Over ten years of BAT architecture experience, shared generously!
Redis achieves sub‑millisecond response times by storing all data in RAM, using a single‑threaded event loop with I/O multiplexing (epoll/select/poll), and employing highly optimized data structures such as skip lists and hash tables that provide O(1) or O(log N) operations.
This guide explains how to maximize Nginx concurrency by configuring worker_processes, worker_connections, keepalive_timeout, and high‑performance I/O options such as sendfile, tcp_nopush, and tcp_nodelay, including formulae, example calculations, recommended values, and system‑level considerations.
This article explains what high concurrency means for Kafka, outlines key performance metrics such as QPS, TPS, throughput and latency, and provides concrete configuration and architectural techniques—including broker optimization, horizontal scaling, network batching, and zero‑copy—to achieve write rates exceeding one million records per second.
This guide explains Spring Cloud Config's role in microservice architectures, covering its core features, architecture components, workflow, and step‑by‑step setup—including server creation, repository configuration, client integration, and optional dynamic refresh—while providing practical code snippets and diagrams.
The article explains how Kafka achieves high throughput by writing messages sequentially to disk, leveraging OS page cache and zero‑copy system calls, using partitioned topics for parallelism, batching and compressing records on both producer and broker sides, and employing asynchronous replication with configurable persistence strategies.
Redis delivers millisecond‑level response and millions of operations per second by storing data entirely in memory, using a single‑threaded event‑driven model, efficient I/O multiplexing with epoll, and highly optimized data structures such as strings, hashes, lists, sets and sorted sets.
This article explains Redis master‑slave replication, covering its purpose for data redundancy, read/write separation, and high availability, and details the full‑synchronization and incremental‑synchronization processes with step‑by‑step diagrams.
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.
