ELK Stack Tutorial

Nowadays it’s not an easy task to overcome legacy problems, so many micro services are used to hustle these problems. In the current market, there are many applications used to provide better legacy services. One of the popular distributed log management systems tools available that is ELK (Elastic Search, Log stash, and Kibana). They are used for searching, data analyzing, and visualizing data log in a real-time environment. So what you are waiting for, let’s start our journey with learning ELK stack concepts.

Introduction to ELK Stack

ELK Stack is a collection of three open-source microservice products such as Elastic Search, Log stash, and Kibana. These three open-source products are developed, managed, and maintained by the company called Elastic. Whereas E stands for ElasticSearch – used for storing data logs, L stands for both shipping as well as data processing and storing data logs and K stands for Kibana is a data visualization tool or web interface tool which is hosted by Nginx or Apache. ELK stack is specially designed to allow multiple users to use data from any source, in any format, and to search, perform, analyze, and visualize the data in real-time. ELK Stack tool offers centralized data logging that will be useful when attempting to find any problems with data servers or any applications. This tool also allows users to search for all the logs in a single space. It also helps to find any issues that will be occur in multiple servers by connecting their logs during a specific time frame. ELK stack tool is a distributed, open-source search and analytic engine, designed for horizontal scalability, reliability, and easy management. It combines the speed of search with the power of analytics via a sophisticated, developer-friendly query language covering structured, unstructured, and time-series data.

ELK Stack Architecture:

The ELK Stack architecture explains the nature of the task, the following figure explains,

ELK Stack architecture consists of three open-source tools, Elastic search, Log stash, and Kibana. Where Elastic search is used for search, data storage, and analysis purposes. Logstash collects the logs, event data, parse, and transforms. Kibana is used to explore, visualize, share, and eliminate the need for complex SQL query execution. Beats is used for data shippers which collect the data at the client place and ship it either to elastic search or log stash. ELK offers built on top of Apache Lucene and supports languages like No SQL data store (for example MongoDB), JSON documents, RESTful APIs that you can interact with the cluster, Node, and clusters.

What is an Elastic Search?

Elastic search is a kind of No SQL database. This is designed on the base of the Lucene search engine, and this is built with RESTful APIS. Elastic search tool offers simple software deployment, increases reliability, and easy tool management systems. Elastic search also provides advanced data queries to perform many tasks such as detailed analysis operations, and also helps to store all kinds of data centrally. This also helps quick execution search of documents. The Elastic search tool also allows the users to store the complex data, searching and analyzing the big amount of data. This tool is also used as the underlying engine to power applications that complete the search requirements. This has been adopted in search engine platforms for modern web applications and mobile development applications. Apart from the quick search, this tool offers complex data analytics and many advanced features.

Features of Elastic Search:

  • Open source search server will be written using Java
  • Mainly used to data index any kind of heterogeneous data
  • Has REST API web –interface application with JSON output
  • Offers full –text search
  • Near Real-time search engine
  • Provides sharded, replicated searchable, JSON software document store
  • Offers schema-free, REST API and JSON software distributed document store
  • Provides multi-language and geolocation support.

Advantages of Elastic Search:

  • The elastic search stores the schema-less data and also generates a data schema for your data.
  • Manipulates the data record by record with the help of multiple document APIs.
  • Perform operations like data filtering and querying the database for insights
  • This tool is designed on the base of Apache Lucene and offers RESTful API
  • Offers horizontal scalability, reliability, and multi-talent capability for real-time use of data indexing to make it faster search
  • Helps users to scale their data vertically and horizontally.

The important terms used in Elastic search


Terms

Usage
Cluster
A cluster is a collection of data nodes which holds the data and provides joined indexing and search capabilities.

Node

A node is an elastic search data instance. It will be created when an elastic search data instance begins.
Index
An index is a collection of important documents which has similar characteristics such as customer data, product catalog. It will be very useful while performing operations like indexing, searching, updating, and deleting many data operations. This allows user to define their data indexes in a single cluster
Document
The document is the basic unit of information which can be indexed. This will be expressed in a JSON key pair. Every single document in Elastic search is associated with a type and unique data-id.

Shard


Every index can be split into many shards to be able to distribute customized data. The shard is just the atomic part of the index, which will be distributed over the data cluster if the user wants to add more data nodes.

What is Logstash?

Logstash is nothing but a data collection of pipeline tools. This open-source tool combines the data inputs and data feeds into the Elastic search. Logstash tool gathers all types of data from multiple sources and later makes it available for further usage. Logstash will unify the data from the disparate sources and normalize the data into the desired destinations. This allows user to cleanse and democratize all data for analysis, and data visualization of user cases.

This consists of three components:

  • Data input: This elastic search passes the data logs to process them to machine-understandable formats
  • Data filters: It is nothing but a set of conditions that are later used to perform specific actions or any events.
  • Data outputs: This helps in decision making to precede events or logs.

Features of Logstash:

  1. Events will be passed through each data phase using the internal queues.
  2. Allows different types of inputs for your data logs
  3. Performs data filtering/ parsing the logs

Advantages of Logstash:

  • Logstash offers to centralize the data processing
  • This logstash analyzes a large variety of structured or unstructured data and events.
  • Offers data plugins to connect with various data types of multiple input data sources and platforms.

What is Kibana?

Kibana is a data visualization open-source tool that completes the ELK stack. This tool is primarily used for visualizing the Elastic search data documents and helps users to develop a quick data insight into it. Kibana data dashboard provides various interactive data flow diagrams, geospatial data, and graphs to visualize the complex SQL queries. This Kibana open-source tool can be used for data searching, viewing, and interacting with data stored in Elastic search directories. Kibana tool helps users to perform advanced-level data analysis and visualize data in a variety of tables, charts, graphs, and maps.

In Kibana there are many different methods that are used for performing searches on user data.

Here are the different methods and description:


Search type

Usage

Free text level searches

Kibana is used for searching specific data strings

Field –level data searches

In this type, the kibana is used for searching the data string within a specific field

Logical data statements

This option is used to combine the data searches into logical statements

Proximity data searches

This tool is used for searching terms within the specific data character proximity

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Features of Kibana:

The following features explain the important advantages of using Kibana:

  • This kibana is considered as a powerful front –end dashboard tool which is capable of visualizing indexed data information from the elastic data cluster.
  • The kibana also enables real-time data search of indexed information
  • This tool also enables a user on a real-time data search of indexed information
  • Helps user to search, view, and interact with the data stored in the elastic search data cluster
  • The tool also executes the queries on data and visualize data results in charts, graphs, tables, and maps
  • Offers configurable dashboard to slice and dice logstash logs in elastic search clusters
  • Provides real-time dashboards which are easily configurable
  • Also enables the real-time search of data indexed information.

Advantages of Kibana:

  • Offers easy data visualization techniques
  • Provides fully data integration with Elastic search
  • Kibana is now considered as a visualization tool
  • Kibana supports real-time data analysis, charting, summarization, and data debugging capabilities
  • Allows sharing of data snapshot of the logs searched through
  • Kibana permits saving the dashboard and managing the multiple dashboards.

Case studies of ELK Stack:

A Case study explains the type of industries which are adopting ELK stack as a primary tool, such as;

  • Netflix (Entertainment platform):

Netflix heavily depends on the ELK Stack tool. This company is using the ELK stack primarily to monitor and analyze the customer service operation’s security log. It allows users to perform tasks like index, store, and search the documents from more than 15+ clusters which consist of 800 nodes.

  •  LinkedIn ( Recruitment platform):

This is one of the social media marketing and recruiting sites, which uses the ELK stack to monitor the performance and data security purposes. The IT recruitment team integrates the ELK tool with Kafka to provide the support for their data load in a real-time environment. The ELK operations include more than 100+ clusters across six different multiple data centers.

  • Tripwire (Data security management platform):

Tripwire is a mostly used world-wide data security information event management system tool. This Tripwire uses the ELK to support information packet data log analysis.

  • Medium(Blog-publishing platform):

This medium system is one of the famous blog-publishing platforms. This Medium uses the ELK stack to debug the production issues while publishing the contents/blogs. And also it uses the ELK tool to identify DynamoDB hotspots. One important thing is that the Medium Company supports more than 25 million unique readers across the world as well as thousands of published posts per week.

Advantages of ELK Stack:

  • There are lots of advantages of using ELK stack;
  • ELK tool works best when data logs from various apps of an enterprise converge into a single ELK instances
  • This ELK tool offers amazing data insights and also eliminates the need for data into a hundred different multiple log data sources
  • Rapid on-premise software installation
  • ELK tool offers a host of programming language clients which includes Ruby, Python, Perl, NET, Java, and JavaScript, and more.
  • Availability of libraries for different programming and scripting languages.
  • ELK open-source tool also scales as needed, without updating any clients
  • Offers to keep alive optimization, dynamic scalability, and health monitoring.
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ELK Stack installation steps:

The following steps explain how to install the ELK stack;

Steps:

Step 1:Go to the web link https://www.elastic.co/downloads

Step 2: Select and download Elastic search

Step 3: Select and download Log stash

Step 4: Select and download Kinbana

Step 5: Unzip all the three tool’s files to get the folder files as shown in the figure;

Now install the Elastic search tool:

Step 6: Now you need to open the elastic search folder and go to its bin folder

Step 7: Users need to double click on the elastic search. Bat file to start the -> elastic search server.

Step 8: Now the user needs to wait for the elastic search server to start.

Step 9: To verify whether the elastic search server has started or not go to the browser and type localhost:9200 as shown in the figure,

Now install Kibana:

Step 10: Now you need to open the Kibana folder -> go to its bin folder.

Step 11: Double click on the kibana .bat file -> start the elastic search server.

Step 12: Now all you need to do is wait for the kibana server to restart.

Step 13: to verify whether the Kibana server has started or not got to the browser -> just type localhost:5601.

Installing Log stash:

Step 14: Now you need to open the log stash folder.

Step 15: To verify your log stash installation, -> just open the command prompt -> go to log stash folder,

I bin logstash –e  ‘input { stdin {} } output { stdout {} } ‘

Step 16: The user needs to wait until “pipeline main started” appears on the command prompt.

Step 17: Now enter a message at the command prompt -> hit enter.

Step 18: log stash appends timestamp -> the IP address information to the message -> then it displays on the command prompt.

That’s it about the ELK stack installation process.

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Conclusion:
In this article, I have tried my best to explain the important features of the ELK Stack tool. ELK Stack open-source tool is one of the popular tools designed to analyze the data, testing, and visualizing the data. Most of the bigger companies now a day are showing a keen interest in adopting the ELK stack to increase productivity and produce a good outcome. I hope this article may help a few of you learn and unleash your skillset in the ELK stack tool and enables users to interact with experts through social media forums.