Tag Archives: python
Private yum repository with Amazon S3

While a public yum repository is easy to set up with S3, going private is more difficult. The privacy must be enforced by some plugin that can retrieve files from the S3 bucket using the API with stored credentials (maybe). Storing credentials can be avoided on EC2 machines that are assigned a proper role, but this is not possible in any other scenario.

Nevertheless, the first steps are common for both public and private setups:

1. Create the proper directory structure

This is achievable with the “createrepo” binary that can be installed on both RedHat and Debian-based systems (e.g. Fedora/Centos or Ubuntu). Running this program results in a “repodata” sub-directory being created with a couple of files that store parsed rpm information.

2. Sync the local repository with the S3 bucket

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Publishing to Atlassian Confluence (with Python)

Introduction

This text is about automating the interaction with Atlassian Confluence, a commercial product widely used in the corporate world for publishing documentation or other content that needs sharing with a certain team, department or throughout the whole organization. This product is usually integrated with the other Atlassian tools such as Bamboo or JIRA.

By “automating the interaction” I understand document publishing; one may extract data from other components of the system using automated tools and may want this data published in a nice format (if possible). This is indeed possible through the REST interface that Atlassian provides for all its products. The REST functionality does not cover all the features or functions, though, but it’s enough for our scenario. The reference for the Confluence Cloud version can be found here. Please note that the interface may be a bit different between versions, so please check the API reference for your particular Confluence version.

Now that we have the tools and the method, let’s get to the implementation – and obviously, to some code.

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A lesson on failure: “Going Cloud”

More than a year ago, during a time when I barely knew anything about Cloud Computing or AWS, I was assigned along with a couple of colleagues on bringing an existing code base from “alpha” to “production” and ensure a smooth deployment to the Amazon Cloud. The customer wanted to “go live” in less than 3 months and be able to handle tens of thousands of visitors that would click on banners and fill their bank accounts; well, most likely they were just wishing for a good exit. On a side note, one of the photoplasty sections of the cracked.com website has an image about this type of business.

Starting the project

Things initially went to some direction – we dealt with many functionality issues, being able to fix and test more than 100 bugs and glitches; after all, this was the thing we knew best how to do and we also put in the long hours required for getting things done. We weren’t bothered by the cloud setup issues – the customer fiercely guarded the “keys to the kingdom” and agreed on instance and resource set-ups on a case-by-case basis only, all with the desire of keeping the Amazon bill as low as possible. We thought this was fine – it was their home, they knew best what they needed.

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