geospatial-data-lake
Zappa
geospatial-data-lake | Zappa | |
---|---|---|
5 | 36 | |
32 | 3,060 | |
- | 1.8% | |
0.0 | 7.5 | |
about 1 year ago | 11 days ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
geospatial-data-lake
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A curated list of questionable installation instructions
One option is to trust on first use, checksum the installation script and at least casually verify the diff each time the checksum changes[1].
Pros:
- Protects against simple hijacking.
- Reproducible as long as the installer doesn't also call out to a moving target, such as example.com/releases/latest.
Cons:
- Build breaks as soon as the installer is bumped. If it's bumped often (or just before an important release) this can cause pain.
- TOFU may not be acceptable, but of course you could review the code thoroughly before even the first use.
[1] https://github.com/linz/geostore/blob/b3cd162605109da8a3a688...
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Ask HN: Good Python projects to read for modern Python?
I'd recommend a project from work, Geostore[1]. Highlights:
- 100% test coverage (with some typical exceptions like `if __name__ == "__main__":` blocks)
- Randomises test sequence and inputs reproducibly
- Passes Pylint with max McCabe complexity of 6
- Passes `mypy --strict`
- Formatted using Black and isort
[1] https://github.com/linz/geostore
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Python Best Practices for a New Project in 2021
The current work project[1] has all of these: Pyenv, Poetry, Pytest, pytest-cov with 100% branch coverage, pre-commit, Pylint rather than Flake8, Black, mypy (with a stricter configuration than recommended here), and finally isort. These are all super helpful.
There's also a simpler template repo[2] with almost all of these.
[1] https://github.com/linz/geostore/
[2] https://github.com/linz/template-python-hello-world
- Codecov bash uploader was compromised
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AWS CloudFormation Best Practices
As someone who's used CDK for a few months and never handcoded CF, that sounds completely correct. If you're comfortable with Python, here's a simple but non-trivial architecture you can check out: https://github.com/linz/geospatial-data-lake/blob/master/app....
Zappa
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Jets: The Ruby Serverless Framework
If people aren't familiar, there's a similar project for Python that's fantastic: https://github.com/zappa/Zappa
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Building serverless websites (lambdas written with python) - do I use FastAPI or plain old python?
Chalice was a consequence, a reaction from AWS to the release of (Zappa Framework)[https://github.com/zappa/Zappa] that provide a very good alternative to migrate very quickly a Django/Flask or any WSGI compliant solution in Python.
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Best way to host Django DRF on AWS? (so many competing options)
Use Zappa https://github.com/zappa/Zappa and host as a Lambda, simple setup and deployment, Lambda only costs when processing requests, no servers to mess around with
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How to deploy a project from git lab backend where I used django on backend and database
One of my favorite options that is probably the most cost-effective is to deploy using a 'severless' model on AWS Lambda using zappa which supports deploying Python webapps to AWS in this way. Zappa also makes it super easy to deploy in just a couple commands! The README includes instructions for everything you might need, including handling sensitive information like your database passwords, running django management commands, setting up DNS, etc.
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I’m a Brazilian salesforce developer and want to work with django stack. Any tips?
Deployment works nicely with Docker. I often use AWS AppRunner because it's really easy and just scales. Some people use AWS Lambda with Zappa but I don't recommend it unless you really want to spend less than $15 a month. You will probably need Django Storages to save uploads to an S3 bucket. At some stage you might want to put a CloudFront distribution in front of everything but the configuration of the caching behaviour might be a bit confusing when you do it the first time.
- lambda API deployment
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Why or why not use AWS Lambda instead of a web framework for your REST APIs? (Business projects)
It doesn't have to be an either-or! I have several apps in production that were developed on Django or Flask, and deployed to Lambda using Zappa.
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Backend Server with Django Rest API
If you need a relational DB, you can use AWS Aurora or RDS and use cloud functions ('lambda' in AWS) that you can invoke with HTTP to process the document first. Zappa will do a lot of the configuration for you if you go that route.
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Easiest/Best way to deploy django to AWS?
Lambda + API gateway, this library bundles a Django application into a lambda https://github.com/zappa/Zappa . 1 million free invokes from aws, scale to zero, plugs into your RDS
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We clone a running VM in 2 seconds
I use Zappa, it just schedules a frequent execution of the lambda: https://github.com/zappa/Zappa#keeping-the-server-warm
What are some alternatives?
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
template-python-hello-world - :triangular_ruler: Python Hello World | Minimal template for Python development
mangum - AWS Lambda support for ASGI applications
asgi-correlation-id - Request ID propagation for ASGI apps
chalice - Python Serverless Microframework for AWS
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
Poetry - Python packaging and dependency management made easy
dev-tasks - Automated development tasks for my own projects
aws-sqs-jobs-processer - Serverless jobs processor on AWS
pip - The Python package installer
sample-django-docker - A sample of using Django with Docker and docker-compose