In this chapter, we learned how Apache Lucene scoring works internally. We've also seen how to use the scripting capabilities of Elasticsearch and how to index and search documents in different languages. We've used different queries to alter the score of our documents and modify it so it fits our use case. We've learned about index-time boosting, what synonyms are, and how they can help us. Finally, we've seen how to check why a particular document was a part of the result set and how its score was calculated.

In the next chapter, we'll go beyond full-text searching. We'll see what aggregations are and how we can use them to analyze our data. We'll also see faceting, which also allows us to aggregate our data and bring meaning to it. We'll use suggesters to implement spellchecking and autocomplete, and we'll use prospective search to find out which documents match particular queries. We'll index binary files and use geospatial capabilities to search our data with the use of geographical...