This posts tests some popular Python tools (sqlalchemy_data_model_visualizer , sqlalchemy_schemadisplay, and eralchemy2) to generate ERD (Entity-Relation Diagram) from sqlalchemy models.
The test code can be found in this Github repo.
The ForeEach activity in Azure Data Factory has some important limitations. One of them is when working with the batch mode, it would be nice to embed only pipeline activities inside.
| Name | Scope | web framework middleware | VSCode Extension |
|---|---|---|---|
| scalene | cpu, gpu, memory, duration | partially | yes |
| cProfile (Python native, function level only and cli only) | duration | no | no |
| VizTracer | duration | unknown | yes |
| profyle (based on Viztracer) | duration | yes | no |
| pyinstrument | duration | yes | no |
| py-spy | duration | no | no |
| yappi (cli only) | duration | unknown | no |
| austin | duration | unknown | yes |
Interesting reading:
Standard method to run asyncio task is as simple as asyncio.run(main()). But in Databricks, it is not that simple. With the same command, you will get the following error:
import asyncio
async def main():
await asyncio.sleep(1)
asyncio.run(main())
RuntimeError: asyncio.run() cannot be called from a running event loop
Indeed, in Databricks, we've already in a running loop:
Load a local json file into a PyArrow table, then write it to a parquet file in Azure Blob Storage without using pandas.
The most secure way to use secrets in a Dockerfile is to use the --secret flag in the docker build command. This way, the secret is not stored in the image, and it is not visible in the Dockerfile.
A common use case in Python world is to install packages from a private PyPI repository in a Dockerfile. Suppose during the CICD pipeline, there's an environment variable called PIP_INDEX_URL where holds this private PyPI credentials.
Check the official Build secrets doc.
Flask is a little bit old-fashioned today (I know it's still widely used), as it's not async native, among others. When I prepared my fastapi-demo this weekend, I discovered a new framework called Quart, which is maintained by Pallet Project, the same community maintaining Flask. They said "Quart is an asyncio re-implementation of the popular Flask micro framework API. This means that if you understand Flask you understand Quart.". So I decided to give it a try.
This posts describes the differences on selectinload, joinedload, subqueryload, these 3 popular eager loading techniques in Sqlalchemy (so as to SQLModel)
This post is based on the official Azure documentations (Asynchronous messaging options, Compare Azure messaging services, Enterprise integration using message broker and events, Azure Well-Architected Framework) and describes a resume of differences and uses cases for Azure messaging service, including Service Bus, Event Grid, Event Hubs. The official documentations are very good and comprehensive, this post is for my personal reference as a quick reminder.
MS Graph API's endpoint for retrieving users, GET /users can return all users of the tenant. The default limit is 100 users per page, and the maximum limit is 999 users per page. If there are more than 999 users, the response will contain a @odata.nextLink field, which is a URL to the next page of users. For a big company having a large number of users (50,000, 100,000, or even more), and it can be time-consuming to retrieve all users.
While MS Graph API provides generous throttling limits, we should find a way to parallelize the queries. This post explores sharding as a strategy to retrieve all users in a matter of seconds. The idea is to get all users by dividing users based on the first character of the userPrincipalName field.For instance, shard 1 would encompass users whose userPrincipalName starts with a, shard 2 would handle users starting with b, and so forth.