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Graph Data Processing with Cypher

You're reading from   Graph Data Processing with Cypher A practical guide to building graph traversal queries using the Cypher syntax on Neo4j

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804611074
Length 332 pages
Edition 1st Edition
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Author (1):
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Ravindranatha Anthapu Ravindranatha Anthapu
Author Profile Icon Ravindranatha Anthapu
Ravindranatha Anthapu
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Toc

Table of Contents (18) Chapters Close

Preface 1. Part 1: Cypher Introduction
2. Chapter 1: Introduction to Neo4j and Cypher FREE CHAPTER 3. Chapter 2: Components of Cypher 4. Part 2: Working with Cypher
5. Chapter 3: Loading Data with Cypher 6. Chapter 4: Querying Graph 7. Chapter 5: Filtering, Sorting, and Aggregations 8. Chapter 6: List Expressions, UNION, and Subqueries 9. Part 3: Advanced Cypher Concepts
10. Chapter 7: Working with Lists and Maps 11. Chapter 8: Advanced Query Patterns 12. Chapter 9: Query Tuning 13. Chapter 10: Using APOC Utilities 14. Chapter 11: Cypher Ecosystem 15. Chapter 12: Tips and Tricks 16. Index 17. Other Books You May Enjoy

Working with count stores

Neo4j maintains certain data statistics as count stores. For example, there are node count stores that maintain the counts of nodes for each label type. Since our dataset is small, we will use PROFILE to understand how much work the database would be doing in terms of db hits, with and without count stores, for a given type of work. We will also take a look at how to leverage count stores to build more performant queries.

Let’s look at a sample node count store query:

PROFILE MATCH (n:Patient)
RETURN count(n)

This is a very basic query, and it leverages count stores instead of counting the nodes that have the Patient label.

We can see from the screenshot, the database uses NodeCountFromCountStore@neo4j, which looks for the totals from the count store. We can see that it takes one db hit. The performance is constant, no matter how large the database grows.

Figure 8.19 – A query using the node count store

We...

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