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Scaling the Web: Lessons from Jetty, Bitronix, Terracotta, Quartz – Ludovic Orban
Episode description:
Ludovic Orban (Jetty Maintainer and Performance Engineer at WebTide) joins Marco to break down what “web scale” really means, why most systems don’t need Google-level architecture, and how modern Java servers achieve massive throughput. From Jetty’s design philosophy to multi-core CPU realities, distributed transactions, Terracotta’s wild JVM clustering experiments, and the limits of AI-generated code, this episode dives deep into how high-performance Java is built today.
💡 In this episode:
What “web scale” actually means for real-world systems
Jetty vs Tomcat and how Jetty became a fast, modern web server
CPU evolution, memory bottlenecks, and multi-core performance
Distributed transactions, XA pitfalls, and compensating patterns
Terracotta’s clustered JVM: engineering lessons and war stories
Why developers struggle with performance (and how to learn it)
AI, coding assistance, and why Ludovic doesn’t trust generated code
⏱️ Timestamps:
(00:00) Teaser
(01:10) Who is Ludovic Orban?
(02:04) What “web scale” really means, and who actually needs it
(03:54) How hardware changes reshaped software performance
(06:28) Cloud, containers, and why performance still matters
(07:28) Jetty vs Tomcat: history, adoption & performance
(10:47) What makes Jetty “fast” in practice
(13:21) How the Jetty team prioritizes performance
(15:10) Recent work: fixing complex HTTP/2 bugs
(16:38) How WebTide supports customers beyond Jetty issues
(17:52) Bitronix: Why Ludovic built his own transaction manager
(20:45) Open-source challenges. The rise and fall of Bitronix
(24:19) Distributed transactions vs compensating transactions
(27:07) Where to learn more: Atomikos and modern approaches
(28:25) Terracotta: clustering JVMs and wild engineering stories
(31:20) What Terracotta taught him about the JVM
(33:48) Real-world Java performance mistakes developers make
(40:22) Why learning performance is so hard
(45:40) Kubernetes, abstraction, and performance visibility
(48:50) Hardware that excites Ludovic: Oxide Computer
(50:42) His take on AI and why he doesn’t trust generated code
(53:30) Lessons from Jetty, Terracotta, Bitronix, and Quartz
(56:10) Rapid-fire questions
(01:01:15) Giveaway and outro
New episodes every other Wednesday. Subscribe for more deep, developer-focused conversations.
🎥 Watch the full episode on YouTube: https://youtu.be/fJvg5zTKHeE
View all episodes
By IntelliJ IDEAEpisode description:
Ludovic Orban (Jetty Maintainer and Performance Engineer at WebTide) joins Marco to break down what “web scale” really means, why most systems don’t need Google-level architecture, and how modern Java servers achieve massive throughput. From Jetty’s design philosophy to multi-core CPU realities, distributed transactions, Terracotta’s wild JVM clustering experiments, and the limits of AI-generated code, this episode dives deep into how high-performance Java is built today.
💡 In this episode:
What “web scale” actually means for real-world systems
Jetty vs Tomcat and how Jetty became a fast, modern web server
CPU evolution, memory bottlenecks, and multi-core performance
Distributed transactions, XA pitfalls, and compensating patterns
Terracotta’s clustered JVM: engineering lessons and war stories
Why developers struggle with performance (and how to learn it)
AI, coding assistance, and why Ludovic doesn’t trust generated code
⏱️ Timestamps:
(00:00) Teaser
(01:10) Who is Ludovic Orban?
(02:04) What “web scale” really means, and who actually needs it
(03:54) How hardware changes reshaped software performance
(06:28) Cloud, containers, and why performance still matters
(07:28) Jetty vs Tomcat: history, adoption & performance
(10:47) What makes Jetty “fast” in practice
(13:21) How the Jetty team prioritizes performance
(15:10) Recent work: fixing complex HTTP/2 bugs
(16:38) How WebTide supports customers beyond Jetty issues
(17:52) Bitronix: Why Ludovic built his own transaction manager
(20:45) Open-source challenges. The rise and fall of Bitronix
(24:19) Distributed transactions vs compensating transactions
(27:07) Where to learn more: Atomikos and modern approaches
(28:25) Terracotta: clustering JVMs and wild engineering stories
(31:20) What Terracotta taught him about the JVM
(33:48) Real-world Java performance mistakes developers make
(40:22) Why learning performance is so hard
(45:40) Kubernetes, abstraction, and performance visibility
(48:50) Hardware that excites Ludovic: Oxide Computer
(50:42) His take on AI and why he doesn’t trust generated code
(53:30) Lessons from Jetty, Terracotta, Bitronix, and Quartz
(56:10) Rapid-fire questions
(01:01:15) Giveaway and outro
New episodes every other Wednesday. Subscribe for more deep, developer-focused conversations.
🎥 Watch the full episode on YouTube: https://youtu.be/fJvg5zTKHeE