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
1
Section 1: Fundamentals and Core APIs
8
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
13
Section 3: Programming with the Elasticsearch Client
16
Section 4: Elastic Stack
20
Section 5: Advanced Features

Index persistence

Elasticsearch solves the persistence in different ways. The transaction log, translog, and the temporary storage in-memory buffer are used during index operations. Later, the data in the in-memory buffer will move to a new segment. Finally, segments will be flushed to the disk storage. A few APIs that manage the persistent stage of the indexed data are as follows:

  • Clear Cache: When Elasticsearch determines that a bitset is likely to be reused in the future, it will be cached directly in memory and reuse it as needed. This API allows you to clear all caches or specific caches such as query, request, and field data for one or more indices.

The following is an example of clearing the query cache of the cf_view index:

The following is an example of clearing the shard request cache of the cf_view index:

The following is an example of clearing the field data cache...