LINQ Dev Practice

A focused C# LINQ practice repository built around a realistic marketplace dataset, a 150-question catalog, and a deterministic judge.

In short: write LINQ, run scenarios, discover truths. Occasionally also discover off-by-one bugs.

Why this repo exists

Most LINQ examples online are tiny and clean. Real data is neither.

This project gives you practical, messy, relational-style JSON data so you can practice:

• filtering and projections
• joins and relationship traversal
• grouping, aggregates, and ranking
• time-based reporting and trends
• set operations and funnel analysis
• anomaly and data-quality checks

What you get

• Assets/ — JSON datasets for a multi-seller commerce domain
• UserSolutions.cs — your practice surface
• LINQSolutions/JudgeAnswers.cs — canonical answer behavior
• Docs/DATASET_OVERVIEW.md — dataset explanation + Mermaid ER diagram
• Docs/QUESTIONS.md — full 150-question bank
• Docs/QUESTIONS_WITH_ANSWERS.md — guided walkthroughs
• Docs/LINQ_QUERY_GUIDE.md — operators and patterns reference

Quick start

dotnet build
dotnet run

Running dotnet run opens an interactive launcher where you can practice, inspect datasets, or run a full audit.

Command reference

dotnet run -- --list
dotnet run -- --datasets
dotnet run -- Q5
dotnet run -- Q5 --include-real
dotnet run -- Q5,Q6
dotnet run -- --all-user
dotnet run -- --all-user --include-real
dotnet run -- --judge Q141,Q150
dotnet run -- --audit

• dotnet run → opens interactive launcher
• --list → prints full catalog with explicit input/output contracts
• --datasets → prints dataset details and relationships
• Q5 → runs your UserSolutions answer for selected question(s)
• --include-real → includes full asset-backed scenario
• --all-user → runs every UserSolutions method
• --judge / --reference → runs canonical judge answers
• --audit → runs judge answers across all registered scenarios

Scenario model

Each question can run against up to three scenario types:

• Dummy happy path
• Dummy edge/boundary
• Real dataset

By default, selected-question practice runs the fast dummy scenarios. Add --include-real when you want production-style pain (the educational kind).

Dataset domain snapshot

The dataset models a multi-seller Indian e-commerce platform including:

• customers, loyalty, addresses, login behavior
• sellers, suppliers, products, categories
• orders, order items, payments, shipments, refunds
• reviews, support tickets, event tracking
• warehouses and inventory snapshots

See Docs/DATASET_OVERVIEW.md for ER relationships and details.

Repository layout

• Assets/ data files
• Docs/ guides and question bank
• LINQSolutions/ catalog, judge answers, and contexts
• Models/ contracts and runner models
• Services/ dataset loading, judging, helpers

Contributing

See CONTRIBUTING.md.

If you spot a mismatch in wording, expected outputs, or dataset assumptions, open an issue or PR. Thoughtful fixes are always welcome.

License

Licensed under the MIT License.

Note

This project includes AI-assisted content and structuring. Human review is still the final judge (as it should be).