Benchmarking Apache Spark and Hadoop MapReduce on Big Data Classification

TL;DR

This paper compares Apache Spark and Hadoop MapReduce for classification tasks in Big Data, evaluating performance, accuracy, and scalability to guide tool selection.

Contribution

It provides a comprehensive benchmark using multiple metrics, including execution time, accuracy, and scalability, which is novel in considering task-specific performance.

Findings

Spark is 5 times faster than MapReduce in training.

Spark's performance degrades with larger input workloads.

MapReduce achieves slightly better accuracy (~3%) than Spark.

Abstract

Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term Big Data Mining. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consideration when choosing the right data management framework based on the task at hand. In this paper, we present a comprehensive benchmark for two widely used Big Data analytics tools, namely Apache Spark and Hadoop MapReduce, on a common data mining task, i.e., classification. We employ several evaluation metrics to compare the performance of the benchmarked frameworks, such as execution time, accuracy, and scalability. These metrics are specialized to measure the performance for classification task. To the best of our knowledge, there is no previous study in the literature that employs all these metrics while taking into consideration task-specific concerns. We show that Spark is 5 times faster than MapReduce on training the model. Nevertheless, the performance of Spark degrades when the input workload gets larger. Scaling the environment by additional clusters significantly improves the performance of Spark. However, similar enhancement is not observed in Hadoop. Machine learning utility of MapReduce tend to have better accuracy scores than that of Spark, like around 3%, even in small size data sets.