This commit allows to trade precision for memory when storing geo points.
This new field data impl accepts a `precision` parameter that controls the
maximum expected error for storing coordinates. This option can be updated on
a live index with the PUT mapping API.
Default precision is 1cm, which requires 8 bytes per geo-point (50% memory
saving compared to using 2 doubles).
Close#4386
Instead of using the '-f' parameter to start elasticsearch in the
foreground, this is now the default modus.
In order to start elasticsearch in the background, the '-d' parameter
can be used.
Closes#4392
The `text` query was replaced by the `match` query and has been
deprecated for quite a while.
The `field` query should be replaced by a `query_string` query with
the `default_field` specified.
Fixes#4033
This commit changes field data configuration updates so that they are
immediately taken into account for loading new segments. The way it works
is that field data configuration is now cached separately from the field
data cache, meaning that it is now possible to clear the field data
configuration from IndexFieldDataService while the cache will stay around. On
the next time that Elasticsearch will reload field data configuration, it will
check if there is already a cache entry, and reuse it if it exists.
To disable field data loading, all that is required is to change the field
data format to "none" (supported by all field data types) using the update
mapping API. Elasticsearch will then refuse to load field data on any new
segment, but field data which has been loaded on the previous segments will
remain available. So you need to clear the field data cache in order to
reclaim memory (otherwise memory will be reclaimed slower, as segments get
merged).
Close#4430Close#4431
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 adds support for Lucene's SimpleQueryParser by adding a new type
of query called the `simple_query_string`. The `simple_query_string`
query is designed to be able to parse human-entered queries without
throwing any exceptions.
Resolves#4159
In order to be sure that memory mapped lucene directories are working
one can configure the kernel about how many memory mapped areas
a process may have. This setting ensure for the debian and redhat initscripts
as well as the systemd startup, that this setting is set high enough.
Closes#4397
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
Add FieldDataTermsFilter that compares terms out of
the fielddata cache. When filtering on a large
set of terms this filter can be considerably faster
than using a standard lucene terms filter.
Add the "fielddata" execution mode to the
terms filter parser to enable the use of
the new FieldDataTermsFilter.
Add supporting tests and documentation.
Closes#4209
The 'default' / 'standard' analyzer can be a trappy default sicne it filters
english stopwords by default. Yet a default should not be dedicated to a certain language
since elasticsearch is used in many different scenarios where a standard analysis chain
with specialization to english full-text might be rather counter productive.
This commit changes the 'standard' analyzer to use an empty stopword list for indices
that are created from 1.0.0.Beta1 version onwards but will maintain backwards compatibiliy
for older indices.
Closes#3775
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
This allows the RegexpQueryBuilder to be used in span queries
Added tests for all span multi term queries.
Also updated the documentation and removed mentioning of numeric range
queries for span queries (they have to be terms).
Closes#3392
This new API allows to get the mapping for a specific set of fields rather than get the whole index mapping and traverse it.
The fields to be retrieved can be specified by their full path, index name and field name and will be resolved in this order.
In case multiple field match, the first one will be returned.
Since we are now generating the output (rather then fall back to the stored mapping), you can specify `include_defaults`=true on the request to have default values returned.
Closes#3941
The setting causes the upper bound for a range query/filter to be rounded up,
therefore the name `round_ceil` seems to make more sense.
Also this commit removes the redundant fourth parameter to DateMathParser.parse(..)
which was never used.
was: parse(String text, long now, boolean roundUp, boolean upperInclusive)
is now: parse(String text, long now, boolean roundCeil)
closes#3914
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 description of the timeout parameter was worded misleadingly; it implied that the API would wait until the cluster reached the desired level and then stayed at that level for the timeout. I've tweaked the sentence to remove the risk of confusion.
Adrien Grand
Boaz Leskes
Chris Simpson
spenceralger
Spencer Alger
Grégory Quatannens
Clinton Gormley
Alexander Reelsen
Martijn van Groningen
Lee Hinman
Simon Willnauer
Shay Banon
Markus Fischer
Nik Everett
Yousef
Conrad Pankoff
uboness
Andrew Raines
Britta Weber
Jun Ohtani
Matt Weber
Luca Cavanna
Olivier Favre
Igor Motov
Cory G Watson
Karel Minarik
Ben McCann
Dan Everton
Opak Alex
David Pilato
Richard Boulton