Over ten years of BAT architecture experience, shared generously!
This guide explains practical Kafka configurations—such as acks, idempotence, replication factor, and offset handling—to ensure messages are reliably produced, stored, and consumed without loss across network glitches or broker failures.
This article explains why Redis can handle massive concurrent traffic by storing all data in RAM, using a single‑threaded event loop, leveraging Linux epoll for I/O multiplexing, and employing highly optimized in‑memory data structures that keep latency in the microsecond range.
This article explains the key techniques Kafka uses to maximize throughput, including sequential disk writes with zero‑copy, partition‑based parallelism, batch processing with compression, and configurable asynchronous replication for balancing performance and reliability.
The article explains what cache penetration is in high‑concurrency architectures, why it occurs, and presents four practical mitigation strategies—including caching empty values, using Bloom filters, validating request data, and applying rate‑limiting—to protect backend databases from overload.
This article explains Nginx’s core concurrency mechanisms—including its multi‑process architecture, event‑driven model, I/O multiplexing techniques like epoll, and non‑blocking I/O—highlighting how they provide high stability, low resource consumption, and excellent performance for high‑traffic network services.
This article explains Redis master‑slave replication, covering the roles of master and replica, the full‑copy synchronization process, the incremental sync mechanism, and the step‑by‑step workflow illustrated with diagrams to help engineers implement reliable high‑availability data stores.
This guide explains how to tune Nginx for maximum performance by leveraging multi‑core CPUs, increasing concurrent connections, enabling zero‑copy file transfer, and optimizing keep‑alive settings, with concrete configuration examples and practical recommendations.
Learn how Nginx’s powerful caching mechanism can slash response times by up to tenfold, reduce backend load, and increase throughput, with detailed explanations of the caching workflow, essential configuration directives, micro‑cache tricks, and FastCGI caching examples for high‑traffic services.
This guide explains how to build a million‑request‑per‑second microservice system by properly splitting business domains, partitioning data with vertical and horizontal sharding, applying robust rate‑limiting techniques, and implementing circuit‑breaking and degradation strategies to maintain stability.
This article explains the fundamentals of distributed communication, outlines essential elements such as serialization, transport protocols, service discovery, load balancing, security, and reliability, and reviews popular frameworks like gRPC, Dubbo, Thrift, and OpenFeign for building robust backend architectures.
