Neo4j In Action Pdf ❲2024-2026❳

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

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" .

SQL would need multiple JOINs. In Neo4j: neo4j in action pdf

“It took 2 milliseconds,” Sam said. “And we didn’t even index anything yet.” Alex needed to know: how is Alice connected to a known criminal, Mr. X?

MATCH (p:Person name: 'Charlie')-[:VISITED|KNOWS]->(common)<-[:VISITED|KNOWS]-(other:Person) WHERE p <> other RETURN other.name, count(common) AS similarity ORDER BY similarity DESC This returned unknown associates—perfect for expanding investigations. The agency integrated Neo4j with Kafka. Every new tip became a new relationship. A trigger query ran every minute: SQL would need multiple JOINs

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.

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.” the agency linked a money trail

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.

Sam partitioned data by case and used for speed. No more JOIN explosions. Epilogue: The Conviction Using Neo4j, the agency linked a money trail, phone calls, and meeting locations across 12 suspects. The prosecutor presented a graph visualization—not as evidence, but as an investigation tool. The jury understood instantly.

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: