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Mike Chen's Internet Architecture

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Latest from Mike Chen's Internet Architecture

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Mike Chen's Internet Architecture

Dec 3, 2025 · Big Data

Kafka High‑Throughput Tricks: Sequential Writes, Zero‑Copy, Partitioning

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.

CompressionKafkabatching

0 likes · 4 min read

Kafka High‑Throughput Tricks: Sequential Writes, Zero‑Copy, Partitioning

Mike Chen's Internet Architecture

Dec 3, 2025 · Databases

Why Redis Achieves Millisecond Latency and Million QPS: Core Design Principles

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.

I/O multiplexingIn-Memory DatabaseRedis

0 likes · 4 min read

Why Redis Achieves Millisecond Latency and Million QPS: Core Design Principles

Mike Chen's Internet Architecture

Dec 1, 2025 · Databases

How Redis Master‑Slave Replication Enables Real‑Time Sync, Read/Write Separation, and Failover

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.

DatabaseMaster‑SlaveRedis

0 likes · 4 min read

How Redis Master‑Slave Replication Enables Real‑Time Sync, Read/Write Separation, and Failover

Mike Chen's Internet Architecture

Nov 27, 2025 · Backend Development

How to Prevent Kafka Message Loss: Producer, Broker, and Consumer Settings

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.

Broker ConfigConsumer ConfigKafka

0 likes · 4 min read

How to Prevent Kafka Message Loss: Producer, Broker, and Consumer Settings

Mike Chen's Internet Architecture

Nov 26, 2025 · Databases

How Redis Achieves Million-Connection Performance: Memory, Single Thread, and Epoll

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.

In-Memory DatabaseRedisdata structures

0 likes · 5 min read

How Redis Achieves Million-Connection Performance: Memory, Single Thread, and Epoll

Mike Chen's Internet Architecture

Nov 25, 2025 · Big Data

How Kafka Achieves High Throughput: Sequential Writes, Zero‑Copy, Partitioning, and More

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.

CompressionKafkaasync-replication

0 likes · 5 min read

How Kafka Achieves High Throughput: Sequential Writes, Zero‑Copy, Partitioning, and More

Mike Chen's Internet Architecture

Nov 24, 2025 · Backend Development

How to Prevent Cache Penetration in High‑Concurrency Systems

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.

Bloom filterCachecache penetration

0 likes · 6 min read

How to Prevent Cache Penetration in High‑Concurrency Systems

Mike Chen's Internet Architecture

Nov 18, 2025 · Backend Development

Understanding Nginx’s Core Concurrency Model: Multi‑Process, Event‑Driven, and Non‑Blocking I/O

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.

IO Multiplexingbackendconcurrency

0 likes · 5 min read

Understanding Nginx’s Core Concurrency Model: Multi‑Process, Event‑Driven, and Non‑Blocking I/O

Mike Chen's Internet Architecture

Nov 17, 2025 · Databases

Understanding Redis Master‑Slave Replication: Full and Incremental Sync Explained

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.

Master‑SlaveRedis

0 likes · 4 min read

Understanding Redis Master‑Slave Replication: Full and Incremental Sync Explained

Mike Chen's Internet Architecture

Nov 16, 2025 · Operations

Boost Nginx Performance: 5 Essential Settings for Ten‑Fold Throughput

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.

NginxServer configurationbackend

0 likes · 3 min read

Boost Nginx Performance: 5 Essential Settings for Ten‑Fold Throughput