Video Transcript (English)

Congratulations on completing this unit on analyzers in Atlas Search!

In this unit, we explored the crucial role text analyzers within Atlas Search play in preparing text for efficient indexing and search.

We then applied these concepts to a theoretical movie catalog application.

We first covered built-in analyzers, including standard, whitespace, and language, and how they function.

Then we discovered how to use a multi analyzer to improve our search results. Specifically, we combined the standard analyzer with the keyword analyzer for our movie catalog application to allow for a standard search, or an exact match.

Then, we created a custom analyzer to fit the specific needs of our application. Specifically, we used the Atlas UI to create a custom analyzer that prevented HTML tags from interfering with search terms in the plot field of our movie collection.

Finally, we explored how to use tokenizers and filters, two components of analyzers.

We covered the standard, whitespace, keyword, nGram and edgeGram tokenizers.

Then, we used character filters to map numbers to Roman numerals in movie titles. We also used token filters to prevent accent characters from interfering with search results.

Now you can use your understanding of Atlas Search analyzers to fine-tune search functionality in your application.

To learn more about Atlas Search analyzers, be sure to refer to the MongoDB documentation.