Mike Chen's Internet Architecture
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Mike Chen's Internet Architecture

Over ten years of BAT architecture experience, shared generously!

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Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 24, 2025 · Operations

How to Deploy a Two‑Location Three‑Center Disaster‑Recovery Architecture for High Availability

This guide explains the two‑location three‑center disaster‑recovery pattern, describing its purpose, typical deployment across two cities and three data centers, and step‑by‑step recommendations for same‑city dual‑active or primary‑backup setups, remote backup strategies, traffic routing, and essential monitoring.

GSLBSLBdisaster recovery
0 likes · 5 min read
How to Deploy a Two‑Location Three‑Center Disaster‑Recovery Architecture for High Availability
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 22, 2025 · Fundamentals

Why Is StringBuilder Faster Than StringBuffer? Understanding Java’s Mutable vs Immutable Strings

This article explains the fundamental differences between Java's String, StringBuilder, and StringBuffer—covering immutability, thread‑safety, internal caching mechanisms, and performance characteristics—while providing concrete code examples that illustrate how each class behaves in single‑threaded and multi‑threaded scenarios.

JavaStringStringBuffer
0 likes · 6 min read
Why Is StringBuilder Faster Than StringBuffer? Understanding Java’s Mutable vs Immutable Strings
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 22, 2025 · Operations

How to Diagnose and Resolve Kafka Consumer Lag Quickly

When Kafka consumers fall behind, this guide walks you through confirming the backlog, pinpointing bottlenecks in production, consumption, or brokers, and applying concrete steps—such as checking offsets, comparing TPS, inspecting consumer logic, and adjusting partitions—to efficiently eliminate lag.

Consumer LagKafka
0 likes · 4 min read
How to Diagnose and Resolve Kafka Consumer Lag Quickly
Mike Chen's Internet Architecture
Mike Chen's Internet Architecture
Dec 21, 2025 · Backend Development

How Elasticsearch Scales to Billions of Queries: Sharding, Inverted Index, Distributed Execution, and Replication

Elasticsearch achieves billion‑scale search performance by combining horizontal sharding, immutable inverted‑index segments, a two‑stage distributed Query/FETCH model, and multiple replicas with a coordinator node to ensure high concurrency, scalability, and availability.

Distributed QueryElasticsearchSharding
0 likes · 4 min read
How Elasticsearch Scales to Billions of Queries: Sharding, Inverted Index, Distributed Execution, and Replication