Data page layouts for relational databases on deep memory hierarchies

Ailamaki, Anastassia; DeWitt, David J.; Hill, Mark D.

doi:10.1007/s00778-002-0074-9

Cited by 89 publications

(61 citation statements)

References 16 publications

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“…For instance, the workload characterization studies that analyze micro-architectural behavior of the OLTP workloads demonstrate that transactions exhibit significant stalls during execution [2,7,22,62]; a result we corroborate in Section 6.2. It has also been shown that sharedeverything systems have frequent shared read-write accesses [9,22], which are difficult to predict [56].…”

Section: Shared-everything Database Deploymentssupporting

confidence: 71%

Characterization of the Impact of Hardware Islands on OLTP

Porobic

Pandis

Branco³

et al. 2015

The VLDB Journal

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Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.

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Section: Shared-everything Database Deploymentssupporting

confidence: 71%

Characterization of the Impact of Hardware Islands on OLTP

Porobic

Pandis

Branco³

et al. 2015

The VLDB Journal

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“…Our experiments used a typical database page size of 8 KB. The page layout follows a simplified N -ary storage model (NSM) [1]: each page consists of N equi-length records (N = 64).…”

Section: Workloadmentioning

confidence: 99%

Wear-Aware Algorithms for PCM-Based Database Buffer Pools

Chen

et al. 2014

Web-Age Information Management

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Abstract. PCM can be used to overcome the capacity limit and energy issues of conventional DRAM-based main memory. This paper explores how the database buffer manager can deal with the write endurance problem, which is unique to PCM-based buffer pools and not considered by conventional buffer algorithms. We introduce a range of novel buffer algorithms addressing this problem, called wear-aware buffer algorithms, and study their behavior using trace-driven simulations.

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“…Simplified database benchmarks have been previously used for analyzing the efficiency of the processor and memory configurations [4], [12]- [14]. The reduced benchmarks have been shown to produce similar results with a significant reduction in simulation time [14]- [16].…”

Section: Related Workmentioning

confidence: 99%

SCRAP: A Statistical Approach for Creating a Database Query Workload Based on Performance Bottlenecks

Skarie

Debnath

Lilja

et al. 2007

2007 IEEE 10th International Symposium on Workload Characterization

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Abstract-With the tremendous growth in stored data, the role of database systems has become more significant than ever before. Standard query workloads, such as the TPC-C and TPC-H benchmark suites, are used to evaluate and tune the functionality and performance of database systems. Running and configuring benchmarks is a time consuming task. It requires substantial statistical expertise due to the enormous data size and large number of queries in the workload. Subsetting can be used to reduce the number of queries in a workload. An existing workload subsetting technique selected queries based on similarities of the ranks of the queries for low-level characteristics, such as cache miss rates, or based on the execution time required in different computer systems. However, many low-level characteristics are correlated, produce similar behaviors. Also, raw execution time as a metric is too diffuse to capture important performance bottlenecks. Our goal is to select a subset of queries that can reproduce the same bottlenecks in the system as the original workload. In this paper, we propose a statistical approach for creating a database query workload based on performance bottlenecks (SCRAP). Our methodology takes a query workload and a set of system configuration parameters as inputs, and selects a subset of the queries from the workload based on the similarity of performance bottlenecks. Experimental results using the TPC-H benchmark and the PostgreSQL database system, show that the reduced workload and the original workload produce similar performance bottlenecks, and the subset accurately estimates the total execution time.

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Data page layouts for relational databases on deep memory hierarchies

Cited by 89 publications

References 16 publications

Characterization of the Impact of Hardware Islands on OLTP

Characterization of the Impact of Hardware Islands on OLTP

Wear-Aware Algorithms for PCM-Based Database Buffer Pools

SCRAP: A Statistical Approach for Creating a Database Query Workload Based on Performance Bottlenecks

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