Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Nested Aggregations - Part 2

The video tutorial addresses the challenge of aggregating on analyzed text fields in Elasticsearch. Initially, enabling field data is attempted, but it doesn't yield the desired results. The solution involves reindexing the data with a keyword type to allow aggregation on full titles. The process includes setting up a new mapping with a raw subfield and reimporting the data. The tutorial highlights the importance of planning data indexing strategies to avoid reindexing.

Why is it problematic to aggregate on an analyzed text field in Elasticsearch?

• It doesn't recognize full titles as single entities.

• It requires more memory than available.

• It automatically deletes the index.

• It only works with numeric fields.

What was the initial solution attempted to resolve the aggregation issue?

• Changing the data type to integer.

• Enabling field data on the title field.

• Reindexing the entire dataset.

• Using a different database.

What was the limitation of using field data for aggregation?

• It was too slow.

• It required additional hardware.

• It didn't keep titles together as single entities.

• It caused data loss.

What change was made to the mapping to allow proper aggregation?

• Increasing the server memory.

• Adding a raw keyword subfield.

• Using a different programming language.

• Switching to a different database.

What tool was used to reimport the data after reindexing?

• A Java application.

• A Python script.

• A SQL query.

• A shell script.

What was the outcome of using the new mapping with a raw keyword subfield?

• The query failed to execute.

• The query executed successfully with desired results.

• The data was corrupted.

• The server crashed.

What is a key takeaway regarding data indexing strategies?

• Avoid using keyword fields.

• Plan how data will be used before indexing.

• Never use Elasticsearch for large datasets.

• Always use analyzed text fields.