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