A Beginner's Guide to ELK Stack

Logging is a vital part of keeping every system alive and healthy. It allows developers and system administrators to troubleshoot issues, monitor system behavior, and gain insight into the performance of their applications. Logs contain valuable information such as stack traces and information about where data is coming from, which can help you reassemble the crime scene of a server crash. However, with the ever-increasing volume and complexity of logs, analyzing and managing them can become overwhelming. That's where ELK Stack comes into play.

ELK Stack is a powerful tool for collecting, analyzing, and visualizing log data. It consists of three open-source components: Elasticsearch, Logstash, and Kibana. In this article, I will provide a beginner's guide to ELK Stack, including an overview of each component and how they work together to help you manage your logs more effectively. Whether you are a developer, a system administrator, or a data analyst, understanding ELK Stack can help you make better use of your log data and keep your systems running smoothly.

1. What is ELK Stack?

ELK, or more specifically, the ELK stack, is a reference to Elasticsearch, Logstash, and Kibana, three open-source tools built by Elastic.

Elasticsearch is a search engine that stores and indexes data, while Logstash is a data processing pipeline that collects, enriches, and transports data, and Kibana is a visualization and analytics platform that allows you to interact with and explore your data. Together, these three components form a powerful tool for managing log data.

Now that we have a global overview of ELK Stack, let’s go deep into each of its specific components.

- Elasticsearch

Elasticsearch is a flexible document database engine that specializes in the ability to write interesting queries to get insight into your data. It is a common tool used by many applications to facilitate search capabilities. It provides a RESTful interface and has many client
libraries available. It also exposes an HTTP API and has a default port of 9200. As part of the ELK stack, it is used to store your logs for long-term retention.

- Logstash

Logstash is a tool for consuming and transforming log data provided in many different formats into a clean and consistent format. Its functionality encompasses tasks such as monitoring for JSON sent via User Datagram Protocol (UDP), analyzing Nginx or Apache log files, and parsing syslog logs. It even enables advanced data transformations that necessitate external data, like converting IP addresses into geolocations. Additionally, there are numerous open-source libraries available in various programming languages for delivering data to Logstash. Among the most widely used libraries in the Node.js environment is winston-logstash.

- Kibana

As already said, Kibana is a data visualization and exploration tool that is part of the ELK stack. It provides users with an intuitive web interface that enables them to create interactive dashboards, reports, and visualizations that allow them to gain insights into their data.

Kibana has a wide range of features, including advanced charting options, data filtering and aggregation, and geospatial analysis. Its visualization capabilities allow users to create custom charts and graphs that help them to identify trends and patterns in their data, as well as to detect anomalies or outliers. Additionally, Kibana has collaboration features that allow users to share their visualizations and dashboards with other team members, promoting teamwork and collaboration.

2. How to use ELK Stack to provide logging information?

To use ELK Stack to provide logging information, you will need to follow these basic steps:

• You will need to download and configure each of the three components. You can either download each component separately or use a package manager like apt or yum to install them. Once you have installed each component, you will need to configure them to work together.

You can access the installation procedures for each component by referring to the links associated with the name of each component: Elasticsearch, Logstash, Kibanna

• The next step is configuring Logstash to collect log data from your applications or services. A common approach to transmitting logs is via UDP. While this approach cannot guarantee log delivery, it does permit the transfer of a large volume of logs without generating excessive network congestion. To implement this method, you need to modify your Logstash configuration file. Depending on your distribution, you may be able to create a file, such as /etc/logstash/conf.d/20-udp.conf, and restart the Logstash service. Within this configuration file, you will need to add an entry similar to the one shown in the following listing:

input { 
udp { 
port => 1337 
host => "0.0.0.0" 
codec => "json" 
} 

The port number 1337, in the last listing is arbitrary and can be replaced with any port of your choice. By default, the host entry is set to 0.0.0.0, allowing all incoming connections. However, if you prefer to restrict incoming requests to local connections only, you can set the host entry to localhost.

Once the configuration is in place, you will need to restart the Logstash service, which can often be done with a simple command, such as $ sudo service logstash restart. Once the service is running, Logstash will be able to receive properly formatted JSON data sent via UDP.

In order to send log messages from your application, you will need to use a library that adheres to a specific message format. The library may require some configuration (e.g., specifying your application name), but will provide the necessary basic attributes when transmitting a log (e.g., the current timestamp).

Example Logstash Log Entry:

{
 "@timestamp": "2023-02-01T13:37:15.185Z",
 "@version": "1",
 "fields": {
 "category": "server",
 "purged": "92",
 "level": "DEBUG"
 },
 "message": "Global Garbage Collection Finished.",
 "type": "lara"
}
}

• Once you have got the logs from logstash, you need to filter and sent them to Elasticsearch for indexing and storage.

• The final step is to use Kibana to visualize and explore your log data. The visualization will take the form of tables, charts, graphs, and reports.

Summary

In conclusion, the ELK stack offers a robust and powerful solution for organizations looking to streamline their log collection and analysis processes. From Elasticsearch's efficient data storage and retrieval to Logstash's data collection and transformation capabilities, and Kibana's powerful data visualization features, the ELK stack provides a complete solution for your data analysis needs.

Thanks for reading! If you have any questions or feedback, please leave a comment below.

Mentions:
The cover image featured in this article is not my property. I got it from Elastic, the company that develops and maintains the ELK Stack.

Connect with me on various platforms