The process of breaking the text of a field into individual terms to be indexed. This consists of tokenizing the text into terms, and then optionally filtering the tokenized terms (for example, lowercasing and removing stop words). Whoosh includes several different analyzers.
The set of documents you are indexing.
The individual pieces of content you want to make searchable. The word “documents” might imply files, but the data source could really be anything – articles in a content management system, blog posts in a blogging system, chunks of a very large file, rows returned from an SQL query, individual email messages from a mailbox file, or whatever. When you get search results from Whoosh, the results are a list of documents, whatever “documents” means in your search engine.
Each document contains a set of fields. Typical fields might be “title”, “content”, “url”, “keywords”, “status”, “date”, etc. Fields can be indexed (so they’re searchable) and/or stored with the document. Storing the field makes it available in search results. For example, you typically want to store the “title” field so your search results can display it.
Forward index
A table listing every document and the words that appear in the document. Whoosh lets you store term vectors that are a kind of forward index.
The process of examining documents in the corpus and adding them to the reverse index.
The reverse index lists every word in the corpus, and for each word, a list of documents in which that word appears, along with some optional information (such as the number of times the word appears in that document). These items in the list, containing a document number and any extra information, are called postings. In Whoosh the information stored in postings is customizable for each field.
Reverse index
Basically a table listing every word in the corpus, and for each word, the list of documents in which it appears. It can be more complicated (the index can also list how many times the word appears in each document, the positions at which it appears, etc.) but that’s how it basically works.
Whoosh requires that you specify the fields of the index before you begin indexing. The Schema associates field names with metadata about the field, such as the format of the postings and whether the contents of the field are stored in the index.
Term vector
A forward index for a certain field in a certain document. You can specify in the Schema that a given field should store term vectors.