Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Analyzers

The video tutorial covers the concept of mappings in Elasticsearch, explaining how they define the schema for data storage and indexing. It discusses using the curl command to send JSON requests, the importance of specifying field types, and the role of analyzers and token filters in text processing. The tutorial also explores character filters, tokenization, handling stop words, and using language-specific analyzers. Finally, it demonstrates creating a real mapping for movie data.

What is the primary purpose of a mapping in Elasticsearch?

• To visualize data in a dashboard

• To connect Elasticsearch with other databases

• To encrypt data for security

• To define the schema for data storage and indexing

Which command is used to send HTTP requests to Elasticsearch?

• curl

• ssh

• wget

• ping

What is the significance of specifying a field type in a mapping?

• It encrypts the data for security

• It helps Elasticsearch interpret the data correctly

• It connects the data to external APIs

• It determines the color of the data in the dashboard

What is a key function of an analyzer in Elasticsearch?

• To visualize data in real-time

• To connect Elasticsearch to cloud services

• To encrypt data for secure storage

• To filter and process text data for indexing

Which component of an analyzer is responsible for splitting text into tokens?

• Token filter

• Tokenizer

• Character filter

• Data parser

What is the main advantage of using language-specific analyzers?

• They enhance data encryption

• They increase the speed of data retrieval

• They allow for language-specific text processing

• They reduce the size of the index

Which analyzer would you use if you want to preserve punctuation in the text?

• Language-specific analyzer

• Whitespace analyzer

• Simple analyzer

• Standard analyzer