What is SQLAlchemy and what are its top alternatives?
Top Alternatives to SQLAlchemy
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. ...
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. ...
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. ...
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. ...
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 is a suite of open source projects around domain models. The flagship project is Hibernate ORM, the Object Relational Mapper. ...
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 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
- Rapid development604
- Open source448
- Great community388
- Easy to learn338
- Beautiful code202
- Great packages180
- Great libraries168
- Comes with auth and crud admin panel53
- Great documentation49
- Great for web47
- Great orm32
- Great for api28
- All included22
- Web Apps18
- Used by top startups14
- Easy setup11
- Convention over configuration8
- Allows for very rapid development with great libraries5
- The Django community5
- Its elegant and practical3
- Great MVC and templating engine3
- Easy to use2
- Easy to develop end to end AI Models2
- Fast prototyping2
- Full stack2
- Batteries included2
- Easy Structure , useful inbuilt library2
- Great peformance1
- Many libraries1
- Zero code burden to change databases1
- Have not found anything that it can't do1
- Very quick to get something up and running1
- Just the right level of abstraction1
- Python community1
- Full-Text Search1
- King of backend world1
- Underpowered templating24
- Underpowered ORM19
- Autoreload restarts whole server18
- URL dispatcher ignores HTTP method15
- Internal subcomponents coupling10
- Not nodejs7
- Configuration hell4
- Not as clean and nice documentation like Laravel3
- Overwhelming folder structure2
- Not typed2
- Bloated admin panel included2
- InEffective Multithreading1
related Django posts
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’s worth mentioning Uploadcare is not a front-end-focused SPA: we aren’t running at high levels of complexity. If it were, we’d 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.
- Easy data frame management18
- Extensive file format compatibility1
related Pandas posts
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.
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.
- 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: 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.
- 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.
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.
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
related Entity Framework posts
- Easy to start5
- High Performance4
- Open Source4
related peewee posts
- Widely used527
- Open source485
- High availability180
- Cross-platform support158
- Great community103
- Full-text indexing and searching75
- Fast, open, available25
- SSL support14
- Enterprise Version8
- Easy to set up on all platforms7
- Easy, light, scalable1
- Relational database1
- NoSQL access to JSON data type1
- Sequel Pro (best SQL GUI)1
- Replica Support1
- Owned by a company with their own agenda13
- Can't roll back schema changes1
related MySQL posts
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.
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’ll explore some of the drawbacks we found with Postgres and explain the decision to build Schemaless and other backend services on top of MySQL:
- Easy ORM13
- Easy transaction definition7
- Is integrated with spring jpa1
- Can't control proxy associations when entity graph used3
related Hibernate posts
- Good ORM for node.js29
- Easy setup22
- Support MySQL & MariaDB, PostgreSQL, MSSQL, Sqlite12
- Open source9
- Promise Based6
- Atrocious documentation, buggy, issues closed by bots2
- Recommend for mongoose users1
- Docs are awful20
- Relations can be confusing3
related Sequelize posts
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?
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.
- Great abstraction, easy to use, good docs13
- Easy setup5