SQLAlchemy

I'm considering moving from Flask to Quart, does anyone have some experience with this migration?

I expect possible problems with connexion which we use as OpenAPI specification.

Would be good if someone can point downsides of moving to the Quart framework so I can double-check if my plan is worth doing.

Other libs and tools used in the project: SQLAlchemy, alembic, PostgreSQL, Datadog

cons for now:

• Refactoring uncertainty (not sure how big of a task is it)
• Connexion might not work with Quart (moving to another library)
• ...

Merging Django ORM with SQLAlchemy for Easier Data Analysis (Video Inside)

Development of products with Django framework is usually easy and straightforward; great documentation, many tools out of the box, plenty of open source libraries and big community. Django ORM takes full control about SQL layer protecting you from mistakes, and underlying details of queries so you can spend more time on designing and building your application structure in Python code. However, sometimes such behavior may hurt - for example, when you’re building a project related to data analysis. Building advanced queries with Django is not very easy; it’s hard to read (in Python) and hard to understand what’s going on in SQL-level without logging or printing generated SQL queries somewhere. Moreover, such queries could not be efficient enough, so this will hit you back when you load more data into DB to play with. In one moment, you can find yourself doing too much raw SQL through Django cursor, and this is the moment when you should do a break and take a look on another interesting tool, which is placed right between ORM layer and the layer of raw SQL queries.

Unlike our frontend, we chose Flask, a microframework, for our backend. We use it with Python 3 and Gunicorn.

One of the reasons was that I have significant experience with this framework. However, it also was a rather straightforward choice given that our backend almost only serves REST APIs, and that most of the work is talking to the database with SQLAlchemy .

We could have gone with something like Hug but it is kind of early. We might revisit that decision for new services later on.

Recently we were looking at a few robust and cost-effective ways of replicating the data that resides in our production MongoDB to a PostgreSQL database for data warehousing and business intelligence.

We set ourselves the following criteria for the optimal tool that would do this job: - The data replication must be near real-time, yet it should NOT impact the production database - The data replication must be horizontally scalable (based on the load), asynchronous & crash-resilient

Based on the above criteria, we selected the following tools to perform the end to end data replication:

We chose MongoDB Stitch for picking up the changes in the source database. It is the serverless platform from MongoDB. One of the services offered by MongoDB Stitch is Stitch Triggers. Using stitch triggers, you can execute a serverless function (in Node.js) in real time in response to changes in the database. When there are a lot of database changes, Stitch automatically "feeds forward" these changes through an asynchronous queue.

We chose Amazon SQS as the pipe / message backbone for communicating the changes from MongoDB to our own replication service. Interestingly enough, MongoDB stitch offers integration with AWS services.

In the Node.js function, we wrote minimal functionality to communicate the database changes (insert / update / delete / replace) to Amazon SQS.

Next we wrote a minimal micro-service in Python to listen to the message events on SQS, pickup the data payload & mirror the DB changes on to the target Data warehouse. We implemented source data to target data translation by modelling target table structures through SQLAlchemy . We deployed this micro-service as AWS Lambda with Zappa. With Zappa, deploying your services as event-driven & horizontally scalable Lambda service is dumb-easy.

In the end, we got to implement a highly scalable near realtime Change Data Replication service that "works" and deployed to production in a matter of few days!

In the early days, the Uber backend system was a monolithic software architecture with several app servers and a single database. The system was mainly written in Python and used SQLALchemy as the ORM-layer to the database.

In early 2014, Uber faced the fact that given their trip growth —roughly 20% per month—the solution for storing trips was going to run out of steam, both in terms of storage volume and IOPS (Input/Output Operations Per Second) by the end of the year.

To fix this problem, project Mezzanine was launched. The whole system, which Uber simply calls Schemaless in homage to its design, is written in Python. The initial version took about 5 months from idea to production deployment.