This cleans up a few rough edged in the `variable_width_histogram`,
mostly found by @wwang500:
1. Setting its tuning parameters in an unexpected order could cause the
request to fail.
2. We checked that the maximum number of buckets was both less than
50000 and MAX_BUCKETS. This drops the 50000.
3. Fixes a divide by 0 that can occur of the `shard_size` is 1.
4. Fixes a divide by 0 that can occur if the `shard_size * 3` overflows
a signed int.
5. Requires `shard_size * 3 / 4` to be at least `buckets`. If it is less
than `buckets` we will very consistently return fewer buckets than
requested. For the most part we expect folks to leave it at the
default. If they change it, we expect it to be much bigger than
`buckets`.
6. Allocate a smaller `mergeMap` in when initially bucketing requests
that don't use the entire `shard_size * 3 / 4`. Its just a waste.
7. Default `shard_size` to `10 * buckets` rather than `100`. It *looks*
like that was our intention the whole time. And it feels like it'd
keep the algorithm humming along more smoothly.
8. Default the `initial_buffer` to `min(10 * shard_size, 50000)` like
we've documented it rather than `5000`. Like the point above, this
feels like the right thing to do to keep the algorithm happy.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Moves the highlighting docs from the deprecated 'Request Body Search'
chapter to the new subpage of the 'Run a search chapter' section.
No substantive changes were made to the content.
This request:
```
POST /_search
{
"aggs": {
"a": {
"adjacency_matrix": {
"filters": {
"1": {
"terms": { "t": { "index": "lookup", "id": "1", "path": "t" } }
}
}
}
}
}
}
```
Would fail with a 500 error and a message like:
```
{
"error": {
"root_cause": [
{
"type": "illegal_state_exception",
"reason":"async actions are left after rewrite"
}
]
}
}
```
This fixes that by moving the query rewrite phase from a synchronous
call on the data nodes into the standard aggregation rewrite phase which
can properly handle the asynchronous actions.
Adds an explicit check to `variable_width_histogram` to stop it from
trying to collect from many buckets because it can't. I tried to make it
do so but that is more than an afternoon's project, sadly. So for now we
just disallow it.
Relates to #42035
We're tracking this aggregation's experimental-progress in #58573. We'd
like a little time to be able to make backwards incompatible changes to
the aggregation because we're not 100% sure about the request and
response format yet.
Implements a new histogram aggregation called `variable_width_histogram` which
dynamically determines bucket intervals based on document groupings. These
groups are determined by running a one-pass clustering algorithm on each shard
and then reducing each shard's clusters using an agglomerative
clustering algorithm.
This PR addresses #9572.
The shard-level clustering is done in one pass to minimize memory overhead. The
algorithm was lightly inspired by
[this paper](https://ieeexplore.ieee.org/abstract/document/1198387). It fetches
a small number of documents to sample the data and determine initial clusters.
Subsequent documents are then placed into one of these clusters, or a new one
if they are an outlier. This algorithm is described in more details in the
aggregation's docs.
At reduce time, a
[hierarchical agglomerative clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering)
algorithm inspired by [this paper](https://arxiv.org/abs/1802.00304)
continually merges the closest buckets from all shards (based on their
centroids) until the target number of buckets is reached.
The final values produced by this aggregation are approximate. Each bucket's
min value is used as its key in the histogram. Furthermore, buckets are merged
based on their centroids and not their bounds. So it is possible that adjacent
buckets will overlap after reduction. Because each bucket's key is its min,
this overlap is not shown in the final histogram. However, when such overlap
occurs, we set the key of the bucket with the larger centroid to the midpoint
between its minimum and the smaller bucket’s maximum:
`min[large] = (min[large] + max[small]) / 2`. This heuristic is expected to
increases the accuracy of the clustering.
Nodes are unable to share centroids during the shard-level clustering phase. In
the future, resolving https://github.com/elastic/elasticsearch/issues/50863
would let us solve this issue.
It doesn’t make sense for this aggregation to support the `min_doc_count`
parameter, since clusters are determined dynamically. The `order` parameter is
not supported here to keep this large PR from becoming too complex.
Changes:
* Condenses and relocates the `docvalue_fields` example to the 'Run a search'
page.
* Adds docs for the `docvalue_fields` request body parameter.
* Updates several related xrefs.
Co-authored-by: debadair <debadair@elastic.co>
Per 49554 I added standard deviation sampling and variance sampling to the extended stats interface.
Closes#49554
Co-authored-by: Igor Motov <igor@motovs.org>
* Make it more clear that you can use `month` or `1M`.
* Explain rounding rules
* Consistently use "time zone" instead of "timezone". It looks like both
are right but I see "time zone" much more. And the parameter in
elasticsearch is `time_zone` so we may as well line up.
Closes#56760
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
This aggregation will perform normalizations of metrics
for a given series of data in the form of bucket values.
The aggregations supports the following normalizations
- rescale 0-1
- rescale 0-100
- percentage of sum
- mean normalization
- z-score normalization
- softmax normalization
To specify which normalization is to be used, it can be specified
in the normalize agg's `normalizer` field.
For example:
```
{
"normalize": {
"buckets_path": <>,
"normalizer": "percent"
}
}
```
Closes#51005.
Similar to what the moving function aggregation does, except merging windows of percentiles
sketches together instead of cumulatively merging final metrics
Implements value_count and avg aggregations over Histogram fields as discussed in #53285
- value_count returns the sum of all counts array of the histograms
- avg computes a weighted average of the values array of the histogram by multiplying each value with its associated element in the counts array
Removes an example from the "Document counts are approximate" section of the
terms agg documentation.
As #52377 details, the example was no longer accurate in 7.x or 6.8. Document
counts were more precise than the example presented.
We've opened issue #56025 to discuss re-adding an example later.
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
* Aggs must specify a `field` or `script` (or both)
This adds a validation to VSParserHelper to ensure that a field or
script or both are specified by the user. This is technically
required today already, but throws an exception much deeper
in the agg framework and has a very unintuitive error for the user
(as well as eating more resources instead of failing early)
* Fix StringStats test
* Add yaml test
* Skip test on older versions
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Adds support for filters to T-Test aggregation. The filters can be used to
select populations based on some criteria and use values from the same or
different fields.
Closes#53692
Adds t_test metric aggregation that can perform paired and unpaired two-sample
t-tests. In this PR support for filters in unpaired is still missing. It will
be added in a follow-up PR.
Relates to #53692
* Removes experimental.
* Replaces `"v"` (for value) with `"m"` (for metric).
* Move the note about tiebreaking into the list of limitations of the
sort.
* Explain how you ask for `metrics`.
* Clean up some wording.
* Link to the docs from `top_metrics`.
Closes#51813
This changes the `top_metrics` aggregation to return metrics in their
original type. Since it only supports numerics, that means that dates,
longs, and doubles will come back as stored, with their appropriate
formatter applied.
The `top_metrics` agg is kind of like `top_hits` but it only works on
doc values so it *should* be faster.
At this point it is fairly limited in that it only supports a single,
numeric sort and a single, numeric metric. And it only fetches the "very
topest" document worth of metric. We plan to support returning a
configurable number of top metrics, requesting more than one metric and
more than one sort. And, eventually, non-numeric sorts and metrics. The
trick is doing those things fairly efficiently.
Co-Authored by: Zachary Tong <zach@elastic.co>
The method parameter is not used in the percentile aggs, instead
the method is determined by the presence of `hdr` or `tdigest`
objects.
Relates to #8324
It is fairly common to filter the geo point candidates in
geohash_grid and geotile_grid aggregations according to some
viewable bounding box. This change introduces the option of
specifying this filter directly in the tiling aggregation.
This is even more relevant to `geo_shape` where the bounds will restrict
the shape to be within the bounds
this optional `bounds` parameter is parsed in an equivalent fashion to
the bounds specified in the geo_bounding_box query.
Adds support for the `offset` parameter to the `date_histogram` source
of composite aggs. The `offset` parameter is supported by the normal
`date_histogram` aggregation and is useful for folks that need to
measure things from, say, 6am one day to 6am the next day.
This is implemented by creating a new `Rounding` that knows how to
handle offsets and delegates to other rounding implementations. That
implementation doesn't fully implement the `Rounding` contract, namely
`nextRoundingValue`. That method isn't used by composite aggs so I can't
be sure that any implementation that I add will be correct. I propose to
leave it throwing `UnsupportedOperationException` until I need it.
Closes#48757
If `geo_point fields` are multi-valued, using `geo_centroid` as a
sub-agg to `geohash_grid` could result in centroids outside of bucket
boundaries.
This adds a related warning to the geo_centroid agg docs.
* Docs: Refine note about `after_key`
I was curious about composite aggregations, specifically I wanted to
know how to write a composite aggregation that had all of its buckets
filtered out so you *had* to use the `after_key`. Then I saw that we've
declared composite aggregations not to work with pipelines in #44180. So
I'm not sure you *can* do that any more. Which makes the note about
`after_key` inaccurate. This rejiggers that section of the docs a little
so it is more obvious that you send the `after_key` back to us. And so
it is more obvious that you should *only* use the `after_key` that we
give you rather than try to work it out for yourself.
* Apply suggestions from code review
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Percentile aggregations are non-deterministic. A percentile aggregation
can produce different results even when using the same data.
Based on [this discuss post][0], the non-deterministic property stems
from processes in Lucene that can affect the order in which docs are
provided to the aggregation.
This adds a warning stating that the aggregation is non-deterministic
and what that means.
[0]: https://discuss.elastic.co/t/different-results-for-same-query/111757
Co-authored-by: Daniel Huang <danielhuang@tencent.com>
This is a spinoff of #48130 that generalizes the proposal to allow early termination with the composite aggregation when leading sources match a prefix or the entire index sort specification.
In such case the composite aggregation can use the index sort natural order to early terminate the collection when it reaches a composite key that is greater than the bottom of the queue.
The optimization is also applicable when a query other than match_all is provided. However the optimization is deactivated for sources that match the index sort in the following cases:
* Multi-valued source, in such case early termination is not possible.
* missing_bucket is set to true
The example snippets in the percentile rank agg docs use a test dataset
named `latency`, which is generated from docs/gradle.build.
At some point the dataset and example snippets were updated, but the
text surrounding the snippets was not. This means the text and the
example snippets shown no longer match up.
This corrects that by changing the snippets using /TESTRESPONSE magic comments.
This PR adds a new metric aggregation called string_stats that operates on string terms of a document and returns the following:
min_length: The length of the shortest term
max_length: The length of the longest term
avg_length: The average length of all terms
distribution: The probability distribution of all characters appearing in all terms
entropy: The total Shannon entropy value calculated for all terms
This aggregation has been implemented as an analytics plugin.
This commit removes types entirely from BulkRequest, both as a global
parameter and as individual entries on update/index/delete lines.
Relates to #41059
* Minor improvement to the nested aggregation docs
* The attributes name and resellers.name were rather confusing,
especially since the first one was dynamically mapped and not shown
in the documentation (you had to read the test to see it). This
change introduces a unique name for the nested attribute and adds
the example document to the documentation.
* Change the index name from "index" to something more speaking.
* Update docs/reference/aggregations/bucket/nested-aggregation.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
* Update docs/reference/aggregations/bucket/nested-aggregation.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
* Update docs/reference/aggregations/bucket/nested-aggregation.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
This adds a pipeline aggregation that calculates the cumulative
cardinality of a field. It does this by iteratively merging in the
HLL sketch from consecutive buckets and emitting the cardinality up
to that point.
This is useful for things like finding the total "new" users that have
visited a website (as opposed to "repeat" visitors).
This is a Basic+ aggregation and adds a new Data Science plugin
to house it and future advanced analytics/data science aggregations.
This adjusts the `buckets_path` parser so that pipeline aggs can
select specific buckets (via their bucket keys) instead of fetching
the entire set of buckets. This is useful for bucket_script in
particular, which might want specific buckets for calculations.
It's possible to workaround this with `filter` aggs, but the workaround
is hacky and probably less performant.
- Adjusts documentation
- Adds a barebones AggregatorTestCase for bucket_script
- Tweaks AggTestCase to use getMockScriptService() for reductions and
pipelines. Previously pipelines could just pass in a script service
for testing, but this didnt work for regular aggs. The new
getMockScriptService() method fixes that issue, but needs to be used
for pipelines too. This had a knock-on effect of touching MovFn,
AvgBucket and ScriptedMetric
Introduce shift field to MovingFunction aggregation.
By default, shift = 0. Behavior, in this case, is the same as before.
Increasing shift by 1 moves starting window position by 1 to the right.
To simply include current bucket to the window, use shift = 1
For center alignment (n/2 values before and after the current bucket), use shift = window / 2
For right alignment (n values after the current bucket), use shift = window.
This adds a `rare_terms` aggregation. It is an aggregation designed
to identify the long-tail of keywords, e.g. terms that are "rare" or
have low doc counts.
This aggregation is designed to be more memory efficient than the
alternative, which is setting a terms aggregation to size: LONG_MAX
(or worse, ordering a terms agg by count ascending, which has
unbounded error).
This aggregation works by maintaining a map of terms that have
been seen. A counter associated with each value is incremented
when we see the term again. If the counter surpasses a predefined
threshold, the term is removed from the map and inserted into a cuckoo
filter. If a future term is found in the cuckoo filter we assume it
was previously removed from the map and is "common".
The map keys are the "rare" terms after collection is done.
The date_histogram accepts an interval which can be either a calendar
interval (DST-aware, leap seconds, arbitrary length of months, etc) or
fixed interval (strict multiples of SI units). Unfortunately this is inferred
by first trying to parse as a calendar interval, then falling back to fixed
if that fails.
This leads to confusing arrangement where `1d` == calendar, but
`2d` == fixed. And if you want a day of fixed time, you have to
specify `24h` (e.g. the next smallest unit). This arrangement is very
error-prone for users.
This PR adds `calendar_interval` and `fixed_interval` parameters to any
code that uses intervals (date_histogram, rollup, composite, datafeed, etc).
Calendar only accepts calendar intervals, fixed accepts any combination of
units (meaning `1d` can be used to specify `24h` in fixed time), and both
are mutually exclusive.
The old interval behavior is deprecated and will throw a deprecation warning.
It is also mutually exclusive with the two new parameters. In the future the
old dual-purpose interval will be removed.
The change applies to both REST and java clients.
Adds some validation to prevent duplicate source names from being
used in the composite agg.
Also refactored to use a ConstructingObjectParser and removed the
private ctor and setter for sources, making it mandatory.
This section should be at the same sub-level as other sections in the
auto date-histogram docs, otherwise it is rendered on to another page
and is confusing for users to understand what it's in reference to.
This helps avoid memory issues when computing deep sub-aggregations. Because it
should be rare to use sub-aggregations with significant terms, we opted to always
choose breadth first as opposed to exposing a `collect_mode` option.
Closes#28652.
Nik Everett
James Rodewig
István Zoltán Szabó
Adam Locke
Christos Soulios
David Kyle
James Dorfman
Cris da Rocha
Tal Levy
andrewjohnson2
Igor Motov
Benjamin Trent
Gabriel Petrovay
Gil Raphaelli
Ignacio Vera
AB Prashanth
Zachary Tong
Paweł Krześniak
Lisa Cawley
Anton Dollmaier
Mark Tozzi
Elvis Saravia
Florian Kelbert
Jim Ferenczi
Ignacio Vera
Przemko Robakowski
Ian Danforth
Alan Woodward
Mark Tozzi
Javier Ruiz
markharwood
Philipp Krenn
Zachary Tong
LHearen
Nikita Glashenko
Flavio Pompermaier
Sandeep Kanabar
Paul Sanwald
Jason Tedor
Antonio Matarrese
Ian
Samuel Cifuentes García
Hannes Van De Vreken
Alexander Reelsen