MaxScale Multiplexing and Caching – Part 1

Part 1 – Complete Guide for High-Concurrency Workloads

This multi-part series breaks down each section into easy logical steps.

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Introduction

High-concurrency database workloads are challenging for any organization. Whether it’s a major e-commerce event, financial trading spikes, or peak traffic in SaaS applications, managing thousands of simultaneous connections while maintaining low latency is critical. Many teams initially rely on traditional connection pooling or external proxies, but these solutions can become complex, resource-heavy, and hard to scale.

In this blog, we explore how MariaDB MaxScale can simplify and optimize your database architecture through multiplexing and caching. We’ll provide a detailed, practical guide covering:

• Understanding MaxScale thread architecture
• Multiplexing for high concurrency
• Caching strategies (local, Memcached, Redis)
• Combined multiplexing and caching for superior throughput
• Real-world testing with Sysbench
• Best practices, tuning, and warnings

By following this guide, you’ll learn how to reduce backend load, improve throughput, and maintain predictable performance even under extreme spikes, just like the customers who rely on MaxScale for their high-demand workloads.

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Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7 | Page 8