Highlighting
editHighlighting
editHighlighters 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.
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
.
The _all
field cannot be extracted from _source
, so it can only
be used for highlighting if it is explicitly stored.
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:
GET /_search { "query" : { "match": { "content": "kimchy" } }, "highlight" : { "fields" : { "content" : {} } } }
Elasticsearch supports three highlighters: unified
, plain
, and fvh
(fast vector
highlighter). You can specify the highlighter type
you want to use
for each field.
Unified highlighter
editThe 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.
Plain highlighter
editThe 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.
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.
Fast vector highlighter
editThe 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_scanner
. -
Requires setting
term_vector
towith_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
The fvh
highlighter does not support span queries. If you need support for
span queries, try an alternative highlighter, such as the unified
highlighter.
Offsets Strategy
editTo 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 tooffsets
in the mapping, theunified
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 usingterm_vectors
. -
Term vectors. If
term_vector
information is provided by settingterm_vector
towith_positions_offsets
in the mapping, theunified
highlighter automatically uses theterm_vector
to highlight the field. It’s fast especially for large fields (>1MB
) and for highlighting multi-term queries likeprefix
orwildcard
because it can access the dictionary of terms for each document. Thefvh
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. Theplain
highlighter always uses plain highlighting.
Plain highlighting for large texts may require substantial amount of time and memory.
To protect against this, the maximum number of text characters to be analyzed will be
limited to 1000000 in the next major Elastic version. The default limit is not set for this version,
but can be set for a particular index with the index setting index.highlight.max_analyzed_offset
.
Highlighting Settings
editHighlighting 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_scanner
-
Specifies how to break the highlighted fragments:
chars
,sentence
, orword
. Only valid for theunified
andfvh
highlighters. Defaults tosentence
for theunified
highlighter. Defaults tochars
for thefvh
highlighter.-
chars
-
Use the characters specified by
boundary_chars
as highlighting boundaries. Theboundary_max_scan
setting controls how far to scan for boundary characters. Only valid for thefvh
highlighter. -
sentence
-
Break highlighted fragments at the next sentence boundary, as determined by Java’s BreakIterator. You can specify the locale to use with
boundary_scanner_locale
.When used with the
unified
highlighter, thesentence
scanner splits sentences bigger thanfragment_size
at the first word boundary next tofragment_size
. You can setfragment_size
to 0 to never split any sentence. -
word
-
Break highlighted fragments at the next word boundary, as determined
by Java’s 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 Locale Language Tag documentation. The default value is Locale.ROOT. - encoder
-
Indicates if the snippet should be HTML encoded:
default
(no encoding) orhtml
(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 and keyword fields that start withcomment_
.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
orspan
. Only valid for theplain
highlighter. Defaults tospan
.-
simple
- Breaks up text into same-sized fragments.
-
span
- Breaks up text into same-sized fragments, but tried 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.
Elasticsearch 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 thehighlight_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 haveterm_vector
set towith_positions_offsets
, but only the field to which the matches are combined is loaded so only that field benefits from havingstore
set toyes
. Only valid for thefvh
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 toscore
will output the most relevant fragments first. Each highlighter applies its own logic to compute relevancy scores. See the document 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 usingmatched_fields
,phrase_limit
phrases per matched field are considered. Raising the limit increases query time and consumes more memory. Only supported by thefvh
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
tofalse
to highlight all fields. Defaults totrue
. - tags_schema
-
Set to
styled
to use the built-in tag schema. Thestyled
schema defines the followingpre_tags
and definespost_tags
as</em>
.<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">
Highlighting Examples
editOverride global settings
editYou can specify highlighter settings globally and selectively override them for individual fields.
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "number_of_fragments" : 3, "fragment_size" : 150, "fields" : { "_all" : { "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" } } } }
Specify a highlight query
editYou 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.
GET /_search { "stored_fields": [ "_id" ], "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 } }, "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 } } } } } }
Set highlighter type
editThe 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:
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "fields" : { "comment" : {"type" : "plain"} } } }
Configure highlighting tags
editBy default, the highlighting will wrap highlighted text in <em>
and
</em>
. This can be controlled by setting pre_tags
and post_tags
,
for example:
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "pre_tags" : ["<tag1>"], "post_tags" : ["</tag1>"], "fields" : { "_all" : {} } } }
When using the fast vector highlighter, you can specify additional tags and the "importance" is ordered.
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "pre_tags" : ["<tag1>", "<tag2>"], "post_tags" : ["</tag1>", "</tag2>"], "fields" : { "_all" : {} } } }
You can also use the built-in styled
tag schema:
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "tags_schema" : "styled", "fields" : { "comment" : {} } } }
Highlight on source
editForces the highlighting to highlight fields based on the source even if fields
are stored separately. Defaults to false
.
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "fields" : { "comment" : {"force_source" : true} } } }
Combine matches on multiple fields
editThis 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.
GET /_search { "query": { "query_string": { "query": "comment.plain:running scissors", "fields": ["comment"] } }, "highlight": { "order": "score", "fields": { "comment": { "matched_fields": ["comment", "comment.plain"], "type" : "fvh" } } } }
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.
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" } } } }
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.
GET /_search { "query": { "query_string": { "query": "running scissors", "fields": ["comment", "comment.plain^10"] } }, "highlight": { "order": "score", "fields": { "comment": { "matched_fields": ["comment.plain"], "type" : "fvh" } } } }
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.
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.
There is a small amount of overhead involved with setting
matched_fields
to a non-empty array so always prefer
"highlight": { "fields": { "comment": {} } }
to
"highlight": { "fields": { "comment": { "matched_fields": ["comment"], "type" : "fvh" } } }
Explicitly order highlighted fields
editElasticsearch 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:
GET /_search { "highlight": { "fields": [ { "title": {} }, { "text": {} } ] } }
None of the highlighters built into Elasticsearch care about the order that the fields are highlighted but a plugin might.
Control highlighted fragments
editEach 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:
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "fields" : { "comment" : {"fragment_size" : 150, "number_of_fragments" : 3} } } }
On top of this it is possible to specify that highlighted fragments need to be sorted by score:
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "order" : "score", "fields" : { "comment" : {"fragment_size" : 150, "number_of_fragments" : 3} } } }
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.
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "fields" : { "_all" : {}, "blog.title" : {"number_of_fragments" : 0} } } }
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.
GET /_search { "query" : { "match": { "user": "kimchy" } }, "highlight" : { "fields" : { "comment" : { "fragment_size" : 150, "number_of_fragments" : 3, "no_match_size": 150 } } } }
Highlight using the postings list
editHere is an example of setting the comment
field in the index mapping to
allow for highlighting using the postings:
PUT /example { "mappings": { "doc" : { "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):
PUT /example { "mappings": { "doc" : { "properties": { "comment" : { "type": "text", "term_vector" : "with_positions_offsets" } } } } }
Specify a fragmenter for the plain highlighter
editWhen using the plain
highlighter, you can choose between the simple
and
span
fragmenters:
GET twitter/_search { "query" : { "match_phrase": { "message": "number 1" } }, "highlight" : { "fields" : { "message" : { "type": "plain", "fragment_size" : 15, "number_of_fragments" : 3, "fragmenter": "simple" } } } }
Response:
{ ... "hits": { "total": 1, "max_score": 1.601195, "hits": [ { "_index": "twitter", "_type": "_doc", "_id": "1", "_score": 1.601195, "_source": { "user": "test", "message": "some message with the number 1", "date": "2009-11-15T14:12:12", "likes": 1 }, "highlight": { "message": [ " with the <em>number</em>", " <em>1</em>" ] } } ] } }
GET twitter/_search { "query" : { "match_phrase": { "message": "number 1" } }, "highlight" : { "fields" : { "message" : { "type": "plain", "fragment_size" : 15, "number_of_fragments" : 3, "fragmenter": "span" } } } }
Response:
{ ... "hits": { "total": 1, "max_score": 1.601195, "hits": [ { "_index": "twitter", "_type": "_doc", "_id": "1", "_score": 1.601195, "_source": { "user": "test", "message": "some message with the number 1", "date": "2009-11-15T14:12:12", "likes": 1 }, "highlight": { "message": [ " with the <em>number</em> <em>1</em>" ] } } ] } }
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.
How highlighters work internally
editGiven 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?
How to break a text into fragments?
editRelevant 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.
How to find the best fragments?
editRelevant 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.
How to highlight the query terms in a fragment?
editRelevant 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.
An example of the work of the unified highlighter
editLet’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.
PUT test_index { "mappings": { "_doc": { "properties": { "content" : { "type" : "text", "analyzer" : "english" } } } } }
We put the following document into the index:
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." }
And we ran the following query with a highlight request:
GET test_index/_search { "query": { "match_phrase" : {"content" : "only fox"} }, "highlight": { "type" : "unified", "number_of_fragments" : 3, "fields": { "content": {} } } }
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.