Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →
Top 15 Python cloud-native Projects
-
-
-
InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
-
opta
The next generation of Infrastructure-as-Code. Work with high-level constructs instead of getting lost in low-level cloud configuration.
-
couler
Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
-
-
-
hera
Hera is an Argo Python SDK. Hera aims to make construction and submission of various Argo Project resources easy and accessible to everyone! Hera abstracts away low-level setup details while still maintaining a consistent vocabulary with Argo. ⭐️ Remember to star!
-
WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
-
-
-
-
opteryx
🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
-
sample-microservices
Sample cloud-first application forked from GoogleCloudPlatform/microservices-demo with added support for REST and Thrift APIs.
-
SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
We will install Ambassador Gateway which is an open-source Kubernetes-native API gateway for microservices. We will use it as a reverse proxy to manage external access to services within our Kubernetes cluster.
author seems to be describing the kind of patterns you might make with https://argoproj.github.io/argo-workflows/ . or see for example https://github.com/couler-proj/couler , which is an sdk for describing tasks that may be submitted to different workflow engines on the backend.
it's a little confusing to me that the author seems to object to "pipelines" and then equate them with messaging-queues. for me at least, "pipeline" vs "workflow-engine" vs "scheduler" are all basically synonyms in this context. those things may or may not be implemented with a message-queue for persistence, but the persistence layer itself is usually below the level of abstraction that $current_problem is really concerned with. like the author says, eventually you have to track state/timestamps/logs, but you get that from the beginning if you start with a workflow engine.
i agree with author that message-queues should not be a knee-jerk response to most problems because the LoE for edge-cases/observability/monitoring is huge. (maybe reach for a queue only if you may actually overwhelm whatever the "scheduler" can handle.) but don't build the scheduler from scratch either.. use argowf, kubeflow, or a more opinionated framework like airflow, mlflow, databricks, aws lamda or step-functions. all/any of these should have config or api that's robust enough to express rate-limit/retry stuff. almost any of these choices has better observability out-of-the-box than you can easily get from a queue. but most importantly.. they provide idioms for handling failure that data-science folks and junior devs can work with. the right way to structure code is just much more clear and things like structuring messages/events, subclassing workers, repeating/retrying tasks, is just harder to mess up.
The code segment in the main() function in the backend.py file demonstrates the integration of NATS for even-driven messaging, continuous weather monitoring, and alerting. We use the nats.py library to integrate NATS within Python code. First, we establish a connection to the NATs server running in Docker at nats://localhost:4222.
That's why I'm working on the GeoParquet spec [0]! It gives you both compression-by-default and super fast reads and writes! So it's usually as small as gzipped CSV, if not smaller, while being faster to read and write than GeoPackage.
Try using `GeoDataFrame.to_parquet` and `GeoPandas.read_parquet`
[0]: https://github.com/opengeospatial/geoparquet
Project mention: Toyota blames factory shutdown in Japan on ‘insufficient disk space’ | news.ycombinator.com | 2023-09-07
Project mention: Show HN: Netchecks – A Kubernetes tool to validate assumptions about the network | news.ycombinator.com | 2023-11-29
Project mention: Jump into Microservices Testing with Docker Compose and Skyramp | dev.to | 2023-11-30Skyramp provides a sample project, sample-microservices, which serves as an excellent starting point for demonstrating testing and mocking with a full-featured distributed application. The application is based on Google's Online Boutique repo, which is an e-commerce store consisting of 11 different microservices. The docker-compose-demo branch referenced above showcases how Skyramp can be seamlessly integrated with Docker Compose for testing microservices with no local setup required. You can also clone the repository and explore the structure of the microservices setup for your own purposes.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python cloud-native related posts
- Friends don't let friends export to CSV
- Revolutionizing Real-Time Alerts with AI, NATs and Streamlit
- Jina.ai: Self-host Multimodal models
- Jump into Microservices Testing with Docker Compose and Skyramp
- Securing Front-end Applications in Kubernetes with SSL/TLS
- Turbocharge Your Debugging with Skyramp's Hot Code Reload
- Custom domains for Airtable
-
A note from our sponsor - InfluxDB
www.influxdata.com | 26 Apr 2024
Index
What are some of the best open-source cloud-native projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | jina | 20,009 |
| 2 | emissary | 4,276 |
| 3 | opta | 907 |
| 4 | couler | 885 |
| 5 | nats.py | 810 |
| 6 | geoparquet | 719 |
| 7 | hera | 484 |
| 8 | Kubernetes-Volume-Autoscaler | 245 |
| 9 | netchecks | 149 |
| 10 | container-service-extension | 78 |
| 11 | zen3geo | 70 |
| 12 | django-hurricane | 69 |
| 13 | jina-financial-qa-search | 68 |
| 14 | opteryx | 43 |
| 15 | sample-microservices | 12 |
Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com