The full text of this article


Improving execution speed of incremental runs of MapReduce using provenance
by Anu Mary Chacko; Anish Gupta; S. Madhu; S.D. Madhu Kumar
International Journal of Big Data Intelligence (IJBDI), Vol. 4, No. 3, 2017


Abstract: Hadoop MapReduce is an analytic tool used to solve big data problems that are parallelisable. MapReduce jobs need to be rerun frequently for data changes. Many times these data changes are made by appending new data to the existing file. So in a rerun, if we can reuse the output of the previous run and limit the job execution to the new data, we can reduce the overall job execution time. In the literature, there are schemes that use the concept of memoisation, storing the intermediate result, etc. to implement efficient incremental rerun. In this paper, we explain how provenance can be used to implement transparent incremental MapReduce for 'append only' input files. Our approach requires no additional storage or modification of existing Hadoop infrastructure or scheduler. Experimental evaluation of running MapReduce on multinode cluster with provenance stored in HBase gave good results for incremental runs in cases of an addition of new file/new data.

Online publication date: Fri, 28-Jul-2017


is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Big Data Intelligence (IJBDI):
Login with your Inderscience username and password:


    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email