External Memory Searching and Indexing Fundamentals

When datasets grow too large to fit into RAM, traditional in-memory search algorithms fail. Understanding how to design and analyze algorithms that minimize slow disk I/O is critical for modern database engines and large-scale data processing. This text-based course guides you through the foundational principles of external memory searching, transitioning from basic I/O models to complex indexing structures. You will learn how to analyze algorithm performance using the I/O complexity model and apply these concepts to real-world database storage engines. What you'll learn: 1. Understand the external memory model and how it differs from traditional RAM models. 2. Analyze the structure, search, and insertion operations of B-Trees and B+ Trees. 3. Explore external sorting algorithms, including multiway merge sort, to organize massive files. 4. Compare cache-aware and cache-oblivious data structures for modern hardware architectures. 5. Examine Log-Structured Merge (LSM) Trees and their application in write-heavy storage systems. 6. Practice evaluating query performance and disk access costs through structured written exercises and conceptual quizzes. Starting with fundamental definitions of memory hierarchies and block transfers, the course progresses through tree-based indexing, external sorting, and modern log-structured storage patterns. This course is designed for beginners, software developers, database enthusiasts, and computer science students who want to understand how databases manage data under the hood, with no advanced prerequisites required. Start reading today to master the core algorithms that power modern high-performance database engines.