The mixed workload CH-benCHmark

Cole, Richard L.; Funke, Florian; Giakoumakis, Leo; Guy, Wey; Kemper, Alfons; Krompaß, Stefan; Kuno, Harumi; Nambiar, Raghunath; Neumann, Thomas; Poess, Meikel; Sattler, Kai-Uwe; Seibold, Michael; Simon, Eric; Waas, Florian

doi:10.1145/1988842.1988850

Cited by 99 publications

(65 citation statements)

References 3 publications

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“…As a new direction to benchmarking mixed workloads, we adopt the CH-benCHmark [5], which considers concurrent OLAP and OLTP clients in a mixed workload inspired by TPC-C and TPC-H. We find the CH-benCHmark an adequate solution since it allows to scale the number of concurrent transactional and analytical clients independently. …”

Section: Real-time Reportingmentioning

confidence: 99%

“…We evaluate the CH-benCHmark, which we implement using C++ and ODBC, and provide it as open source 5 . Through our analysis, we detail how (a) data freshness, (b) flexibility, and (c) scheduling affect the performance of mixed workloads while we scale the number of concurrent clients.…”

Section: Contributionsmentioning

confidence: 99%

“…TPC-C is used to analyze the performance of transactional workloads in a scenario of order processing, while TPC-H analyzes the performance of analytical workloads in the context of a wholesale supplier. The goal of the CHbenCHmark [5] is to combine TPC-C and TPC-H in a unified schema, in order to analyze the performance of the mixed OLTP and OLAP workload. Next, we give an overview of the CH-benCHmark, and how we adapt and implement it.…”

Section: Setting Up the Ch-benchmarkmentioning

confidence: 99%

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Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling

Psaroudakis

Wolf

May

et al. 2015

Lecture Notes in Computer Science

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Abstract. The common "one size does not fit all" paradigm isolates transactional and analytical workloads into separate, specialized database systems. Operational data is periodically replicated to a data warehouse for analytics. Competitiveness of enterprises today, however, depends on real-time reporting on operational data, necessitating an integration of transactional and analytical processing in a single database system. The mixed workload should be able to query and modify common data in a shared schema. The database needs to provide performance guarantees for transactional workloads, and, at the same time, efficiently evaluate complex analytical queries. In this paper, we share our analysis of the performance of two main-memory databases that support mixed workloads, SAP HANA and HyPer, while evaluating the mixed workload CHbenCHmark. By examining their similarities and differences, we identify the factors that affect performance while scaling the number of concurrent transactional and analytical clients. The three main factors are (a) data freshness, i.e., how recent is the data processed by analytical queries, (b) flexibility, i.e., restricting transactional features in order to increase optimization choices and enhance performance, and (c) scheduling, i.e., how the mixed workload utilizes resources. Specifically for scheduling, we show that the absence of workload management under cases of high concurrency leads to analytical workloads overwhelming the system and severely hurting the performance of transactional workloads.

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Section: Real-time Reportingmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Setting Up the Ch-benchmarkmentioning

confidence: 99%

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Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling

Psaroudakis

Wolf

May

et al. 2015

Lecture Notes in Computer Science

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“…2) The CH-benchmark [11], a merge between the TPC-C and TPC-H benchmarks, modeling a very specific use case: the selling and shipping of products from warehouses in one country. 3) A custom set of queries on the CNET product catalog dataset [4] designed to reflect the workload of such Parameters used for the model a product catalog web application.…”

Section: Discussionmentioning

confidence: 99%

CPU and cache efficient management of memory-resident databases

Pirk

Funke²,

Grund

et al. 2013

2013 IEEE 29th International Conference on Data Engineering (ICDE)

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Abstract-Memory-Resident Database Management Systems (MRDBMS) have to be optimized for two resources: CPU cycles and memory bandwidth. To optimize for bandwidth in mixed OLTP/OLAP scenarios, the hybrid or Partially Decomposed Storage Model (PDSM) has been proposed. However, in current implementations, bandwidth savings achieved by partial decomposition come at increased CPU costs. To achieve the aspired bandwidth savings without sacrificing CPU efficiency, we combine partially decomposed storage with Just-in-Time (JiT) compilation of queries, thus eliminating CPU inefficient function calls. Since existing cost based optimization components are not designed for JiT-compiled query execution, we also develop a novel approach to cost modeling and subsequent storage layout optimization. Our evaluation shows that the JiT-based processor maintains the bandwidth savings of previously presented hybrid query processors but outperforms them by two orders of magnitude due to increased CPU efficiency.

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“…That was traditionally not possible within a single database engine. The combined processing functionality has inspired people to consider combined OLTP and OLAP benchmarks, as in the mixed CH benchmark [5]. It combines a transaction mix from TPC-C with a query stream inspired from TPC-H running on the same data, and thus stresses combined processing functionality.…”

Section: Hypermentioning

confidence: 99%

Engineering high-performance database engines

Neumann

2014

Proc. VLDB Endow.

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Developing a database engine is both challenging and rewarding. Database engines are very complex software artifacts that have to scale to large data sizes and large hardware configurations, and developing such systems usually means choosing between different trade-offs at various points of development.This papers gives a survey over two different database engines, the disk-based SPARQL-processing engine RDF-3X, and the relational main-memory engine HyPer. It discusses the design choices that were made during development, and highlights optimization techniques that are important for both systems.

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The mixed workload CH-benCHmark

Cited by 99 publications

References 3 publications

Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling

Scaling Up Mixed Workloads: A Battle of Data Freshness, Flexibility, and Scheduling

CPU and cache efficient management of memory-resident databases

Engineering high-performance database engines

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