Chapter 2. Understanding Document Analysis and Creating Mappings
Search is hard, and it becomes harder when both speed and relevancy are required together. There are lots of configurable options Elasticsearch provides out-of-the-box to take control before you start putting the data into it. Elasticsearch is schemaless. I gave a brief idea in the previous chapter of why it is not completely schemaless and how it creates a schema right after indexing the very first document for all the fields existing in that document. However, the schema matters a lot for a better and more relevant search. Equally important is understanding the theory behind the phases of document indexing and search.
In this chapter, we will cover the following topics:
- Full text search and inverted indices
- Document analysis
- Introducing Lucene analyzers
- Creating custom analyzers
- Elasticsearch mappings
