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4mbench: Performance Benchmark of Manufacturing Business Database

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Performance Evaluation and Benchmarking (TPCTC 2022)

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Abstract

A massive number of networked sensors are incorporated into the manufacturing business field, enabling its whole manufacturing process to be monitored and transformed into digital records. Intensive analysis of these digital assets has the potential to offer novel solutions: identifying a problematic piece of the production line and reducing its yield loss. This paper presents 4mbench, a performance benchmark for decision support database in the manufacturing business. 4mbench has employed the 4m (man, machine, material and method) model in order to organize manufacturing event records into relational database and allow business questions to be queried on those event records. This paper presents an overall design of 4mbench that simulates food processing and packaging business operations; specifically, a database schema, a dataset generation rule and a set of queries are introduced. In addition, this paper presents an experimental case study that we conducted with 4mbench on PostgreSQL. This study revealed that the existing query optimization might yield detrimental query execution plans that offered significantly (up to four orders of magnitude) longer execution time. We hope that 4mbench provides researchers and developers with opportunities to explore the scope of further performance optimization on manufacturing business database.

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Notes

  1. 1.

    Potentially, the schema may be further composed of similar fact tables that store status information of workers and procedures. We have omitted them for benchmark simplicity.

  2. 2.

    Currently, the simulation is not fully event-driven. In reality, an operation unit may accept and process multiple materials at a time. But, the current implementation does not consider this parallelism for simplicity. Instead, we have opted to set smaller latency values (divided by the degree of parallelism) such that it can simulate the process parallelism approximately.

  3. 3.

    The detailed index definition is disclosed at the source code repository [4].

  4. 4.

    The choice of the date may affect the query workload amount of m4Q.2. Thus, this paper does not deep dive into the performance comparison between different dataset scales for m4Q.2.

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Acknowledgements

This work has been in part supported by “Big Data Value Co-creation Platform Engineering” social cooperation program at Institute of Industrial Science, The University of Tokyo with Hitachi, Ltd.

Author information

Authors and Affiliations

  1. The University of Tokyo, Meguro-ku, Tokyo, Japan

    Kazuo Goda & Yuto Hayamizu

  2. Hitachi, Ltd., Yokohama, Kanagawwa, Japan

    Norifumi Nishikawa & Shinji Fujiwara

Authors
  1. Kazuo Goda
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  2. Yuto Hayamizu
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  3. Norifumi Nishikawa
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  4. Shinji Fujiwara
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Corresponding author

Correspondence to Kazuo Goda .

Editor information

Editors and Affiliations

  1. Advanced Micro Devices Inc., Santa Clara, CA, USA

    Raghunath Nambiar

  2. Oracle Corporation, Redwood City, CA, USA

    Meikel Poess

A Experiment Configuration

A Experiment Configuration

1.1 A.1 SQL Description of Test Query (4mQ.3)

figure a
figure b

1.2 A.2 [DATE] Variable Substitution

Dataset

4mQ2

4mQ3

4mQ4

4mQ5

4mQ6

Small scale

2022-04-10

2022-04-01

2022-04-08

2022-04-03

2022-04-06

Middle scale

2022-04-15

2022-04-29

2022-04-22

2022-04-15

2022-04-15

Large scale

2022-04-25

2022-06-22

2022-06-20

2022-07-28

2022-07-03

1.3 A.3 Configuration Parameters of PostgreSQL

Parameter

Default value

Tuned value

max_connections

100

10

shared_buffers

128 MB

16 GB

temp_buffers

8 MB

16 GB

work_mem

4 MB

16 GB

maintenance_work_mem

64 MB

16 GB

effectiv_io_concurrency

1

256

maintenance_io_concurrency

10

128

max_worker_processes

8

48

max_parallel_workers_per_gather

2

24

max_parallel_workers

8

24

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Goda, K., Hayamizu, Y., Nishikawa, N., Fujiwara, S. (2023). 4mbench: Performance Benchmark of Manufacturing Business Database. In: Nambiar, R., Poess, M. (eds) Performance Evaluation and Benchmarking. TPCTC 2022. Lecture Notes in Computer Science, vol 13860. Springer, Cham. https://doi.org/10.1007/978-3-031-29576-8_7

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  • DOI: https://doi.org/10.1007/978-3-031-29576-8_7

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  • Online ISBN: 978-3-031-29576-8

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