maglietti commented on code in PR #288: URL: https://github.com/apache/ignite-website/pull/288#discussion_r2557782458
########## _src/_blog/apache-ignite-3-architecture-part-1.pug: ########## @@ -0,0 +1,286 @@ +--- +title: "Apache Ignite 3 Architecture Series: Part 1 — When Multi-System Complexity Compounds at Scale" +author: "Michael Aglietti" +date: 2025-11-25 +tags: + - apache + - ignite +--- + +p Apache Ignite 3 shows what really happens once your “good enough” multi-system setup starts cracking under high-volume load. This piece breaks down why the old stack stalls at scale and how a unified, memory-first architecture removes the latency tax entirely. + +<!-- end --> + +p Your high-velocity application began with smart architectural choices: PostgreSQL for reliable transactions, Redis for fast cache access, and custom processing for domain-specific logic. These decisions powered early success and growth. + +p But success changes the game. Your system now handles thousands of events per second, and customers expect microsecond-level response times. The same architectural choices that enabled growth now create performance bottlenecks that compound with every additional event. + +p At high event volumes, data movement between systems becomes the primary performance constraint. + +hr + +h3 The Scale Reality for High-Velocity Applications + +p As event volume grows, compromises that once seemed reasonable become critical bottlenecks. Consider a financial trading platform, gaming backend, or IoT processor handling tens of thousands of operations per second. + +h3 Event Processing Under Pressure + +p High-frequency event characteristics +ul + li Events arrive faster than traditional batch processing can handle + li Each event requires immediate consistency checks against live data + li Results must update multiple downstream systems simultaneously + li Network delays compound into user-visible lag + li Traffic spikes create systemic pressure — traditional stacks drop connections or crash when overwhelmed + +p The compounding effect +p At 100 events / s, 2 ms network latency adds negligible overhead. +p At 10 000 events / s, that same latency produces a 20-second processing backlog. +p At 50 000 + events / s, traditional systems collapse — dropping connections and losing data when they’re needed most. +p The math scales against you. + +hr + +h3 When Smart Choices Become Scaling Limits + +p Initial Architecture — works well at low scale + +pre.mermaid. Review Comment: Mermaid is not working on the site. Please render this graph locally and put the image in place. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
