Book Image

Advanced Elasticsearch 7.0

By : Wai Tak Wong
Book Image

Advanced Elasticsearch 7.0

By: Wai Tak Wong

Overview of this book

Building enterprise-grade distributed applications and executing systematic search operations call for a strong understanding of Elasticsearch and expertise in using its core APIs and latest features. This book will help you master the advanced functionalities of Elasticsearch and understand how you can develop a sophisticated, real-time search engine confidently. In addition to this, you'll also learn to run machine learning jobs in Elasticsearch to speed up routine tasks. You'll get started by learning to use Elasticsearch features on Hadoop and Spark and make search results faster, thereby improving the speed of query results and enhancing the customer experience. You'll then get up to speed with performing analytics by building a metrics pipeline, defining queries, and using Kibana for intuitive visualizations that help provide decision-makers with better insights. The book will later guide you through using Logstash with examples to collect, parse, and enrich logs before indexing them in Elasticsearch. By the end of this book, you will have comprehensive knowledge of advanced topics such as Apache Spark support, machine learning using Elasticsearch and scikit-learn, and real-time analytics, along with the expertise you need to increase business productivity, perform analytics, and get the very best out of Elasticsearch.
Table of Contents (25 chapters)
Free Chapter
Section 1: Fundamentals and Core APIs
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
Section 3: Programming with the Elasticsearch Client
Section 4: Elastic Stack
Section 5: Advanced Features

Conditional execution in pipelines

As we mentioned in the Processors section, the optional if parameter is designed to let users define conditions for executing the pipeline processor. Let's demo a simple case with an example. The rating field of the documents in the index of cf_etf is a single space string when no rating is given for ETF in the original source. We can use the remove processor to remove the rating field in such a condition before the indexing operation, as shown in the following code block:

"pipeline": {
"description":"remove the rating field if the rating is equal to a single space string",
"remove": {
"field": "rating",
"if": "ctx.rating == ' '"

You will recall the dividend information...