CODD: a dataless approach to big data testing

The construction and development of the so-called Big Data systems has occupied centerstage in the data management community in recent years. However, there has been comparatively little attention paid to the testing of such systems, an essential pre-requisite for successful deployment. This is surp...

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Bibliographic Details
Main Authors: S., Ashoke, Haritsa, Jayant R.
Format: Conference Proceeding
Language:English
Online Access:Get full text
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Summary:The construction and development of the so-called Big Data systems has occupied centerstage in the data management community in recent years. However, there has been comparatively little attention paid to the testing of such systems, an essential pre-requisite for successful deployment. This is surprising given that traditional testing techniques, which typically involve construction of representative databases and regression query suites, are completely impractical at Big Data scale -- simply due to the time and space overheads involved in their execution. For instance, consider the situation where a database engineer wishes to evaluate the query optimizer's behavior on a futuristic Big Data setup featuring "yottabyte" (10 24 bytes) sized relational tables. Obviously, just generating this data, let alone storing it, is practically infeasible even on the best of systems.
ISSN:2150-8097
2150-8097
DOI:10.14778/2824032.2824123