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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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Toc

Table of Contents (12) Chapters Close

Preface 1. Introducing Elastic Stack FREE CHAPTER 2. Getting Started with Elasticsearch 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

Modeling time series data


Often, we have a need to store time series data in Elasticsearch. Typically, one would create a single index to hold all documents. This typical approach of one big index to hold all documents has its own limitations, especially for the following reasons:

  • Scaling the index with an unpredictable volume over time
  • Changing the mapping over time
  • Automatically deleting older documents

Let's look at how each problem manifests itself when we choose a single monolithic index.

Scaling the index with unpredictable volume over time

One of the most difficult choices when creating an Elasticsearch cluster and its indices is deciding how many primary shards should be created and how many replica shards should be created.

Let's understand how the number of shards becomes important in the following sub sections:

  • Unit of parallelism in Elasticsearch:
    • The effect of the number of shards on the relevance score
    • The effect of the number of shards on the accuracy of aggregations

Unit of parallelism...

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