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

High-concurrency PHP 8 e-commerce core featuring MySQL pessimistic database locking, transactional inventory management, and Docker setup.

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.

This Repository Contains Implementation for Database Locking with Spring Data JPA and Test It Using JMeter to Send Concurrent Requests to Test Optimistic Locking

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.

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.

Node.js concurrency patterns explored through practical race conditions, synchronization strategies, and consistency guarantees.