Modern Python offers powerful features such as asynchronous programming (using async and await) and memory-efficient data processing with generators and iterators. Leveraging these advancements, primarily found in Python 3, can significantly improve your code’s performance and maintainability. This often necessitates migrating existing Python 2 projects. The migration process can vary from a straightforward conversion to a substantial refactoring. To help you navigate this, we’ll discuss some of the crucial differences between the two Python versions.
1. PRINT:
This one is easy – most of the time it’s just about translating code from:
print "abcd";
to:
print("abcd")
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
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.