Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - N-Grams - Part 2

The video tutorial explains how to reindex movie data in Elasticsearch by deleting the existing index and creating a new analyzer using edge ngrams. It demonstrates the process of testing the analyzer with sample text and mapping it to the title field for autocompletion. The tutorial also covers querying with a standard analyzer to improve search results and discusses potential issues with ngram-based approaches.

What is the first step in reindexing the movies data?

• Create a new analyzer

• Apply the analyzer to the title field

• Delete the existing index

• Test the analyzer

What type of filter is used in the autocomplete analyzer?

• Lowercase

• Ngram

• Standard

• Edge ngram

Which tokenizer is used in the custom autocomplete analyzer?

• Keyword tokenizer

• Whitespace tokenizer

• Pattern tokenizer

• Standard tokenizer

What is the purpose of testing the analyzer with a sample text string?

• To delete the existing index

• To verify the analyzer's functionality

• To apply the analyzer to the title field

• To create a new analyzer

Why is it important to use different analyzers for indexing and querying?

• To allow for more complex queries

• To increase the speed of indexing

• To ensure queries are not split in the same way as indexing

• To reduce the size of the index

What issue arises when using the same analyzer for both indexing and querying?

• The index size increases

• Queries are split into unigrams, bigrams, and trigrams

• Queries become case-sensitive

• Indexing becomes slower

What alternative approach is suggested for handling edge cases in autocompletion?

• Using a different tokenizer

• Using completion suggestions

• Increasing the max gram size

• Applying more filters