geospatial-data-lake
helm
geospatial-data-lake | helm | |
---|---|---|
5 | 206 | |
32 | 26,081 | |
- | 0.7% | |
0.0 | 8.9 | |
about 1 year ago | 3 days ago | |
Python | Go | |
MIT License | Apache License 2.0 |
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....
helm
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Kubernetes CI/CD Pipelines
Applying Kubernetes manifests individually is problematic because files can get overlooked. Packaging your applications as Helm charts lets you version your manifests and easily repeat deployments into different environments. Helm tracks the state of each deployment as a "release" in your cluster.
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deploying a minio service to kubernetes
helm
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How to take down production with a single Helm command
Explanation here: https://github.com/helm/helm/issues/12681#issuecomment-19593...
Looks like it's a bug in Helm, but actually isn't Helm's fault, the issue was introduced by Fedora Linux.
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Building a VoIP Network with Routr on DigitalOcean Kubernetes: Part I
Helm (Get from here https://helm.sh/)
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The 2024 Web Hosting Report
It’s also well understood that having a k8s cluster is not enough to make developers able to host their services - you need a devops team to work with them, using tools like delivery pipelines, Helm, kustomize, infra as code, service mesh, ingress, secrets management, key management - the list goes on! Developer Portals like Backstage, Port and Cortex have started to emerge to help manage some of this complexity.
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Deploying a Web Service on a Cloud VPS Using Kubernetes MicroK8s: A Comprehensive Guide
Kubernetes orchestrates deployments and manages resources through yaml configuration files. While Kubernetes supports a wide array of resources and configurations, our aim in this tutorial is to maintain simplicity. For the sake of clarity and ease of understanding, we will use yaml configurations with hardcoded values. This method simplifies the learning process but isn’t ideal for production environments due to the need for manual updates with each new deployment. Although there are methods to streamline and automate this process, such as using Helm charts or bash scripts, we’ll not delve into those techniques to keep the tutorial manageable and avoid fatigue — you might be quite tired by that point!
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Deploy Kubernetes in Minutes: Effortless Infrastructure Creation and Application Deployment with Cluster.dev and Helm Charts
Helm is a package manager that automates Kubernetes applications' creation, packaging, configuration, and deployment by combining your configuration files into a single reusable package. This eliminates the requirement to create the mentioned Kubernetes resources by ourselves since they have been implemented within the Helm chart. All we need to do is configure it as needed to match our requirements. From the public Helm chart repository, we can get the charts for common software packages like Consul, Jenkins SonarQube, etc. We can also create our own Helm charts for our custom applications so that we don’t need to repeat ourselves and simplify deployments.
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Kubernets Helm Chart
We can search for charts https://helm.sh/ . Charts can be pulled(downloaded) and optionally unpacked(untar).
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Introduction to Helm: Comparison to its less-scary cousin APT
Generally I felt as if I was diving in the deepest of waters without the correct equipement and that was horrifying. Unfortunately to me, I had to dive even deeper before getting equiped with tools like ArgoCD, and k8slens. I had to start working with... HELM.
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🎀 Five tools to make your K8s experience more enjoyable 🎀
Within the architecture of Cyclops, a central component is the Helm engine. Helm is very popular within the Kubernetes community; chances are you have already run into it. The popularity of Helm plays to Cyclops's strength because of its straightforward integration.
What are some alternatives?
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
crossplane - The Cloud Native Control Plane
template-python-hello-world - :triangular_ruler: Python Hello World | Minimal template for Python development
kubespray - Deploy a Production Ready Kubernetes Cluster
asgi-correlation-id - Request ID propagation for ASGI apps
Packer - Packer is a tool for creating identical machine images for multiple platforms from a single source configuration.
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
krew - 📦 Find and install kubectl plugins
dev-tasks - Automated development tasks for my own projects
skaffold - Easy and Repeatable Kubernetes Development
pip - The Python package installer
dapr-demo - Distributed application runtime demo with ASP.NET Core, Apache Kafka and Redis on Kubernetes cluster.