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Talks & Seminars
Title: Predicting SQL Query Execution Time for Large Data Volume
Dr. Rekha Singhal, Tata Consultancy Services
Date & Time: October 6, 2016 15:00
Venue: Conference Room, 01st Floor, C Block, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) Building
Abstract:
Most of the database applications experience performance violation over a period of time due to increase in the underlying data volume. Tuning SQL queries in production requires additional efforts and cost. Time constraints during application development do not permit testing SQL queries with high data volumes. Having the capability to predict SQL query execution time for large data volumes can alert the developers to tune SQL queries or database design upfront, in such scenarios. This presentation will talk about a model having this capability to estimate SQL query execution time for large data volume. This has been developed in TCS Research and is incorporated in CODD as well. We have used a modular approach of mapping a compound SQL query execution plan to a sequential execution of a set of elementary steps. The execution time of a SQL query in isolation is predicted as summation of estimated execution time of all its elementary steps. We have built analytical models for estimating execution time of different IO access, DB cache access and SQL operators as function of data size for each such step. The proposed model dynamically adapts itself to the structure of the query execution plans and characteristics of the underlying hardware. We have evaluated the model by generating synthetic queries for all combinations of elementary steps for a range of data sizes. The model has also been validated with TPC-H benchmarks and three real life application and shows an average prediction error to be within 10%.
Speaker Profile:
Rekha Singhal is working as Senior Scientist with TCS Innovation Lab and leading Big Data Performance initiatives. One of the products, Revival 2000, developed under her guidance at CDAC had received NASSCOM Technology award and has been deployed in the Government MMM project. Her research interests are Big Data System Performance, Query Performance Prediction, IP Storage Area Networks and Health IT. She is in TPC of ICPE, CMG India, BigDF Workshop with HiPC, ParLearning workshop with IPDPS and WOSSP workshop with SC. She has successfully conducted two PABS workshop along with ICPE 2015 & 2016. She has taught in prestigious Institutes such as NITIE, TISS and ITM. She has a Ph.D and M.Tech. from IIT Delhi.
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