Performance of Containerized Database Management Systems

Rehmann, Kim-Thomas; Folkerts, Enno

doi:10.1145/3209950.3209953

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…Bare-metal operation refers to code that runs without distinction between payload and OS close to the hardware, without intermediary layers. This in contrast to, for example, Ref [RF18],. which uses the term to denote code that runs without containers or virtual machines, but still relies on heavyweight, multi-million-LoC OS kernels.…”

contrasting

confidence: 76%

“…The general idea of using existing OS-level isolation mechanisms to reduce the amount of inference between latency (or otherwise) sensitive database workloads and the rest of a system has also been pursued by Rehmann et al [RF18]: The authors use Docker containers to isolate database instances from system and competing payload noise. Their work essentially implements limiting the CPU quota available to tasks, and pinning databaserelevant operations to specific CPUs in the system.…”

Section: Narasayya Et Al [Na13mentioning

confidence: 99%

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Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack

Mauerer,

Ramsauer,

Lucas

et al. 2021

Preprint

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When multiple tenants compete for resources, database performance tends to suffer. Yet there are scenarios where guaranteed sub-millisecond latencies are crucial, such as in real-time data processing, IoT devices, or when operating in safety-critical environments. In this paper, we study how to make query latencies deterministic in the face of noise (whether caused by other tenants or unrelated operating system tasks). We perform controlled experiments with an in-memory database engine in a multi-tenant setting, where we successively eradicate noisy interference from within the system software stack, to the point where the engine runs close to bare-metal on the underlying hardware.We show that we can achieve query latencies comparable to the database engine running as the sole tenant, but without noticeably impacting the workload of competing tenants. We discuss these results in the context of ongoing efforts to build custom operating systems for database workloads, and point out that for certain use cases, the margin for improvement is rather narrow. In fact, for scenarios like ours, existing operating systems might just be good enough, provided that they are expertly configured. We then critically discuss these findings in the light of a broader family of database systems (e.g. including disk-based), and how to extend the approach of this paper accordingly.

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contrasting

confidence: 76%

Section: Narasayya Et Al [Na13mentioning

confidence: 99%

Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack

Mauerer,

Ramsauer,

Lucas

et al. 2021

Preprint

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“…For SAP HANA Cloud, performance testing is still a challenge. SAP HANA runs in Kubernetes pods, which raises concerns with noisy neighbor effects, CPU overprovisioning, network issues, or hardware capabilities [70]. The complexity of the cloud infrastructure raises challenges regarding the robustness of performance test execution [41] and requires further elaboration and tooling.…”

Section: Special Properties Of Performance Testsmentioning

confidence: 99%

Testing Very Large Database Management Systems: The Case of SAP HANA

et al. 2022

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Software Testing is an established activity in the software development process to ensure and improve the quality of a software. Consequently, there exists a wide range of literature, popular information, and even multiple ISO standards covering this topic. However, we found that testing very large database management systems (DBMS) requires special adaptations of the generally available guidance for software testing and requires to solve specific challenges that may not be relevant for other areas or smaller software projects. We therefore discuss the testing of SAP HANA, a very large software project with millions of lines of code, to share insights about our approach, best practices, and unsolved challenges that are open for further research.

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“…The comparative analysis of the resource isolation capabilities of VMs and containers is provided by [16,12,8]. While [12] apply resource-specific microbenchmarks, [16] and [8] use DBMS and respective DBMS workloads to evaluate the resource capabilities. All of these evaluation indicate a stronger resource isolation of VMs, especially for disk-bound workloads.…”

Section: Performance Overhead and Resource Isolationmentioning

confidence: 99%

The Impact of the Storage Tier: A Baseline Performance Analysis of Containerized DBMS

Seybold¹,

Hauser²,

Eisenhart³

et al. 2018

Lecture Notes in Computer Science

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Containers emerged as cloud resource offerings. While the advantages of containers, such as easing the application deployment, orchestration and adaptation, work well for stateless applications, the feasibility of containerization of stateful applications, such as database management system (DBMS), still remains unclear due to potential performance overhead. The myriad of container operation models and storage backends even raises the complexity of operating a containerized DBMS. Here, we present an extensible evaluation methodology to identify performance overhead of a containerized DBMS by combining three operational models and two storage backends. For each combination a memory-bound and disk-bound workload is applied. The results show a clear performance overhead for containerized DBMS on top of virtual machines (VMs) compared to physical resources. Further, a containerized DBMS on top of VMs with different storage backends results in a tolerable performance overhead. Building upon these baseline results, we derive a set of open evaluation challenges for containerized DBMSs.

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Performance of Containerized Database Management Systems

Cited by 4 publications

References 10 publications

Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack

Silentium! Run-Analyse-Eradicate the Noise out of the DB/OS Stack

Testing Very Large Database Management Systems: The Case of SAP HANA

The Impact of the Storage Tier: A Baseline Performance Analysis of Containerized DBMS

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