A demonstration of spatialhadoop: An efficient mapreduce framework for spatial data

Ahmed Eldawy, Mohamed F Mokbel

Research output: Contribution to journalConference articlepeer-review

151 Scopus citations

Abstract

This demo presents SpatialHadoop as the first full-fledged MapReduce framework with native support for spatial data. Spatial- Hadoop is a comprehensive extension to Hadoop that pushes spatial data inside the core functionality of Hadoop. SpatialHadoop runs existing Hadoop programs as is, yet, it achieves order(s) of magnitude better performance than Hadoop when dealing with spatial data. SpatialHadoop employs a simple spatial high level language, a two-level spatial index structure, basic spatial components built inside the MapReduce layer, and three basic spatial operations: range queries, k-NN queries, and spatial join. Other spatial operations can be similarly deployed in SpatialHadoop. We demonstrate a real system prototype of SpatialHadoop running on an Amazon EC2 cluster against two sets of real spatial data obtained from Tiger Files and OpenStreetMap with sizes 60GB and 300GB, respectively.

Original languageEnglish (US)
Pages (from-to)1230-1233
Number of pages4
JournalProceedings of the VLDB Endowment
Volume6
Issue number12
DOIs
StatePublished - Aug 2013
Event39th International Conference on Very Large Data Bases, VLDB 2012 - Trento, Italy
Duration: Aug 26 2013Aug 30 2013

Fingerprint

Dive into the research topics of 'A demonstration of spatialhadoop: An efficient mapreduce framework for spatial data'. Together they form a unique fingerprint.

Cite this