exitflynn

Auto-Scaling Cloud Native applications using Kubernetes

This'll involve us writing some sample API's to work with, dockerizing them, pushing the images to Dockerhub, pulilng and deploying them on Kubernetes, configuring appropriate networking strategies for interconnectivity, enabling metrics collection and monitoring via Prometheus, plug them to Grafana for visualisation, stress testing and using k8s' HPA (Horizontal Pod Autoscaler) to autoscale the cluster under (simulated) high traffic.

Where we can take this further:

• Autoscaling Based on Prometheus Custom Metrics (also via using Prometheus Operator)
• [Comparative Analysis / Optimized Autoscaling](https://www.diva-portal.org/smash/get/diva

exitflynn

GSoC '23 @ SunPy : Scraper Rewrite

minor edit: The PR was unmerged when the GSoC period ended, however now that it's merged to main, I've updated parts to reflect that.

Personal Information

• Student: Akshit Tyagi
• email: tyagiakshit833@gmail.com
• GitHub: @exitflynn

Project Information

exitflynn

import requests
import json

r = requests.get("http://p2sa.esac.esa.int/p2sa-sl-tap/tap/sync?REQUEST=doQuery&LANG=ADQL&FORMAT=JSON&PHASE=RUN&QUERY=SELECT * FROM p2sa.v_file WHERE (file_date >= '2014-10-01 00:00:00') AND (file_date <= '2014-10-31 00:00:00') AND (file_type in ('DSLP_DERIVED')) ORDER BY file_date ASC")

r.json()

"""
=================================(OUTPUT)==================================