Parallel OLAP query processing in database clusters with data replication

Lima, Alexandre A. B.; Furtado, Camille; Valduriez, Patrick; Mattoso, Marta

doi:10.1007/s10619-009-7037-8

Cited by 42 publications

(32 citation statements)

References 15 publications

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“…Results presented by Lima et al show this dynamic load balancing strategy is quite effective even under severe data skew conditions.…”

Section: Pargresmentioning

confidence: 97%

“…AVP implementation in ParGRES requires full database replication . However, an implementation using partial database replication is described by Lima et al and its adoption in ParGRES is a future work.…”

Section: Pargresmentioning

confidence: 99%

“…There are some requirements for VP to be effective. First, to produce correct results, virtual partitioning of T must be disjoint and complete . Second, for performance reasons, each subquery must access only those disk pages that contain tuples belonging to its respective virtual partition.…”

Section: Pargresmentioning

confidence: 99%

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OLAP parallel query processing in clouds with C‐ParGRES

Ribeiro

Lima

Oliveira

2019

Concurrency and Computation

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Summary The advent of big data technologies has changed the way many companies manage their data. Several companies moved their data to the cloud using the concept of database‐as‐a‐service (DBaaS). Moving databases to the cloud presents several challenges related to flexible and scalable management of data. Although some of these companies migrated to NoSQL databases, most still rely on relational databases in the cloud to manage data, especially data that is critical to the decision making process. Online analytical processing (OLAP) queries take a long time to be processed, thus demanding high‐performance capabilities from their associated database systems to get results in a feasible time. In this article, we propose a middleware solution that can be deployed in any cloud provider, named C‐ParGRES, which explores database replication and interquery and intraquery parallelism to efficiently support OLAP queries in the cloud. C‐ParGRES is an extension of ParGRES, an open‐source database cluster middleware for high‐performance OLAP query processing in clusters. C‐ParGRES exploits cloud capabilities such as on‐demand resource provisioning and elasticity. In addition, C‐ParGRES can create multiple and independent virtual clusters for different database and users. We evaluate C‐ParGRES with two real‐world OLAP applications, both from the Brazilian Institute of Geography and Statistics. Results show that C‐ParGRES is a cost‐effective solution for OLAP query processing in the cloud.

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“…Results presented by Lima et al show this dynamic load balancing strategy is quite effective even under severe data skew conditions.…”

Section: Pargresmentioning

confidence: 97%

Section: Pargresmentioning

confidence: 99%

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OLAP parallel query processing in clouds with C‐ParGRES

Ribeiro

Lima

Oliveira

2019

Concurrency and Computation

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“…We call those High Cardinality Dimensions (HCD). Table I [16], [17], [18]), which both are well-known data warehouse partitioning strategies.…”

Section: Tpc-h*d Fragmentation Schemamentioning

confidence: 99%

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

Cuzzocrea

Xu²,

Grasso

2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Following our previous research results, in this paper we provide two authoritative application scenarios that build on top of OLAP*, a middleware for parallel processing of OLAP queries that truly realizes effective and efficiently OLAP over Big Data. We have provided two authoritative case studies, namely parallel OLAP data cube processing and virtual OLAP data cube design, for which we also propose a comprehensive performance evaluation and analysis. Derived analysis clearly confirms the benefits of our proposed framework.

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“…Partition sizes keep increasing while the increase in query execution time is proportional to the increase in partition size. Such work is expanded in [25], where partial replication is used instead of full database replication. In [30], authors present ParGRES, which is a middleware that implements the AVP technique.…”

Section: Query Scheduling and Dynamic Replication In Distributed Datamentioning

confidence: 99%

Quality of experience in distributed databases

Costa

Furtado

2011

Distrib Parallel Databases

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This paper proposes an approach to improve the level of Quality of Experience (QoE) that distributed database systems provide.Quality of Experience is a measure of users' satisfaction when using a certain service or application. Therefore, the main objective of this paper is to provide mechanisms to increase users' satisfaction when accessing distributed database systems.In traditional database systems, users cannot specify execution-related constraints. Then, the database system cannot evaluate if user expectations are satisfied and neither the system can take corrective actions when necessary.In this work, we present the QoE-oriented distributed database system (QoE-DDB). It allow users to specify Data Access Requirements (DARs) and aims to please users by satisfying the DARs they define. We define a set of types of Data Access Requirements and propose some SQL extensions that enable users to specify execution-related requirements. Proposed types of DARs include execution deadline and priority, execution start and finish times, data availability and freshness degrees, and disconnected execution mode.In our QoE-DDB, each user's command is transformed into one or more tasks that are executed by data services. Community modules and local data services negotiate Service Level Objectives (SLOs) for each task, which improves the system's dependability. We propose both QoE-oriented scheduling and dynamic data placement strategies. Proposed architecture and scheduling strategies enable the system to be used in a wide range of distributed environments, from tightly-coupled homogeneous environments (e.g. composed by off-the-shelf computers connected by a LAN) Communicated by Amit Sheth. R.L.d.C. Costa ( ) · P. Furtado 362 Distrib Parallel Databases (2011) 29:361-396 to highly heterogeneous and geographically distributed systems, where data services have some degree of autonomy.Traditional performance indicators (e.g. throughput and response time) are not adequate to measure the QoE a system provides. We also propose some specialized Key Performance Indicators (KPIs) to estimate the QoE level a database system provides.Finally, we present experimental results obtained through the use of benchmark data and queries together with a prototype that implements proposed strategies. In our experiments, we consider realistic scenarios and compare proposed scheduling strategies with their best-effort oriented counterparts. Obtained results prove the importance of our QoE-oriented approach.

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Parallel OLAP query processing in database clusters with data replication

Cited by 42 publications

References 15 publications

OLAP parallel query processing in clouds with C‐ParGRES

OLAP parallel query processing in clouds with C‐ParGRES

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

Quality of experience in distributed databases

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