Book Image

Elasticsearch Essentials

Book Image

Elasticsearch Essentials

Overview of this book

With constantly evolving and growing datasets, organizations have the need to find actionable insights for their business. ElasticSearch, which is the world's most advanced search and analytics engine, brings the ability to make massive amounts of data usable in a matter of milliseconds. It not only gives you the power to build blazing fast search solutions over a massive amount of data, but can also serve as a NoSQL data store. This guide will take you on a tour to become a competent developer quickly with a solid knowledge level and understanding of the ElasticSearch core concepts. Starting from the beginning, this book will cover these core concepts, setting up ElasticSearch and various plugins, working with analyzers, and creating mappings. This book provides complete coverage of working with ElasticSearch using Python and performing CRUD operations and aggregation-based analytics, handling document relationships in the NoSQL world, working with geospatial data, and taking data backups. Finally, we’ll show you how to set up and scale ElasticSearch clusters in production environments as well as providing some best practices.
Table of Contents (18 chapters)
Elasticsearch Essentials
About the Author
About the Reviewer

Metric aggregations

As explained in the previous sections, metric aggregations allow you to find out the statistical measurement of the data, which includes the following:

  • Computing basic statistics

    • Computing in a combined way: stats aggregation

    • Computing separately : min, max, sum, value_count, aggregations

  • Computing extended statistics: extended_stats aggregation

  • Computing distinct counts: cardinality aggregation


    Metric aggregations are fundamentally categorized in two forms:

    • single-value metric: min, max, sum, value_count, avg, and cardinality aggregations

    • multi-value metric: stats and extended_stats aggregations

Computing basic stats

The basic statistics include: min, max, sum, count, and avg. These statistics can be computed in the following two ways and can only be performed on numeric fields.

Combined stats

All the stats mentioned previously can be calculated with a single aggregation query.

Python example

query = {
 "aggs": {
   "follower_counts_stats": {
     "stats": {