This commit adds the ability to associate a bit of state with each
individual aggregation.
The aggregation response can be hard to stitch back together without
having a reference to the aggregation request. In many cases this is not
available, many json serializer frameworks cache types globally or have a
static deserialisation override mechanism. In these cases making the
original request available, if at all possible, would be a hack.
The old facets returned `_type` which was just enough metadata to know
what the originating facet type in the request was.
This PR takes `_type` one step further by introducing ANY arbitrary meta
data. This could be further <strike>ab</strike>used for instance by
generic/automated aggregations that include UI state (color information,
thresholds, user input states, etc) per aggregation.
This commit adds a new field to the response of the terms aggregation called
`sum_other_doc_count` which is equal to the sum of the doc counts of the buckets
that did not make it to the list of top buckets. It is typically useful to have
a sector called eg. `other` when using terms aggregations to build pie charts.
Example query and response:
```json
GET test/_search?search_type=count
{
"aggs": {
"colors": {
"terms": {
"field": "color",
"size": 3
}
}
}
}
```
```json
{
[...],
"aggregations": {
"colors": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 4,
"buckets": [
{
"key": "blue",
"doc_count": 65
},
{
"key": "red",
"doc_count": 14
},
{
"key": "brown",
"doc_count": 3
}
]
}
}
}
```
Close#8213
This is functionally equivalent to before, so there should be no
user-visible impact, except I added a NOTE in the docs warning about
the interaction of pagination and rescoring.
Closes#6232Closes#7707
By letting the fetch phase understand the nested docs structure we can serve nested docs as hits.
The `top_hits` aggregation can because of this commit be placed in a `nested` or `reverse_nested` aggregation.
Closes#7164
This change removes the script_type parameter form the Scripted Metric Aggregation and adds support for _file and _id suffixes to the init_script, map_script, combine_script and reduce_script parameters to make defining the source of the script consistent with the other APIs which use the ScriptService
Changes the name of the field in the scripted metrics aggregation from 'aggregation' to 'value' to be more in line with the other metrics aggregations like 'avg'
The terms aggregation can now support sorting on multiple criteria by replacing the sort object with an array or sort object whose order signifies the priority of the sort. The existing syntax for sorting on a single criteria also still works.
Contributes to #6917
Replaces #7588
The terms aggregation can now support sorting on multiple criteria by replacing the sort object with an array or sort object whose order signifies the priority of the sort. The existing syntax for sorting on a single criteria also still works.
Contributes to #6917
Aggregations are collection-wide statistics, which is incompatible with the
collection mode of search_type=SCAN since it doesn't collect all matches on
calls to the search API.
Close#7429
Aggregations are collection-wide statistics so they would always be the same.
In order to save CPU/bandwidth, we can just return them on the first page.
Same as #1642 but for aggregations.
The current implementation of 'date_histogram' does not understand
the `factor` parameter. Since the docs shouldn't raise false hopes,
I removed the section.
Closes#7277
A multi-bucket aggregation where multiple filters can be defined (each filter defines a bucket). The buckets will collect all the documents that match their associated filter.
This aggregation can be very useful when one wants to compare analytics between different criterias. It can also be accomplished using multiple definitions of the single filter aggregation, but here, the user will only need to define the sub-aggregations only once.
Closes#6118
Implements a new Exists API allowing users to do fast exists check on any matched documents for a given query.
This API should be faster then using the Count API as it will:
- early terminate the search execution once any document is found to exist
- return the response as soon as the first shard reports matched documents
closes#6995
This is only applicable when the order is set to _count. The upper bound of the error in the doc count is calculated by summing the doc count of the last term on each shard which did not return the term. The implementation calculates the error by summing the doc count for the last term on each shard for which the term IS returned and then subtracts this value from the sum of the doc counts for the last term from ALL shards.
Closes#6696
Allow users to control document collection termination, if a specified terminate_after number is
set. Upon setting the newly added parameter, the response will include a boolean terminated_early
flag, indicating if the document collection for any shard terminated early.
closes#6876
A new option `prune` has been added to allow users to control phrase suggestion pruning when `collate`
is set. If the new option is set, the phrase suggestion option will contain a boolean `collate_match`
indicating whether the respective result had hits in collation.
CLoses#6927
The newly added collate option will let the user provide a template query/filter which will be executed for every phrase suggestions generated to ensure that the suggestion matches at least one document for the filter/query.
The user can also add routing preference `preference` to route the collate query/filter and additional `params` to inject into the collate template.
Closes#3482
This commit adds the infrastructure to allow pluging in different
measures for computing the significance of a term.
Significance measures can be provided externally by overriding
- SignificanceHeuristic
- SignificanceHeuristicBuilder
- SignificanceHeuristicParser
closes#6561
Percentile Rank Aggregation is the reverse of the Percetiles aggregation. It determines the percentile rank (the proportion of values less than a given value) of the provided array of values.
Closes#6386
A new "breadth_first" results collection mode allows upper branches of aggregation tree to be calculated and then pruned
to a smaller selection before advancing into executing collection on child branches.
Closes#6128
The existing Note about the shorthand suggest syntax was poorly worded and confusing. Please check whether the way I've phrased it now is still correct as to what the shorthand form actually does and doesn't do: the original wording did not provide me enough information to be sure.
Thanks!
The GeoBounds Aggregation is a new single bucket aggregation which outputs the coordinates of a bounding box containing all the points from all the documents passed to the aggregation as well as the doc count. Geobound Aggregation also use a wrap_logitude parameter which specifies whether the resulting bounding box is permitted to overlap the international date line. This option defaults to true.
This aggregation introduces the idea of MetricsAggregation which do not return double values and cannot be used for sorting. The existing MetricsAggregation has been renamed to NumericMetricsAggregation and is a subclass of MetricsAggregation. MetricsAggregations do not store doc counts and do not support child aggregations.
Closes#5634
Because json objects are unordered this also adds an explicit order syntax
that looks like
"highlight": {
"fields": [
{"title":{ /*params*/ }},
{"text":{ /*params*/ }}
]
}
This is not useful for any of the builtin highlighters but will be useful
in plugins.
Closes#4649
Our improvements to t-digest have been pushed upstream and t-digest also got
some additional nice improvements around memory usage and speedups of quantile
estimation. So it makes sense to use it as a dependency now.
This also allows to remove the test dependency on Apache Mahout.
Close#6142
By default More Like This API excludes the queried document from the response.
However, when debugging or when comparing scores across different queries, it
could be useful to have the best possible matched hit. So this option lets users
explicitly specify the desired behavior.
Closes#6067
In the Google Groups forum there appears to be some confusion as to what mlt
does. This documentation update should hopefully help demystifying this
feature, and provide some understanding as to how to use its parameters.
Closes#6092
- Randomized integration tests for the benchmark API.
- Negative tests for cases where the cluster cannot run benchmarks.
- Return 404 on missing benchmark name.
- Allow to specify 'types' as an array in the JSON syntax when describing a benchmark competition.
- Don't record slowest for single-request competitions.
Closes#6003, #5906, #5903, #5904
Significant terms internally maintain a priority queue per shard with a size potentially
lower than the number of terms. This queue uses the score as criterion to determine if
a bucket is kept or not. If many terms with low subsetDF score very high
but the `min_doc_count` is set high, this might result in no terms being
returned because the pq is filled with low frequent terms which are all sorted
out in the end.
This can be avoided by increasing the `shard_size` parameter to a higher value.
However, it is not immediately clear to which value this parameter must be set
because we can not know how many terms with low frequency are scored higher that
the high frequent terms that we are actually interested in.
On the other hand, if there is no routing of docs to shards involved, we can maybe
assume that the documents of classes and also the terms therein are distributed evenly
across shards. In that case it might be easier to not add documents to the pq that have
subsetDF <= `shard_min_doc_count` which can be set to something like
`min_doc_count`/number of shards because we would assume that even when summing up
the subsetDF across shards `min_doc_count` will not be reached.
closes#5998closes#6041
The default precision was way too exact and could lead people to
think that geo context suggestions are not working. This patch now
requires you to set the precision in the mapping, as elasticsearch itself
can never tell exactly, what the required precision for the users
suggestions are.
Closes#5621
By default the date_/histogram returns all the buckets within the range of the data itself, that is, the documents with the smallest values (on which with histogram) will determine the min bucket (the bucket with the smallest key) and the documents with the highest values will determine the max bucket (the bucket with the highest key). Often, when when requesting empty buckets (min_doc_count : 0), this causes a confusion, specifically, when the data is also filtered.
To understand why, let's look at an example:
Lets say the you're filtering your request to get all docs from the last month, and in the date_histogram aggs you'd like to slice the data per day. You also specify min_doc_count:0 so that you'd still get empty buckets for those days to which no document belongs. By default, if the first document that fall in this last month also happen to fall on the first day of the **second week** of the month, the date_histogram will **not** return empty buckets for all those days prior to that second week. The reason for that is that by default the histogram aggregations only start building buckets when they encounter documents (hence, missing on all the days of the first week in our example).
With extended_bounds, you now can "force" the histogram aggregations to start building buckets on a specific min values and also keep on building buckets up to a max value (even if there are no documents anymore). Using extended_bounds only makes sense when min_doc_count is 0 (the empty buckets will never be returned if the min_doc_count is greater than 0).
Note that (as the name suggest) extended_bounds is **not** filtering buckets. Meaning, if the min bounds is higher than the values extracted from the documents, the documents will still dictate what the min bucket will be (and the same goes to the extended_bounds.max and the max bucket). For filtering buckets, one should nest the histogram agg under a range filter agg with the appropriate min/max.
Closes#5224
Significance is related to the changes in document frequency observed between everyday use in the corpus and
frequency observed in the result set. The asciidocs include extensive details on the applications of this feature.
Closes#5146
This aggregation computes unique term counts using the hyperloglog++ algorithm
which uses linear counting to estimate low cardinalities and hyperloglog on
higher cardinalities.
Since this algorithm works on hashes, it is useful for high-cardinality fields
to store the hash of values directly in the index, which is the purpose of
the new `murmur3` field type. This is less necessary on low-cardinality
string fields because the aggregator is smart enough to only compute the hash
once per unique value per segment thanks to ordinals, or on numeric fields
since hashing them is very fast.
Close#5426
================
This commit extends the `CompletionSuggester` by context
informations. In example such a context informations can
be a simple string representing a category reducing the
suggestions in order to this category.
Three base implementations of these context informations
have been setup in this commit.
- a Category Context
- a Geo Context
All the mapping for these context informations are
specified within a context field in the completion
field that should use this kind of information.
Boaz Leskes
Joel Taddei
Clinton Gormley
Veres Lajos
Martijn Laarman
Areek Zillur
Adrien Grand
Brian Kim
Michael McCandless
Sergii Golubev
Andrew O'Brien
Son
Martijn van Groningen
Colin Goodheart-Smithe
mdzor
Jordan Snodgrass
markharwood
Lee Hinman
smayzak
David Pilato
shrinidhichaudhari
londocr
Britta Weber
Konrad Feldmeier
Mpampis Kostas
uboness
Stefan Antoni
Kurt Hurtado
Simon Willnauer
Brian Murphy
Florian Hopf
Andrii Gakhov
Carsten Brandt
Duncan Angus Wilkie
Chris
stephlag
Luke Fender
fransflippo
Jad Naous
javanna
leonardo menezes
Nik Everett
Tiago Alves Macambira
Rashid Khan
Alex Ksikes
Andrew Selden
gabriel-tessier
Audrey
Scott Wilkerson
Malte Schirnacher
Karl Meisterheim
Alexander Reelsen
Hannes Korte
Randy Stauner
rphadake
Florian Schilling