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Pack RPM/DEB for obsolete distros

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#257 0 則留言 0 個 reaction 已指派 0 人 在 GitHub 檢視

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評估

難度
4/5
預估耗時
3-5 天
新手友好度
30/100
Issue 類型
功能
描述清晰度
基本清楚
活躍度
停滯
技術堆疊
python

研究方向

審查 PR #248 以及套件測試與發布矩陣,然後參閱連結的關於建置 RPMs 的 wiki 草稿。檢查 CentOS 7、Debian 9 和 Ubuntu 18.04(可能還包括 16.04)的相依性可用性,包括 Python 3.6 的需求。完成的標準是:支援的 RPM/DEB 套件能夠針對選定的已停止支援的發行版,在其相依性可用的情況下完成建置、測試與發布。

由索引模型根據 Issue 內容生成。

描述

backlog question

Follows up #248

In PR #248 we had introduced RPM and DEB packages for distros which have Python 3.7 or newer in its package managers by default. Extending package testing and publish matrices is a complicated task. Why is that?

First, the code works with Python 3.6 or newer, so if system doesn't have it one (including our CI) must set it up manually. Second, running code with Python 3.6 requires dataclasses backport package. And here we come to the packages issue.

It seems rather useless to provide a package for a distro if distribution repositories (or some external popular repos like EPEL) don't have its dependencies. And the case is they mostly don't.

RPM with dataclasses backport is available for EPEL 8 only. It is not so hard to rebuild (see https://github.com/tarantool/tarantool-python/wiki/Building-RPMs draft), but we will need to provide and maintain external packages in Tarantool repo. I also haven't seen any DEBs for dataclasses.

dataclasses is not the only one troublesome package. For example, CentOS 7 (including EPEL 7) does not provide any pandas RPM for Python 3. Rebuilding pandas RPM from EPEL 8 on EPEL 7 is rather challenging task. We also shouldn't forget that pandas has many dependencies and it is possible that we would need to rebuild them to.

Distros planed in this ticket:

  • centos:7
  • debian:9
  • ubuntu:18.04 (and possibly 16.04)

centos:8 is EOL so we don't plan to provide a package for it.

主要語言
Python
星號
108
分支
50
PR 合併指標
30 天內沒有已合併 PR

環境準備

這個專案沒有提供開發容器、Dockerfile 或貢獻指南,環境需要你自己搭建:先看它的 README,通用步驟見我們的新手貢獻指南。

從這裡開始

  1. 先讀完整個 Issue,再讀專案的貢獻指南。
  2. 在 Issue 下留言說明你要接手 —— 這能避免兩個人做同樣的事。
  3. Fork 儲存庫,在一個分支上完成修改。
  4. 送出 Pull Request,並在描述裡引用這個 Issue 編號。

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