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Explore EP-4: Search in 100 Crores of Data in Milliseconds | Bloom vs Cuckoo vs Binary Fuse with Real code with 23,289 views, 541 likes, and 57 comments. Experience the impact of this video content that has captured audience attention.

Episode 4 - Search like a pro: How I Search in10-100 Crore Records in Milliseconds using Bloom/Cuckoo/XOR/Binary Fuse filters 00:00 - Introduction & The 100 Crore Data Problem 02:30 - Animated Promo & Visualisation 02:58 - Real-World Use Cases: UPI, Banking & Cloudflare 04:26 - Why Traditional DBs & Indexing Fall Short at Scale 05:44 - Probabilistic Data Structures: Bloom, Cuckoo, Xor & Binary Fuse 07:48 - High-Level Design (HLD): Fast Validation Architecture 14:14 - Filter Comparison: Deletion, Accuracy & Memory Trade-offs 19:10 - Brainstorming Filter Choices & Google Chrome Case Study with AI 24:39 - Low-Level Design (LLD) & Request Flow Pipeline 33:27 - Project Setup: Node.js, Rust, MongoDB & ElasticSearch 34:36 - Load Testing with Grafana K6: Bloom Filter Benchmarks 39:39 - Binary Fuse Filter Performance & Memory Analysis 41:51 - Rust Implementation & 32-bit Cuckoo Filter Scaling 42:44 - Cloud Cost Analysis: Scaling to 300 Crore Records 48:00 - Live Postman Demo & Real-World Query Debugging 53:23 - Summary & What's Next Share your feedbacks, requests & suggestions with us - https://forms.gle/KnDMvco3oJ1fZBut5 Fast search is everything. When your data grows from thousands to crores, normal database queries fail. Indexing helps, but it is not enough. In this video, I will show you how big companies like Google handle this. We will start with common solutions and then move to the real pro solutions. **In this video you will learn:** * Why search becomes slow with big data * Why indexing, partitioning, sharding and Elastic Search are not always enough * How caching and Redis solves only half the problem * What are probabilistic filters * Bloom Filters vs Cuckoo Filters vs XOR vs Binary Fuse Filters * Which filter Google actually uses and why * Full HLD and LLD breakdown * Live coding and real implementation * Load test results with numbers * Can Rust or Go make it 10x faster? We will compare everything at the end so you know exactly which filter to use for your project. If you are preparing for system design interviews or building a scalable backend, do not miss this. Subscribe for weekly system design episodes. Comment your use case, I will reply which filter is best for you. Code (GitHub): https://github.com/xDAnkit/system-design-journey Assets: https://drive.google.com/drive/folders/1FBt1N8Jk8T5Qi_TSMZup9mKulVNmj7aC?usp=sharing System Design S1 Playlist: https://youtube.com/playlist?list=PLSvDIozU0pRk&si=etRaAC_X_RfuDyUS - EP-1: https://www.youtube.com/watch?v=DCYaJb8Bzdk - EP-2: https://www.youtube.com/watch?v=PMD1fat6QdY - EP-3: https://www.youtube.com/watch?v=pgLHXxW3Mg0 Question for you: What is the hardest part of System Design for you? Comment below. Would love to connect ♥️ LinkedIn - https://linkedin.com/in/xdankit Instagram - https://instagram.com/xdankit Facebook - https://facebook.com/xdankit #SystemDesign #BackendEngineering #BloomFilter #BinaryFuse #CuckooFilter #HighLevelDesign #LowLevelDesign #Golang #Redis

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