The Fan-out/Fan-in pattern enhances concurrency by distributing tasks among multiple workers (fan-out) and collecting their results (fan-in). It involves generating work items, processing them across workers, and gathering results without blocking. This pattern optimizes CPU usage and is suitable for various applications like parallel data processing and API handling. Continue reading →

The Pipeline pattern facilitates concurrent processing by connecting several stages that transform data. The implementation example includes generating random numbers, squaring them, and adding ten. Each stage operates independently through channels, ensuring synchronization. This pattern enhances scalability, resource efficiency, and flow control, making it suitable for various data processing applications. Continue reading →

This series explores essential concurrency patterns in Go, essential for building scalable systems in today’s multi-core environments. Covering 12 patterns with practical examples and use cases, it aims to deepen Go developers’ understanding of concurrency, enhancing their ability to design robust applications. The first focus is on the Pipeline Pattern. Continue reading →

Concurrency control in relational databases ensures data integrity when multiple transactions happen concurrently. Various methods include locking, preventing conflicts by restricting data access; optimistic concurrency control, assuming rare conflicts and rolling back and retrying when they occur; and timestamp ordering, prioritizing transactions based on their start time. The method choice depends on the database needs... Continue reading →