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Ability to scan private git repositories (e.g. bitbucket onpremise) #3
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HelmQA specifically has split SaaS and CI/CD modes. We have successfully implemented CLI-based CI/CD here for example: appuio/charts#22 - Perhaps this comes closer to your desired solution? Thank you for your other feedback. It will be considered for the evolving implementation and packaging. As there are not many users yet, you may want to share more details about your intended use case, and therefore get a customised solution which fits your situation. |
This is a integration of helmqa instance running at helmqa-zhaw-prod-demos.appuioapp.ch in a CI pipeline, but it is still using helmqa as a webserver and sends a URL to it to ask helmqa webserver to validate this github repo. What i am dreaming of is NOT: What i need is to feed a tgz file to it: At least URLs that point directly to a tgz file (archive) would help: |
OK, understood. The code is in principle prepared for that, but during the refactoring to OOP the main.py script lost some parameters. Essentially, you could say in main.py: 'bucket = Bucket(path = /my/local/path[.tgz])' and you should get the desired result working on directories and tgzs. We will provide a convenience script for this purpose. |
Do you know when the script will be available? |
Any news on this? |
Some progress - sorry for the delay. There is now preliminary support for running the following: |
HelmQA webserver is not able to access a private bitbucket repo (at the moment), and i do not want HelmQA webserver to know my passwords or keys.
So there is the need to push a archive (zip, tgz) to HelmQA webserver or to get rid of the webserver and to execute helmQA as a cli within the CI/CD server (and to point it to a local (work-)dir or archive file).
Cross-chart statistic is for most developers not very important (the second goal, if at all) - i guess.
The primary goal of your tool should be to report concrete acionable tasks - so a cli without the "cross chart / cross repo"-stuff is welcomed. In a alpine-based docker image as a dockerized command line tool. Will end up in a very small docker image size - i guess less then 30 MB (compared to your 659MB docker image).
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