XDB

Binnig, Carsten; Salama, Abdallah; Müller, Alexander; Zamanian, Erfan; Kornmayer, H.; Lising, Sven

doi:10.1145/2523616.2525947

Proceedings of the 4th Annual Symposium on Cloud Computing 2013

DOI: 10.1145/2523616.2525947

|View full text |Cite

XDB

Carsten Binnig

Abdallah Salama

Alexander Müller

et al.

Abstract: Parallel database systems are major platforms for supporting analytical queries over large data sets. However, in order to offer SQL-like services for data analytics in the cloud, providers such as Amazon and Google do often build their own systems (e.g., BigTable). One reason is that existing database systems do not fulfill important requirements such as elasticity and fine-grained faulttolerance. In this poster, we present XDB [2, 3], a parallel database system which implements two novel concepts:(1) a parti… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Other2

Relationship

Self Cite1

Independent1

Authors

Journals

Cited by 2 publications

References 3 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

Locality-aware Partitioning in Parallel Database Systems

Zamanian

Binnig

Salama³

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

Parallel database systems horizontally partition large amounts of structured data in order to provide parallel data processing capabilities for analytical workloads in sharednothing clusters. One major challenge when horizontally partitioning large amounts of data is to reduce the network costs for a given workload and a database schema. A common technique to reduce the network costs in parallel database systems is to co-partition tables on their join key in order to avoid expensive remote join operations. However, existing partitioning schemes are limited in that respect since only subsets of tables in complex schemata sharing the same join key can be co-partitioned unless tables are fully replicated.In this paper we present a novel partitioning scheme called predicate-based reference partition (or PREF for short) that allows to co-partition sets of tables based on given join predicates. Moreover, based on PREF, we present two automatic partitioning design algorithms to maximize data-locality. One algorithm only needs the schema and data whereas the other algorithm additionally takes the workload as input.In our experiments we show that our automated design algorithms can partition database schemata of different complexity and thus help to effectively reduce the runtime of queries under a given workload when compared to existing partitioning approaches.

show abstract

Locality-aware Partitioning in Parallel Database Systems

Zamanian

Binnig

Salama³

2015

Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data

Self Cite

View full text Add to dashboard Cite

show abstract

A Novel Approach to Accessing Large Images in the Database

Ren¹

2015

Advances in Social Science, Education and Humanities Research

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

XDB

Cited by 2 publications

References 3 publications

Locality-aware Partitioning in Parallel Database Systems

Locality-aware Partitioning in Parallel Database Systems

A Novel Approach to Accessing Large Images in the Database

Contact Info

Product

Resources

About