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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

Estimating algorithm memory usage

For each algorithm in the production quality tier, the GDS implements an estimate procedure similar to the one studied in the preceding paragraph for projected graphs. It can be used by appending estimate to any algorithm execution mode:

CALL gds.<ALGO>.<MODE>.estimate(graphNameOrConfig: String|Map, configuration: Map)
The algorithms in the alpha tier do not benefit from this feature.

The returned parameters are as follows:

YIELD 
requiredMemory,
treeView,
mapView,
bytesMin,
bytesMax,
heapPercentageMin,
heapPercentageMax,
nodeCount,
relationshipCount

The parameter names are self-explanatory and similar to the projected graph estimation procedure. By estimating the memory usage of both the projected graph and the algorithms you want to run on them, you can get a fairly good estimate of the heap required and use a well-sized server for your analysis.

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