. Postman will be used for creating and testing APIs due to its convenience.

    • Machine Learning: We decided to go with PyTorch for machine learning since it is one of the most popular libraries. It is also known to have an easier learning curve than other popular libraries such as Tensorflow. This is important because our team lacks ML experience and learning the tool as fast as possible would increase productivity.

    • Data Analysis: Some common Python libraries will be used to analyze our data. These include NumPy, Pandas , and matplotlib. These tools combined will help us learn the properties and characteristics of our data. Jupyter notebook will be used to help organize the data analysis process, and improve the code readability.

    Client side

    • UI: We decided to use React for the UI because it helps organize the data and variables of the application into components, making it very convenient to maintain our dashboard. Since React is one of the most popular front end frameworks right now, there will be a lot of support for it as well as a lot of potential new hires that are familiar with the framework. CSS 3 and HTML5 will be used for the basic styling and structure of the web app, as they are the most widely used front end languages.

    • State Management: We decided to use Redux to manage the state of the application since it works naturally to React. Our team also already has experience working with Redux which gave it a slight edge over the other state management libraries.

    • Data Visualization: We decided to use the React-based library Victory to visualize the data. They have very user friendly documentation on their official website which we find easy to learn from.

    Cache

    • Caching: We decided between Redis and memcached because they are two of the most popular open-source cache engines. We ultimately decided to use Redis to improve our web app performance mainly due to the extra functionalities it provides such as fine-tuning cache contents and durability.

    Database

    • Database: We decided to use a NoSQL database over a relational database because of its flexibility from not having a predefined schema. The user behavior analytics has to be flexible since the data we plan to store may change frequently. We decided on MongoDB because it is lightweight and we can easily host the database with MongoDB Atlas . Everyone on our team also has experience working with MongoDB.

    Infrastructure

    • Deployment: We decided to use Heroku over AWS, Azure, Google Cloud because it is free. Although there are advantages to the other cloud services, Heroku makes the most sense to our team because our primary goal is to build an MVP.

    Other Tools

    • Communication Slack will be used as the primary source of communication. It provides all the features needed for basic discussions. In terms of more interactive meetings, Zoom will be used for its video calls and screen sharing capabilities.

    • Source Control The project will be stored on GitHub and all code changes will be done though pull requests. This will help us keep the codebase clean and make it easy to revert changes when we need to.

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    Guillaume Simler

    Jupyter Anaconda Pandas IPython

    A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

    The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

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    Entity Framework logo

    Entity Framework

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    An object-relational mapper that enables .NET developers to work with relational data
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        peewee logo

        peewee

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        A small, expressive ORM -- supports postgresql, mysql and sqlite
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        PROS OF PEEWEE
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          Easy to start
        • 4
          Free
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        CONS OF PEEWEE
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          MySQL logo

          MySQL

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            Easy
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            Widely used
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            Open source
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            Cross-platform support
          • 103
            Great community
          • 77
            Secure
          • 75
            Full-text indexing and searching
          • 25
            Fast, open, available
          • 14
            SSL support
          • 13
            Robust
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            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
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            Sequel Pro (best SQL GUI)
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            Replica Support
          CONS OF MYSQL
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            Owned by a company with their own agenda
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          Tim Abbott

          We've been using PostgreSQL since the very early days of Zulip, but we actually didn't use it from the beginning. Zulip started out as a MySQL project back in 2012, because we'd heard it was a good choice for a startup with a wide community. However, we found that even though we were using the Django ORM for most of our database access, we spent a lot of time fighting with MySQL. Issues ranged from bad collation defaults, to bad query plans which required a lot of manual query tweaks.

          We ended up getting so frustrated that we tried out PostgresQL, and the results were fantastic. We didn't have to do any real customization (just some tuning settings for how big a server we had), and all of our most important queries were faster out of the box. As a result, we were able to delete a bunch of custom queries escaping the ORM that we'd written to make the MySQL query planner happy (because postgres just did the right thing automatically).

          And then after that, we've just gotten a ton of value out of postgres. We use its excellent built-in full-text search, which has helped us avoid needing to bring in a tool like Elasticsearch, and we've really enjoyed features like its partial indexes, which saved us a lot of work adding unnecessary extra tables to get good performance for things like our "unread messages" and "starred messages" indexes.

          I can't recommend it highly enough.

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          Conor Myhrvold
          Tech Brand Mgr, Office of CTO at Uber | 20 upvotes 路 918.4K views

          Our most popular (& controversial!) article to date on the Uber Engineering blog in 3+ yrs. Why we moved from PostgreSQL to MySQL. In essence, it was due to a variety of limitations of Postgres at the time. Fun fact -- earlier in Uber's history we'd actually moved from MySQL to Postgres before switching back for good, & though we published the article in Summer 2016 we haven't looked back since:

          The early architecture of Uber consisted of a monolithic backend application written in Python that used Postgres for data persistence. Since that time, the architecture of Uber has changed significantly, to a model of microservices and new data platforms. Specifically, in many of the cases where we previously used Postgres, we now use Schemaless, a novel database sharding layer built on top of MySQL (https://eng.uber.com/schemaless-part-one/). In this article, we鈥檒l explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:

          https://eng.uber.com/mysql-migration/

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          What is the best way to increase your income as a freelancer in 2019? What frameworks should be the best to learn? React Node.js Docker Kubernetes Sequelize Mongoose MongoDB ExpressJS hapi Based on trends I've picked up a JS full stack. If you need to work under startups you may replace React with Vue.js . If you want to work in outsourcing Angular 2+ may be better.

          What is your opinion?

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          Max Musing
          Founder & CEO at BaseDash | 0 upvote 路 4.1K views

          Hey Mohd. Those are pretty good areas to start with. For front end, focus on getting really good at JavaScript and React. Specifically, learn how to manage state in complex apps. You might want to learn Redux to help with this. Make sure you're using semantic elements in your JSX and get good at using flexbox in your CSS 3. I wouldn't necessarily use Bootstrap, it's good practice to build components yourself. A good exercise is to try to replicate a website like StackOverflow or ProductHunt from scratch.

          On backend, ExpressJS is a good place to start. Learn how to build semantic REST APIs that pull data from a database. MongoDB is fine, but I would suggest using Mongoose along with it to avoid nesting objects too much. You could alternatively use a SQL database like PostgreSQL, with an ORM like Sequelize to manage the data.

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          Doctrine 2 logo

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