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Algorithm Engineering: How Textbook Algorithms Actually Run in Production Systems
Every production system runs an algorithm you already studied — at a layer you have not yet connected. Git addresses files by their hash. A load balancer is consistent hashing. A build system is a topological sort. A query planner is backtracking. Algorithm Engineering opens each chapter with a real system, then reverse-engineers the algorithm inside it — so the algorithms you know on paper become the ones you recognise on sight, in code, and in interviews.
WHAT YOU'LL BE ABLE TO DO
Read any system for its algorithm — find the bottleneck, name the data structure and the algorithm, know the complexity.
Recognise the interview pattern behind the production system — two pointers, sliding window, BFS/DFS, backtracking, union-find, tries, dynamic programming. Each chapter names the interview problem shapes that share its algorithmic signature.
Know why the theoretically better algorithm sometimes loses — cache misses, memory access patterns, and amortized cost decide real performance as often as Big-O does.
Choose the right algorithm on purpose — a decision framework driven by input size, update frequency, query type, and consistency requirement.
Trade exactness for scale when it pays — Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, and when approximation is the correct engineering answer.WHO THIS BOOK IS FOR
Software engineers who learned the textbook complexity and now need to ship code that runs at scale — and who want their interview preparation grounded in how algorithms actually appear in real systems, not in isolated puzzles. A strong companion for coding interview and system design interview preparation.
WHAT'S INSIDE
22 chapters across 5 parts — pattern recognition, graph algorithms in systems, dynamic programming engineering, randomisation and analysis, and algorithm analysis in practice.
Application-first structure — every chapter opens with a production system (Git, Kubernetes, Prometheus, DNS, PostgreSQL, MapReduce) and works back to the algorithm running inside it.
Pattern-recognition callouts — every chapter names the interview problem shapes that share its signature, so the book doubles as interview prep without reading like a problem workbook.
Exercises at three levels — Understand, Apply, Design — every Level 3 a system-design problem. Runnable Python throughout.
TOPICS COVERED
Consistent hashing and virtual nodes · content-addressable storage · rolling hashes · B-tree indexes · tries and longest-prefix match · MapReduce and external merge sort · quickselect · two pointers, sliding windows, prefix sums · Fenwick trees · KMP · BFS, DFS, and backtracking · Dijkstra and A* routing · topological sort · bipartite and stable matching · graph partitioning · memoization as caching · edit distance and LCS · knapsack and bin packing · Bloom filter, HyperLogLog, Count-Min Sketch · reservoir sampling · amortized analysis and union-find · cache-conscious algorithms · benchmarking and profiling · algorithm selection.
ABOUT THE SERIES
Book 2 of The Computing Series — an eight-book curriculum from first principles to product strategy. Book 1 (Foundations of Algorithmic Thinking) establishes the mathematics; Book 2 puts those algorithms to work in real systems; the series continues through infrastructure, system design, code architecture, leadership, and product strategy. Book 0 (The Engineer's Map) is the reference companion.
ABOUT THE AUTHOR
Harish Kumar is a software engineer and systems thinker. The Computing Series compresses the full stack of engineering knowledge — from first principles to product strategy — into one connected learning system.
WHAT YOU'LL BE ABLE TO DO
Read any system for its algorithm — find the bottleneck, name the data structure and the algorithm, know the complexity.
Recognise the interview pattern behind the production system — two pointers, sliding window, BFS/DFS, backtracking, union-find, tries, dynamic programming. Each chapter names the interview problem shapes that share its algorithmic signature.
Know why the theoretically better algorithm sometimes loses — cache misses, memory access patterns, and amortized cost decide real performance as often as Big-O does.
Choose the right algorithm on purpose — a decision framework driven by input size, update frequency, query type, and consistency requirement.
Trade exactness for scale when it pays — Bloom filters, HyperLogLog, Count-Min Sketch, reservoir sampling, and when approximation is the correct engineering answer.WHO THIS BOOK IS FOR
Software engineers who learned the textbook complexity and now need to ship code that runs at scale — and who want their interview preparation grounded in how algorithms actually appear in real systems, not in isolated puzzles. A strong companion for coding interview and system design interview preparation.
WHAT'S INSIDE
22 chapters across 5 parts — pattern recognition, graph algorithms in systems, dynamic programming engineering, randomisation and analysis, and algorithm analysis in practice.
Application-first structure — every chapter opens with a production system (Git, Kubernetes, Prometheus, DNS, PostgreSQL, MapReduce) and works back to the algorithm running inside it.
Pattern-recognition callouts — every chapter names the interview problem shapes that share its signature, so the book doubles as interview prep without reading like a problem workbook.
Exercises at three levels — Understand, Apply, Design — every Level 3 a system-design problem. Runnable Python throughout.
TOPICS COVERED
Consistent hashing and virtual nodes · content-addressable storage · rolling hashes · B-tree indexes · tries and longest-prefix match · MapReduce and external merge sort · quickselect · two pointers, sliding windows, prefix sums · Fenwick trees · KMP · BFS, DFS, and backtracking · Dijkstra and A* routing · topological sort · bipartite and stable matching · graph partitioning · memoization as caching · edit distance and LCS · knapsack and bin packing · Bloom filter, HyperLogLog, Count-Min Sketch · reservoir sampling · amortized analysis and union-find · cache-conscious algorithms · benchmarking and profiling · algorithm selection.
ABOUT THE SERIES
Book 2 of The Computing Series — an eight-book curriculum from first principles to product strategy. Book 1 (Foundations of Algorithmic Thinking) establishes the mathematics; Book 2 puts those algorithms to work in real systems; the series continues through infrastructure, system design, code architecture, leadership, and product strategy. Book 0 (The Engineer's Map) is the reference companion.
ABOUT THE AUTHOR
Harish Kumar is a software engineer and systems thinker. The Computing Series compresses the full stack of engineering knowledge — from first principles to product strategy — into one connected learning system.
264 pages, Paperback
Published September 30, 2026
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