Metadata-Version: 2.1
Name: yarn-dev-tools
Version: 2.0.0
Summary: 
Home-page: https://github.com/szilard-nemeth/yarn-dev-tools
Keywords: YARN,development,dev environment
Author: Szilard Nemeth
Author-email: szilard.nemeth88@gmail.com
Requires-Python: >=3.8.12,<4.0.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: bs4
Requires-Dist: dacite
Requires-Dist: dataclasses-json
Requires-Dist: gitpython
Requires-Dist: google-api-wrapper2 (==1.0.5)
Requires-Dist: humanize
Requires-Dist: jira
Requires-Dist: marshmallow
Requires-Dist: pymongo
Requires-Dist: python-common-lib (==1.0.6)
Project-URL: Repository, https://github.com/szilard-nemeth/yarn-dev-tools
Description-Content-Type: text/markdown

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# YARN-dev-tools

This project contains various developer helper scripts in order to simplify every day tasks related to Apache Hadoop YARN development.

## Main dependencies

* [gitpython](https://gitpython.readthedocs.io/en/stable/) - GitPython is a python library used to interact with git repositories, high-level like git-porcelain, or low-level like git-plumbing.
* [tabulate](https://pypi.org/project/tabulate/) - python-tabulate: Pretty-print tabular data in Python, a library and a command-line utility.
* [bs4](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) - Beautiful Soup is a Python library for pulling data out of HTML and XML files.

* TODO: Missing dependencies

## Contributing

TODO 

## Authors

* **Szilard Nemeth** - *Initial work* - [Szilard Nemeth](https://github.com/szilard-nemeth)

## License

TODO 

## Acknowledgments

TODO

# Getting started

In order to use this tool, you need to have at least Python 3.8 installed.

## Use yarn-dev-tools from package (Recommended)
If you don't want to tinker with the source code, you can download [yarn-dev-tools](https://pypi.org/project/yarn-dev-tools/#history) from PyPi as well.
This is probably the easiest way to use it.
You don't need to install anything manually as I created a [script](initial_setup.sh) that performs the installation automatically.
The script has a `setup-vars` function at the beginning that defines some environment variables:

These are the following:
- `YARNDEVTOOLS_ROOT`: Specifies the directory where the Python virtualenv will be created and yarn-dev-tools will be installed to this virtualenv.
- `HADOOP_DEV_DIR` Should be set to the upstream Hadoop repository root, e.g.: "~/development/apache/hadoop/"
- `CLOUDERA_HADOOP_ROOT` Should be set to the downstream Hadoop repository root, e.g.: "~/development/cloudera/hadoop/"

The latter two environment variables is better to be added to your bashrc / zshrc file (depending on what shell you are using) to keep them between the shells.

## Use yarn-dev-tools from source
If you want to use yarn-dev-tools from source, first you need to install its dependencies.
The project root contains a pyproject.toml file that has all the dependencies listed.
The project uses Poetry to resolve the dependencies so you need to [install poetry](https://python-poetry.org/docs/#installation) as well.
Simply go to the root of this project and execute `poetry install --without localdev`.
Alternatively, you can run `make` from the root of the project.

## Setting up handy aliases to use yarn-dev-tools
If you completed the installation (either by source or by package), you may want to define some shell aliases to use the tool more easily.
In my system, I have [these](
https://github.com/szilard-nemeth/linux-env/blob/master/workplace-specific/cloudera/scripts/yarn/setup-yarn-dev-tools-aliases.sh).
Please make sure to source this script so that the command 'yarndevtools' will be available since it's defined as a function.
It is important to specify `HADOOP_DEV_DIR` and `CLOUDERA_HADOOP_ROOT` as mentioned above, before sourcing the script.

After these steps, you will have a basic set of aliases that is enough to get you started.


# Setting up yarn-dev-tools with Cloudera CDSW

## Initial setup
1. Upload the initial setup scripts to the CDSW files, to the root directory (/home/cdsw)
- [initial-cdsw-setup.sh](yarndevtools/cdsw/scripts/initial-cdsw-setup.sh)
- [install-requirements.sh](yarndevtools/cdsw/scripts/install-requirements.sh)

2. Create a new CDSW session.
Wait for the session to be launched and open up a terminal by Clicking "Terminal access" on the top menu bar.


3. Execute this command:
```
~/initial-cdsw-setup.sh user cloudera
```


The script performs the following actions: 
1. Downloads the scripts that are cloning the upstream and downstream Hadoop repositories + installing yarndevtools itself as a python module.
The download location is: `/home/cdsw/scripts`<br>
Please note that the files will be downloaded from the GitHub master branch of this repository!
- [clone_downstream_repos.sh](yarndevtools/cdsw/scripts/clone_downstream_repos.sh)
- [clone_upstream_repos.sh](yarndevtools/cdsw/scripts/clone_upstream_repos.sh)

2. Executes the script described in step 2. 
This can take some time, especially cloning Hadoop.
Note: The individual CDSW jobs should make sure for themselves to clone the repositories.

3. Copies the [python-based job configs](yarndevtools/cdsw/job_configs) for all jobs to `/home/cdsw/jobs`

4. All you have to do in CDSW is to set up the projects and their starter scripts like this:

| Project                                                                | Starter script location         | Arguments for script          |
|------------------------------------------------------------------------|---------------------------------|-------------------------------|
| Jira umbrella data fetcher (Formerly: Jira umbrella checker reporting) | scripts/start_job.py            | jira-umbrella-data-fetcher    |
| Unit test result aggregator                                            | scripts/start_job.py            | unit-test-result-aggregator   |
| Unit test result fetcher (Formerly: Unit test result reporting)        | scripts/start_job.py            | unit-test-result-fetcher      |
| Branch comparator (Formerly: Downstream branchdiff reporting)          | scripts/start_job.py            | branch-comparator             |
| Review sheet backport updater                                          | scripts/start_job.py | review-sheet-backport-updater |
| Reviewsync                                                             | scripts/start_job.py | reviewsync                    |

# Use-cases


### Examples for YARN backporter
To backport YARN-6221 to 2 branches, run these commands:
```
yarn-backport YARN-6221 COMPX-6664 cdpd-master
yarn-backport YARN-6221 COMPX-6664 CDH-7.1-maint --no-fetch
```
The first argument is the upstream Jira ID<br>
The second argument is the downstream Jira ID.<br>
The third argument is the downstream branch.<br>
The `--no-fetch` option is a means to skip git fetch on both repos.

### How to backport to an already existing relation chain?
1. Go to Gerrit UI and download the patch.
For example: 
```
git fetch "https://gerrit.sjc.cloudera.com/cdh/hadoop" refs/changes/29/156429/5 && git checkout FETCH_HEAD
```
2. Checkout a new branch
```
git checkout -b my-relation-chain 
```

3. Run backporter with: 
```
yarn-backport YARN-10314 COMPX-7855 CDH-7.1.7.1000 --no-fetch --downstream_base_ref my-relation-chain
```
where:<br>
The first argument is the upstream Jira ID<br>
The second argument is the downstream Jira ID.<br>
The third argument is the downstream branch.<br>
The `--no-fetch` option is a means to skip git fetch on both repos.<br>
The `--downstream_base_ref <local-branch` is a way to use a local branch to base the backport on so the Git remote name won't be prepended.


Finally, I set up two aliases for pushing the changes to the downstream repo:
```
alias git-push-to-cdpdmaster="git push <REMOTE> HEAD:refs/for/cdpd-master%<REVIEWER_LIST>"
alias git-push-to-cdh71maint="git push <REMOTE> HEAD:refs/for/CDH-7.1-maint%<REVIEWER_LIST>"
```
where REVIEWER_LIST is in this format: "r=user1,r=user2,r=user3,..."


# Contributing

## Setup of pre-commit

Configure precommit as described in [this blogpost](https://ljvmiranda921.github.io/notebook/2018/06/21/precommits-using-black-and-flake8/).

Commands:
1. Install precommit: `pip install pre-commit`
2. Make sure to add pre-commit to your path. For example, on a Mac system, pre-commit is installed here: 
   `$HOME/Library/Python/3.8/bin/pre-commit`.
2. Execute `pre-commit install` to install git hooks in your `.git/` directory.

## Running the tests

TODO

## Troubleshooting

### Installation issues
In case you're facing a similar issue:
```
An error has occurred: InvalidManifestError: 
=====> /<userhome>/.cache/pre-commit/repoBP08UH/.pre-commit-hooks.yaml does not exist
Check the log at /<userhome>/.cache/pre-commit/pre-commit.log
```
, please run: `pre-commit autoupdate`

More info can be found [here](https://github.com/pre-commit/pre-commit/issues/577).
