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Hands-On Graph Analytics with Neo4j

You're reading from   Hands-On Graph Analytics with Neo4j Perform graph processing and visualization techniques using connected data across your enterprise

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781839212611
Length 510 pages
Edition 1st Edition
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Author (1):
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 Scifo Scifo
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Scifo
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Toc

Table of Contents (18) Chapters Close

Preface 1. Section 1: Graph Modeling with Neo4j
2. Graph Databases FREE CHAPTER 3. The Cypher Query Language 4. Empowering Your Business with Pure Cypher 5. Section 2: Graph Algorithms
6. The Graph Data Science Library and Path Finding 7. Spatial Data 8. Node Importance 9. Community Detection and Similarity Measures 10. Section 3: Machine Learning on Graphs
11. Using Graph-based Features in Machine Learning 12. Predicting Relationships 13. Graph Embedding - from Graphs to Matrices 14. Section 4: Neo4j for Production
15. Using Neo4j in Your Web Application 16. Neo4j at Scale 17. Other Books You May Enjoy

Eager operations

If you look closely, Neo4j Desktop is showing a small warning sign next to the query text editor. If you click on this warning, it will show an explanation about it. In our case, it says this:

The execution plan for this query contains the Eager operator, which forces all dependent data to be materialized in main memory before proceeding

This is not directly related to a data import, but this is often the first time we face this warning message, so let's try to understand it.

The Neo4j documentation defines the Eager operator in this sentence:

For isolation purposes, the operator ensures that operations affecting subsequent operations are executed fully for the whole dataset before continuing execution.

In other words, each statement of the query is executed for each row of the file, before moving to the other row. This is usually not a problem because a Cypher statement will deal with a hundred nodes or so, but when importing large data files, the overhead...

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