Cypher 103 with ladybugdb

Hands-On LadybugDB Cypher

1. A Comprehensive, Example-Driven Guide to Graph Querying with LadybugDB

Getting Started

What Is a Graph? — The Mental Model You Already Have

Graphs vs. Relational Databases — Why Tables Struggle with Connections

1. The JOIN tax
2. The “relationship table explosion”
3. When relational is still the right choice

Graphs vs. Document Databases — Why Nesting Isn’t Connecting

1. The problems with embedding relationships
2. Side-by-side comparison
3. When document databases are still the right choice

The Labeled Property Graph Model — Why Developers Love It

1. The four building blocks
2. Why LPG maps naturally to object-oriented code
3. LPG vs. RDF — why LPG won developer hearts
4. The takeaway
5. 🏋️ Exercises

Chapter 0: Why LadybugDB Is a Great First Graph Database

1. Zero infrastructure, instant feedback
2. Schema-first teaches you to think in graphs
3. Full Cypher — not a subset
4. Columnar storage means fast even on a laptop
5. Interoperability — connect to what you already know
6. Built-in visualization
7. Open source and free
8. The bottom line
9. 🏋️ Exercises

Chapter 0.1: Installing LadybugDB

1. Command Line (CLI Shell)
2. Python
3. Node.js
4. Rust
5. Java
6. Go
7. C/C++
8. Nightly builds
9. On-disk vs. in-memory
10. 🏋️ Exercises

Chapter 0.2: Ladybug Explorer — Visualize and Explore Your Graph

1. Installing and launching
2. The four panels
3. AI-assisted query generation
4. Launch options reference
5. Recommended workflow for this book
6. 🏋️ Exercises

Part I: Foundations

Chapter 1: Introduction to LadybugDB and Cypher

1. What is LadybugDB?
2. Cypher at a glance
3. Our running example: The Agent Network
4. Setting up LadybugDB
5. 🏋️ Exercises

Chapter 2: Cypher Syntax Fundamentals

1. Statements, clauses, and terminators
2. Multi-line statements
3. Comments
4. Case insensitivity
5. Naming rules
6. Escaping reserved words
7. Unicode support
8. Parameters
9. Reserved keywords
10. Putting it all together: anatomy of a well-written query
11. 🏋️ Exercises

Chapter 3: Data Types

1. Numeric types
2. Boolean
3. String
4. UUID
5. NULL
6. Temporal types
7. SERIAL — auto-incrementing IDs
8. Composite types
9. BLOB
10. JSON
11. Logical graph types
12. 🏋️ Exercises

Part II: Defining Your Graph

Chapter 4: Data Definition Language (DDL) — Creating the Schema

1. Creating node tables
2. Creating relationship tables
3. Altering tables
4. Dropping tables
5. Typed graphs vs. open type graphs
6. 🏋️ Exercises

Part III: Populating the Graph

Chapter 5: Creating Data — INSERT Operations

1. Inserting nodes
2. Inserting relationships
3. General CREATE semantics
4. MERGE — “get or create”
5. CREATE TABLE AS — bulk creation from queries
6. 🏋️ Exercises

Part IV: Querying the Graph

Chapter 6: MATCH — Pattern Matching

1. Matching nodes
2. Matching relationships
3. Matching multiple patterns
4. Equality predicates as syntactic sugar
5. 🏋️ Exercises

Chapter 7: OPTIONAL MATCH

1. 🏋️ Exercises

Chapter 8: WHERE — Filtering Results

1. Boolean operators
2. Comparison operators
3. String functions in WHERE
4. NULL handling in WHERE
5. List membership with IN
6. WHERE subquery on a relationship
7. 🏋️ Exercises

Chapter 9: RETURN — Projecting Results

1. Returning properties
2. Return all properties with *
3. RETURN *
4. DISTINCT
5. Group by and aggregations
6. 🏋️ Exercises

Chapter 10: ORDER BY, SKIP, and LIMIT

1. ORDER BY
2. SKIP
3. LIMIT
4. Pagination pattern
5. 🏋️ Exercises

Chapter 11: WITH — Intermediate Projections

1. WITH for aggregation then filtering
2. WITH for top-k
3. Chaining WITH multiple times
4. 🏋️ Exercises

Chapter 12: UNION

1. UNION (removes duplicates)
2. UNION ALL (keeps duplicates)
3. 🏋️ Exercises

Chapter 13: UNWIND — Exploding Lists

1. Basic usage
2. Creating multiple nodes from a list
3. UNWIND with WHERE (requires WITH)
4. UNWIND nested lists
5. 🏋️ Exercises

Chapter 14: Recursive Relationships and Path Queries

1. Variable-length paths
2. Walk, trail, and acyclic semantics
3. Filtering inside recursive patterns
4. Projecting intermediate properties
5. Shortest path
6. 🏋️ Exercises

Chapter 15: Named Paths

1. Basic named path
2. Extracting nodes and relationships
3. Multiple named paths
4. 🏋️ Exercises

Part V: Modifying the Graph

Chapter 16: SET — Updating Properties

1. Update a node property
2. Set to NULL (remove a value)
3. Update a relationship property
4. Bulk update
5. RETURN after update
6. 🏋️ Exercises

Chapter 17: DELETE — Removing Data

1. Delete a node
2. DETACH DELETE
3. Delete a relationship
4. Delete everything
5. 🏋️ Exercises

Part VI: Advanced Query Features

Chapter 18: Subqueries — EXISTS and COUNT

1. EXISTS
2. COUNT subquery
3. 🏋️ Exercises

Chapter 19: Expressions, Operators and Functions

1. CASE expression
2. Comparison operators
3. Logical operators
4. Numeric operators
5. String operators and functions
6. Date and timestamp operators
7. NULL operators
8. List operators
9. Aggregate functions
10. Casting
11. Node and relationship functions
12. 🏋️ Exercises

Chapter 20: Macros

1. 🏋️ Exercises

Chapter 21: Transactions

1. Basic transaction syntax
2. Rolling back
3. Transaction isolation
4. 🏋️ Exercises

Part VII: Real-World Patterns

Chapter 22: Putting It All Together — Complex Queries

1. Query 1: Full agent inventory with company and tools
2. Query 2: Agents certified by TrustChain with their capabilities
3. Query 3: Companies with legal agreements — network view
4. Query 4: Find all cooperation chains from Atlas
5. Query 5: Companies whose certified agents cooperate
6. Query 6: Agent task load — who is overloaded?
7. Query 7: Shortest cooperation path between Cipher and Dash
8. Query 8: Companies reachable through legal agreements from TrustChain
9. Query 9: CASE-based agent status report
10. Query 10: Using UNWIND to batch-assign agents to a new task
11. Query 11: Schema evolution — adding a new dimension
12. Query 12: WITH pipeline — average reputation by company, then filter
13. 🏋️ Exercises

Chapter 23: Typed Graphs vs. Neo4j — A Detailed Comparison

1. Philosophical difference
2. Why this matters for agent ecosystems
3. Semantic differences
4. Typed graph as ontology
5. Open type graphs — best of both worlds
6. 🏋️ Exercises

Chapter 24: LOAD FROM and External Data

1. 🏋️ Exercises

Chapter 25: Attach/Detach External Databases

1. 🏋️ Exercises

Chapter 26: The Social Graph Memory — Our Running Example Explained

1. What is a social graph memory?
2. Why “memory” and not just “database”?
3. The social graph memory as the foundation for agentic AI
4. Relationship to knowledge graphs and RAG
5. 🏋️ Exercises

Chapter 27: Why Modern AI Needs Graphs

1. The problem: AI systems work with strings, not things
2. Vector RAG: good, but not enough
3. GraphRAG: the convergence of graphs and AI
4. Why graphs specifically?
5. Agentic AI: where graphs become essential
6. The future: graphs as the backbone of AI systems
7. Connecting back to our example
8. 🏋️ Exercises

Part VIII: Reference

Appendix A: Complete Schema for the Agent Network Example

Appendix B: Quick Reference — Cypher Clauses

Appendix C: Quick Reference — Data Types

Appendix D: Quick Reference — Key Differences from Neo4j

About This Book

Appendix E: Exercise Solutions

1. Graph Fundamentals Solutions
2. Chapter 0 Solutions
3. Chapter 0.1 Solutions
4. Chapter 0.2 Solutions
5. Chapter 1 Solutions
6. Chapter 2 Solutions
7. Chapter 3 Solutions
8. Chapter 4 Solutions
9. Chapter 5 Solutions
10. Chapter 6 Solutions
11. Chapter 7 Solutions
12. Chapter 8 Solutions
13. Chapter 9 Solutions
14. Chapter 10 Solutions
15. Chapter 11 Solutions
16. Chapter 12 Solutions
17. Chapter 13 Solutions
18. Chapter 14 Solutions
19. Chapter 15 Solutions
20. Chapter 16 Solutions
21. Chapter 17 Solutions
22. Chapter 18 Solutions
23. Chapter 19 Solutions
24. Chapter 20 Solutions
25. Chapter 21 Solutions
26. Chapter 22 Solutions
27. Chapter 23 Solutions
28. Chapter 24 Solutions
29. Chapter 25 Solutions
30. Chapter 26 Solutions
31. Chapter 27 Solutions