Experiences and Lessons in Practice Using TPCx-BB Benchmarks

摘要

The TPCx-BigBench (TPCx-BB) is a TPC Express benchmark, which is designed to measure the performance of big data analytics systems. It contains 30 use cases that simulate big data processing, big data storage, big data analytics, and reporting. We have used this benchmark to evaluate the performance of software and hardware components for big data systems. It has very good coverage on different data types and provides enough scalability to address data size and node scaling problems. We have gained lots of meaningful insights through this benchmark to design analytic systems. In the meantime, we also found we cannot merely rely on TPCx-BB to evaluate and design an end-to-end big data systems. There are some gaps between an analytics system and a real end-to-end system. The whole data flow of a real end-to-end system should include data ingestion, which moves data from where it is originated into a system where it can be stored and analyzed such as Hadoop. Data ingestion may be challenging for businesses at a reasonable speed in order to maintain a competitive advantage. However, TPCx-BB cannot help on performance evaluation of software and hardware for data ingestion. Big data is composed of three dimensions: Volume, Variety, and Velocity. The Velocity refers to the high speed in data processing: real-time or near real-time. With big data technology widely used, real-time and near real-time processing become more popular. There is very strict limitation on bandwidth and latency for real-time processing. TPCx-BB cannot help on performance evaluation of software and hardware for real-time processing. This paper mainly discusses these experiences and lessons in practice using TPCx-BB. Then, we provide some advices to extend TPCx-BB to cover data ingestion and real-time processing. We also share some ideas how to implement TPCx-BB coverage.