Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads

TL;DR

This paper provides an empirical analysis of modern MapReduce workloads across various industries, highlighting their interactive nature and diversity, and proposes initial steps toward a standardized benchmark for such workloads.

Contribution

It characterizes new, interactive MapReduce workloads from real-world deployments and introduces foundational ideas for a MapReduce benchmark.

Findings

Workloads are diverse and driven by interactive analysis.

Traditional assumptions about MapReduce, like uniform data access, do not hold.

Workloads include many small, query-like jobs.

Abstract

Within the past few years, organizations in diverse industries have adopted MapReduce-based systems for large-scale data processing. Along with these new users, important new workloads have emerged which feature many small, short, and increasingly interactive jobs in addition to the large, long-running batch jobs for which MapReduce was originally designed. As interactive, large-scale query processing is a strength of the RDBMS community, it is important that lessons from that field be carried over and applied where possible in this new domain. However, these new workloads have not yet been described in the literature. We fill this gap with an empirical analysis of MapReduce traces from six separate business-critical deployments inside Facebook and at Cloudera customers in e-commerce, telecommunications, media, and retail. Our key contribution is a characterization of new MapReduce workloads which are driven in part by interactive analysis, and which make heavy use of query-like programming frameworks on top of MapReduce. These workloads display diverse behaviors which invalidate prior assumptions about MapReduce such as uniform data access, regular diurnal patterns, and prevalence of large jobs. A secondary contribution is a first step towards creating a TPC-like data processing benchmark for MapReduce.