In this chapter, we learned how to index tree-like structures using Elasticsearch. In addition to that, we indexed data that is not flat and modified the structure of already-created indices. Finally, we learned how to handle relationships by using nested documents and by using the Elasticsearch parent-child functionality.

In the next chapter, we'll focus on making our search even better. We will see how Apache Lucene scoring works and why it matters so much. We will learn how to use the Elasticsearch function-score query to adjust the importance of our documents using different functions and we'll leverage the provided scripting capabilities. We will search the content in different languages and discuss when index time-boosting makes sense. We'll use synonyms to match words with the same meaning and we'll learn how to check why a given document was found by a query. Finally, we'll influence queries with boosts, and we will learn how to understand the score calculation done by Elasticsearch...