How We Upload Multi-GB Files via REST (Without Blowing Up Memory)

Originally published on Hashnode The Challenge Processing large payloads through REST APIs presents a fundamental challenge: how do you accept, validate, encrypt, and store multi-gigabyte data streams without exhausting server memory or degrading response times? Traditional approaches that buffer entire payloads into memory fail spectacularly when data sizes exceed available heap space. The naive solution of simply increasing memory creates a cascading problem—fewer concurrent requests, higher infrastructure costs, and unpredictable OutOfMemoryError failures. On the other hand, the typical “upload first, validate later” pattern used by many large systems fails to provide client feedback within the same request. An Elegant Solution: The Streaming Pipeline Stream-oriented architecture can process arbitrarily large payloads with constant, minimal memory overhead. The secret lies in treating the data as a continuous flow of bytes rather than a discrete object to be loaded into memory. Arch