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Advanced Elasticsearch 7.0

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Advanced Elasticsearch 7.0

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

Handling failures in pipelines

As discussed in the Ingest APIs section of this chapter, a pipeline is formed by a list of supported processors that are executed sequentially. If an exception occurs, the whole process is halted. Let's show an exception with an example. The processor in the pipeline is to remove the rating field from the indexing document. However, the rating field is optional and it may not be present. When an error occurs, you can check out the root clause in the error field. When the field rating is missing in the remove processor, it shows you that the reason is field [rating] not present as part of path [rating]:

If the error can be ignored, you can set the optional ingore_failure parameter to true to silently ignore the failure and continue the execution of the next processor. Another choice is to use the on_failure parameter to catch the exception...

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