Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Flattened Datatype

The video tutorial explains the concept of mapping explosion in Elasticsearch and introduces the flattened data type as a solution. It demonstrates how dynamic mappings can lead to performance issues when handling documents with many inner fields. The tutorial provides a hands-on example using syslog data and shows how to index documents while avoiding mapping explosion. It also covers the limitations of using the flattened data type, such as reduced search capabilities and lack of result highlighting.

What is a mapping explosion in Elasticsearch?

• A rapid increase in the number of Elasticsearch nodes

• An error in the Elasticsearch query syntax

• A situation where too many fields are mapped, causing performance issues

• A sudden increase in document size

How does Elasticsearch handle documents with many inner fields by default?

• It combines all fields into a single field

• It deletes the inner fields

• It ignores the inner fields

• It maps each subfield as an individual field

What is the purpose of the flattened data type in Elasticsearch?

• To enhance search capabilities

• To map all subfields as individual fields

• To prevent mapping explosions by mapping inner fields as a single field

• To increase the number of Elasticsearch nodes

What is the role of the cluster state in Elasticsearch?

• It controls the Elasticsearch query syntax

• It manages user access permissions

• It holds the mapping information of every index

• It stores the data of all documents

What happens to the cluster state when a new field is added to an index?

• It decreases in size

• It deletes old mappings

• It changes and requires synchronization across nodes

• It remains unchanged

How does the flattened data type affect the mapping of inner fields?

• Inner fields are deleted

• Inner fields are combined into a single field

• Inner fields are not mapped at all

• Inner fields are mapped as individual fields

What is a limitation of using the flattened data type in Elasticsearch?

• It enables result highlighting

• It supports numerical calculations

• It allows full-text search

• It treats fields as keywords, limiting search capabilities

Which of the following queries is supported by the flattened data type?

• Numerical calculations

• Term and prefix queries

• Full-text search

• Highlighting queries

Why might a partial match not return results when using the flattened data type?

• Because fields are combined

• Because fields are deleted

• Because fields are highlighted

• Because fields are not analyzed

How can you search for a specific inner object in a flattened field?

• By using the field name directly

• By using a prefix query

• By using dot notation

• By using a wildcard