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“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?
SQL would need multiple JOINs. In Neo4j:
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 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) |
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.
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
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.
“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?
SQL would need multiple JOINs. In Neo4j: 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.” “It took 2 milliseconds,” Sam said
“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) | 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.
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
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.