Concurrency

If you have ever tried to push a RAG or Multi-Agent system written in Python (using LangChain or AutoGen) into a Production environment with thousands of concurrent requests, you have likely tasted the pain. Servers run out of RAM, CPUs become bottlenecked, and latency skyrockets uncontrollably. The root cause does not lie in the LLMs. The root cause lies in the Orchestration Architecture you are using. In Part 1 of this series, we will dissect why Python falls short in the Agentic era, and why Golang, combined with the Eino (CloudWeGo) framework, is the “ultimate weapon” for building the brain of next-generation e-commerce search systems. ...

Prerequisite: Part 6 of the System Design Masterclass. Read Part 5: Kafka & Event-Driven to understand event sourcing patterns before tackling lock coordination. Answer-first: Distributed locks solve the mutual exclusion problem across independent servers — ensuring only one server can modify a shared resource at a time. Redis Redlock provides high-performance locking using majority quorum across multiple master nodes; etcd provides stronger guarantees via Raft consensus at the cost of higher latency. ...

Answer-first: Production Go concurrency patterns: errgroup worker pools, semaphore-based rate limiting, bounded queues, and graceful backpressure for microservices. Every Go engineer eventually writes the same mistake: a loop that launches goroutines unconditionally. In a demo with 10 items, this works beautifully. In production with 50,000 incoming webhook events, it spawns 50,000 goroutines simultaneously, exhausts memory, and triggers the OOM killer. Kubernetes restarts the pod. The on-call engineer gets paged at 3 AM. ...