Name
Abstract | ||
|---|---|---|
Distributed big data processing and analytics applications demand a comprehensive end-to-end architecture stack consisting of big data technologies. However, there are many possible architecture patterns (e.g. Lambda, Kappa or Pipeline architectures) to choose from when implementing the application requirements. A big data technology in isolation may be best performing for a particular application, but its performance in connection with other technologies depends on the connectors and the environment. Similarly, existing big data benchmarks evaluate the performance of different technologies in isolation, but no work has been done on benchmarking big data architecture stacks as a whole. For example, BigBench (TPCx-BB) may be used to evaluate the performance of Spark, but is it applicable to PySpark or to Spark with Kafka stack as well? What is the impact of having different programming environments and/or any other technology like Spark? This vision paper proposes a new category of benchmark, called ABench, to fill this gap and discusses key aspects necessary for the performance evaluation of different big data architecture stacks.
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Year | Venue | Field |
|---|---|---|
2018 | ICPE Companion | Big data processing,Computer architecture,Architecture,Spark (mathematics),Stack (abstract data type),Electronic engineering,Engineering,Analytics,Big data,Architectural pattern,Benchmarking |
DocType | ISBN | Citations |
Conference | 978-1-4503-5629-9 | 1 |
PageRank | References | Authors |
0.35 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Todor Ivanov | 1 | 5 | 3.50 |
| Rekha Singhal | 2 | 16 | 8.22 |