Adapting TPC-C benchmark to measure performance of multi-document transactions in MongoDB

Kamsky, Asya

doi:10.14778/3352063.3352140

Cited by 20 publications

(7 citation statements)

References 16 publications

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“…Alternative Data Schemas Database managers may model the relationships among data without database reference. There are two mainstream methods as presented in prior work [25]: (1) embedding all sub-documents into the parent document; (2) using a separate collection to record all relations among documents. We call them embedding and separating approaches.…”

Section: Discussionmentioning

confidence: 99%

PMLiteDB: Streamlining Access Paths for High-Performance Persistent Memory Document Database Systems

Jin

Wei

Sha

et al. 2023

IEEE Trans. Comput.

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This paper points out an important challenge for document databases to enjoy the fast byte-addressable persistent memory, such that excessive data indexing and movement become the performance bottlenecks. The advent of byte-addressable persistent memory opens an important opportunity for document databases to read and write durable data fetching them into DRAM. Reaping the benefit of persistent memory is not straightforward, as existing document databases are tailored for disk storage. They assume that the disk and DRAM data movement dominates the performance. However, data indexing becomes the performance bottleneck when porting the document databases to persistent memory. The paper proposes PMLiteDB, the first persistent memory document database with streamlined access paths. PMLiteDB introduces two techniques, direct reading and selective caching. Direct reading streamlines the translation from document IDs to the address of documents whenever possible by swizzling the IDs into persistent memory(PM) references. It guarantees to use only up-to-date PM references when document movements invalidate associated references. Selective caching reduces data movements between DRAM and persistent memory by selectively caching persistent memory data page with DRAM buffer. For accesses on other pages, the mechanism directly load the data on persistent memory. Compared to the design that adopts persistent memory as a fast disk without exploiting the byte-addressability, PMLiteDB achieves 2.33× on average and up to 6.18× speedup.

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Section: Discussionmentioning

confidence: 99%

PMLiteDB: Streamlining Access Paths for High-Performance Persistent Memory Document Database Systems

Jin

Wei

Sha

et al. 2023

IEEE Trans. Comput.

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“…Our evaluation includes three workloads: the Yahoo Cloud Serving Benchmark (YCSB) [37] v0.11, TPC-C [38], a popular OLTP benchmark adapted for NoSQL systems [39]; and an analytical processing workload modeled after the popular TPC-H [40] benchmark. In all cases the workload driver executes on a dedicated server.…”

Section: Discussionmentioning

confidence: 99%

“…We experimented with TPC-C [38], a popular online transaction processing benchmark emulating a commerce system with five types of transactions. While initially designed to test traditional RDBMS systems, we used a recent implementation that adapts it to match the NoSQL document data model and to test transactional features [39] The database is populated to simulate 500 warehouses resulting to 66 GB of data size (41.5 GB on disk space using compression). To have a continuous view of performance, we adapted the benchmark to report throughput and response time for each query every 2 seconds.…”

Section: Tpc-cmentioning

confidence: 99%

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Addressing the Read-Performance Impact of Reconfigurations in Replicated Key-Value Stores

Papaioannou

Magoutis

2022

IEEE Trans. Parallel Distrib. Syst.

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Raw data are often orders of magnitude larger than main memory for many applications. As the performance of storage devices is still significantly slower than main memory, systems still rely on memory caching to improve performance. Data replication schemes are prevalent in data stores for high availability and reliability. In such schemes, while data updates are propagated to all replicas (either synchronously or in the background), reads are usually served by only a subset of replica group members (e.g., as in primary-backup and quorum systems). As a result, non-serving replicas cannot keep their memory cache state updated; thus, during a reconfiguration or a fail-over action, the system suffers from a high read-performance impact for a significant amount of time due to cold-cache misses. In our study we observed up to 70% hit after a reconfiguration due to cold cache misses, taking almost 18 minutes in some cases to fully restore to the pre-reconfiguration level of performance.In this paper we propose a mechanism to maintain up-to-date read caches across replicas by sending read hints to the non-serving replicas to keep their caches warm. Thus the system is able to seamlessly achieve the same performance level even in the face of a replica group re-organization. This is especially important under the read-intensive workloads that are common today. Our evaluation shows that our mechanism has significant benefits during reconfigurations, with low performance impact under periods of resource strain. Given its advisory nature, the maintenance of read hints can be reduced or held off if needed during such periods.

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“…Despite an immense variety of database benchmarks like TPC 1 for relational databases, YCSB [5] for key-value stores, and LDBC [7] for graph databases, several studies on document stores reported that existing benchmarks do not cover their specific workloads and data (e. g., TPC-C [12], YCSB [10]). Especially, the missing support of JSON arrays and nested objects led to several initiatives on the data generation side such as attempts that translate TPC-H data to JSON [15], and more recently, initial work on extending the well-known Wisconsin benchmark by a JSON data generator [11].…”

Section: Introductionmentioning

confidence: 99%

DeepBench

Belloni¹,

Ritter²,

Schröder³

et al. 2022

Proceedings of the 2022 Workshop on 9th International Workshop of Testing Database Systems

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The growing popularity of JSON as exchange and storage format in business and analytical applications led to its rapid dissemination, thus making a timely storage and processing of JSON documents crucial for organizations. Consequently, specialized JSON document stores are ubiquitously used for diverse domain-specific workloads, while a JSON-specific benchmark is missing.In this work, we specify DeepBench, an extensible, scalable benchmark that addresses nested JSON data, as well as queries over JSON documents. DeepBench features configurable domain-independent (e. g., varying document sizes, concurrent users) and JSON-specific scale levels (e. g., object, array nesting). The evaluation of wellknown document stores with a prototypical DeepBench implementation shows its versatility and gives new insights into potential weaknesses that were not found by existing, non-JSON benchmarks.

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Adapting TPC-C benchmark to measure performance of multi-document transactions in MongoDB

Cited by 20 publications

References 16 publications

PMLiteDB: Streamlining Access Paths for High-Performance Persistent Memory Document Database Systems

PMLiteDB: Streamlining Access Paths for High-Performance Persistent Memory Document Database Systems

Addressing the Read-Performance Impact of Reconfigurations in Replicated Key-Value Stores

DeepBench

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