RVTools Virtualization Analysis

Analysis Tool for VMWare Inventories

This tool analyzes VMware inventory reports generated by RVTools and produces comprehensive reports and visualizations to assess the virtual machine (VM) inventory and the complexity of migration to the Red Hat OpenShift Virtualization platform.

Overview

• Parses and processes RVTools exports to extract key VM details.

• Generates reports and charts to assess migration feasibility and complexity.

• Uses Jupyter Notebooks for interactive analysis.

• Leverages NumPy and Pandas for data manipulation and calculations.

Supported Operating Systems

This tool has been tested on:

• Fedora 34 and later

• macOS 14 (Sonoma) and later

• Windows 10 and later

Prerequisites

Linux/macOS Requirements

Ensure the following are installed:

• Python (>= 3.10)

• pip (Python package manager)

• Git (for version control)

• xclip (clipboard management for Linux)

• LibreOffice or Microsoft Excel (for spreadsheet processing)

Windows Requirements

Ensure the following are installed:

• Python (>= 3.10)

• Git

• LibreOffice or Microsoft Excel

• Visual Studio (Community Edition) (required for compiling dependencies)

• Rust (required for some Python dependencies)

Directory Structure

rvtools-virt-analysis
│
├── containers/ # Container options for rvtools-analysis
├── data/ # Index.xlsx and RVTools export files (xlsx format)
├── example_rvtools_files/ # Test data for analysis
├── helper_files/ # Custom configuration files (ignores, migration vs redeploy, colors, etc.)
├── img/ # Images and screenshots for documentation
├── saved_csv_files/ # Generated CSV files from various analysis functions
├── useful_scripts/ # Ad-hoc utility scripts (outside Jupyter Notebooks)
├── .git/ # Git repository information
├── .venv/ # Python virtual environment for Jupyter Notebooks
├── LICENSE # License file
├── README.md # This file
└── requirements.txt # Python dependencies (used by setup.sh/startanalyser.bat)

Usage

Setting Up the Environment

1. Clone the Repository

• If you have your SSH public key uploaded:

  git clone ssh://git@gitlab.consulting.redhat.com:2222/customer-success/consulting-delivery-automation/vma-tools/rvtools-virt-analysis.git

• If you do not have your SSH key uploaded, use HTTPS instead:

  git clone https://gitlab.consulting.redhat.com/customer-success/consulting-delivery-automation/vma-tools/rvtools-virt-analysis.git

  (You will be prompted to enter your GitLab username and password.)

2. Navigate to the Project Directory

cd rvtools-virt-analysis

3. Helper_files ensure they are updated for the environment

The helper files are provided to add flexibility for different use cases vs having to have completely new code.

• environment_mapping.json - has mappings for environment example: Production, prod, prd will all be counted as prod.

• helper_color_file.txt - Sets some default colors to use. (inprogress)

• ignored_patternes.txt - to ignore things such as template, poweredOff,virtual appliances, etc.

• ignored_vm_names_example.txt - if there are known names or name patterns to ignore copy to ignored_vm_names.txt

• migration_patterns_example.txt - if there is a split between migration and redeploy copy to migration_patterns.txt edit and add VM name patterns

• os_filter_patterns.txt - This is the unsupported OS list, to update ref: Documents here

• redeploy_patterns_example.txt - if there is a split between migration and redeploy copy to redeploy_patterns.txt edit and add VM name patterns

• Tmpl_In_Scope_VMs.xlsx

• vm_db_name_patterns.txt - this is used if databases are identified by have db,mmsql,mysql in there names edit and use regex to adapt to environment

4. Set Up a Python Virtual Environment

You can create a Python 3.x virtual environment using venv or virtualenv (or any other tool of your choice).

Automated Setup

A shell script (setup.sh) for Linux/macOS and a batch file (startanalyzer.bat) for Windows are included to automate the setup and launch Jupyter Lab:

• Linux/macOS:

  ./setup.sh

• Windows:

  startanalyzer.bat

Manual Setup

If you prefer to set up the environment manually, follow these steps:

• Linux/macOS:

  • Create a virtual environment inside the project’s root directory:

    python3 -m venv .venv

  • Activate the virtual environment:

    source .venv/bin/activate

• Windows:

  • Create a virtual environment:

    python -m venv .venv

  • Add the virtual environment scripts to your system path:

    set PATH=%USERPROFILE%\mywork\rvtools-virt-analysis\.venv\Scripts;%PATH%

  • Activate the virtual environment:

    .venv\Scripts\activate

Note	The commands above create a hidden .venv directory inside the project. Unless you add .venv/bin (Linux/macOS) or .venv\Scripts (Windows) to your system path, you will need to specify the full path when using pip or python.

4. Set Up a Containerized environment

Alternatively, rvtools-virt-analysis can be run in a containerized environment.

You can see more information on this topic in the containers folder README.

Access Jupyter Labs

Once the container has been successfully built and started, you should see some log entries similar to the following. Port tcp/8888 has been forwarded to the container, so click/open the http://127.0.0.1:8888/lab?token=<token>; link. You should successfully access Jupyter Labs.

    To access the server, open this file in a browser:
        file:///root/.local/share/jupyter/runtime/jpserver-1-open.html
    Or copy and paste one of these URLs:
        http://49d6660cece7:8888/lab?token=<token>
        http://127.0.0.1:8888/lab?token=<token> <<<< Open this link

5. Upgrade pip to the Latest Version

• Linux/macOS:

  .venv/bin/pip install --upgrade pip

• Windows:

  .venv\Scripts\pip.exe install --upgrade pip

6. Install Dependencies

• Linux/macOS:

  .venv/bin/pip install -r requirements.txt

• Windows:

  .venv\Scripts\pip.exe install -r requirements.txt

Preparing the Data

Note	If the RVTools export consists of multiple CSV files instead of a single XLSX file, you can use the CSV parser script (rvtools-csv-parser.py) included in this repository to generate a single XLSX file with the proper tabs.

1. Copy Exported Spreadsheets

• Move or copy the exported spreadsheet files from the client’s RVTools output into the data directory

2. Create an Index File

• Copy the file index_template.xlsx and rename it to index.xlsx inside the data directory.

3. Update index.xlsx

• Follow the instructions inside the file.

• Remove sample entries before proceeding.

4. Verify Exported Worksheets

• Ensure the exported RVTools spreadsheets contain the default set of worksheets.

• The worksheets highlighted in yellow in the following image are required for analysis:

• Mandatory worksheets:

  • vInfo

  • vHost

These sheets must be present with the exact spelling as shown.

5. Ensure Correct File Naming

Warning

All RVTools export files must be in lowercase, including both the filenames and the corresponding names referenced in index.xlsx. Failure to maintain consistent lowercase formatting may result in errors during data processing.

• The exported spreadsheet filenames (excluding the file extension .xlsx) must match the corresponding vCenter instance names.

  • Example:

    • If the vCenter instance is named avcenterinstance123, the exported file should be named: avcenterinstance123.xlsx

6. Ensure you have updated your helper files that are needed to be updated

• rvtools-virt-analysis/helperfiles

Running the JupyterLab Notebook

Note	If you have already executed the setup.sh (Linux/macOS) or startanalyzer.bat (Windows) script, you can skip this section. These scripts automate the setup process by creating and activating the virtual environment (venv), installing all required dependencies using pip, and launching Jupyter Lab.

1. Activate the Python Virtual Environment

• Ensure that the virtual environment is activated in your shell.

2. Start JupyterLab

• Run the following command in the terminal:

  jupyter lab

3. Access the Notebook

• JupyterLab should launch automatically in your default web browser.

• If it does not open automatically, navigate to:

http://localhost:8888/lab

• Once JupyterLab opens, you should see the following interface:

4. Select the Notebook

• In the file explorer pane on the left, open analyze-vm-inventory.ipynb

5. Follow the Notebook Instructions

• The notebook contains step-by-step guidance.

• If the data has been prepared as described earlier, it should run automatically with minimal input.

6 Execute All Notebook Cells

• Run all the cells in sequence.

• The execution should take 60-90+ seconds, depending on the number of vCenter inventory files being processed.

7 Cell Compress files in saved_csv_files directory

• The last cell that runs will compress all the csv files in the rvtools-virt-analysis/saved_csv_files, into compress_assets_analyze_vms.tar.gz located at the rvtools-virt-analysis/. This these are helpful artifacts that are output from various cells that ran, if something needs to be looked up later.

Added helper python scripts.

1. create_reports/create_reports.py Create graphs/charts reports with code* Read the README.md in folder with the script.

2. add_ignored_patterns.py - assist adding names to helper_files/ignored_patterns.txt

3. add_os_pattern.py - assist adding names to helper_files/os_filter_patterns.txt

4. findonly_dbs.py

   Search all XLSX files based on index file for Database VMs
   Uses vCenter column from index file and vinfo tab from XLSX files
   Categorizes databases by type based on VM name patterns
   
   When executed it will ask for input.

5. mergefiels.py - useful when rvtools extracted data is proved as single tab exports of all the data this will combined into proper files.

   Merge multiple CSV/XLSX files into a single file with tabs in specified order
   
   Args:
       source_dir: Directory containing source files
       destination_file: Path to the output file
       file_type: Type of files to process ('csv' or 'xlsx')

6. os_name_search.py

   Search all XLSX files based on index file for Red Hat/SUSE VMs
   Uses vCenter column from index file and vinfo tab from XLSX files
   
   When executed it will ask for inputs.

7. rvtools-csv-parser.py

   Used to parse single csv files at a time to insure they have minimum information.
   
   Asks for input when run

General Notes

• These instructions assume the use of a Linux/macOS/WSL shell such as bash or zsh. If using a different shell, such as Windows PowerShell, adjust the commands accordingly.

• If analyzing more than 18 files, update the nrows=19 variable in the Read the index metadata file section of the Jupyter notebook.

  • Set nrows to the total number of files + 1 (to account for the header row in the index file).

Author(s)

1. Anupam Sengupta (ansengup@redhat.com)

2. Freddy Montero (fmontero@redhat.com)

3. Jon Harding (jharding@redhat.com)

Red Hat Inc.

Issues, Suggestions, or Contributions

If you encounter any issues, have suggestions, or would like to contribute to the project, please follow these guidelines:

1. Reporting Issues

• Clearly describe the problem, including:

  • Steps to reproduce the issue

  • Expected vs. actual behavior

  • Error messages (if any)

  • Relevant system details (OS, Python version, etc.)

  • Submit issues through the project’s GitLab issue tracker.

2. Suggesting Enhancements

• If you have ideas for improvements or additional features:

  • Describe the problem your suggestion aims to solve.

  • Propose a potential solution or implementation approach.

  • Provide any relevant references or examples.

3. Contributing to the Project

• Fork the repository and create a new branch for your changes.

• Follow best practices for code quality and documentation.

• Submit a merge request (MR) with a clear description of your changes.

• Ensure your code is tested before submission.

For general discussions or questions, feel free to reach out via Slack