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Learning Neo4j 3.x

You're reading from   Learning Neo4j 3.x Effective data modeling, performance tuning and data visualization techniques in Neo4j

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781786466143
Length 316 pages
Edition 2nd Edition
Languages
Tools
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Authors (2):
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Jerome Baton Jerome Baton
Author Profile Icon Jerome Baton
Jerome Baton
Rik Van Bruggen Rik Van Bruggen
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Rik Van Bruggen
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Toc

Table of Contents (17) Chapters Close

Preface 1. Graph Theory and Databases 2. Getting Started with Neo4j FREE CHAPTER 3. Modeling Data for Neo4j 4. Getting Started with Cypher 5. Awesome Procedures on Cypher - APOC 6. Extending Cypher 7. Query Performance Tuning 8. Importing Data into Neo4j 9. Going Spatial 10. Security 11. Visualizations for Neo4j 12. Data Refactoring with Neo4j 13. Clustering 14. Use Case Example - Recommendations 15. Use Case Example - Impact Analysis and Simulation 16. Tips and Tricks

Custom aggregators

Some of the most common aggregators in the SQL world are count, sum, min, or max. They are used in conjunction with the GROUP BY instruction. Here is an example of aggregator usage in Cypher:

MATCH (p:Person)-[IS_NAMED]-(ln:LastName)
RETURN ln.lastName, count(p)

Since version 3.2, Neo4j allows you to create your own aggregators, which is fairly simple. To illustrate, we will see how to code a random count: a count aggregator that returns a random value. The Java code is as follows:

package learningneo4j;

import org.neo4j.procedure.*;

public class RandomCount {
@UserAggregationFunction("learningneo4j.randomCount")
@Description( "learningneo4j.randomCount - mostly returns a wrong value " )
public RandomAggregator randomAggregator() {
return new RandomAggregator();
}

public static class RandomAggregator {
private...
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