NDLI: Llama: Leveraging columnar storage for scalable join processing (2011)

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Llama: Leveraging columnar storage for scalable join processing (2011)

Content Provider	CiteSeerX
Author	Lin, Yuting Agrawal, Divyakant Chen, Chun Ooi, Beng Chin Wu, Sai
Description	To achieve high reliability and scalability, most large-scale data warehouse systems have adopted the cluster-based architecture. In this paper, we propose the design of a new cluster-based data ware-house system, Llama, a hybrid data management system which combines the features of row-wise and column-wise database sys-tems. In Llama, columns are formed into correlation groups to pro-vide the basis for the vertical partitioning of tables. Llama employs a distributed file system (DFS) to disseminate data among cluster nodes. Above the DFS, a MapReduce-based query engine is sup-ported. We design a new join algorithm to facilitate fast join pro-cessing. We present a performance study on TPC-H dataset and compare Llama with Hive, a data warehouse infrastructure built on top of Hadoop. The experiment is conducted on EC2. The results show that Llama has an excellent load performance and its query performance is significantly better than the traditional MapReduce framework based on row-wise storage.
File Format	PDF
Language	English
Publisher Date	2011-01-01
Publisher Institution	in the MapReduce framework. SIGMOD
Access Restriction	Open
Subject Keyword	Data Warehouse Infrastructure Excellent Load Performance Hybrid Data Management System New Join Algorithm Fast Join Pro-cessing Cluster-based Architecture Vertical Partitioning Performance Study Query Performance Tpc-h Dataset Mapreduce-based Query Engine Traditional Mapreduce Framework Scalable Join Processing High Reliability New Cluster-based Data Ware-house System File System Leveraging Columnar Storage Row-wise Storage Correlation Group Column-wise Database Sys-tems Cluster Node Large-scale Data Warehouse System
Content Type	Text
Resource Type	Article

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