anchor.diff:              }
           },
           {
  -            "_index" : "test",
  -            "_type" : "type",
  +            "_index" : "marvel",
  +            "_type" : "quotes",
               "_id" : "2"
           }
           ],
  
positive.code: }
  --------------------------------------------------
  
  `more_like_this` can be shortened to `mlt`.
  
  Under the hood, `more_like_this` simply creates multiple `should` clauses in a `bool` query of
  interesting terms extracted from some provided text. The interesting terms are
  selected with respect to their tf-idf scores. These are controlled by
  `min_term_freq`, `min_doc_freq`, and `max_doc_freq`. The number of interesting
  terms is controlled by `max_query_terms`. While the minimum number of clauses
  that must be satisfied is controlled by `minimum_should_match`. The terms
  are extracted from the text in `like` and analyzed by the analyzer associated
  with the field, unless specified by `analyzer`. There are other parameters,
  such as `min_word_length`, `max_word_length` or `stop_words`, to control what
  terms should be considered as interesting. In order to give more weight to
  more interesting terms, each boolean clause associated with a term could be
  boosted by the term tf-idf score times some boosting factor `boost_terms`.
  When a search for multiple documents is issued, More Like This generates a
  `more_like_this` query per document field in `fields`. These `fields` are
  specified as a top level parameter or within each document request.
  
  IMPORTANT: The fields must be indexed and of type `string`. Additionally, when
  using `like` with documents, the fields must be either `stored`, store `term_vector`
  or `_source` must be enabled.
  
  The `more_like_this` top level parameters include:
  
  [cols="<,<",options="header",]
  |=======================================================================
  |Parameter |Description
  |`fields` |A list of the fields to run the more like this query against.
  Defaults to the `_all` field for text and to all possible fields
  for documents.
  
  |`like`|coming[2.0]
  Can either be some text, some documents or a combination of all, *required*.
  A document request follows the same syntax as the
  <<docs-multi-get,Multi Get API>> or <<docs-multi-termvectors,Multi Term Vectors API>>.
  In this case, the text is fetched from `fields` unless specified otherwise in each document request.
  The text is analyzed by the default analyzer at the field, unless overridden by the
  `per_field_analyzer` parameter of the <<docs-termvectors-per-field-analyzer,Term Vectors API>>.
  
  |`like_text` |deprecated[2.0,Replaced by `like`]
  The text to find documents like it, *required* if `ids` or `docs` are
  not specified.
  
  |`ids` or `docs` |deprecated[2.0,Replaced by `like`]
  A list of documents following the same syntax as the
  <<docs-multi-get,Multi GET API>> or <<docs-multi-termvectors,Multi termvectors API>>.
  The text is fetched from `fields` unless specified otherwise in each `doc`.
  The text is analyzed by the default analyzer at the field, unless specified by the
  `per_field_analyzer` parameter of the <<docs-termvectors-per-field-analyzer,Term Vectors API>>.
  
  |`ignore_like`|coming[2.0] The `ignore_like` parameter is used to skip terms
  from the documents specified by `like`.  In other words, we could ask for
  documents `like: "Apple"`, but `ignore_like: "cake crumble tree"`. Follows the
  same syntax as `like`.
  
  |`include` |When using `like` with document requests, specifies whether the documents should be
  included from the search. Defaults to `false`.
  
  |`minimum_should_match`| From the generated query, the number of terms that
  must match following the <<query-dsl-minimum-should-match,minimum should
  syntax>>. (Defaults to `"30%"`).
  
  |`min_term_freq` |The frequency below which terms will be ignored in the
  source doc. The default frequency is `2`.
  
  |`max_query_terms` |The maximum number of query terms that will be
  included in any generated query. Defaults to `25`.
  
  |`stop_words` |An array of stop words. Any word in this set is
  considered "uninteresting" and ignored. Even if your Analyzer allows
  stopwords, you might want to tell the MoreLikeThis code to ignore them,
  as for the purposes of document similarity it seems reasonable to assume
  that "a stop word is never interesting".
  
  |`min_doc_freq` |The frequency at which words will be ignored which do
  not occur in at least this many docs. Defaults to `5`.
  
  |`max_doc_freq` |The maximum frequency in which words may still appear.
  Words that appear in more than this many docs will be ignored. Defaults
  to unbounded.
  
  |`min_word_length` |The minimum word length below which words will be
  ignored. Defaults to `0`.(Old name "min_word_len" is deprecated)
  
  |`max_word_length` |The maximum word length above which words will be
  ignored. Defaults to unbounded (`0`). (Old name "max_word_len" is deprecated)
  
  |`boost_terms` |Sets the boost factor to use when boosting terms.
  Defaults to deactivated (`0`). Any other value activates boosting with given
  boost factor.
  
  |`boost` |Sets the boost value of the query. Defaults to `1.0`.
  
  |`analyzer` |The analyzer that will be used to analyze the `like text`.
  Defaults to the analyzer associated with the first field in `fields`.
  |=======================================================================
  
  
negative.code:         "fields" : ["name.first", "name.last"],
          "like" : [
          {
              "_index" : "marvel",
              "_type" : "quotes",
              "doc" : {
                  "name": {
                      "first": "Ben",
  