Book Image

Mastering Kibana 6.x

Book Image

Mastering Kibana 6.x

Overview of this book

Kibana is one of the popular tools among data enthusiasts for slicing and dicing large datasets and uncovering Business Intelligence (BI) with the help of its rich and powerful visualizations. To begin with, Mastering Kibana 6.x quickly introduces you to the features of Kibana 6.x, before teaching you how to create smart dashboards in no time. You will explore metric analytics and graph exploration, followed by understanding how to quickly customize Kibana dashboards. In addition to this, you will learn advanced analytics such as maps, hits, and list analytics. All this will help you enhance your skills in running and comparing multiple queries and filters, influencing your data visualization skills at scale. With Kibana’s Timelion feature, you can analyze time series data with histograms and stats analytics. By the end of this book, you will have created a speedy machine learning job using X-Pack capabilities.
Table of Contents (21 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Avoiding sparsity


Elasticsearch is a search engine that is built on top of Lucene and passes data to Lucene for storage and searching. Lucene data structures can perform in a better way if the data is stored in dense form; for example, all documents with the same type of fields can create a dense storage rather than storing different types of field in a single document. Lucene identifies documents with doc_id, which has an integer value and varies from 0 to total number of documents in the index. This is how Lucene recognizes Elasticsearch document in the index. These doc_id elements of Elasticsearch documents are used to communicate with Lucene's internal APIs.

For example, if we execute a match query on any term, Lucene will produce an iterator of doc_ids. These doc_ids elements are used to compute the score for the document in the search by retrieving the value of the norm. One byte is reserved for each document to store the norm value, and this is the current norm lookup implementation...