Analysis of Apache Logs Using Hadoop and Hive (original) (raw)
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Web Log analyser is a tool used for finding the statics of web sites. Through Web Log analyzer the web log files are uploaded into the Hadoop Distributed Framework where parallel procession on log files is carried in the form of master and slave structure. Pig scripts are written on the classified log files to satisfy certain query. The log files are maintained by the web servers. By analysing these log files gives an idea about the user in the way like which IP address have generated the most errors, which user is visiting a web page frequently.. This paper discuss about these log files, their formats, access procedures, their uses, the additional parameters that can be used in the log files which in turn gives way to an effective mining and the tools used to process the log files. It also provides the idea of creating an extended log file and learning the user behaviour. Analysing the user activities is particularly useful for studying user behaviour when using highly interactive ...
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The wide spread use of internet and increased web applications accelerate the rampant growth of web content. Every organization produces huge amount of data in different forms like text, audio, video etc., from multiplesources. The log data stored in web servers is a great source of knowledge. The real challenge for any organization is to understand the behavior of their customers. Analyzing such web log data will help the organizations to understand navigational patterns and interests of their users. As the logs are growing in size day by day, the existing database technologies face a bottleneck to process such massive unstructured data. Hadoop provides a best solution to this problem. Hadoop framework comes up with Hadoop Distributed File System, a reliable distributed storage for data and MapReduce, a distributed parallel processing for executing large volumes of complex data. Hadoop ecosystem constitutes of several other tools like Pig, Hive, Flume, Sqoop etc., for effective ana...
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Web usage mining is a process for finding a user navigation patterns in web server access logs. These navigation patterns are further analyzed by various data minig techniques. The discovered navigation patterns can be further used for several things like identifying the frequent patterns of the user, predicting the future request of user, etc. and in the recent years there are huge growth in electronic commerce websites like flipkart, amazon, etc. with an huge amount of online shopping websites, it is necessary to notice that how many users are actually reaching to the websites. When user's access any online website, web access logs are generated on the server. Web access logs data helps us to analyze user behavior that contain information like ip address, user name, url, timestamp, bytes transferred. It is very meaningful to analyze the web access logs which helps us in knowing the emergency trends on electronic commerce. These ecommerce websites generates petabytes of log data every day which is not possible by traditional tools and techniques to store and analyze such log data. In these paper we proposed an hadoop framework which is very reliable for storing such huge amount of data in to HDFS and than we can analyze the unstructured logs data using apache spark framework to find user behaviour. And in these paper we can also analyze the log data using mapreduce framework and finally we can compare the performance on spark and mapreduce framework on analyzing the log data.
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Pretty much every part of life now results in the generation of data. Logs are documentation of events or records of system activities and are created automatically through IT systems. Log data analysis is a process of making sense of these records. Log data often grows quickly and the conventional database solutions run short for dealing with a large volume of log files. Hadoop, having a wide area of applications for Big Data analysis, provides a solution for this problem. In this study, Hadoop was installed on two virtual machines. Log files generated by a Python script were analyzed in order to evaluate the system activities. The aim was to validate the importance of Hadoop in meeting the challenge of dealing with Big Data. The performed experiments show that analyzing logs with Hadoop MapReduce makes the data processing and detection of malfunctions and defects faster and simpler. Keywords— Hadoop, MapReduce, Big Data, log analysis, distributed file systems.
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Abstract—Big Data is an emerging growing dataset beyond the ability of a traditional database tool. Hadoop rides the big data where the massive quantity of information is processed using cluster of commodity hardware. Web server logs are semi-structured files generated by the computer in large volume usually of flat text files. It is utilized efficiently by Mapreduce as it process one line at a time. This paper performs the session identification in log files using Hadoop in a distributed cluster. Apache Hadoop Mapreduce a data processing platform is used in pseudo distributed mode and in fully distributed mode. The framework effectively identifies the session utilized by the web surfer to recognize the unique users and pages accessed by the users. The identified session is analyzed in R to produce a statistical report based on total count of visit per day. The results are compared with non-hadoop approach a java environment, and it results in a better time efficiency, storage and processing speed of the proposed work.