Logstash receives data from multiple sources, performs data processing, and then sends the log information to the stash
, which can mean a store. There are two types of configurations with which to configure Logstash for Docker: pipeline configuration and the settings configuration. We will use pipeline configuration for our demonstration. When the Logstash Docker container runs with pipeline configuration, it will check the path for the logstash.conf file. In our case, the file path is docker_run/pipeline/logstash.conf, as specified in the docker_run_logstash script file. The structure of a Logstash configuration file basically includes three parts: input, filter, and output. You specify the source of the data in the input section, and the destination in the output section. You can manipulate, measure, and create events in the filter section...
Advanced Elasticsearch 7.0
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Advanced Elasticsearch 7.0
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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)
Preface
Overview of Elasticsearch 7
Index APIs
Document APIs
Mapping APIs
Anatomy of an Analyzer
Search APIs
Section 2: Data Modeling, Aggregations Framework, Pipeline, and Data Analytics
Modeling Your Data in the Real World
Aggregation Frameworks
Preprocessing Documents in Ingest Pipelines
Using Elasticsearch for Exploratory Data Analysis
Section 3: Programming with the Elasticsearch Client
Elasticsearch from Java Programming
Elasticsearch from Python Programming
Section 4: Elastic Stack
Using Kibana, Logstash, and Beats
Working with Elasticsearch SQL
Working with Elasticsearch Analysis Plugins
Section 5: Advanced Features
Machine Learning with Elasticsearch
Spark and Elasticsearch for Real-Time Analytics
Building Analytics RESTful Services
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