In the previous chapter, we learned more about Elasticsearch's data analysis capabilities. We used aggregations and faceting to add meaning to the data we indexed. We also introduced the spellcheck and autocomplete functionalities to our application by using Elasticsearch suggesters. We've created the alerting functionality by using a percolator, and we've indexed binary files by using the attachment capability. We've indexed and searched geospatial data, and we've used the scroll API to efficiently fetch a large number of results. Finally, we've used the terms lookup to speed up the queries that fetch a list of terms and use them.
By the end of this chapter, you will have learned the following topics:
Understanding a node's discovery mechanism, configuration, and tuning
Controlling recovery and gateway modules
Preparing Elasticsearch for high query and indexing use cases
Using index templates and dynamic mappings