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Centralized Logging with Logstash and Kibana On CentOS 7
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By Mitchell Anicas
Developer and author at DigitalOcean.
Centralized Logging with Logstash and Kibana On CentOS 7

Introduction

Centralized logging can be very useful when attempting to identify problems with your servers or applications, as it allows you to search through all of your logs in a single place. It is also useful because it allows you to identify issues that span multiple servers by correlating their logs during a specific time frame. This series will teach you how to install Logstash and Kibana on Ubuntu, then how to add more filters to structure your log data. Then it will teach you how to use Kibana.

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In this tutorial, we will go over the installation of the Elasticsearch ELK Stack on CentOS 7—that is, Elasticsearch 2.1.x, Logstash 2.1.x, and Kibana 4.3.x. We will also show you how to configure it to gather and visualize the syslogs of your systems in a centralized location, using Filebeat 1.0.x. Logstash is an open source tool for collecting, parsing, and storing logs for future use. Kibana 4 is a web interface that can be used to search and view the logs that Logstash has indexed.

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In this tutorial, we will show you how to use Topbeat, on a CentOS 7 server, with an ELK stack to gather and visualize infrastructure metrics. Topbeat, which is one of the several “Beats” data shippers that helps send various types of server data to an Elasticsearch instance, allows you to gather information about the CPU, memory, and process activity on your servers.

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One way to increase the effectiveness of your Logstash setup is to collect important application logs and structure the log data by employing filters. In this guide, we will focus primarily on how to add filters for various common application logs.

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The Kibana interface is divided into four sections: Discover, Visualize, Dashboard, and Settings. In this tutorial, we will go over the basics of each section, and demonstrate how each section can be used.

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How To Map User Location with GeoIP and ELK (Elasticsearch, Logstash, and Kibana)

Published on March 31, 2015 · Updated on December 1, 2017

IP Geolocation, the process used to determine the physical location of an IP address, can be leveraged for a variety of purposes, such as content personalization and traffic analysis. In this tutorial, we will show you how to create a visual geo-mapping of the IP addresses of your application’s users, by using a GeoIP database with Elasticsearch, Logstash, and Kibana.

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