A Gentle Introduction to AstraeaDB
A progressive learning path that takes you from "What is a graph?" all the way to running Graph Neural Networks, building GraphRAG pipelines, and deploying production-grade security—one chapter at a time.
Part I: Foundations
Understand why graph databases exist, how they differ from relational tables, and where AstraeaDB fits in the broader landscape.
Chapter 1: Why Graphs?
Discover the limits of relational tables when modeling connected data. Learn the fundamentals of nodes, edges, and properties, and see why graph traversals outperform SQL JOINs for relationship-heavy queries.
Chapter 2: The Graph Database Landscape
Survey the major graph data models (Property Graph vs. RDF), query languages (Cypher, Gremlin, SPARQL, GQL), and see how AstraeaDB's Vector-Property Graph and Rust foundation set it apart from Neo4j, TigerGraph, and others.
Part II: Getting Started
Get AstraeaDB running on your machine and build your first graph in minutes.
Chapter 3: Installation and Setup
Clone the repository, build from source with Cargo, start the server, and connect using the interactive shell. Covers all three transport protocols: JSON-TCP, gRPC, and Arrow Flight.
Chapter 4: Your First Graph
Create nodes and edges, attach properties and labels, query with MATCH/RETURN, and visualize results. A hands-on walkthrough using a small social network as a running example.
Part III: Intermediate
Master the query language, explore traversal algorithms, and understand the network protocols that power AstraeaDB clients.
Chapter 5: The GQL Query Language
Deep dive into AstraeaDB's GQL implementation: pattern matching with MATCH, filtering with WHERE, creating and deleting data, aggregation functions, ORDER BY, LIMIT, and more.
Chapter 6: Graph Traversals
Understand BFS and DFS traversals, shortest-path algorithms, neighbor expansion, and how index-free adjacency makes multi-hop queries efficient at any depth.
Chapter 7: Transport Protocols
Compare JSON-TCP (simple, zero-dependency), gRPC (strongly typed, streaming), and Apache Arrow Flight (zero-copy, DataFrame-native). Learn when to use each and how to configure clients.
Part IV: Advanced
Unlock the AI-first capabilities that make AstraeaDB unique: vector search, temporal queries, graph algorithms, RAG pipelines, and neural network training.
Chapter 8: Vector Search and Semantic Queries
Store embeddings on nodes, run approximate nearest-neighbor search with HNSW, blend vector similarity with graph proximity using hybrid search, and perform semantic walks.
Chapter 9: Temporal Graphs
Model time-varying relationships with validity intervals. Query the graph as it existed at any point in time using neighbors_at(), bfs_at(), and shortest_path_at().
Chapter 10: Graph Algorithms
Run PageRank, Louvain community detection, betweenness centrality, and connected components. Understand the CSR matrix representation and optional GPU acceleration.
Chapter 11: GraphRAG
Build Retrieval-Augmented Generation pipelines: anchor on a node via vector search, extract a subgraph with BFS, linearize it to text, and feed it to an LLM—all in one atomic operation.
Chapter 12: Graph Neural Networks
Train GNNs inside the database. Create differentiable tensors, define message-passing layers, run forward passes, compute loss, and backpropagate—without exporting data to an external framework.
Part V: Production
Harden your deployment with authentication, encryption, performance tuning, and real-world scenario planning.
Chapter 13: Security
Configure RBAC authentication, enable mutual TLS, and explore homomorphic encryption for server-side label matching on encrypted data—the server never sees plaintext node labels.
Chapter 14: Performance and Scaling
Tune the buffer pool, configure pointer swizzling thresholds, understand MVCC and WAL, set up hash/range partitioning, and monitor with built-in metrics.
Chapter 15: Cybersecurity Scenario
A complete end-to-end walkthrough: model a corporate network as a graph, ingest firewall logs, detect lateral movement with traversals, and identify attack paths using graph algorithms.
Appendices
Quick references and cheat sheets for everyday use.
Appendix A: GQL Quick Reference
A compact reference card covering all supported GQL clauses, functions, operators, and patterns with short examples for each.
Appendix B: Client API Reference
Method-by-method documentation for the Python, R, Go, Java, and Rust client libraries, including every operation, parameter, and return type.
Appendix C: Configuration Reference
All server configuration options: ports, buffer pool size, WAL settings, TLS paths, authentication mode, GPU backend selection, and cluster coordination parameters.