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You're reading from  Learning ELK Stack

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Published inNov 2015
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ISBN-139781785887154
Edition1st Edition
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Saurabh Chhajed
Saurabh Chhajed
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Saurabh Chhajed

Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at http://saurzcode.in. Saurabh has also reviewed books by Packt Publishing, Apache Camel Essentials and Java EE 7 Development with NetBeans 8, in the past.
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Configuring Logstash input


In this section, we'll configure Logstash to read data from access logs located on Tomcat, and index it in Elasticsearch, making filters and tokenization of terms in logs as per the grok pattern.

Grok pattern for access logs

As we already saw, some of the commonly used grok patterns are already included with the Logstash installation. Check out the list of Logstash grok patterns on GitHub at https://github.com/logstash-plugins/logstash-patterns-core/tree/master/patterns.

There is already a grok pattern for the Common Apache log format in the Logstash installation as follows:

COMMONAPACHELOG %{IPORHOST:clientip} %{USER:ident} %{USER:auth} \[%{HTTPDATE:timestamp}\] "(?:%{WORD:verb} %{NOTSPACE:request}(?: HTTP/%{NUMBER:httpversion})?|%{DATA:rawrequest})" %{NUMBER:response} (?:%{NUMBER:bytes}|-)

We can directly use COMMONAPACHELOG as a matching pattern for our incoming messages to Logstash as follows:

input{
file{
path =>"/var/lib/tomcat7/logs/localhost_access_logs.txt...
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Learning ELK Stack
Published in: Nov 2015Publisher: ISBN-13: 9781785887154

Author (1)

author image
Saurabh Chhajed

Saurabh Chhajed is a technologist with vast professional experience in building Enterprise applications that span across product and service industries. He has experience building some of the largest recommender engines using big data analytics and machine learning, and also enjoys acting as an evangelist for big data and NoSQL technologies. With his rich technical experience, Saurabh has helped some of the largest financial and industrial companies in USA build their large product suites and distributed applications from scratch. He shares his personal experiences with technology at http://saurzcode.in. Saurabh has also reviewed books by Packt Publishing, Apache Camel Essentials and Java EE 7 Development with NetBeans 8, in the past.
Read more about Saurabh Chhajed