Read heavy VS Write heavy architectures | #subashpalvel | #SubashPalvel |

📌 A single viral post is read a hundred million times, but a profile setting is updated once. In this video, I break down the core architectural differences between Read-Heavy and Write-Heavy systems, comparing CDNs and Redis caches to LSM Trees and compaction.

👇 KEY TAKEAWAYS & WHAT YOU'ALL LEARN:
- 💡 How to scale read-heavy workloads using CDNs, Redis caches, and primary-replica database setups
- ⚡ Mitigating database replication lag and stale reads
- 🛡️ Storage Engine internals: Read-optimized B-Trees vs. Write-optimized LSM-Trees
- 🛑 How MemTables, SSTables, and background compactions ingest high-write bursts
- 🔌 Buffering write peaks using partitioned Apache Kafka messaging queues
- ⚙️ Implementing the CQRS pattern to decouple Command (Write) and Query (Read) system pipelines

⏱️ TIMESTAMPS / CHAPTERS:
00:00 - Introduction (Profiles at Scale)
00:17 - Read/Write Ratios
00:36 - Read-Heavy Scaling Tiers
00:52 - Database Replication Lag
01:07 - Cache-Aside Redis Caching
01:22 - B-Tree Index Optimization
01:38 - Write-Heavy Ingest Streams
01:54 - Kafka Buffering queues
02:08 - Disk IOPS & Append-only Logs
02:23 - Relational B-Tree Internals
02:40 - LSM Trees & MemTable Ingestion
02:56 - SSTables & Background Compaction
03:11 - Storage Engines Compared
03:25 - Tech Stack Database Choices
03:39 - CQRS Decoupling Pattern
03:52 - Command vs. Query Flow Mechanics
04:06 - Case Study: E-Commerce Catalog
04:20 - Case Study: IoT Sensor Fleet
04:36 - Core System Bottlenecks
04:51 - Tuning Read Expiries & Replicas
05:04 - Tuning Write Compactions & Buffers
05:17 - Summary

🔗 RESOURCES & LINKS:
- Connect with me on LinkedIn: https://in.linkedin.com/in/subashpalvel
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Видео Read heavy VS Write heavy architectures | #subashpalvel | #SubashPalvel | канала Subash Palvel