The world, today, is data-driven which means whether it is a small start-up or a large corporate, a huge amount of data is produced. Business data, sales data, customer data, and product data, much of these data is stored in databases and web servers. Inside web servers, you can find the data in web server logs. These logs have raw data that is not structured and may be difficult to understand.
As these logs are usually neglected by large companies, they may lose track of valuable information that can help them enhance their business. Thus, it is important to handle all this log data. But log analysis can be troublesome without a proper tool.
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This is where ELK Stack comes in.
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What is ELK Stack?
ELK Stack is a powerful log analysis tool that is a combination of three tools – Elasticsearch, Logstash, and Kibana. They are three open-source projects, but together they offer an end-to-end analysis solution for log searching, visualizing and analyzing. These logs may be generated from different systems.
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This software stack helps you to take data from any source and format. You can then perform searching, analyzing and visualizing this data for determining patterns, in real-time. With the help of centralized logging, you can identify problems of the webserver and applications. This means you can look for all the logs from a single platform. Issues in multiple servers can also be detected.
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ELK Stack Tutorial: Architecture
Let us take a closer look at the ELK Stack architecture. The components are as follows:
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This is the heart of the software stack and is basically a NoSQL database. It was launched in 2010 and is based on the Apache Lucene search engine. Coded in Java, this tool is open-sourced. This powerful analytics engine allows you to store, analyze and search huge chunks of log data. The best way to obtain data from searching in Elasticsearch is by utilizing its REST API.
Some of its important features are:
- Stores data centrally for looking for it quickly
- Offers advanced queries for better data analysis
- You can use it to index heterogeneous data
- Offers Near Real-Time search, which means that you can find the documents right after they are indexed. So, you can update and add more data to the documents in real-time.
- Offers Geolocation support and Multilanguage support
- Use Multi-document APIs for handling individual records
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Some of the important components of Elasticsearch are:
- Index – They are logical partitions of documents that have similar characteristics
- Node –This is an Elasticsearch instance
- Shard – Indices can be split into horizontal shards or pieces
- Document – JSON objects that are storage units and are stored in an Elasticsearch index
- Cluster – A collection of nodes
This is a tool that fetches data inputs and provides them to the Elastic search. Initially, it was used for collecting and streaming large quantities of data from different data sources. Later on, it was included in the ELK Stack, and then it processed log messages, enhancing them and sending them to the destination.
Logstash makes the collected data available for using it further. It also helps to clean the data for further use and can support a huge array of data types. There is a huge ecosystem of plugins for Logstash, you can enhance its features. Some of the popular plugins include Github, file, exec, heartbeat, http, and iMac.
It has 3 major components:
This is used for passing the logs for processing so that it can be understood by the machine. There are more than 50 input plugins for collecting and processing data from databases and applications.
This consists of the input data for the message field. This is considered the decision-maker for the log that is already been processed.
These are conditions that are used for executing an action or event. Events are handled using internal queues.
This is the tool used for data visualization in the ELK Stack. You can use this to search for Elasticsearch indices and is a simple interface that is browser-based. Using this, you can explore large volumes of data. It has an extensive dashboard that has many features such as graphs, geospatial data, and diagrams. Kibana can be used for searching, interacting and viewing Elasticseach data that is contained in the indices. Learn more about data visualization.
Important features of Kibana are as follows:
- Runs on Windows, Mac, and Linux
- Offers real-time visualization of indexed data
- This runs on Node.js and you get the necessary packages along with the installation package
- It can depict historical information using charts and graphs
- You can develop and save your own graphs
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Another component of the ELK architecture is Beats. They are a set of log shippers that are installed on servers for fetching metrics and data logs. It is coded in the Go programming language and is a lightweight tool. Some of the different types of Beats are:
- Filebeat: It collects log files
- Packetbeat: It collects network data
- Metricbeat: It collects service and system metrics
- Winlogbeat: It is used to collect Windows Event log files
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ELK Stack Tutorial: Installation
Now, we have reached the last section of the ELK Stack tutorial. Let us see the steps required for installing the ELK Stack.
- Visit the official website of ELK Stack – https://www.elastic.co/downloads
- Click to download Elasticsearch
- Then, click to download Logstash
- After that download Kibana
- You will get three zip folders. Unzip them and follow the instructions on the official website to download them individually.
The ELK Stack is used by famous corporations all over the world such as Netflix, Medium and LinkedIn for handling their log data. This is because the tool works great while collecting data from different applications and converge it into a single instance. It is also very useful in vertical and horizontal scaling. Moreover, it supports multiple languages such as Python, Java, Perl and Ruby.
So, if you are a business owner struggling to handle your log data, ELK is the solution. For understanding the basics, keep this ELK Stack tutorial handy.
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