The goal of this project is to study JPA Batch Processing (i.e., inserting, updating, or deleting a set of records in a single command) and JPA Locking.
Examples of applying pessimistic and optimistic concurrency control with AWS DynamoDB.
🔒 Implement distributed locking for .NET background jobs to prevent duplicate execution and ensure data consistency across multiple application instances.
A microservice-based stock brokerage platform and order book analytics engine
Comprehensive Laravel Database Engineering fundamentals covering SQL operations, database transactions, Query Builder, advanced filtering, joins, aggregations, raw queries, query optimization, pagination, cursor-based data retrieval, database migrations, and database seeding.
A robust .NET library for distributed locking of background jobs across multiple application instances, supporting Hangfire, Coravel, and custom implementations with SQL Server, PostgreSQL, and Redis backends.
High-concurrency PHP 8 e-commerce core featuring MySQL pessimistic database locking, transactional inventory management, and Docker setup.
Airline Checkin System APIs
A Spring Boot application demonstrating optimistic and pessimistic database locking to handle concurrent transactions, prevent data inconsistencies, and ensure reliable data integrity in multi-user systems.
Laravel 12 API for slot booking with Redis caching (5-15s TTL), idempotency middleware, and MySQL transaction locks to prevent overselling. Implements hold/confirm/cancel workflow with cache stampede protection.
This Repository Contains Implementation for Database Locking with Spring Data JPA and Test It Using JMeter to Send Concurrent Requests to Test Optimistic Locking
Reproduces and fixes a classic double-spend race condition under real concurrent load (500 goroutines) — naive read-then-write vs. atomic conditional UPDATE in PostgreSQL. Day 15 of a systems engineering challenge.
A project to master database transaction management and isolation levels in PostgreSQL by investigating and reproducing ACID anomalies including Lost Update, Non-Repeatable Reads, Phantom Reads, and Deadlocks.
A robust Ticket Booking API built with Node.js (Express) and PostgreSQL, engineered to handle intense concurrent requests (ticket war simulation) without double-booking bugs using Database Transactions and Pessimistic Locking (FOR UPDATE).
Lost update under contention and three ways to close the gap: atomic UPDATE, pessimistic lock (SELECT ... FOR UPDATE) and optimistic lock (@Version + bounded retry). Spring Boot 4, Spring Data JPA, PostgreSQL, with a bench that measures all of them on the same row.
Node.js concurrency patterns explored through practical race conditions, synchronization strategies, and consistency guarantees.
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