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ElasticSearch (Vocabulary (Similarity algorithm (takes into account (Term…

- - - - Term frequency
        
        How often does the term appear in the field? The more often, the more relevant. A field containing five mentions of the same term is more likely to be relevant than a field containing just one mention.
      - Inverse document frequency
        
        How often does each term appear in the index? The more often, the less relevant. Terms that appear in many documents have a lower weight than more-uncommon terms.
      - Field-length norm
        
        How long is the field? The longer it is, the less likely it is that words in the field will be relevant. A term appearing in a short title field carries more weight than the same term appearing in a long content field.
- - - - can be empty
- - - - analyzes the query string
        
        f you run a match query against a full-text field
      - searches for that exact value
        
        If you use it on a field containing an exact value
        
        such as a number
        
        a date
        
        a Boolean
        
        or a not_analyzed string field
    - - allows to run the same match query on multiple fields
    - - allows you to find numbers or dates that fall into a specified range
      - operators
        
        gt
        
        gte
        
        lt
        
        lte
    - - is used to search by exact values
    - - are used to find documents in which the specified field either
        
        has one or more values (exists)
        
        or doesn’t have any values (missing)
    - - keeps only documents that contain all of the search terms, in the same positions relative to each other
  - - - Clauses that must match for the document to be included
    - - If these clauses match, they increase the _score; otherwise, they have no effect. They are simply used to refine the relevance score for each document.
    - - Clauses that must match, but are run in non-scoring, filtering mode.
      - These clauses do not contribute to the score, instead they simply include/exclude documents based on their criteria.
  - - - Determines if a document matches and how well it does
        
        Best matching the words full text search
        
        Containing the word run, but maybe also matching runs, running, jog, or sprint
        
        Containing the words quick, brown, and fox—the closer together they are, the more relevant the document
        
        Tagged with lucene, search, or java—the more tags, the more relevant the document
    - - Does this document match?
        
        Is the created date in the range 2013 - 2014?
        
        Does the status field contain the term published?
        
        Is the lat_lon field within 10km of a specified point?
  - - - most_fields
      - best_field
    - - cross_fields
        
        first analyzes the query string to produce a list of terms
        
        and then it searches for each term in any field