Below is a proposed architecture and implementation plan for your Python command line tool that validates and simulates GitLab CI configurations.

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1. Overview

Your tool will take a root GitLab CI YAML file, recursively include and merge all referenced files, and then process the jobs, workflows, and variables. It will simulate how GitLab expands jobs (including the “extends” mechanism with its dictionary merge and array overwrite rules) and how variable interpolation is applied in different contexts (e.g., merge request vs. schedule) as specified by an external configuration file.

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2. High-Level Architecture

Main Components
	•	File Loader & Parser
	•	Responsibilities:
	•	Read the root .gitlab-ci.yml file.
	•	Recursively resolve and load any included YAML files.
	•	Merge all YAML fragments into a single unified configuration dictionary.
	•	Tools/Libraries:
	•	Use PyYAML or an equivalent YAML parser.
	•	Job Processor & Expander
	•	Responsibilities:
	•	Identify job definitions (usually keys that are not reserved keywords).
	•	Process the extends keyword:
	•	Recursively merge parent job definitions into child jobs.
	•	Implement the logic for merging dictionaries (deep merge) and overwriting arrays.
	•	Key Considerations:
	•	Handle conflicts in keys.
	•	Respect GitLab CI’s rules for merging (e.g., arrays are overwritten rather than merged).
	•	Workflow Handler
	•	Responsibilities:
	•	Parse and validate the workflow: section.
	•	Simulate workflow rules (e.g., only run pipelines if certain conditions are met).
	•	Integration:
	•	This module will work with the configuration values (see below) to decide whether a pipeline would be triggered.
	•	Configuration Manager
	•	Responsibilities:
	•	Read an external configuration file (YAML/JSON) that specifies variable values for different simulation contexts (e.g., merge request, scheduled run).
	•	Provide these variable values to the variable expander.
	•	Key Considerations:
	•	Support multiple configuration profiles and possibly environment overrides.
	•	Variable Expander
	•	Responsibilities:
	•	Traverse the expanded job definitions.
	•	Substitute variables using the provided configuration and any default values from the GitLab CI file.
	•	Take workflows into account to determine which variables should be used in a given context.
	•	Challenges:
	•	Implement a robust substitution engine that simulates GitLab’s behavior.
	•	Simulation Engine
	•	Responsibilities:
	•	Using the processed job definitions, workflows, and variable expansions, simulate the pipeline execution.
	•	Validate the overall configuration.
	•	Optionally, produce a “dry-run” output of the pipeline structure.
	•	Command Line Interface (CLI)
	•	Responsibilities:
	•	Provide user commands to:
	•	Validate the configuration.
	•	Run simulations with specified configurations.
	•	Use Python’s argparse or a similar library for parsing command-line arguments.
	•	User Interaction:
	•	Allow passing in the root GitLab CI file, the configuration file, and options for simulation (e.g., pipeline type).

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3. Modules and Their Responsibilities

A. Loader Module
	•	Functionality:
	•	load_yaml(filepath): Reads and parses a YAML file.
	•	resolve_includes(config_dict, base_path): Recursively locate and merge included YAML files.
	•	Design Tip:
	•	Use recursion and careful path resolution to handle nested includes.

B. Job Expander Module
	•	Functionality:
	•	expand_job(job_dict, all_jobs): Given a job definition and a dictionary of all available jobs, apply the extends mechanism.
	•	deep_merge(base: dict, override: dict): Custom function for merging dictionaries according to GitLab rules.
	•	Design Tip:
	•	Write unit tests to ensure your merge logic aligns with GitLab’s documented behavior.

C. Workflow Module
	•	Functionality:
	•	parse_workflow(config_dict): Identify and validate the workflow section.
	•	evaluate_workflow(workflow, variables): Determine if a pipeline should run based on conditions.
	•	Design Tip:
	•	Keep the workflow evaluation logic decoupled from job processing for modularity.

D. Configuration Module
	•	Functionality:
	•	load_simulation_config(config_filepath): Parse the simulation configuration file.
	•	Manage profiles for different pipeline types (e.g., merge request, scheduled).
	•	Design Tip:
	•	Allow for dynamic profiles to be selected via CLI options.

E. Variable Expander Module
	•	Functionality:
	•	expand_variables(job_dict, variables): Walk through job definitions and replace placeholders with actual values.
	•	Design Tip:
	•	Implement robust parsing for variable syntax (e.g., ${VAR_NAME}) and support nested substitutions if needed.

F. Simulation Engine Module
	•	Functionality:
	•	simulate_pipeline(expanded_jobs, workflows, variables): Validate the final configuration and simulate the pipeline.
	•	Optionally, generate logs or a graphical representation of job dependencies.
	•	Design Tip:
	•	Keep simulation logic separate from configuration parsing to enable easier debugging and unit testing.

G. CLI Module
	•	Functionality:
	•	Parse command-line arguments (e.g., file paths, simulation options).
	•	Call into the loader, job expander, workflow handler, configuration manager, and simulation engine in the correct order.
	•	Design Tip:
	•	Use sub-commands if you plan to extend functionality (e.g., validate, simulate).

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4. Data Flow and Execution Order
	1.	Input Processing:
	•	CLI receives input paths for the GitLab CI file and simulation configuration file.
	2.	File Loading:
	•	Loader module reads the root YAML and all included files, returning a merged configuration dictionary.
	3.	Job Extraction & Expansion:
	•	Extract job definitions.
	•	Expand jobs by processing extends entries with the job expander module.
	4.	Workflow Processing:
	•	Parse the workflow section and evaluate it based on the configuration manager’s variable values.
	5.	Variable Expansion:
	•	The variable expander applies configuration-provided variable values to the expanded jobs.
	6.	Simulation:
	•	The simulation engine takes the final job definitions and workflow decisions to simulate the pipeline.
	7.	Output:
	•	Results (validation output, simulation summary, or error messages) are printed to the console or logged.

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5. Implementation Plan

Phase 1: Initial Setup and File Parsing
	•	Tasks:
	•	Set up the project structure (using a virtual environment, organizing modules into packages).
	•	Implement the Loader Module:
	•	Write load_yaml() to read a file.
	•	Implement resolve_includes() for recursive include resolution.
	•	Write tests for file loading and YAML merging.

Phase 2: Job Processing and Expanding ‘extends’
	•	Tasks:
	•	Develop the Job Expander Module:
	•	Define the rules for dictionary deep merge and array overwriting.
	•	Implement expand_job() and deep_merge().
	•	Test with various GitLab CI job definitions that use extends.

Phase 3: Workflow and Configuration Handling
	•	Tasks:
	•	Build the Workflow Module to parse and evaluate workflows.
	•	Create the Configuration Module to load external simulation configuration.
	•	Write unit tests to verify workflow logic and configuration loading.

Phase 4: Variable Expansion and Simulation Engine
	•	Tasks:
	•	Develop the Variable Expander Module:
	•	Implement a function to replace variable placeholders.
	•	Build the Simulation Engine:
	•	Combine expanded jobs, workflow evaluation, and variable expansion to simulate a pipeline.
	•	Integrate logging to provide detailed output on the simulation steps.

Phase 5: CLI Integration and Final Testing
	•	Tasks:
	•	Create a CLI module using argparse:
	•	Add commands for validation and simulation.
	•	Integrate all modules and run end-to-end tests.
	•	Write documentation and usage examples.

Phase 6: Refactoring and Documentation
	•	Tasks:
	•	Refactor code for readability and modularity.
	•	Write comprehensive documentation (usage instructions, developer guide).
	•	Add any additional features such as support for custom merge strategies or detailed simulation logs.

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6. Conclusion

This plan lays out a modular and maintainable architecture for your GitLab CI simulator in Python. By dividing the application into clear modules (loader, job expander, workflow handler, configuration manager, variable expander, simulation engine, and CLI), you can develop, test, and extend each part independently. This architecture mirrors the actual GitLab CI processing stages, making it easier to validate and simulate CI pipelines accurately.

Feel free to ask if you need further details on any module or assistance with the implementation specifics.
