Distributed Log Analysis on the Cloud Using MapReduce

TL;DR

This paper presents a scalable, cloud-based distributed log analysis system utilizing MapReduce, capable of processing various web server logs efficiently and automatically resizing based on data analysis needs.

Contribution

It introduces a flexible, cloud-deployed MapReduce framework for web log analysis that automatically adapts to data size and supports multiple log formats.

Findings

System efficiently processes large web logs

Supports multiple log formats like Apache, IIS, Squid

Automatically resizes cluster based on workload

Abstract

In this paper we describe our work on designing a web based, distributed data analysis system based on the popular MapReduce framework deployed on a small cloud; developed specifically for analyzing web server logs. The log analysis system consists of several cluster nodes, it splits the large log files on a distributed file system and quickly processes them using MapReduce programming model. The cluster is created using an open source cloud infrastructure, which allows us to easily expand the computational power by adding new nodes. This gives us the ability to automatically resize the cluster according to the data analysis requirements. We implemented MapReduce programs for basic log analysis needs like frequency analysis, error detection, busy hour detection etc. as well as more complex analyses which require running several jobs. The system can automatically identify and analyze several web server log types such as Apache, IIS, Squid etc. We use open source projects for creating the cloud infrastructure and running MapReduce jobs.