By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators discuss new findings in Database Management. According to news originating from Doha, Qatar, by VerticalNews correspondents, research stated, "Hadoop MapReduce has evolved to an important industry standard for massive parallel data processing and has become widely adopted for a variety of use-cases. Recent works have shown that indexes can improve the performance of selective MapReduce jobs dramatically."
Our news journalists obtained a quote from the research from Qatar Foundation, "However, one major weakness of existing approaches is high index creation costs. We present HAIL (Hadoop Aggressive Indexing Library), a novel indexing approach for HDFS and Hadoop MapReduce. HAIL creates different clustered indexes over terabytes of data with minimal, often invisible costs, and it dramatically improves runtimes of several classes of MapReduce jobs. HAIL features two different indexing pipelines, static indexing and adaptive indexing. HAIL static indexing efficiently indexes datasets while uploading them to HDFS. Thereby, HAIL leverages the default replication of Hadoop and enhances it with logical replication. This allows HAIL to create multiple clustered indexes for a dataset, e.g., one for each physical replica. Still, in terms of upload time, HAIL matches or even improves over the performance of standard HDFS. Additionally, HAIL adaptive indexing allows for automatic, incremental indexing at job runtime with minimal runtime overhead. For example, HAIL adaptive indexing can completely index a dataset as byproduct of only four MapReduce jobs while incurring an overhead as low as 11 % for the very first of those job only. In our experiments, we show that HAIL improves job runtimes by up to 68 over Hadoop."
According to the news editors, the research concluded: "This article is an extended version of the VLDB 2012 paper (Dittrich et al. in PVLDB 5(11):1591-1602, 2012)."
For more information on this research see: Towards zero-overhead static and adaptive indexing in Hadoop. VLDB Journal, 2014;23(3):469-494. VLDB Journal can be contacted at: Springer, 233 Spring St, New York, NY 10013, USA. (Springer - www.springer.com; VLDB Journal - www.springerlink.com/content/1066-8888/)
The news correspondents report that additional information may be obtained from S. Richter, Qatar Fdn, Qatar Comp Res Inst, Doha, Qatar. Additional authors for this research include J.A. Quiane-Ruiz, S. Schuh and J. Dittrich.
Keywords for this news article include: Doha, Asia, Qatar, Database Management
Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC