Mastering Elastic Stack

By Gupta Ravi Kumar and Gupta Yuvraj

About This Book

• Your one-stop solution to perform advanced analytics with Elasticsearch, Logstash, and Kibana
• Learn how to make better sense of your data by searching, analyzing, and logging data in a systematic way
• This highly practical guide takes you through an advanced implementation on the ELK stack in your enterprise environment

Who This Book Is For

This book cater to developers using the Elastic stack in their day-to-day work who are familiar with the basics of Elasticsearch, Logstash, and Kibana, and now want to become an expert at using the Elastic stack for data analytics.

• Language: English
• Publisher: Packt Publishing
• Release date: Feb 28, 2017
• ISBN: 9781786468055

Book preview

Table of Contents

Mastering Elastic Stack

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Why subscribe?

Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Errata

Piracy

Questions

1. Elastic Stack Overview

Introduction to ELK Stack

Logstash

Elasticsearch

Kibana

The birth of Elastic Stack

Beat

Who uses Elastic Stack?

Salesforce

CERN

Green Man Gaming

Stack competitors

Setting up Elastic Stack

Installation of Java

Installation of Java on Ubuntu 14.04

Installation of Java on Windows

Installation of Elasticsearch

Installation of Elasticsearch on Ubuntu 14.04

Installation of Elasticsearch on Windows

Installation of Elasticsearch as a service

Installation of Kibana

Installation of Kibana on Ubuntu 14.04

Installation of Kibana on Windows

Installation of Logstash

Installation of Logstash on Ubuntu 14.04

Installation of Logstash on Windows

Installation of Filebeat

Installation of Filebeat on Ubuntu 14.04

Installation of Filebeat on Windows

X-Pack

Summary

2. Stepping into Elasticsearch

The beginning of Elasticsearch

Key features

Understanding the architecture

Recommended cluster configurations

Minimum master nodes

Local cluster settings

Understanding document processing

Elasticsearch APIs

Document APIs

Single document APIs

Index API

Get API

Delete API

Update API

Multi-document APIs

Multi-get API

Bulk API

Search APIs

Search API

Query parameters

Search shard API

Multi-search APIs

Count API

Validate API

Explain API

Profile API

Field stat API

Indices APIs

Managing indices

Creating an index

Checking if an index exists

Getting index information

Managing index settings

Getting index stats

Getting index segments

Getting index recovery information

Getting shard stores information

Index aliases

Mappings

Closing, opening, and deleting an index

Other operations

Cat APIs

Cluster APIs

Query DSL

Aggregations

Bucket

Metrics aggregations

Avg aggregation

Min aggregation

Max aggregation

Percentiles Aggregation

Sum aggregation

Value count aggregation

Cardinality aggregation

Stats aggregation

Extended stats aggregation

A note for painless scripting

Summary

3. Exploring Logstash and Its Plugins

Introduction to Logstash

Why do we need Logstash?

Features of Logstash

Logstash Plugin Architecture

Logstash Configuration File Structure

Value types

Array

Boolean

Bytes

Codec

Comments

Hash

Number

String

Use of Conditionals

Types of Plugins

Input plugins

Filter plugins

Output plugins

Codec plugins

Exploring Input Plugins

stdin

file

path

udp

Exploring Filter Plugins

grok

mutate

csv

Exploring Output Plugins

stdout

file

elasticsearch

Exploring Codec Plugins

rubydebug

json

avro

multiline

Plugins Command-Line Options

Listing of Plugins

Installing a plugin

Removing a plugin

Updating a plugin

Packing a plugin

Unpacking a plugin

Logstash command-line options

Logstash Tips and Tricks

Referencing fields and Its values

Adding custom-created grok patterns

Logstash does not show any output

When an input file has already been completely read

When an input file is not modified since 1 day

Logstash Configuration for Parsing Logs

Sample Catalina logs

Sample Tomcat logs

Grok pattern for Catalina logs

Grok pattern for Tomcat logs

Logstash configuration file

Monitoring APIs

Node info API

OS Info

JVM info

Pipleine Info

Plugins Info API

Node stats API

JVM stats

Process stats

Pipeline stats

Hot threads API

Threads

Human

Ignore idle threads

Summary

4. Kibana Interface

Kibana and its offerings

Kibana interface

Exploring the discover interface

Time Filter

Quick time filter

Relative time filter

Absolute time filter

Auto-refresh

Querying and Searching data

Full-text searches

Range searches

Boolean searches

Proximity search

Wildcard searches

Regular expressions search

Grouping

Fields and filters

Filtering the field

Functionalities of filters

Discovery page options

Exploring the visualize interface

Understanding aggregations

Bucket aggregations

Metric aggregations

Visualization Canvas

Area chart

Data table

Line chart

Bubble chart

Markdown widget

Metric

Pie chart

Tag clouds

Tile map

Time series

Vertical bar chart

Exploring the Dashboard interface

Understanding Timelion

Exploring Dev Tools

Exploring the Management interface

Index patterns

Saved objects

Advanced Settings

Status

Putting it all together

Input data

Creating a Logstash configuration file

Using Kibana

Top states based on 2003 RUCC

Top states based on 2003 UIC

Top five area names with less than high school diploma 1970

Top five area names with high school diploma 1970

Percentage of adults having less than high school diploma in 1970 by area and state

Top states  as per their count and their top 2013 RUCC

Insights

Creating a dashboard in Kibana

Summary

5. Using Beats

Introduction to Beats

How Beats differ from Logstash

How Beats fits into Elastic Stack

An overview of the different types of Beats

Beats by Elastic Team

Packetbeat

Metricbeat

Filebeat

Winlogbeat

Libbeat

Beats by community

Dockbeat

Lmsensorbeat

Exploring Elastic Team Beats

Understanding Filebeat

Filebeat Prospectors Configuration

Processors configuration

Defining a processor

Output Configuration

Elasticsearch Output Configuration

Logstash Output Configuration

Logging Configuration

Understanding Metricbeat

System Module

CPU metricset

Disk I/O metricset

Filesystem metricset

FsStat metricset

Load metricset

Memory metricset

Network metricset

Process Metricset

Installation of Metricbeat

Installation of Metricbeat on Ubuntu 14.04

Understanding Packetbeat

Installation of Packetbeat

Installation of Packetbeat on Ubuntu 14.04

Exploring Community Beats

Understanding Elasticbeat

Installation of Elasticbeat

Installation of Elasticbeat on Ubuntu 14.04

Elasticbeat configuration

Beats in action with Elastic Stack

Exploring Metricbeat with Logstash and Kibana

Step 1-Configuring Metricbeat to send data to Logstash

Step 2-Creating a Logstash configuration file

Step 3-Downloading and loading the sample Beats dashboard

Step 4-Viewing the sample Beats dashboard

Exploring Elasticbeat with Elasticsearch and Kibana

Step 1-Configuring Elasticbeat to send data to Elasticsearch

Step 2-Downloading and loading the Elasticbeat dashboard

Step 3-Viewing the sample Beats dashboard

Summary

6. Elastic Stack in Action

Understanding problem scenario

Understanding the architecture

Preparing Elastic Stack pipeline

What to capture?

Updated architecture

Configuring Elastic Stack components

Setting up Elasticsearch

Setting up agents/Beats

Packetbeat

Metricbeat

Filebeat

Setting up Logstash

grok for nginxlogs

grok for liferaylogs

grok for openDJ logs.

Config File

Setting up Kibana

Setting up Kibana Dashboards

PacketBeat

MetricBeat

Checking DB (MySQL) Performance

Analyzing CPU usage

Keeping an eye on memory

Checking logs

Finding most visited pages

Visitors' map

Number of visitors in a time frame

Request Types

Error type-log levels

Top referrers

Top agents

Alerting using Logstash e-mail capability

Using a message broker

Summary

7. Customizing Elastic Stack

Extending Elasticsearch

Elasticsearch development environment

Anatomy of an Elasticsearch Java plugin

Building the plugin

Extending Logstash

Generating a plugin

Anatomy of the plugin

weather.rb file

Plugin logic implementation

Reading data from API end point

Preparing an event

Publish the event

Building and installing a plugin

Testing our plugin

Extending Beats

libbeat framework

Creating a beat

Anatomy of a Beat

Beat configuration

weatherbeat.go file

Implementing beat logic

Adding the Configuration

Reading data from API

Parsing the data

Preparing an event

Publishing the event

Running the beat

Extending Kibana

Setting up Kibana development environment

Generating the plugin

Anatomy of a plugin

Summary

8. Elasticsearch APIs

The cluster APIs

Cluster health

Cluster State

Cluster stats

Pending tasks

Cluster reroute

Cluster update settings

Node stats

Nodes info API

Task Management API

The cat APIs

Elasticsearch modules

Cluster module

Discovery module

Gateway module

HTTP module

Indices module

Network module

Node client

Plugins module

Scripting

Snapshot/restore module

Thread pools

Transport module

Tribe nodes module

Ingest nodes

Elasticsearch clients

Supported clients

Community contributed clients

Java API

Connecting to a Cluster

Admin tasks

Managing indices

Creating an index

Getting index settings

Updating index settings

Refreshing an index

Managing clusters

Getting cluster tasks

Getting cluster health

Index-level tasks

Managing documents

Indexing a document

Getting a document

Deleting a document

Updating a document

Query DSL and search API

Aggregations

Elasticsearch plugins

Discovery plugins

Ingest plugins

Elasticsearch SQL

Summary

9. X-Pack: Security and Monitoring

Introduction to X-Pack

Installation of X-Pack

Installing X-Pack in Elasticsearch

Installing X-Pack in Kibana

Installing X-Pack on offline systems

Uninstalling X-Pack

Security

Listing of all users in security

Listing of roles in security

Understanding roles in security

Understanding Cluster Privileges

Understanding Run As privileges

Understanding Indices privileges

Decoding default user roles

kibana_user

superuser

transport_client

Adding a role in security

Updating a role in security

Understanding Field Level Security

Adding a user in security

Updating user details in security

Changing the password of a user in security

Deleting a role in security

Deleting a user in security

Viewing X-Pack information

Enabling and disabling of X-Pack features

Monitoring

Exploring monitoring statistics for Elasticsearch

Discovering the Overview tab

Discovering the Indices tab

Discovering the Nodes tab

Exploring monitoring statistics for Kibana

Understanding Profiler

Summary

10. X-Pack: Alerting, Graph, and Reporting

Alerting and notification

Working of watcher

Trigger

Schedule trigger

Input

Simple input

Search input

HTTP input

Chain input

Conditions

Always condition

Never condition

Compare condition

Array compare condition

Script condition

Transforms

Search transform

Script transform

Chain transform

Actions

Throttling

Graph

Working of Graph

Graph UI

Reporting

Summary

11. Best Practices

Why do we require best practices?

Understanding your use case

Managing configuration files

Elasticsearch - elasticsearch.yml

Kibana - kibana.yml

Choosing the right set of hardware

Memory

Java heap size

Swapping memory

Disks

Sizing disk space

I/O

CPU

Network

Searching and indexing performance

Filter cache

Fielddata size

Indexing buffer

Sizing the Elasticsearch cluster

Choosing the right kind of node

Master and data node

Master node

Data node

Ingest node

No master, no data, and no ingest node

Determining the number of nodes

Determining the number of shards

Reducing disk space

Logstash configuration file

Categorizing multiple sources of data

Using conditionals

Using custom grok patterns

Simplifying _grokparsefailure

Mapping of fields

Dynamic templating

Testing configuration

Re-indexing data

Using aliases

Summary

12. Case Study-Meetup

Understanding meetup scenario

Setting things up

A bit of Meetup API understanding

Setting up Elasticsearch

Preparing Logstash

Setting up Kibana

Analyzing data using Kibana

Filtering Content

Number of Meetups by Country

Top 10 meetup cities in world

Meetups trends by duration

Meetups by RSVP Counts

Number of Groups by country

Number of Groups by join mode

Popular Categories

Popular Topics

Meetup Venue Map

Meetups on Map

Just the number of things

Getting Notified

Summary

Mastering Elastic Stack

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Mastering Elastic Stack

Copyright © 2017 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

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First published: February 2017 

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Credits

About the Authors

Ravi Kumar Gupta is an author, reviewer, and open source software evangelist. He pursued an MS degree in software system at BITS Pilani and a B.Tech at LNMIIT, Jaipur. His technological forte is portal management and development.

He is currently working with Azilen Technologies, where he acts as a Technical Architect and Project Manager. His previous assignment was as a lead consultant with CIGNEX Datamatics. He was a core member of the open source group at TCS, where he started working on Liferay and other UI technologies. During his career, he has been involved in building enterprise solutions using the latest technologies with rich user interfaces and open source tools.

He loves to spend time writing, learning, and discussing new technologies. His interest in search engines and that small project on crawler during college time made him a technology lover. He is one of the authors of Test-Driven JavaScript Development, Packt Publishing. He is an active member of the Liferay forum. He also writes technical articles for his blog at TechD of Computer World (http://techdc.blogspot.in).

He has been a Liferay trainer at TCS and CIGNEX, where he has provided training on Liferay 5.x and 6.x versions. He was also a reviewer for Learning Bootstrap, Packt Publishing.

He can be reached on Skype at kravigupta, on Twitter at @kravigupta, and on LinkedIn at https://in.linkedin.com/in/kravigupta.

Seven blessing and my gratitude to my wife, Kriti. Despite tough times, she motivated me throughout the writing period. Support from my wife and my family, specially my father and mother-in-law helped me a lot. I can’t forget my co-author, Yuvraj, for his excellent support and understanding. He has been a great friend and help. Without him, it was not possible to finish. I would also like to thanks PACKT team, reviewers and editorial team for their cooperation. I truly appreciate you guys. Thank you.

Yuvraj Gupta is an author and a keen technologist with interest towards Big Data, Data Analytics, Data Visualization, and Cloud Computing. He has been working as a Big Data Consultant primarily in domain of Big Data Testing. He loves to spend time writing on various social platforms. He is an avid gadget lover, a foodie, a sports enthusiast and love to watch tv-series or movies. He always keep himself updated with the latest happenings in technology. He has authored a book titled Kibana Essentials with Packt Publishers. He can be reached at [e-mail] or at LinkedIn www.linkedin.com/in/guptayuvraj.

I would like to thank my family and friends for encouraging and motivating me to write the book. I would like to thank the reviewers and the whole team of PacktPub who were involved in producing this book without their support it would never have been possible. I would like to thank everyone else who helped me directly or indirectly in writing this book. Also I would like to thank my teachers, professors, Gurus, schools and university for playing an important part in providing me with the education which has helped me to gain knowledge. Lastly but not the least I would like to thanks my co-author Ravi without whose help, guidance and support, the book would never have been completed.

About the Reviewer

Marcelo Ochoa works at the system laboratory of Facultad de Ciencias Exactas of the Universidad Nacional del Centro de la Provincia de Buenos Aires and is the CTO at Scotas.com, a company that specializes in near real-time search solutions using Apache Solr and Oracle. He divides his time between university jobs and external projects related to Oracle and big data technologies. He has worked on several Oracle-related projects, such as the translation of Oracle manuals and multimedia CBTs. His background is in database, network, web, and Java technologies. In the XML world, he is known as the developer of the DB Generator for the Apache Cocoon project. He has worked on the open source projects DBPrism and DBPrism CMS, the Lucene-Oracle integration using the Oracle JVM Directory implementation, and the https://restlet.com/ project, where he worked on the Oracle XDB Restlet Adapter, which is an alternative to writing native REST web services inside a database resident JVM. Since 2006, he has been part of an Oracle ACE program. Oracle ACEs are known for their strong credentials as Oracle community enthusiasts and advocates, with candidates nominated by ACEs in the Oracle technology and applications communities. He has coauthored Oracle Database Programming using Java and Web Services by Digital Press and Professional XML Databases by Wrox Press, and has been a technical reviewers for several Packt books, such as Apache Solr 4 Cookbook, ElasticSearch Server and others.

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Preface

Even structured data is useless if it can’t help you to take strategic decisions and improve existing system. If you love to play with data, or your job requires you to process custom log formats, design a scalable analysis system, and manage logs to do real-time data analysis, this book is your one-stop solution. By combining the massively popular Elasticsearch, Logstash, Beats and Kibana, ELK Stack has advanced to Elastic Stack that delivers actionable insights in near real time from almost any type of structured or unstructured data.

This book brushes up your basic knowledge of implementing the Elastic Stack and then dives deeper into complex and advanced scenarios. We’ll help you with data analytics challenges and take you through practical scenario of an intranet portal to understand utilization of Elastic Stack components. You will be able to grasp advanced techniques for log analysis and visualization. Newly announced features such as Beats and X-Pack are also covered in detail with examples.

Toward the end, you will see how to use the Elastic stack for real-world case studies and we’ll show you some best practices and troubleshooting techniques for the Elastic Stack.

What this book covers

Chapter 1, Elastic Stack Overview, covers the shift from ELK Stack to Elastic Stack followed by setup of various components of Elastic Stack.

Chapter 2, Stepping into Elasticsearch, takes us to how Elasticsearch started as a project, how Elasticsearch works and covering various Elasticsearch API’s and Aggregations.

Chapter 3, Exploring Logstash and Its Plugins, covers introduction of Logstash along with understanding it’s architecture. It also covers the various plugins with suitable examples. At the end, a Logstash configuration file is shown for parsing logs.

Chapter 4, Kibana Interface, teaches about the various interfaces present in Kibana in depth along with an example to demonstrate how to combine all the interfaces to create a dashboard.

Chapter 5, Using Beats, takes us to introducing the beats, understanding how beat differs from Logstash followed by exploring various beats, their functionalities and setup steps. At the end, we explored how to use Beats in Elastic Stack.

Chapter 6, Elastic Stack in Action, covers a real-world use-case of an Intranet Portal server and showcases and how to use Elastic Stack components to solve the problem.

Chapter 7, Customizing Elastic Stack, teaches us how to extend each component of Elastic Stack and how to create a plugin for our use-cases.

Chapter 8, Elasticsearch APIs, takes us to various Elasticsearch API’s along with understanding Elasticsearch modules, Ingest nodes, Discovery pPlugins and how to use Java client to access various Elasticsearch operations.

Chapter 9, X-Pack: Security and Monitoring, covers introduction of X-Pack along with installation of X-Pack. It also covers the usage and functionalities provided by Shield, Marvel and Profiler.

Chapter 10, X-Pack: Alerting, Graph, and Reporting, teaches us about the usage and functionalities of Watcher, Graph and Reporting features.

Chapter 11, Best Practices, takes us to understand why do we need to follow best practices along with listing of various best practices which should be followed which has been categorized into multiple sub-sections.

Chapter 12, Case Study-Meetup, covers complete coverage of understanding the problem statement followed by extending Logstash and creating a plugin to fetch required information. It then takes us to understand how to utilize Elastic Stack components to cover end-to-end understanding of Meetup data and showcasing the powerful capabilities of Elastic Stack for data analytics.

What you need for this book

Following table lists all required software and tools needed to execute example in the book. Wherever requires, links to download the software is also present within the chapter as well.

Who this book is for

If you have heard the word ELK stack and want to learn more about it’s latest development and how it became Elastic Stack, this book is for you. If you use analytics or like to play with visualizations on your data, this book helps you to understand how the components of the stack can help you.

Conventions

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: The next lines of code read the link and assign it to the to the BeautifulSoup function.

A block of code is set as follows:

#import packages into the project

from bs4 import BeautifulSoup

from urllib.request import urlopen

import pandas as pd

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

 

  utf-8>

  viewport content=width=device-width>

 

Any command-line input or output is written as follows:

C:\Python34\Scripts> pip install -upgrade pip C:\Python34\Scripts> pip install pandas

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: In order to download new modules, we will go to Files | Settings | Project Name | Project Interpreter.

Note

Warnings or important notes appear in a box like this.

Tip

Tips and tricks appear like this.

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Chapter 1. Elastic Stack Overview

It's as easy to read a log file of a few MBs or hundreds as it is to keep data of this size in databases or files and still get sense out of it. But then a day comes when this data takes up terabytes, petabytes and grows even faster in future. As data demand pushes, normal text editors or word processing tools would refuse to cope up and would not be able to open such a large dataset. There would be a need to analyze the raw data which can be used to discover insights. You start to find something for huge log management, or something that can index the data properly and make sense out of it. If you Google this, you will stumble upon ELK Stack. Elasticsearch manages your data, Logstash reads the data from different sources, and Kibana makes a fine visualization of it.

Recently, ELK Stack has evolved as Elastic Stack. We will get to know more about it in this chapter, along with setting it up. The following are the points that will be covered in this chapter:

Introduction to ELK Stack

The birth of Elastic Stack

Who uses the Stack

Stack competitors

Setting up Elastic Stack

X-Pack

Introduction to ELK Stack

It all began with Shay Banon, who started an open source project called Elasticsearch, successor of Compass, which gained popularity as one of the top open source database engines. Later, based on the distributed model of working, Kibana was introduced, to visualize the data present in Elasticsearch. Earlier, to put data into Elasticsearch, we had Rivers, which provided us with a specific input via which we inserted data into Elasticsearch.

However, with growing popularity, this setup required a tool via which we could insert data into Elasticsearch and have flexibility to perform various transformations on data (to make unstructured data structured and have full control on how to process the data). Based on this premise, Logstash was born, which was then incorporated into the Stack, and together these three tools, Elasticsearch, Logstash, and Kibana were named ELK Stack.

The following diagram is a simple data pipeline using ELK Stack:

As we can see from the preceding figure, data is read using Logstash and indexed to Elasticsearch. Later, we can use Kibana to read the indices from Elasticsearch and visualize it using charts and lists. Let's understand these components separately, and the role they play in the making of the Stack.

Logstash

As mentioned earlier, Rivers were initially used to put data into Elasticsearch before ELK Stack. For ELK Stack, Logstash is the entry point for all types of data. Logstash has so many plugins to read data from a number of sources, and so many output plugins to submit data to a variety of destinations - one of those is the Elasticsearch plugin, which helps to send data to Elasticsearch.

After Logstash became popular, Rivers eventually got deprecated, as they made the cluster unstable and also performance issues were observed.

Logstash does not just ship data from one end to another; it helps us with collecting raw data and modifying/filtering it to convert it to something meaningful, formatted, and organized. The updated data is then sent to Elasticsearch. If there is no plugin available to support reading data from a specific source, writing the data to a location, or modifying it in your own way, Logstash is flexible enough to allow you to write your own plugins.

Simply put, Logstash is open source, highly flexible, rich with plugins and can read your data from your choice of location. It normalizes data as per your defined configurations, and sends it to a particular destination, as per the requirements.

We will be learning more about Logstash in Chapter 3, Exploring Logstash and Its Plugins and Chapter 7, Customizing Elastic Stack.

Elasticsearch

All of the data read by Logstash is sent to Elasticsearch for indexing. Elasticsearch is not only used to index data, it is also full-text search engine, highly scalable, distributed, and offers many more things too. Elasticsearch manages and maintains your data in the form of indices and offers you to query, access, and aggregate the data using its APIs. Elasticsearch is based on Lucene, thus providing you all of the features that Lucene does.

We will be learning more about Elasticsearch in Chapter 2, Stepping into Elasticsearch, Chapter 7, Customizing Elastic Stack, and Chapter 8, Elasticsearch APIs.

Kibana

Kibana uses Elasticsearch APIs to read/query data from Elasticsearch indices, to visualize and analyze in the form of charts, graphs and tables. Kibana is in the form of a web application, providing you with a highly configurable user interface that lets you query the data, create a number of charts to visualize, and make actual sense out of the data stored.

We will be learning more about Kibana in Chapter 4, Kibana Interface and Chapter 7, Customizing Elastic Stack.

After a robust ELK Stack, as time passed, a few important and complex demands took place, such as authentication, security, notifications, and so on. This demand led to the development of a few other tools such as Watcher (providing alerts and notifications based on changes in data), Shield (authentication and authorization for securing clusters), Marvel (monitoring statistics of the cluster), ES-Hadoop, Curator, and Graph, as requirements arose.

The birth of Elastic Stack

All the jobs of reading data were once done using Logstash, but that's resource consuming. Since Logstash runs on JVM, it consumes a good amount of memory. The community realized the need for improvement and to make the pipelining process resource friendly and lightweight. In 2015, Packetbeat was born, a project which was an effort to make a network packet analyzer that could read from different protocols, parse the data, and ship to Elasticsearch. Being lightweight in nature did the trick and a new concept of Beats was formed. Beats are written in Go programming language. The project evolved, and now ELK stack was no more just Elasticsearch, Logstash, and Kibana;  Beats also became a significant component.

The pipeline now looked as follows:

Beat

A Beat reads data, parses it, and can ship it to either Elasticsearch or Logstash. The difference is that they are lightweight, serve a specific purpose, and are installed as agents. There are a few Beats available such as Metricbeat, Filebeat, Packetbeat, and so on, which are supported and provided by the Elastic Team and a good number of Beats are already written by the community. If you have a specific requirement, you can write your own Beat using the libbeat library.

In simple words, Beats can be treated as very lightweight agents to ship data to either Logstash or Elasticsearch, offering you an infrastructure using the libbeat library to create your own Beats.

We will be learning more about Beats in Chapter 5, Using Beats and Chapter 7, Customizing Elastic Stack.

Together Elasticsearch, Logstash, Kibana, and Beats became Elastic Stack, formally known as ELK Stack. Elastic Stack did not just add Beats to its team; they will be using the same version always. The starting version of the Elastic Stack will be 5.0.0 and the same version will apply to all the components.

This version and release method is not only for Elastic Stack, but for other tools of the Elastic family as well. Due to there being so many tools, there was a problem of unification, wherein each tool had their own version, and every version was not compatible with each other, hence leading to a problem. To solve this, all of the tools will now be built, tested, and released together.

All of these components play a