When the ValuesSource has ordinals, terms ordinals are used as a cache key to
bucket ordinals. This can make terms aggregations on String terms significantly
faster.
Close#4350
The percolator uses this option to deal with the fact that the MemoryIndex doesn't support stored fields,
this is possible b/c the _source of the document being percolated is always present.
Closes#4348
This contribution is based on the feedback given in issue #4254 and
issue #4255, and should clear things up, when suggestions are being
removed and not displayed anymore after deletion of data.
The Fast Vector Highlighter can combine matches on multiple fields to
highlight a single field using `matched_fields`. This is most
intuitive for multifields that analyze the same string in different
ways. Example:
{
"query": {
"query_string": {
"query": "content.plain:running scissors",
"fields": ["content"]
}
},
"highlight": {
"order": "score",
"fields": {
"content": {
"matched_fields": ["content", "content.plain"],
"type" : "fvh"
}
}
}
}
Closes#3750
* Minor alignments (like setter to ctor)
* FuzzySuggester has a unicode aware flag, which is not exposed in the fuzzy completion request parameters
* Made XAnalyzingSuggester flags (PAYLOAD_SEP, END_BYTE, SEP_LABEL) to be written into the postings format, so we can retain backwards compatibility
* The above change also implies, that these flags can be set per instantiated XAnalyzingSuggester
* CompletionPostingsFormatTest now uses a randomProvider for writing data to check for bwc
Use .percolator as the internal (hidden) type name for percolators within the index. Seems nicer name to represent "hidden" types within an index.
closes#4090
Requires field index_options set to "offsets" in order to store positions and offsets in the postings list.
Considerably faster than the plain highlighter since it doesn't require to reanalyze the text to be highlighted: the larger the documents the better the performance gain should be.
Requires less disk space than term_vectors, needed for the fast_vector_highlighter.
Breaks the text into sentences and highlights them. Uses a BreakIterator to find sentences in the text. Plays really well with natural text, not quite the same if the text contains html markup for instance.
Treats the document as the whole corpus, and scores individual sentences as if they were documents in this corpus, using the BM25 algorithm.
Uses forked version of lucene postings highlighter to support:
- per value discrete highlighting for fields that have multiple values, needed when number_of_fragments=0 since we want to return a snippet per value
- manually passing in query terms to avoid calling extract terms multiple times, since we use a different highlighter instance per doc/field, but the query is always the same
The lucene postings highlighter api is quite different compared to the existing highlighters api, the main difference being that it allows to highlight multiple fields in multiple docs with a single call, ensuring sequential IO.
The way it is introduced in elasticsearch in this first round is a compromise trying not to change the current highlight api, which works per document, per field. The main disadvantage is that we lose the sequential IO, but we can always refactor the highlight api to work with multiple documents.
Supports pre_tag, post_tag, number_of_fragments (0 highlights the whole field), require_field_match, no_match_size, order by score and html encoding.
Closes#3704
You can configure the highlighting api to return an excerpt of a field
even if there wasn't a match on the field.
The FVH makes excerpts from the beginning of the string to the first
boundary character after the requested length or the boundary_max_scan,
whichever comes first. The Plain highlighter makes excerpts from the
beginning of the string to the end of the last token before the requested
length.
Closes#1171
The clear scroll api allows clear all resources associated with a `scroll_id` by deleting the `scroll_id` and its associated SearchContext.
Closes#3657
Chris Simpson
Clinton Gormley
Martijn van Groningen
Adrien Grand
Markus Fischer
Nik Everett
Conrad Pankoff
uboness
Boaz Leskes
Britta Weber
Alexander Reelsen
Simon Willnauer
Shay Banon
Luca Cavanna
Lee Hinman
David Pilato