Evaluating NoSQL Databases for OLAP Workloads: A Benchmarking Study of MongoDB, Redis, Kudu and ArangoDB

TL;DR

This study benchmarks MongoDB, Redis, Kudu, and ArangoDB to evaluate their performance in OLAP workloads within a standardized data pipeline involving Spark and BI tools.

Contribution

It provides a comparative analysis of multiple NoSQL databases for OLAP workloads using a unified benchmarking pipeline, which was lacking in prior research.

Findings

Different NoSQL databases exhibit varied performance profiles for OLAP tasks.

The standardized pipeline enables fair comparison across databases.

Insights guide optimal database selection for large-scale analytical workloads.

Abstract

In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (OLAP) tasks. Within the realm of NoSQL databases, various classifications exist based on their data storage mechanisms, making it challenging to select the most suitable one for a given OLAP workload. While each NoSQL database boasts distinct advantages, inherent scalability, adaptability to diverse data formats, and high data availability are universally recognized benefits crucial for managing OLAP workloads effectively. Existing research predominantly evaluates individual databases within custom data pipeline setups, lacking a standardized approach for comparative analysis across different databases to identify the optimal data pipeline for OLAP workloads. In this paper, we present our experimental insights into how various NoSQL databases handle OLAP workloads within a standardized data processing pipeline. Our experimental pipeline comprises Apache Spark for large-scale transformations, data cleansing, and schema normalization, diverse NoSQL databases as data stores, and a Business Intelligence tool for data analysis and visualization.