TPC-DS, taking decision support benchmarking to the next level

Poess, Meikel; Smith, Bryan; Kollár, Ľubor; Larson, Paul A.

doi:10.1145/564691.564759

Cited by 70 publications

(11 citation statements)

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“…Ousterhout et al [23] analyzed the performance of the Big Data Benchmark [28] on 5 Amazon EC2 nodes, for a total of 40 cores, and the TPC-DS benchmark [24] on 20 nodes (160 cores) on EC2. These benchmarks both use Spark SQL [5], which allows SQL queries to be performed over RDDs.…”

Section: System Architecture and Data Movementmentioning

confidence: 99%

Scaling Spark on HPC Systems

Chaimov

Malony

Canon

et al. 2016

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing

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We report our experiences porting Spark to large production HPC systems. While Spark performance in a data center installation (with local disks) is dominated by the network, our results show that file system metadata access latency can dominate in a HPC installation using Lustre: it determines single node performance up to 4⇥ slower than a typical workstation. We evaluate a combination of software techniques and hardware configurations designed to address this problem. For example, on the software side we develop a file pooling layer able to improve per node performance up to 2.8⇥. On the hardware side we evaluate a system with a large NVRAM bu↵er between compute nodes and the backend Lustre file system: this improves scaling at the expense of per-node performance. Overall, our results indicate that scalability is currently limited to O(10 2 ) cores in a HPC installation with Lustre and default Spark. After careful configuration combined with our pooling we can scale up to O(10 4 ). As our analysis indicates, it is feasible to observe much higher scalability in the near future.

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Section: System Architecture and Data Movementmentioning

confidence: 99%

Scaling Spark on HPC Systems

Chaimov

Malony

Canon

et al. 2016

Proceedings of the 25th ACM International Symposium on High-Performance Parallel and Distributed Computing

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“…There are four main requisites of a benchmark (Bog, 2013): relevance, portability, scalability and simplicity. In the context of DW and decision support systems, there are three main benchmarks: TPC-DS (Poess et al, 2002), TPC-H (Moussa, 2012) and Star Schema Benchmark (SSB) (O'Neil et al, 2009). However, they fail in, at least, two requisites.…”

Section: Introductionmentioning

confidence: 99%

Physical Data Warehouse Design on NoSQL Databases - OLAP Query Processing over HBase

Scabora

Brito

Ciferri

et al. 2016

Proceedings of the 18th International Conference on Enterprise Information Systems

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Abstract:Nowadays, data warehousing and online analytical processing (OLAP) are core technologies in business intelligence and therefore have drawn much interest by researchers in the last decade. However, these technologies have been mainly developed for relational database systems in centralized environments. In other words, these technologies have not been designed to be applied in scalable systems such as NoSQL databases. Adapting a data warehousing environment to NoSQL databases introduces several advantages, such as scalability and flexibility. This paper investigates three physical data warehouse designs to adapt the Star Schema Benchmark for its use in NoSQL databases. In particular, our main investigation refers to the OLAP query processing over column-oriented databases using the MapReduce framework. We analyze the impact of distributing attributes among column-families in HBase on the OLAP query performance. Our experiments showed how processing time of OLAP queries was impacted by a physical data warehouse design regarding the number of dimensions accessed and the data volume. We conclude that using distinct distributions of attributes among column-families can improve OLAP query performance in HBase and consequently make the benchmark more suitable for OLAP over NoSQL databases.

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“…However, its schema differs from the traditional star schema. The TPC-DS [9] suppresses this issue with a snowflake schema, but is aimed at data refreshing and its project is still under development. The SSB [8] extends the TPC-H to enable the analysis of historical trends and provides a set of predefined queries to run over its star schema.…”

Section: Introductionmentioning

confidence: 99%

Benchmarking Spatial Data Warehouses

Siqueira

Ciferri

Times

et al. 2010

Data Warehousing and Knowledge Discovery

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Abstract. Spatial data warehouses (SDW) enable analytical multidimensional queries together with spatial analysis. Mainly, three operations are related to SDW query processing performance: (i) joining large fact tables and large spatial and non-spatial dimension tables; (ii) computing one or more costly spatial predicates based on spatial ad hoc query windows; and (iii) aggregating data according to different spatial granularity levels. Several techniques to improve the query processing performance over SDW have been proposed in the literature. However, we identified the lack of a benchmark to carry out a controlled experimental evaluation of such techniques and, principally, to effectively measure the costs of the aforementioned three complex operations. In this paper, we propose a novel spatial data warehouse benchmark, called Spadawan, to provide performance evaluation environments for SDW and enable a further investigation on spatial data redundancy. The Spadawan benchmark is available at http://gbd.dc.ufscar.br/spadawan.

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TPC-DS, taking decision support benchmarking to the next level

Cited by 70 publications

References 1 publication

Scaling Spark on HPC Systems

Scaling Spark on HPC Systems

Physical Data Warehouse Design on NoSQL Databases - OLAP Query Processing over HBase

Benchmarking Spatial Data Warehouses

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