Databases

Language models guess. Knowledge graphs know. Rusty Graph shows you how to build a local AI agent whose memory is a real knowledge graph — typed, validated, reasoned over, and queryable with SPARQL — all inside a single static Rust binary. No JVM. No Docker sidecar. No Python runtime bolted to the side. You will build Ares a research-assistant agent that observes papers, forms beliefs, makes promises to other agents, and tracks the provenance of every fact it holds. Chapter by chapter, Ares grows from an empty Cargo workspace into a full pipeline: > load → reason → validate → query → answer All of it in under a thousand lines of idiomatic Rust, using three crates that actually work today: `oxigraph`, `reasonable`, and `rudof_lib`. `grapfeo` You will learn how to- Model a domain as RDF triples and load them into an embedded store. - Write RDFS and OWL 2 RL axioms that infer trust, identity, and inverse relationships — automatically. - Guard your graph with SHACL shapes that reject bad data at the boundary, not in production. - Query everything with SPARQL, from simple lookups to federated queries across named graphs. - Wire the graph into a hybrid RAG pipeline so your LLM answers are grounded in facts, not vibes. Who it is forRust developers building agents, assistants, or any system where the answer "the model said so" is not good enough. You should be comfortable with Cargo and traits. You do **not** need any prior semantic-web background — every concept is introduced through Ares before any formal definition appears. Why Rust, why nowLocal agents are the next deployment target: a user's laptop, a Raspberry Pi, a WASM sandbox. Python cannot go there comfortably. Rust can. This book is the missing manual for the Rust side of the semantic web — the one that tells you exactly which crates work, where the ecosystem is thin, and how to ship anyway. Stop hoping your model tells the truth. Give it a graph that does.

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.

A high-performance data access layer must resonate with the underlying database system. Knowing the inner workings of a relational database and the data access frameworks in use can make the difference between a high-performance enterprise application and one that barely crawls.

Running ClickHouse at petabyte scale takes more than knowing SQL. This practical guide shows experienced engineers how to design, deploy and operate production clusters, from data modeling and ingestion to Kubernetes, query tuning, observability, disaster recovery and cost control, with real-world patterns and concrete configurations throughout.

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.

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.

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.

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!

What really happens between typing a SQL query and getting a result? Build a database engine from the ground up in Rust and find out. You’ll work through storage, indexing, concurrency, recovery and query processing while turning each chapter’s code into one working system.

Learn Polars, the pandas killer for data analysis.

You don't need a graph database. You need graph thinking inside DuckDB. GraphDuck takes you from SQL adjacency lists to metagraphs, hypergraphs, and hybrid Graph RAG pipelines — all inside DuckDB. Learn to model knowledge graphs, build AI agent memory systems, run graph algorithms, and combine vector search with graph traversal in a single embedded database. Every concept comes with runnable code. No infrastructure required.

What really happens inside a database file? Learn to read binary data like a map, uncover hidden structures and turn raw bytes into a working understanding of how storage engines work. With hands-on examples from SQLite, PostgreSQL, InnoDB and more, this book shows you how to reverse engineer unfamiliar formats from the ground up.

84 hand-picked SQL problems, fully solved and explained — from your first SELECT to interview-ready queries. Seven volumes of pure practice: a problem, the data, the answer, and why it works. No theory chapters, no filler, every query tested against a real database. Solve first, read second.

The Local AI Stack: Building a Sovereign Machine Learning Workstation with Hyper-V, WSL2, and GPU Virtualization Renting GPU time from AWS costs more than the GPU. Every prompt you send to a cloud API is a prompt someone else logs. If your ML work runs on hardware you don't own, on a network you don't control, then you don't own your ML work.

This reference volume consists of revised, edited, cross-referenced, and thematically organized articles from the Software Diagnostics and Observability Institute and the Software Diagnostics Library (former Crash Dump Analysis blog) about software diagnostics, root cause analysis, debugging, crash and hang dump analysis, and software trace and log analysis written from 15 April 2024 to 14 November 2025.