Automated profiling and resource management of pig programs for meeting service level objectives

Abstract

An increasing number of MapReduce applications associated with live business intelligence require completion time guarantees. In this paper, we consider the popular Pig framework that provides a high-level SQL-like abstraction on top of MapReduce engine for processing large data sets. Programs written in such frameworks are compiled into directed acyclic graphs (DAGs) of MapReduce jobs. There is a lack of performance models and analysis tools for automated performance management of such MapReduce jobs. We offer a performance modeling environment for Pig programs that automatically profiles jobs from the past runs and aims to solve the following inter-related problems: (i) estimating the completion time of a Pig program as a function of allocated resources; (ii) estimating the amount of resources (a number of map and reduce slots) required for completing a Pig program with a given (soft) deadline. For solving these problems, initially, we optimize a Pig program execution by enforcing the optimal schedule of its concurrent jobs. For DAGs with concurrent jobs, this optimization helps reducing the program completion time: 10%-27% in our experiments. Moreover, it eliminates possible non-determinism of concurrent jobs' execution in the Pig program, and therefore, enables a more accurate performance model for Pig programs. We validate our approach using a 66-node Hadoop cluster and a diverse set of workloads: PigMix benchmark, TPC-H queries, and customized queries mining a collection of HP Labs' web proxy logs. The proposed scheduling optimization leads to significant resource savings(20%-40% in our experiments) compared with the original, unoptimized solution, and the predicted program completion times are within 10% of the measured ones.