Elasticsearch 7 and Elastic Stack - In Depth and Hands On! - Fuzzy Queries

The video tutorial explains fuzzy matching in search engines, focusing on the Levenshtein edit distance, which quantifies typos and misspellings through substitutions, insertions, and deletions. It demonstrates how to use the fuzziness parameter in search queries to tolerate errors and discusses the auto setting for fuzziness based on string length. Practical examples using movie titles illustrate how fuzzy search works, showing how different levels of fuzziness affect search results.

What is the Levenshtein edit distance and how does it relate to fuzzy matching?

Explain the three different classes of errors that contribute to the Levenshtein distance.

How does the concept of insertion affect the Levenshtein distance?

What happens when a search query has a fuzziness parameter set to 2?

What is the significance of specifying a fuzziness parameter in a search query?

Describe the auto setting for fuzziness and its implications for short strings.

How can you test the effectiveness of fuzzy matching in a search engine?