In this chapter, we extended our knowledge about query handling and data analysis. First of all, we discussed query rescore, which can help us when we need to recalculate the score of the top documents returned by a query. We also learned how to control multimatching queries. After that, we looked at two new aggregation types—one allowing us to get significant terms from a set of results and the other allowing documents grouping: a highly anticipated feature. We also discussed differences in relationship handling and approaches we can take when using Elasticsearch. Finally, we extended our knowledge about the Elasticsearch scripting module and we've learned what changes were introduced after Elasticsearch 1.0.

In the next chapter, we will try to improve our user query experience. We will start with user spelling mistakes and how Elasticsearch can help us by turning mistakes into good queries. We will also see what approaches we can take to handle user spelling mistake situations. After that, we will discuss improving query relevance on a given example. We will show you a query returning poor results and we will tune the query to match our needs.