Splunk: Listen to your Data

Hunk™: Splunk Analytics for Hadoop

Hunk helps us to explore, analyse and visualize. It is fast compared to rest of the lot.Hunk is a next evolution in big data analytics.Hunk helps to convert data stored in Hadoop clusters into strategic assets. Deriving value and insights out of huge data stored by organisations has proven a challenge.
Hunk works on all major distributions of Apache Hadoop including those from Amazon Web Services, Cloudera, Hortonworks, IBM, MapR and Pivotal. 
Others who are giving a tough competition to splunk are Logstash ,Kibana , Graylog2 ,LogLogic, LogRhythm,Graylog2 ,Elasticsearch etc. Splunk provides a increasingly useful alternative to commercial log analysis tools. 
Although Splunk is the wonderful log analysis tool but also there are a lot of open source alternatives and competitors of Splunk. If you cannot afford the high price of Splunk, you can get some open source and free log analysis tools which provide almost same functionality of Splunk. I have listed down 20 free and open source alternatives and competitors of Splunk Log Analysis Tool. Following is the list of all these open source alternatives of Splunk.

1. Scribe - Real time log aggregation used in Facebook

Scribe is a server for aggregating log data that's streamed in real time from clients. It is designed to be scalable and reliable. It is developed and maintained by Facebook. It is designed to scale to a very large number of nodes and be robust to network and node failures. There is a scribe server running on every node in the system, configured to aggregate messages and send them to a central scribe server (or servers) in larger groups.

        

2. Logstash - Centralized log storage, indexing, and searching

Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use. Logstash comes with a web interface for searching and drilling into all of your logs.

      

3. Octopussy - Perl/XML Logs Analyzer, Alerter & Reporter

Octopussy is a Log analyzer tool. It analyzes the log, generates reports and alerts the admin. It has LDAP support to maintain users list. It exports report by Email, FTP & SCP. Scheduled reports could be generated. RRD tool to generate graphs.

      

4. Awstats - Advanced web, streaming, ftp and mail server statistics

AWStats is a powerful tool that generates advanced web, streaming, ftp or mail server statistics graphically. It can analyze log files from all major server tools like Apache log files, WebStar, IIS and a lot of other web, proxy, wap, streaming servers, mail servers and some ftp servers. This log analyzer works as a CGI or from command line and shows you all possible information your log contains, in few graphical web pages.

      

5. nxlog - Multi platform Log management

 nxlog is a modular, multi-threaded, high-performance log management solution with multi-platform support. In concept it is similar to syslog-ng or rsyslog but is not limited to unix/syslog only. It can collect logs from files in various formats, receive logs from the network remotely over UDP, TCP or TLS/SSL . It supports platform specific sources such as the Windows Eventlog, Linux kernel logs, Android logs, local syslog etc.

      

6. Graylog2 - Open Source Log Management

Graylog2 is an open source log management solution that stores your logs in ElasticSearch. It consists of a server written in Java that accepts your syslog messages via TCP, UDP or AMQP and stores it in the database. The second part is a web interface that allows you to manage the log messages from your web browser. Take a look at the screenshots or the latest release info page to get a feeling of what you can do with Graylog2.

      

7. Fluentd - Data collector, Log Everything in JSON

Fluentd is an event collector system. It is a generalized version of syslogd, which handles JSON objects for its log messages. It collects logs from various data sources and writes them to files, database or other types of storages.

      

8. Meniscus - The Python Event Logging Service

Meniscus is a Python based system for event collection, transit and processing in the large. It's primary use case is for large-scale Cloud logging, but can be used in many other scenarios including usage reporting and API tracing. Its components include Collection, Transport, Storage, Event Processing & Enhancement, Complex Event Processing, Analytics.

      

9. lucene-log4j - Log4j file rolling appender which indexes log with Lucene

lucene-log4j solves a recurrent problem that production support team face whenever a live incident happens: filtering production log statements to match a session/transaction/user ID. It works by extending Log4j's RollingFileAppender with Lucene indexing routines. Then with a LuceneLogSearchServlet, you get access to your log using web front end.

    

10. Chainsaw - log viewer and analysis tool

Chainsaw is a companion application to Log4j written by members of the Log4j development community. Chainsaw can read log files formatted in Log4j's XMLLayout, receive events from remote locations, read events from a DB, it can even work with the JDK 1.4 logging events.

11. Logsandra - log management using Cassandra

Logsandra is a log management application written in Python and using Cassandra as back-end. It is written as demo for cassandra but it is worth to take a look. It provides support to create your own parser.

    

12. Clarity - Web interface for the grep

Clarity is a Splunk like web interface for your server log files. It supports searching (using grep) as well as trailing log files in realtime. It has been written using the event based architecture based on EventMachine and so allows real-time search of very large log files.

    

13. Webalizer - fast web server log file analysis

The Webalizer is a fast web server log file analysis program. It produces highly detailed, easily configurable usage reports in HTML format, for viewing with a standard web browser. It handles standard Common logfile format (CLF) server logs, several variations of the NCSA Combined logfile format, wu-ftpd/proftpd xferlog (FTP) format logs, Squid proxy server native format, and W3C Extended log formats.

    

14. Zenoss - Open Source IT Management

Zenoss Core is an open source IT monitoring product that delivers the functionality to effectively manage the configuration, health, performance of networks, servers and applications through a single, integrated software package.

    

15. OtrosLogViewer - Log parser and Viewer

OtrosLogViewer can read log files formatted in Log4j (pattern and XMLL yout), java.util.logging. Source of events can be local or remote file (ftp, sftp, sa ba, http) or sockets. It has many powerful features like filtering marking, formatting, adding notes, etc. It could also format SOAP messages in logs.

     

16. Kafka - A high-throughput distributed messaging system

Kafka provides a publish-subscribe solution that can handle all activity stream data and processing on a consumer-scale web site. This kind of activity (page views, searches, and other user actions) are a key ingredient in many of the social feature on the modern web. This data is typically handled by "logging" and ad hoc log aggregation solutions due to the throughput requirements. This kind of ad hoc solution is a viable solution to providing logging data to Hadoop.

    

17. Kibana - Web Interface for Logstash and ElasticSearch

Kibana is a highly scalable interface for Logstash and ElasticSearch that allows you to efficiently search, graph, analyze and otherwise make sense of a mountain of logs. Kibana will load balance against your Elasticsearch cluster. Logstash's daily rolling indicies let you scale to huge datasets, while Kibana's sequential querying gets you most relevant data quickly, with more as it becomes available.

    

18. Pylogdb - A Python-powered, column-oriented database suitable for web log analysis

pylogdb is a database suitable for web log analysis.

19. Epylog - a Syslog parser

Epylog is a syslog parser which runs periodically, looks at your logs, processes some of the entries in order to present them in a more comprehensible format, and then mails you the output. It is written specifically for large network clusters where a lot of machines (around 50 and upwards) log to the same loghost using syslog or syslog-ng.

  

20. Indihiang - IIS and Apache log analyzing tool

Indihiang Project is a web log analyzing tool. This tool analyzes IIS and Apache Web logs and generates real time reports. It has Web Log Viewer and analyzer. It is capable to analyze the trend from the logs. This tool also integrate with windows Explorer so you can attach a log file in to indihiang tool via context menu.

Compiling and Executing a java native interface code

In order to create a java native interface we need to write a class which contains native functions. Suppose SystemCheck.java is the java file containing the native functions.
Keep SystemCheck.java in the package com/tp/pc/schedule/system

For compiling 
 javac com/tp/pc/schedule/system/System.java

This will create a class com/tp/pc/schedule/system/System.class

Create a header File for the class
javah -d inc com.torresnetworks.policycontrol.schedule.system.System

Execute 
java -Djava.library.path=/path/to/native/library -jar system.jar

GitHub: An Open Source Developer's Tool

GitHub is a code sharing and publishing service. It is a social networking site for programmers.What is so speacial about GitHub? At the heart of GitHub is Git, an open source project started by Linus Torvalds. Git, like other version control systems, manages and stores revisions of projects. Git can control word docs and project files as well.

The difference between other version control systems live CVS and Subversion is that they are centralized but Git is distributed. In distributed version coltrol systems if you want to make changes you need to copy the whole repository to your own system. After making changes on the local copy you can check in the changes to the central system. You don’t have to connect to the server every time you make a change.

GitHub is a Git repository hosting service. Git is a command line tool but GitHub provides web based  graphical user interface. In addition to that it provides access control and other features, such as a wikis and basic task management tools. Following are the three features of GitHub:
Fork: Forking is the most important feature of GitHub  which means that the repository of one user can be transfered to another account. This way you can modify a repository under your account on which you don’t have write access
Pull Request:If you like to share the changes made, you can send a notification called a “pull request” to the original owner. 
Merge: If the Pull Request is already made that user can then, with a click of a button, merge the changes found in your repo with the original repo.

I think this is the best approach an open source project should be executed.
If you want to contribute to an open source project then GitHub provides the best and easiest approach. Earlier we use to manually download the project’s source code, make your changes locally, create a list of changes called a “patch” and then e-mail the patch to the project’s maintainer. The maintainer would then have to evaluate this patch, possibly sent by a total stranger, and decide whether to merge the changes.

GitHub is growing where each day many repositories are forked and many more are merged. On 23 December 2013, GitHub announced that it had reached 10 million repositories. There is no hard limit on the size of repository but the guideline says that it should not exceed one gigabyte.  There is a check for files larger than 100MB in a push; if any such files exist, the push will be rejected.

Big data : A new Buzzword

Big data is the new buzzword in the market. Big data is nothing but processing of large sets of structured and unstructured data which is generated at a very high speed. A scientific analysis of unstructured data is a business imperative for accurate forecasts, informed decision-making and an enhanced customer experience.

Big Data analytical solutions is the  technology transition at a massive global scale, which is going to impact  every day interactions, decisions and shape every aspect of  business, governance, social interactions, education, healthcare, telecom and above all climate change and water management.The key challenges are the data privacy and security, real-time data flows, interaction with different technologies and big data in a cloud.

The world of information technology is driven by Data. IT helps to process raw data into some meaningful data. In the last more than two decades RDBMS played a vital role to handle data but the drawback of the RDBMS is that it can not scale up beyond a certain point. Secondly most of the RDBMS are not able to manage unstructured data sets like word docs, PDFs, XML, image files etc. In the recent past with the advent of smart phones , iPads and other  smart devices the data (both structured as well as unstructured) generated is huge and is growing exponentially day by day. It is predicted that the volume, velocity and the variety of this data growth is endless. This has posed a challenge to the engineers to analyse/process big data with the same velocity with which it is generated taking care of the variety of data. Data management is controlling Data Volume, Velocity and Variety.
 Today Big Data problems has grappled almost all the sectors including retails, airlines, automotive, financial services and energy. As per Mckinsey & Company there is a shortage of 140,000 to 190,000 people with analytical expertise and 1.5 million managers and analysts with the skills to understand and make decisions based on the analysis of big data.

Apache Hadoop is the technology which provides new ways of storing and processing massive volumes of structured and unstructured data. Hadoop is heading towards number one enterprise data storage platform in near future as it has the capability to run queries on huge data sets. the Big data technologies include MapReduce, HBase, Pig, Hive, YARN, Zoo Keeper, Sqoop, Flume  and many more.  It is must for the Application Architects, Solution Architects and IT Architects to delve into Big data technology and leverage it to add value to the customer experience.
Apache Spark is a latest addition which has leveraged the distributed file system HDFS, which is at the center of Hadoop Distributed computing infrastructure. Spark is using Resilient Distributed Datasets to perform in memory  distributed computing capability which is swift.