BigBench: Towards an Industry Standard Benchmark for Big Data Analytics

Ahmad Ghazal, Tilmann Rabl, Minqing Hu, Francois Raab, Meikel Poess, Alain Crolotte, and Hans-Arno Jacobsen.

In Proceedings of the ACM SIGMOD Conference, 2013.


There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems.

In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques.

We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.


Related Projects

Tags: big data, benchmarking

Readers who enjoyed the above work, may also like the following:

  • Discussion of BigBench: A Proposed Industry Standard Performance Benchmark for Big Data.
    Chaitanya Baru, Milind Bhandarkar, Carlo Curino, Manuel Danisch, Michael Frank, Bhaskar Gowda, Hans-Arno Jacobsen, Huang Jie, Dileep Kumar, Raghunath Nambiar, Meikel Poess, Francois Raab, Tilmann Rabl, Nishkam Ravi, Kai Sachs, Saptak Sen, Lan Yi, and Choonhan Youn.
    In Sixth TPC Technology Conference on Performance Evaluation & Benchmarking, pages 44-63, 2014. Springer Berlin Heidelberg.
    Tags: bigbench, big data, benchmarking
  • BigBench Specification V0.1.
    Tilmann Rabl, Ahmad Ghazal, Minqing Hu, Alain Crolotte, Francois Raab, Meikel Poess, and Hans-Arno Jacobsen.
    In Proceedings of the 2012 Workshop on Big Data Benchmarking, pages 164-202, 2013.
    Tags: bigbench, big data, benchmarking
  • Big Data Generation.
    Tilmann Rabl and Hans-Arno Jacobsen.
    In Proceedings of the Workshop on Big Data Benchmarking, pages 20-27, 2013.
    Tags: pdgf, big data, benchmarking