Abstract: Apache hadoop is a distributed system for storing large amount of data and processing the data in parallel. This apache hadoop contains HDFS and MAP REDUCE as a core components .HDFS is as file system that can store very large data sets by scaling out across hosts of clusters .Map Reduce is a huge scalable, parallel processing framework that works in concurrent with HDFS.MAP REDUCE contains two main steps map and reduce. Map collects all the jobs carry out by job tracker and reduce module reduces all the map jobs which is carry out by task tracker. Developers use MapReduce for objects like filtering documents by tags, counting words in documents, and takeout links to related data. This paper describes about all the map reduce audit log events for counting the words that are being carry out during the execution of process. Logs are vital part of any computing system, supporting potential from audits to error management. As logs extends and the number of log origin increases (such as in cloud environment), a scalable system is compulsory to efficiently process logs. This procedure explores processing logs with Apache Hadoop from a distinctive Linux system. AUDITING for mapreduce mechanisms is a major concern, how tracing and logging significant events that could take place during a system run. These auditing mechanisms of map reduce audit logs are efficient, scalable, reliable. Map reduce audit logs have been one of the key enabling feature for security auditing.
Keywords: job tracker audit logs, task tracker audit logs, hadoop counter logs.