MATCH (tip:Tip)-[:MENTIONS]->(person:Person) WHERE tip.timestamp > datetime() - duration('PT5M') RETURN person.name, tip.text Within seconds of a new tip mentioning “Mr. X,” Alex’s dashboard lit up. With 2 million nodes and 5 million relationships, SQL queries took minutes. Neo4j used index-free adjacency —traversing relationships is O(1) per hop. The same queries ran in <50 ms.
His tech lead, Sam, introduced Neo4j—a where data is stored as nodes (entities) and relationships (connections). Chapter 2: Building the Knowledge Graph Sam modeled their first case: neo4j in action pdf
CREATE (alice:Person name: 'Alice', age: 34) CREATE (bob:Person name: 'Bob', age: 29) CREATE (alice)-[:KNOWS]->(bob) A witness said: “Bob called a phone number, and that phone was used near the crime scene.” MATCH (tip:Tip)-[:MENTIONS]->(person:Person) WHERE tip
I’m unable to provide a full PDF file or reproduce an entire copyrighted book like Neo4j in Action . However, I can give you a that walks through the key concepts and examples from the book, showing Neo4j in action from start to finish. Story: The Graph-Powered Detective Agency Chapter 1: The Case of the Missing Data Detective Alex Kim ran a small intelligence agency. For years, he stored case data in SQL tables: suspects, locations, vehicles, and tips. But connections were buried in foreign keys and JOINs. Finding how a suspect knew a witness required five table joins—and hours of work. Chapter 2: Building the Knowledge Graph Sam modeled
“The connections don’t lie,” Alex said. “Neither does Neo4j.” | Chapter | Topic | |---------|-------| | 1–2 | Graph thinking, Neo4j basics, Cypher intro | | 3–4 | Data modeling, querying, indexing | | 5–6 | Advanced queries, shortest path, recommendations | | 7–8 | Integration with Java, Spring, REST APIs | | 9–10 | Performance tuning, clustering, high availability | | 11–12 | Real‑world use cases (social, fraud, logistics) |
MATCH (bob:Person name: 'Bob')-[:CALLED]->(phone:Phone) MATCH (phone)<-[:USED]-(suspect:Person)-[:VISITED]->(loc:Location address: 'Main St 42') RETURN suspect.name, phone.number Result: "Charlie" , "555-1234" .
MATCH path = shortestPath( (alice:Person name: 'Alice')-[:KNOWS*..5]-(mrX:Person name: 'Mr. X') ) RETURN path The result: Alice → KNOWS → Bob → KNOWS → Dave → KNOWS → Mr. X