[[highlighting]]
== Highlighting

Highlighters enable you to get highlighted snippets from one or more fields
in your search results so you can show users where the query matches are.
When you request highlights, the response contains an additional `highlight`
element for each search hit that includes the highlighted fields and the
highlighted fragments.

NOTE: Highlighters don't reflect the boolean logic of a query when extracting
 terms to highlight. Thus, for some complex boolean queries (e.g nested boolean
 queries, queries using `minimum_should_match` etc.), parts of documents may be
 highlighted that don't correspond to query matches.

Highlighting requires the actual content of a field. If the field is not
stored (the mapping does not set `store` to `true`), the actual `_source` is
loaded and the relevant field is extracted from `_source`.

For example, to get highlights for the `content` field in each search hit
using the default highlighter, include a `highlight` object in
the request body that specifies the `content` field:

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "match": { "content": "kimchy" }
  },
  "highlight": {
    "fields": {
      "content": {}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

{es} supports three highlighters: `unified`, `plain`, and `fvh` (fast vector
highlighter). You can specify the highlighter `type` you want to use
for each field.

[discrete]
[[unified-highlighter]]
=== Unified highlighter
The `unified` highlighter uses the Lucene Unified Highlighter. This
highlighter breaks the text into sentences and uses the BM25 algorithm to score
individual sentences as if they were documents in the corpus. It also supports
accurate phrase and multi-term (fuzzy, prefix, regex) highlighting. This is the
default highlighter.

[discrete]
[[plain-highlighter]]
=== Plain highlighter
The `plain` highlighter uses the standard Lucene highlighter. It attempts to
reflect the query matching logic in terms of understanding word importance and
any word positioning criteria in phrase queries.

[WARNING]
The `plain` highlighter works best for highlighting simple query matches in a
single field. To accurately reflect query logic, it creates a tiny in-memory
index and re-runs the original query criteria through Lucene's query execution
planner to get access to low-level match information for the current document.
This is repeated for every field and every document that needs to be highlighted.
If you want to highlight a lot of fields in a lot of documents with complex
queries, we recommend using the `unified` highlighter on `postings` or `term_vector` fields.

[discrete]
[[fast-vector-highlighter]]
=== Fast vector highlighter
The `fvh` highlighter uses the Lucene Fast Vector highlighter.
This highlighter can be used on fields with `term_vector` set to
`with_positions_offsets` in the mapping. The fast vector highlighter:

* Can be customized with a <<boundary-scanners,`boundary_scanner`>>.
* Requires setting `term_vector` to `with_positions_offsets` which
  increases the size of the index
* Can combine matches from multiple fields into one result. See
  `matched_fields`
* Can assign different weights to matches at different positions allowing
  for things like phrase matches being sorted above term matches when
  highlighting a Boosting Query that boosts phrase matches over term matches

[WARNING]
The `fvh` highlighter does not support span queries. If you need support for
span queries, try an alternative highlighter, such as the `unified` highlighter.

[discrete]
[[offsets-strategy]]
=== Offsets strategy
To create meaningful search snippets from the terms being queried,
the highlighter needs to know the start and end character offsets of each word
in the original text. These offsets can be obtained from:

* The postings list. If `index_options` is set to `offsets` in the mapping,
the `unified` highlighter uses this information to highlight documents without
re-analyzing the text. It re-runs the original query directly on the postings
and extracts the matching offsets from the index, limiting the collection to
the highlighted documents. This is important if you have large fields because
it doesn't require reanalyzing the text to be highlighted. It also requires less
disk space than using `term_vectors`.

* Term vectors. If `term_vector` information is provided by setting
`term_vector` to `with_positions_offsets` in the mapping, the `unified`
highlighter automatically uses the `term_vector` to highlight the field.
It's fast especially for large fields (> `1MB`) and for highlighting multi-term queries like
`prefix` or `wildcard` because it can access the dictionary of terms for each document.
The `fvh` highlighter always uses term vectors.

* Plain highlighting. This mode is used by the `unified` when there is no other alternative.
It creates a tiny in-memory index and re-runs the original query criteria through
Lucene's query execution planner to get access to low-level match information on
the current document. This is repeated for every field and every document that
needs highlighting. The `plain` highlighter always uses plain highlighting.

[WARNING]
Plain highlighting for large texts may require substantial amount of time and memory.
To protect against this, the maximum number of text characters that will be analyzed has been
limited to 1000000. This default limit can be changed
for a particular index with the index setting <<index-max-analyzed-offset,`index.highlight.max_analyzed_offset`>>.

[discrete]
[[highlighting-settings]]
=== Highlighting settings

Highlighting settings can be set on a global level and overridden at
the field level.

boundary_chars:: A string that contains each boundary character.
Defaults to `.,!? \t\n`.

boundary_max_scan:: How far to scan for boundary characters. Defaults to `20`.

[[boundary-scanners]]
boundary_scanner:: Specifies how to break the highlighted fragments: `chars`,
`sentence`, or `word`. Only valid for the `unified` and `fvh` highlighters.
Defaults to `sentence` for the `unified` highlighter. Defaults to `chars` for
the `fvh` highlighter.
`chars`::: Use the characters specified by `boundary_chars` as highlighting
boundaries. The `boundary_max_scan` setting controls how far to scan for
boundary characters. Only valid for the `fvh` highlighter.
`sentence`::: Break highlighted fragments at the next sentence boundary, as
determined by Java's
https://docs.oracle.com/javase/8/docs/api/java/text/BreakIterator.html[BreakIterator].
You can specify the locale to use with `boundary_scanner_locale`.
+
NOTE: When used with the `unified` highlighter, the `sentence` scanner splits
sentences bigger than `fragment_size` at the first word boundary next to
`fragment_size`. You can set `fragment_size` to 0 to never split any sentence.

`word`::: Break highlighted fragments at the next word boundary, as determined
by Java's https://docs.oracle.com/javase/8/docs/api/java/text/BreakIterator.html[BreakIterator].
You can specify the locale to use with `boundary_scanner_locale`.

boundary_scanner_locale:: Controls which locale is used to search for sentence
and word boundaries. This parameter takes a form of a language tag,
e.g. `"en-US"`,  `"fr-FR"`, `"ja-JP"`. More info can be found in the
https://docs.oracle.com/javase/8/docs/api/java/util/Locale.html#forLanguageTag-java.lang.String-[Locale Language Tag]
documentation. The default value is https://docs.oracle.com/javase/8/docs/api/java/util/Locale.html#ROOT[ Locale.ROOT].

encoder:: Indicates if the snippet should be HTML encoded:
`default` (no encoding) or `html` (HTML-escape the snippet text and then
insert the highlighting tags)

fields:: Specifies the fields to retrieve highlights for. You can use wildcards
to specify fields. For example, you could specify `comment_*` to
get highlights for all <<text,text>> and <<keyword,keyword>> fields
that start with `comment_`.
+
NOTE: Only text and keyword fields are highlighted when you use wildcards.
If you use a custom mapper and want to highlight on a field anyway, you
must explicitly specify that field name.

force_source:: Highlight based on the source even if the field is
stored separately. Defaults to `false`.

fragmenter:: Specifies how text should be broken up in highlight
snippets: `simple` or `span`. Only valid for the `plain` highlighter.
Defaults to `span`.

`simple`::: Breaks up text into same-sized fragments.
`span`::: Breaks up text into same-sized fragments, but tries to avoid
breaking up text between highlighted terms. This is helpful when you're
querying for phrases. Default.

fragment_offset:: Controls the margin from which you want to start
highlighting. Only valid when using the `fvh` highlighter.

fragment_size:: The size of the highlighted fragment in characters. Defaults
to 100.

highlight_query:: Highlight matches for a query other than the search
query. This is especially useful if you use a rescore query because
those are not taken into account by highlighting by default.
+
IMPORTANT: {es} does not validate that `highlight_query` contains
the search query in any way so it is possible to define it so
legitimate query results are not highlighted. Generally, you should
include the search query as part of the `highlight_query`.

matched_fields:: Combine matches on multiple fields to highlight a single field.
This is most intuitive for multifields that analyze the same string in different
ways. All `matched_fields` must have `term_vector` set to
`with_positions_offsets`, but only the field to which
the matches are combined is loaded so only that field benefits from having
`store` set to `yes`. Only valid for the `fvh` highlighter.

no_match_size:: The amount of text you want to return from the beginning
of the field if there are no matching fragments to highlight. Defaults
to 0 (nothing is returned).

number_of_fragments:: The maximum number of fragments to return. If the
number of fragments is set to 0, no fragments are returned. Instead,
the entire field contents are highlighted and returned. This can be
handy when you need to highlight short texts such as a title or
address, but fragmentation is not required. If `number_of_fragments`
is 0, `fragment_size` is ignored. Defaults to 5.

order:: Sorts highlighted fragments by score when set to `score`. By default,
fragments will be output in the order they appear in the field (order: `none`).
Setting this option to `score` will output the most relevant fragments first.
Each highlighter applies its own logic to compute relevancy scores. See
the document <<how-es-highlighters-work-internally, How highlighters work internally>>
for more details how different highlighters find the best fragments.

phrase_limit:: Controls the number of matching phrases in a document that are
considered. Prevents the `fvh` highlighter from analyzing too many phrases
and consuming too much memory. When using `matched_fields`, `phrase_limit`
phrases per matched field are considered. Raising the limit increases query
time and consumes more memory. Only supported by the `fvh` highlighter.
Defaults to 256.

pre_tags:: Use in conjunction with `post_tags` to define the HTML tags
to use for the highlighted text. By default, highlighted text is wrapped
in `<em>` and `</em>` tags. Specify as an array of strings.

post_tags:: Use in conjunction with `pre_tags` to define the HTML tags
to use for the highlighted text. By default, highlighted text is wrapped
in `<em>` and `</em>` tags. Specify as an array of strings.

require_field_match:: By default, only fields that contains a query match are
highlighted. Set `require_field_match` to `false` to highlight all fields.
Defaults to `true`.

[[max-analyzed-offset]]
max_analyzed_offset:: By default, the maximum number of characters
analyzed for a highlight request is bounded by the value defined in the
<<index-max-analyzed-offset, `index.highlight.max_analyzed_offset`>> setting,
and when the number of characters exceeds this limit an error is returned. If
this setting is set to a non-negative value, the highlighting stops at this defined
maximum limit, and the rest of the text is not processed, thus not highlighted and
no error is returned. The <<max-analyzed-offset, `max_analyzed_offset`>> query setting
does *not* override the <<index-max-analyzed-offset, `index.highlight.max_analyzed_offset`>>
which prevails when it's set to lower value than the query setting.

tags_schema:: Set to `styled` to use the built-in tag schema. The `styled`
schema defines the following `pre_tags` and defines `post_tags` as
`</em>`.
+
[source,html]
--------------------------------------------------
<em class="hlt1">, <em class="hlt2">, <em class="hlt3">,
<em class="hlt4">, <em class="hlt5">, <em class="hlt6">,
<em class="hlt7">, <em class="hlt8">, <em class="hlt9">,
<em class="hlt10">
--------------------------------------------------

[[highlighter-type]]
type:: The highlighter to use: `unified`, `plain`, or `fvh`. Defaults to
`unified`.

[discrete]
[[highlighting-examples]]
=== Highlighting examples

* <<override-global-settings, Override global settings>>
* <<specify-highlight-query, Specify a highlight query>>
* <<set-highlighter-type, Set highlighter type>>
* <<configure-tags, Configure highlighting tags>>
* <<highlight-source, Highlight source>>
* <<highlight-all, Highlight all fields>>
* <<matched-fields, Combine matches on multiple fields>>
* <<explicit-field-order, Explicitly order highlighted fields>>
* <<control-highlighted-frags, Control highlighted fragments>>
* <<highlight-postings-list, Highlight using the postings list>>
* <<specify-fragmenter, Specify a fragmenter for the plain highlighter>>

[[override-global-settings]]
[discrete]
== Override global settings

You can specify highlighter settings globally and selectively override them for
individual fields.

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "number_of_fragments" : 3,
    "fragment_size" : 150,
    "fields" : {
      "body" : { "pre_tags" : ["<em>"], "post_tags" : ["</em>"] },
      "blog.title" : { "number_of_fragments" : 0 },
      "blog.author" : { "number_of_fragments" : 0 },
      "blog.comment" : { "number_of_fragments" : 5, "order" : "score" }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[discrete]
[[specify-highlight-query]]
== Specify a highlight query

You can specify a `highlight_query` to take additional information into account
when highlighting. For example, the following query includes both the search
query and rescore query in the `highlight_query`. Without the `highlight_query`,
highlighting would only take the search query into account.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "match": {
      "comment": {
        "query": "foo bar"
      }
    }
  },
  "rescore": {
    "window_size": 50,
    "query": {
      "rescore_query": {
        "match_phrase": {
          "comment": {
            "query": "foo bar",
            "slop": 1
          }
        }
      },
      "rescore_query_weight": 10
    }
  },
  "_source": false,
  "highlight": {
    "order": "score",
    "fields": {
      "comment": {
        "fragment_size": 150,
        "number_of_fragments": 3,
        "highlight_query": {
          "bool": {
            "must": {
              "match": {
                "comment": {
                  "query": "foo bar"
                }
              }
            },
            "should": {
              "match_phrase": {
                "comment": {
                  "query": "foo bar",
                  "slop": 1,
                  "boost": 10.0
                }
              }
            },
            "minimum_should_match": 0
          }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[discrete]
[[set-highlighter-type]]
== Set highlighter type

The `type` field allows to force a specific highlighter type.
The allowed values are: `unified`, `plain` and `fvh`.
The following is an example that forces the use of the plain highlighter:

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "match": { "user.id": "kimchy" }
  },
  "highlight": {
    "fields": {
      "comment": { "type": "plain" }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[[configure-tags]]
[discrete]
== Configure highlighting tags

By default, the highlighting will wrap highlighted text in `<em>` and
`</em>`. This can be controlled by setting `pre_tags` and `post_tags`,
for example:

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "pre_tags" : ["<tag1>"],
    "post_tags" : ["</tag1>"],
    "fields" : {
      "body" : {}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

When using the fast vector highlighter, you can specify additional tags and the
"importance" is ordered.

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "pre_tags" : ["<tag1>", "<tag2>"],
    "post_tags" : ["</tag1>", "</tag2>"],
    "fields" : {
      "body" : {}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

You can also use the built-in `styled` tag schema:

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "tags_schema" : "styled",
    "fields" : {
      "comment" : {}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[discrete]
[[highlight-source]]
== Highlight on source

Forces the highlighting to highlight fields based on the source even if fields
are stored separately. Defaults to `false`.

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "fields" : {
      "comment" : {"force_source" : true}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]


[[highlight-all]]
[discrete]
== Highlight in all fields

By default, only fields that contains a query match are highlighted. Set
`require_field_match` to `false` to highlight all fields.

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "require_field_match": false,
    "fields": {
      "body" : { "pre_tags" : ["<em>"], "post_tags" : ["</em>"] }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[[matched-fields]]
[discrete]
== Combine matches on multiple fields

WARNING: This is only supported by the `fvh` highlighter

The Fast Vector Highlighter can combine matches on multiple fields to
highlight a single field. This is most intuitive for multifields that
analyze the same string in different ways. All `matched_fields` must have
`term_vector` set to `with_positions_offsets` but only the field to which
the matches are combined is loaded so only that field would benefit from having
`store` set to `yes`.

In the following examples, `comment` is analyzed by the `english`
analyzer and `comment.plain` is analyzed by the `standard` analyzer.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "query_string": {
      "query": "comment.plain:running scissors",
      "fields": [ "comment" ]
    }
  },
  "highlight": {
    "order": "score",
    "fields": {
      "comment": {
        "matched_fields": [ "comment", "comment.plain" ],
        "type": "fvh"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

The above matches both "run with scissors" and "running with scissors"
and would highlight "running" and "scissors" but not "run". If both
phrases appear in a large document then "running with scissors" is
sorted above "run with scissors" in the fragments list because there
are more matches in that fragment.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "query_string": {
      "query": "running scissors",
      "fields": ["comment", "comment.plain^10"]
    }
  },
  "highlight": {
    "order": "score",
    "fields": {
      "comment": {
        "matched_fields": ["comment", "comment.plain"],
        "type" : "fvh"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

The above highlights "run" as well as "running" and "scissors" but
still sorts "running with scissors" above "run with scissors" because
the plain match ("running") is boosted.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "query_string": {
      "query": "running scissors",
      "fields": [ "comment", "comment.plain^10" ]
    }
  },
  "highlight": {
    "order": "score",
    "fields": {
      "comment": {
        "matched_fields": [ "comment.plain" ],
        "type": "fvh"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

The above query wouldn't highlight "run" or "scissor" but shows that
it is just fine not to list the field to which the matches are combined
(`comment`) in the matched fields.

[NOTE]
Technically it is also fine to add fields to `matched_fields` that
don't share the same underlying string as the field to which the matches
are combined. The results might not make much sense and if one of the
matches is off the end of the text then the whole query will fail.

[NOTE]
===================================================================
There is a small amount of overhead involved with setting
`matched_fields` to a non-empty array so always prefer
[source,js]
--------------------------------------------------
    "highlight": {
        "fields": {
            "comment": {}
        }
    }
--------------------------------------------------
// NOTCONSOLE
to
[source,js]
--------------------------------------------------
    "highlight": {
        "fields": {
            "comment": {
                "matched_fields": ["comment"],
                "type" : "fvh"
            }
        }
    }
--------------------------------------------------
// NOTCONSOLE
===================================================================


[[explicit-field-order]]
[discrete]
== Explicitly order highlighted fields
Elasticsearch highlights the fields in the order that they are sent, but per the
JSON spec, objects are unordered. If you need to be explicit about the order
in which fields are highlighted specify the `fields` as an array:

[source,console]
--------------------------------------------------
GET /_search
{
  "highlight": {
    "fields": [
      { "title": {} },
      { "text": {} }
    ]
  }
}
--------------------------------------------------
// TEST[setup:my_index]

None of the highlighters built into Elasticsearch care about the order that the
fields are highlighted but a plugin might.




[discrete]
[[control-highlighted-frags]]
== Control highlighted fragments

Each field highlighted can control the size of the highlighted fragment
in characters (defaults to `100`), and the maximum number of fragments
to return (defaults to `5`).
For example:

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "fields" : {
      "comment" : {"fragment_size" : 150, "number_of_fragments" : 3}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

On top of this it is possible to specify that highlighted fragments need
to be sorted by score:

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "order" : "score",
    "fields" : {
      "comment" : {"fragment_size" : 150, "number_of_fragments" : 3}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

If the `number_of_fragments` value is set to `0` then no fragments are
produced, instead the whole content of the field is returned, and of
course it is highlighted. This can be very handy if short texts (like
document title or address) need to be highlighted but no fragmentation
is required. Note that `fragment_size` is ignored in this case.

[source,console]
--------------------------------------------------
GET /_search
{
  "query" : {
    "match": { "user.id": "kimchy" }
  },
  "highlight" : {
    "fields" : {
      "body" : {},
      "blog.title" : {"number_of_fragments" : 0}
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

When using `fvh` one can use `fragment_offset`
parameter to control the margin to start highlighting from.

In the case where there is no matching fragment to highlight, the default is
to not return anything. Instead, we can return a snippet of text from the
beginning of the field by setting `no_match_size` (default `0`) to the length
of the text that you want returned. The actual length may be shorter or longer than
specified as it tries to break on a word boundary.

[source,console]
--------------------------------------------------
GET /_search
{
  "query": {
    "match": { "user.id": "kimchy" }
  },
  "highlight": {
    "fields": {
      "comment": {
        "fragment_size": 150,
        "number_of_fragments": 3,
        "no_match_size": 150
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:my_index]

[discrete]
[[highlight-postings-list]]
== Highlight using the postings list

Here is an example of setting the `comment` field in the index mapping to
allow for highlighting using the postings:

[source,console]
--------------------------------------------------
PUT /example
{
  "mappings": {
    "properties": {
      "comment" : {
        "type": "text",
        "index_options" : "offsets"
      }
    }
  }
}
--------------------------------------------------

Here is an example of setting the `comment` field to allow for
highlighting using the `term_vectors` (this will cause the index to be bigger):

[source,console]
--------------------------------------------------
PUT /example
{
  "mappings": {
    "properties": {
      "comment" : {
        "type": "text",
        "term_vector" : "with_positions_offsets"
      }
    }
  }
}
--------------------------------------------------

[discrete]
[[specify-fragmenter]]
== Specify a fragmenter for the plain highlighter

When using the `plain` highlighter, you can choose between the `simple` and
`span` fragmenters:

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "match_phrase": { "message": "number 1" }
  },
  "highlight": {
    "fields": {
      "message": {
        "type": "plain",
        "fragment_size": 15,
        "number_of_fragments": 3,
        "fragmenter": "simple"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:messages]

Response:

[source,console-result]
--------------------------------------------------
{
  ...
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.6011951,
    "hits": [
      {
        "_index": "my-index-000001",
        "_type": "_doc",
        "_id": "1",
        "_score": 1.6011951,
        "_source": {
          "message": "some message with the number 1",
          "context": "bar"
        },
        "highlight": {
          "message": [
            " with the <em>number</em>",
            " <em>1</em>"
          ]
        }
      }
    ]
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,/]

[source,console]
--------------------------------------------------
GET my-index-000001/_search
{
  "query": {
    "match_phrase": { "message": "number 1" }
  },
  "highlight": {
    "fields": {
      "message": {
        "type": "plain",
        "fragment_size": 15,
        "number_of_fragments": 3,
        "fragmenter": "span"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:messages]

Response:

[source,console-result]
--------------------------------------------------
{
  ...
  "hits": {
    "total": {
      "value": 1,
      "relation": "eq"
    },
    "max_score": 1.6011951,
    "hits": [
      {
        "_index": "my-index-000001",
        "_type": "_doc",
        "_id": "1",
        "_score": 1.6011951,
        "_source": {
          "message": "some message with the number 1",
          "context": "bar"
        },
        "highlight": {
          "message": [
            " with the <em>number</em> <em>1</em>"
          ]
        }
      }
    ]
  }
}
--------------------------------------------------
// TESTRESPONSE[s/\.\.\./"took": $body.took,"timed_out": false,"_shards": $body._shards,/]

If the `number_of_fragments` option is set to `0`,
`NullFragmenter` is used which does not fragment the text at all.
This is useful for highlighting the entire contents of a document or field.


[discrete]
[[how-es-highlighters-work-internally]]
== How highlighters work internally

Given a query and a text (the content of a document field), the goal of a
highlighter is to find the best text fragments for the query, and highlight
the query terms in the found fragments. For this, a highlighter needs to
address several questions:

- How break a text into fragments?
- How to find the best fragments among all fragments?
- How to highlight the query terms in a fragment?

[discrete]
=== How to break a text into fragments?
Relevant settings: `fragment_size`, `fragmenter`, `type` of highlighter,
`boundary_chars`, `boundary_max_scan`, `boundary_scanner`, `boundary_scanner_locale`.

Plain highlighter begins with analyzing the text using the given analyzer,
and creating a token stream from it. Plain highlighter uses a very simple
algorithm to break the token stream into fragments. It loops through terms in the token stream,
and every time the current term's end_offset exceeds `fragment_size` multiplied by the number of
created fragments, a new fragment is created. A little more computation is done with using `span`
fragmenter to avoid breaking up text between highlighted terms. But overall, since the breaking is
done only by `fragment_size`, some fragments can be quite odd, e.g. beginning
with a punctuation mark.

Unified or FVH highlighters do a better job of breaking up a text into
fragments by utilizing Java's `BreakIterator`. This ensures that a fragment
is a valid sentence as long as `fragment_size` allows for this.

[discrete]
=== How to find the best fragments?
Relevant settings: `number_of_fragments`.

To find the best, most relevant, fragments, a highlighter needs to score
each fragment in respect to the given query. The goal is to score only those
terms that participated in generating the 'hit' on the document.
For some complex queries, this is still work in progress.

The plain highlighter creates an in-memory index from the current token stream,
and re-runs the original query criteria through Lucene's query execution planner
to get access to low-level match information for the current text.
For more complex queries the original query could be converted to a span query,
as span queries can handle phrases more accurately. Then this obtained low-level match
information is used to score each individual fragment. The scoring method of the plain
highlighter is quite simple. Each fragment is scored by the number of unique
query terms found in this fragment. The score of individual term is equal to its boost,
which is by default is 1. Thus, by default, a fragment that contains one unique query term,
will get a score of 1; and a fragment that contains two unique query terms,
will get a score of 2 and so on. The fragments are then sorted by their scores,
so the highest scored fragments will be output first.

FVH doesn't need to analyze the text and build an in-memory index, as it uses
pre-indexed document term vectors, and finds among them terms that correspond to the query.
FVH scores each fragment by the number of query terms found in this fragment.
Similarly to plain highlighter, score of individual term is equal to its boost value.
In contrast to plain highlighter, all query terms are counted, not only unique terms.

Unified highlighter can use pre-indexed term vectors or pre-indexed terms offsets,
if they are available. Otherwise, similar to Plain Highlighter, it has to create
an in-memory index from the text. Unified highlighter uses the BM25 scoring model
to score fragments.

[discrete]
=== How to highlight the query terms in a fragment?
Relevant settings:  `pre-tags`, `post-tags`.

The goal is to highlight only those terms that participated in generating the 'hit' on the document.
For some complex boolean queries, this is still work in progress, as highlighters don't reflect
the boolean logic of a query and only extract leaf (terms, phrases, prefix etc) queries.

Plain highlighter given the token stream and the original text, recomposes the original text to
highlight only terms from the token stream that are contained in the low-level match information
structure from the previous step.

FVH and unified highlighter use intermediate data structures to represent
fragments in some raw form, and then populate them with actual text.

A highlighter uses `pre-tags`, `post-tags` to encode highlighted terms.

[discrete]
=== An example of the work of the unified highlighter

Let's look in more details how unified highlighter works.

First, we create a index with a text field `content`, that will be indexed
using `english` analyzer, and will be indexed without offsets or term vectors.

[source,js]
--------------------------------------------------
PUT test_index
{
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "english"
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

We put the following document into the index:

[source,js]
--------------------------------------------------
PUT test_index/_doc/doc1
{
  "content" : "For you I'm only a fox like a hundred thousand other foxes. But if you tame me, we'll need each other. You'll be the only boy in the world for me. I'll be the only fox in the world for you."
}
--------------------------------------------------
// NOTCONSOLE


And we ran the following query with a highlight request:

[source,js]
--------------------------------------------------
GET test_index/_search
{
  "query": {
    "match_phrase" : {"content" : "only fox"}
  },
  "highlight": {
    "type" : "unified",
    "number_of_fragments" : 3,
    "fields": {
      "content": {}
    }
  }
}
--------------------------------------------------
// NOTCONSOLE


After `doc1` is found as a hit for this query, this hit will be passed to the
unified highlighter for highlighting the field `content` of the document.
Since the field `content` was not indexed either with offsets or term vectors,
its raw field value will be analyzed, and in-memory index will be built from
the terms that match the query:

    {"token":"onli","start_offset":12,"end_offset":16,"position":3},
    {"token":"fox","start_offset":19,"end_offset":22,"position":5},
    {"token":"fox","start_offset":53,"end_offset":58,"position":11},
    {"token":"onli","start_offset":117,"end_offset":121,"position":24},
    {"token":"onli","start_offset":159,"end_offset":163,"position":34},
    {"token":"fox","start_offset":164,"end_offset":167,"position":35}

Our complex phrase query will be converted to the span query:
`spanNear([text:onli, text:fox], 0, true)`, meaning that we are looking for
terms "onli: and "fox" within 0 distance from each other, and in the given
order. The span query will be run against the created before in-memory index,
to find the following match:

    {"term":"onli", "start_offset":159, "end_offset":163},
    {"term":"fox", "start_offset":164, "end_offset":167}

In our example, we have got a single match, but there could be several matches.
Given the matches, the unified highlighter breaks the text of the field into
so called "passages". Each passage must contain at least one match.
The unified highlighter with the use of Java's `BreakIterator` ensures that each
passage represents a full sentence as long as it doesn't exceed `fragment_size`.
For our example, we have got a single passage with the following properties
(showing only a subset of the properties here):

    Passage:
        startOffset: 147
        endOffset: 189
        score: 3.7158387
        matchStarts: [159, 164]
        matchEnds: [163, 167]
        numMatches: 2

Notice how a passage has a score, calculated using the BM25 scoring formula
adapted for passages. Scores allow us to choose the best scoring
passages if there are more passages available than the requested
by the user `number_of_fragments`. Scores also let us to sort passages by
`order: "score"` if requested by the user.

As the final step, the unified highlighter will extract from the field's text
a string corresponding to each passage:

    "I'll be the only fox in the world for you."

and will format with the tags <em> and </em> all matches in this string
using the passages's `matchStarts` and `matchEnds` information:

    I'll be the <em>only</em> <em>fox</em> in the world for you.

This kind of formatted strings are the final result of the highlighter returned
to the user.
