Title :
Benchmarking Apache Accumulo BigData Distributed Table Store Using Its Continuous Test Suite
Author :
Sen, Rahul ; Farris, Andrew ; Guerra, Peter
Author_Institution :
Booz Allen Hamilton, Seattle, WA, USA
fDate :
June 27 2013-July 2 2013
Abstract :
In this paper, we present results of benchmarking Apache Accumulo distributed table store using the continuous tests suite included in its open source distribution. The continuous test suite contains tests that build and traverse a very large linked list, implemented via a simple table-row indexing mechanism. This underlying design provides insight for developing applications dealing with complex relationship among data sets as typically found in graph analytics applications. The benchmark study investigated sustained continuous mode stress testing and identified optimum configurations for very high-throughput data ingest, sequential and random query operations. Apache Accumulo also has the unique feature of cell level data access security, and the benchmark evaluates the processing overhead for this feature. We also tested high-speed table data verification and validation. These benchmark tests were run on a large cluster optimized for large-scale analytics and we present the performance figures for Apache Accumulo found in the study.
Keywords :
data analysis; distributed processing; program testing; security of data; Apache Accumulo distributed table store; big data distributed table store; cell level data access security; continuous test suite; graph analytics application; open source distribution; random query operation; sequential query operation; table data validation; table data verification; table-row indexing mechanism; Benchmark testing; Indexes; Radio access networks; Scalability; Security; Servers; Throughput; Apache Accumulo; Benchmark; BigData; BigTable Implementation; NoSQL; Scalable Table Store;
Conference_Titel :
Big Data (BigData Congress), 2013 IEEE International Congress on
Conference_Location :
Santa Clara, CA
Print_ISBN :
978-0-7695-5006-0
DOI :
10.1109/BigData.Congress.2013.51