How claude17/football-ticket-booking-system_database-design-sql-queries works
1. This repository is a database-centric design artifact for a Football Ticket Booking System. It does not contain an application layer or backend service; the main deliverables are the ERD description in README.md and the SQL query set in QUERY.sql.
2. The core data model has three entities: Users, Matches, and Bookings. These form the entire architectural boundary of the system, with Bookings acting as the transactional bridge between a fan and a match.
3. Users stores customer identity and contact data, plus a role field that suggests the model can distinguish different kinds of accounts, even though the repository does not implement role-based behavior.
4. Matches represents inventory for football events. It stores the fixture, competition category, ticket price, and availability/status, which together define what can be booked.
5. Bookings is the operational table where the purchase/reservation flow is recorded. It links to both Users and Matches through foreign keys, and also stores seat, payment state, and total cost, making it the key table for all booking-related reporting.
6. The main relationship pattern is one-to-many from Users to Bookings, and many-to-one from Bookings to Matches. Architecturally, this means a user can create multiple bookings, while each booking points to exactly one user and one match.
7. The README describes a logical one-to-one between a booking record and a specific seat reservation. That implies booking uniqueness is centered on seat allocation per match/user combination, although the actual enforcement rules are not visible in the repo.
8. The SQL file demonstrates common data-access patterns against this schema: filtering upcoming available matches, text search on users, null handling for incomplete payments, joins across all three tables, outer joins to preserve users with no bookings, aggregation via average comparison, and pagination via top-N/offset behavior.
9. The data flow is straightforward: match inventory is queried from Matches, user identity from Users, and then Bookings combines them to produce booking history, financial totals, and payment status views. Most reporting queries are centered on joining Bookings to the other two tables.
10. The project’s technology boundary is PostgreSQL SQL. There is no ORM, API server, frontend, or deployment infrastructure in the repository, so the architecture diagram should focus on the relational schema and the query/reporting layer rather than an application stack.
11. External tooling is minimal and documentation-oriented: Draw.io is used for the ERD diagram, and GitHub is only the hosting/version-control platform. The architectural emphasis is therefore on schema design, referential integrity, and query behavior, not on runtime components.
12. If drawing the system, the clearest view is: users browse/search matches, bookings tie users to matches, and queries extract operational reports from the joined tables. The repository models the data foundation for a ticket booking platform rather than the full platform itself.
Main entry points: README.md README · QUERY.sql SQL Queries
Components 5
Core schema
Relational model for the ticket booking domain.
- Users · Customer identity, contact data, and role metadata.
- Matches · Football fixtures, competition category, ticket price, and availability.
- Bookings · Transactional booking records linking a user to a match with seat and payment state.
Repo artifacts
Documentation and SQL query set that describe and exercise the schema.
- README · ERD and domain explanation for the database design.
README.md - SQL Queries · Reporting and data-access patterns over the booking schema.
QUERY.sql
Connections 10
- Users → Bookings: 1-to-many · A user can have many bookings.
- Matches → Bookings: 1-to-many · A match can be referenced by many bookings.
- Bookings → Users: FK · Each booking points to one user.
- Bookings → Matches: FK · Each booking points to one match.
- README → Users: describes · Documentation defines the customer entity.
- README → Matches: describes · Documentation defines the match inventory entity.
- README → Bookings: describes · Documentation defines the transactional booking entity.
- SQL Queries → Users: reads · Queries filter and search user data.
- SQL Queries → Matches: reads · Queries select upcoming and available matches.
- SQL Queries → Bookings: joins · Queries combine bookings with users and matches for reporting.