6.10. Summary

You now have a better understanding of how scoring works inside Elasticsearch as well as how documents interact with the field data cache, so let’s review what this chapter was about:

The frequency of a term and the number of times that term occurs in a document are used to calculate the score of a term inside a query.

Elasticsearch has a lot of tools to customize and modify scoring.

Scoring impact can be lessened by rescoring a subset of documents.

Use the explain API to understand how a document has been scored.

The function_score query gives you ultimate control over scoring your documents.

Understanding the field data cache can help you understand how your Elasticsearch cluster uses memory.

Alternatives like doc_values can be used if the field data cache is using too much memory.

In chapter 7 we’ll move on to how you can not only get the results of a query but also explore data from a different angle using aggregations.