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Amazon Dataset

Benchmarking graph database scalability through large-scale E-commerce review and product relationship networks

Dataset Overview

The Amazon Review Data (2018) is an extensive collection of customer reviews and product metadata spanning from May 1996 to October 2018. It serves as a major benchmark for research in recommendation systems, natural language processing, and consumer behavior analysis. The full dataset contains approximately 233.1 million reviews across dozens of product categories, including detailed information such as ratings, text, product images, and co-purchasing links. To facilitate experimentation, the data is available in various scales, ranging from the massive raw review files to smaller "5-core" subsets where each user and item has at least five reviews. More

Total node count: 323,139,730

Total edge count: 742,265,681

Dataset Download

You may download this dataset directly from the dataset website. Go to here, scroll down to the File section. To download the complete dataset, you may be required to complete a Google form provided by the dataset authors.

You can click the button below and download the files prepared by us for smoother import into graph database systems:

Benchmark Workloads

Select a platform below to view the specific data loading commands and queries used to benchmark performance.

Runtime Results

* The unit of all results shown below are in milliseconds (ms)

Date (Instance) PuppyGraph Neo4j TigerGraph GraphFramess
Q1 error 9,420,000 2,092 62,075
Q2 error 12,840,000 did not implement 63,518
Q3 189,443 17,640,000 1,435 58,817
Q4 error 1,560,000 51,867 106,748
Q5 error 18,000 2,555 80,859
Q6 error 5,000 2,672 113,616
Q7 248,644 5,400,000 3,628 79,041
Q8 error 12,300,000 36,013 56,145
Q9 error 1,440,000 552 14,662
Q10 error forever 1,854 12,424
Q11 error 660,000 632 19,234