In this chapter, we learned more things about Elasticsearch data analysis capabilities. We used aggregations and faceting to bring meaning to the data we indexed. We also introduced the spellchecking and autocomplete functionalities to our application by using the Elasticsearch suggesters. We created the alerting functionality by using a percolator, and we indexed binary files by using the attachment functionality. We indexed and searched geospatial data and used the scroll API to efficiently fetch a large number of results. Finally, we used the terms lookup mechanism to speed up the querying process that fetches a list of terms.

In the next chapter, we'll focus on Elasticsearch clusters and how to handle them. We'll see what node discovery is, how it is used, and how to alter its configuration. We'll learn about the gateway and recovery modules, and we will alter their configuration. We will also see what the buffers in Elasticsearch are, where they are used, and how to configure...