Books

This book provides an introduction to the high-level concepts behind query engines and walks through all aspects of building a fully working SQL query engine in Kotlin.

Metagraphs for Agentic AI: Beyond Triples, Beyond HypergraphsFrom Knowledge Graphs to Knowledge ArchitecturesThe triple is not enough.Every AI engineer building agent memory hits the same wall. You model a meeting as a knowledge graph triple — and immediately lose the fact that five people were in the room, a decision was made, and that decision caused three downstream actions. You reify. You flatten. You create workarounds. And your "knowledge graph" becomes a tangle of auxiliary nodes that machines can traverse but no human can reason about.This book shows you the way out. What You'll Learn Metagraphs are graph structures where edges connect sets of nodes to sets of nodes — and where edges themselves can be referenced as first-class nodes. They are the missing data structure for AI agents that need to remember, reason, and coordinate like humans do.This book takes you on a complete journey:Hypergraphs first. You'll learn what they are, why they matter, and where they break down. You'll implement them three ways — in SQL, in LadybugDB (Cypher), and in TypeDB — so you understand the tradeoffs viscerally, not just theoretically.Then metagraphs. You'll see how metagraphs solve the fundamental hypergraph problem (edges that can't be nodes), explore RDF named graphs as a lightweight metagraph, and implement full metagraphs in the same three database paradigms with production-ready, commented code.Then the big ideas. Semantic Spacetime. Holonic systems. Human cognitive architecture mapped to graph structures. Multi-agent coordination. Promise Theory for autonomous AI networks. This is where metagraphs stop being a data structure and become an architecture for intelligence. Who This Book Is For You're a software engineer, AI researcher, or knowledge graph practitioner who builds real systems. You've used Neo4j or RDF stores. You've built RAG pipelines. You've felt the limits. You want to know what comes next.No PhD required. Every concept comes with working code in SQL, Cypher, TypeQL, SPARQL, and Python. What Makes This Book Different This isn't a theoretical monograph. It's the distillation of two and a half years of research, 130+ published articles, and hands-on implementation at the intersection of knowledge graphs and agentic AI.Every chapter bridges theory and practice. You'll read about Basu and Blanning's formal metagraph definition — and then build the schema in PostgreSQL. You'll learn Mark Burgess's Promise Theory — and then model a multi-agent coordination protocol as a six-layer promise graph. You'll understand why labeled property graphs are secretly metagraphs — and what that means for your Neo4j deployment today. 18 Chapters. Three Parts. One Argument. Part I — The Hypergraph Foundation (7 chapters): From the knowledge representation crisis through hypergraph theory to three complete database implementations.Part II — The Metagraph Solution (5 chapters): Metagraphs as the answer, RDF named graphs as a bridge, and three full metagraph implementations with detailed commentary.Part III — Theory Meets Practice (6 chapters): Semantic Spacetime, labeled property graphs, AI memory and human cognition, holonic systems, agent-to-agent interaction, and Promise Graphs for network-of-networks coordination. The Core Thesis If you want AI agents that reason like humans, you need knowledge structures that capture how humans actually organize knowledge — not as flat collections of facts, but as nested, hierarchical, context-rich, temporally-aware structures where relationships themselves carry meaning and can be the subject of further reasoning.Metagraphs are that structure. This book shows you why, and how to build with them.

This series of test-driven small coding puzzles lets you code a database from scratch (no dependencies).We'll cover KV storage engines, LSM-Tree indexes, SQL, concurrent transactions, ACID, etc.

Learn databases from the bottom up by coding your own, in small steps, and with simple Go code (language agnostic).Atomicity & durability. A DB is more than files!Persist data with fsync.Crash recovery.KV store based on B-tree.Disk-based data structures.Space management with a free list.Relational DB on top of KV.Learn how tables and indexes are related to B-trees.SQL-like query language; parser & interpreter.Concurrent transactions with copy-on-write data structures.

Build real-world software by coding a Redis server from scratch.Network programming. The next level of programming is programming for multiple machines. Think HTTP servers, RPCs, databases, distributed systems.Data structures. Redis is the best example of applying data structures to real-world problems. Why stop at theoretical, textbook-level knowledge when you can learn from production software?Low-level C. C was, is, and will be widely used for systems programming and infrastructure software. It’s a gateway to many low-level projects.From scratch. A quote from Richard Feynman: “What I cannot create, I do not understand”. You should test your learning with real-world projects!

What really happens when PostgreSQL executes your query? Follow a SQL statement through every stage of PostgreSQL's internal pipeline—from raw text to returned results—and gain the deep understanding that transforms how you write, tune, and debug database applications.

Learn Polars, the pandas killer for data analysis.

Master distributed systems through visual diagrams — from clock drift and CAP to Paxos, Raft, and distributed transactions, explained with clear illustrations instead of dense academic papers.

When your database outgrows simple optimizations, it's time to think bigger. Lift the Elephant goes beyond query tuning to reveal actionable strategies for scaling PostgreSQL, from partitioning to high-availability architectures. Built on lessons from scaling Hubstaff, this is your playbook for navigating the challenges of database growth.

STOP building fragile AI wrappers. START designing resilient AI systems. Lots of companies are trying to make their small AI experiments into big products, but they don't have a good plan. Engineers need a practical guide to build these new AI systems the right way - so they can handle scale, be reliable, and won't cost too much. This book is that guide. It explains how to design systems that use AI models. This book breaks down the architecture of real AI applications, like an AI-powered code editor or a smart learning app. It gives you a deep, practical look at the real-world challenges and solutions for building these systems. It discusses system design concepts for systems that use LLMs.

Building a chat feature over your product docs? You need a vector database.Adding recommendations to your app? Vector database.Searching 100 million images by visual similarity? Definitely a vector database.Yet most engineers stumble into these projects unprepared. They don't understand the trade-offs between IVF, HNSW, and Product Quantization. They pick the wrong similarity metric. They scale the wrong way.This book is the missing manual. It covers:The mathematics of embeddings and why they workHow to choose the right algorithm for your latency and accuracy constraintsDeep dives into Pinecone, Milvus, Weaviate, Qdrant, and ChromaBuilding production RAG systems that actually workIndustry case studies from Spotify to JPMorgan Chase20+ ready-to-run recipes for common scenarios

Explore the power of Rust with "Rust Projects: Write a Redis Clone". This hands-on guide takes you through building a Redis-inspired database from the ground up, introducing key programming concepts like TCP connections, the RESP protocol, and concurrency. Following the CodeCrafters challenge, this book gradually builds your skills, making complex topics accessible. Whether you're new to Rust or looking to deepen your understanding, this project-based journey offers practical, real-world insights into modern systems programming. The book contains 40% discount code for CodeCrafters.io!

Readers will learn discrete mathematical abstracts as well as its implementation in algorithm and data structures shown in various programming languages, such as C, C++, PHP, Java, C#, Python and Dart. This book combines two major components of Mathematics and Computer Science under one roof.

Slow SQL queries waste time, increase costs, and limit scalability. Mastering SQL Performance Optimization teaches you how to analyze execution plans, optimize queries and indexes, and solve real-world performance bottlenecks across today's leading database platforms.