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A distributed weather data visualization platform

Home Page: https://github.com/airavata-courses/garuda#garuda

License: Apache License 2.0

Java 0.67% Python 1.59% JavaScript 47.76% Dockerfile 0.06% Shell 0.19% HTML 42.69% CSS 4.33% Less 2.71%
nexrad node java javascript docker docker-compose python flask aws maven maven-plugins rabbitmq queue mongodb react expressjs

garuda's Issues

Project 1 Feedback (Scapsulators)

Just ran your project, and I have a couple of comments and suggestions -

Build time took around 12-14 minutes, was easy to follow from the readme files.

  1. Implementation of CircleCI for continous builds on commits
  2. Using docker-hub instead of individually building each microservice in your main repo
  3. Time slot selection doesn't seem so intuitive at all
  4. Could've explored more properties instead of just reflectivity
  5. Also not sure how you're handling the error when there's no data for the querry

Rest great work! Loved your project as such

Reviewed by Team Scapsulators (https://github.com/airavata-courses/scapsulators)

UI not responsive

When the same request is made multiple times, nothing happens on the UI.

More informative map plots.

I would like to point out that the weather data on the map can be made more informative by providing some visual aids like a heat map or reflectivity index.

Project 1 Feedback [Team Zilean]

Likes:

  • System architecture looks very similar to the way we thought.

Observations:

  • Each microservice needs to make an API call to the DB middleware microservice to make DB operations. Here, it increases one network hop which might hamper performance.

Piyush Nalawade
Team Zilean ๐Ÿฆ„

Feedback related to Project 1 (Team Neo)

Congratulations on coming up with a smooth UI and data flow.

UI seems very minimal and intuitive.
Below is the resultant data:-

Screen Shot 2022-02-09 at 4 49 31 PM

There are some points I will which we want to bring to your knowledge:-

  • You tried well to persist the data to db for any search before returning it to the user. But It seems some of the time durations have huge data that it's not getting retrieved properly because of the MongoDB limitations. Maybe you can compress the lat-long data to already compressed data and then store to DB. So that merging documents won't reach the limit.
  • Docker Images can be reduced in size using alpine os images of Node, python and Java(Not sure about this)
  • Build time can be removed by pushing images on Docker Hub

Review Done by Team Neo(https://github.com/airavata-courses/neo)

Project 1 Peer Review - Team Epsilon

Hi Team,

I tried to set up your Project on my System which is a MAC running (Monterey v12.1) in which port 5000 is being already used by the OS and I saw that your api gateway runs on PORT 5000, so I tried to update it to an open port in my system (Port 6000) but it still didn't work and then I found out that you have written the port in files other than the configuration files and even after changing them it didn't work.

I would suggest not to write PORT details directly in the code like it was done in garuda.apigateway/server.py and you can also try to look what can be done so that such issues with PORTS can be handled with less changes or changes at a single place like changing the port in docker-compose.yml maybe should change the PORT dependency everywhere.
You can have a look at "Environment" tag for docker-compose.

Screen Shot 2022-02-09 at 10 18 32 PM

Screen Shot 2022-02-09 at 10 27 53 PM

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