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Learning Elastic Stack 6.0

You're reading from   Learning Elastic Stack 6.0 A beginner's guide to distributed search, analytics, and visualization using Elasticsearch, Logstash and Kibana

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781787281868
Length 434 pages
Edition 1st Edition
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Authors (2):
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Sharath Kumar Sharath Kumar
Author Profile Icon Sharath Kumar
Sharath Kumar
Pranav Shukla Pranav Shukla
Author Profile Icon Pranav Shukla
Pranav Shukla
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Table of Contents (12) Chapters Close

Preface 1. Introducing Elastic Stack 2. Getting Started with Elasticsearch FREE CHAPTER 3. Searching-What is Relevant 4. Analytics with Elasticsearch 5. Analyzing Log Data 6. Building Data Pipelines with Logstash 7. Visualizing data with Kibana 8. Elastic X-Pack 9. Running Elastic Stack in Production 10. Building a Sensor Data Analytics Application 11. Monitoring Server Infrastructure

Log analysis challenges


Logs are defined as records of incidents or observations. Logs are generated by a wide variety of resources such as systems, applications, devices, humans, and so on. A log is typically made of two things; that is, a timestamp (time the event was generated) and data (the information related to the event):

Log = Timestamp + Data

Logs are typically used for the following:

  • Troubleshooting: When a bug or issue is reported, the first place to look for what might have caused the issue is the logs. For example, when looking at an exception stack trace in the logs one might easily find the root cause of the issue.
  • To understand system/application behavior: When an application/system is running, it's like a black box, and in order to investigate or understand what's happening within the system/application one has to rely on logs. For example, one might log the time taken by various code blocks within the application and can use it for understanding the bottlenecks and fine-tuning...
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