At some point, we changed the supported-type test to also catch
assertion errors. This has the side effect of also catching the
`fail()` call inside the try-catch, which silently smothered some
failures.
This modifies the test to throw at the end of the try-catch
block to prevent from accidentally catching itself.
Catching the AssertionError is convenient because there are other locations
that do throw an assertion in tests (due to hitting an assertion
before the exception is thrown) so I think we should keep it around.
Also includes a variety of fixes to other tests which were failing
but being silently smothered.
The searcher was randomly wrapping its reader as slow, parallel, or filtered.
This was causing casting issues in the normalizer tests. By removing the
wrapping, the problem goes away.
Closes#57164
Merging logic is currently split between FieldMapper, with its merge() method, and
MappedFieldType, which checks for merging compatibility. The compatibility checks
are called from a third class, MappingMergeValidator. This makes it difficult to reason
about what is or is not compatible in updates, and even what is in fact updateable - we
have a number of tests that check compatibility on changes in mapping configuration
that are not in fact possible.
This commit refactors the compatibility logic so that it all sits on FieldMapper, and
makes it called at merge time. It adds a new FieldMapperTestCase base class that
FieldMapper tests can extend, and moves the compatibility testing machinery from
FieldTypeTestCase to here.
Relates to #56814
Mapper.Builder currently has some complex generics on it to allow fluent builder
construction. However, the second parameter, a return type from the build() method,
is unnecessary, as we can use covariant return types. This commit removes this second
generic parameter.
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"
}
}
```
Similar to what the moving function aggregation does, except merging windows of percentiles
sketches together instead of cumulatively merging final metrics
Right now all implementations of the `terms` agg allocate a new
`Aggregator` per bucket. This uses a bunch of memory. Exactly how much
isn't clear but each `Aggregator` ends up making its own objects to read
doc values which have non-trivial buffers. And it forces all of it
sub-aggregations to do the same. We allocate a new `Aggregator` per
bucket for two reasons:
1. We didn't have an appropriate data structure to track the
sub-ordinals of each parent bucket.
2. You can only make a single call to `runDeferredCollections(long...)`
per `Aggregator` which was the only way to delay collection of
sub-aggregations.
This change switches the method that builds aggregation results from
building them one at a time to building all of the results for the
entire aggregator at the same time.
It also adds a fairly simplistic data structure to track the sub-ordinals
for `long`-keyed buckets.
It uses both of those to power numeric `terms` aggregations and removes
the per-bucket allocation of their `Aggregator`. This fairly
substantially reduces memory consumption of numeric `terms` aggregations
that are not the "top level", especially when those aggregations contain
many sub-aggregations. It also is a pretty big speed up, especially when
the aggregation is under a non-selective aggregation like
the `date_histogram`.
I picked numeric `terms` aggregations because those have the simplest
implementation. At least, I could kind of fit it in my head. And I
haven't fully understood the "bytes"-based terms aggregations, but I
imagine I'll be able to make similar optimizations to them in follow up
changes.
Rounding dates on a shard that contains a daylight savings time transition
is currently something like 1400% slower than when a shard contains dates
only on one side of the DST transition. And it makes a ton of short lived
garbage. This replaces that implementation with one that benchmarks to
having around 30% overhead instead of the 1400%. And it doesn't generate
any garbage per search hit.
Some background:
There are two ways to round in ES:
* Round to the nearest time unit (Day/Hour/Week/Month/etc)
* Round to the nearest time *interval* (3 days/2 weeks/etc)
I'm only optimizing the first one in this change and plan to do the second
in a follow up. It turns out that rounding to the nearest unit really *is*
two problems: when the unit rounds to midnight (day/week/month/year) and
when it doesn't (hour/minute/second). Rounding to midnight is consistently
about 25% faster and rounding to individual hour or minutes.
This optimization relies on being able to *usually* figure out what the
minimum and maximum dates are on the shard. This is similar to an existing
optimization where we rewrite time zones that aren't fixed
(think America/New_York and its daylight savings time transitions) into
fixed time zones so long as there isn't a daylight savings time transition
on the shard (UTC-5 or UTC-4 for America/New_York). Once I implement
time interval rounding the time zone rewriting optimization *should* no
longer be needed.
This optimization doesn't come into play for `composite` or
`auto_date_histogram` aggs because neither have been migrated to the new
`DATE` `ValuesSourceType` which is where that range lookup happens. When
they are they will be able to pick up the optimization without much work.
I expect this to be substantial for `auto_date_histogram` but less so for
`composite` because it deals with fewer values.
Note: My 30% overhead figure comes from small numbers of daylight savings
time transitions. That overhead gets higher when there are more
transitions in logarithmic fashion. When there are two thousand years
worth of transitions my algorithm ends up being 250% slower than rounding
without a time zone, but java time is 47000% slower at that point,
allocating memory as fast as it possibly can.
`FieldMapper#parseCreateField` accepts the parse context, plus a list of fields
as an output parameter. These fields are immediately added to the document
through `ParseContext#doc()`.
This commit simplifies the signature by removing the list of fields, and having
the mappers add the fields directly to `ParseContext#doc()`. I think this is
nicer for implementors, because previously fields could be added either through
the list, or the context (through `add`, `addWithKey`, etc.)
Backports #55933 to 7.x
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
This commit converts the remaining isXXXAllowed methods to instead of
use isAllowed with a Feature value. There are a couple other methods
that are static, as well as some licensed features that check the
license directly, but those will be dealt with in other followups.
Backports #55826 to 7.x
Modified AggregatorTestCase.searchAndReduce() method so that it returns an empty aggregation result when no documents have been inserted.
Also refactored several aggregation tests so they do not re-implement method AggregatorTestCase.testCase()
Fixes#55824
Implements Sum aggregation over Histogram fields by summing the value of each bucket multiplied by their count as requested in #53285
Backports #55681 to 7.x
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)
Some aggregations, such as the Terms* family, will use an alternate
class to represent unmapped shard results (while the rest of the aggs
use the same object but with some form of "empty" or "nullish" values
to represent unmapped).
This was problematic with AbstractWireSerializingTestCase because it
expects the instanceReader to always match the original class. Instead,
we need to use the NamedWriteable version so that the registry
can be consulted for the proper deserialization reader.
* Add ValuesSource Registry and associated logic (#54281)
* Remove ValuesSourceType argument to ValuesSourceAggregationBuilder (#48638)
* ValuesSourceRegistry Prototype (#48758)
* Remove generics from ValuesSource related classes (#49606)
* fix percentile aggregation tests (#50712)
* Basic thread safety for ValuesSourceRegistry (#50340)
* Remove target value type from ValuesSourceAggregationBuilder (#49943)
* Cleanup default values source type (#50992)
* CoreValuesSourceType no longer implements Writable (#51276)
* Remove genereics & hard coded ValuesSource references from Matrix Stats (#51131)
* Put values source types on fields (#51503)
* Remove VST Any (#51539)
* Rewire terms agg to use new VS registry (#51182)
Also adds some basic AggTestCases for untested code
paths (and boilerplate for future tests once the IT are
converted over)
* Wire Cardinality aggregation to work with the ValuesSourceRegistry (#51337)
* Wire Percentiles aggregator into new VS framework (#51639)
This required a bit of a refactor to percentiles itself. Before,
the Builder would switch on the chosen algo to generate an
algo-specific factory. This doesn't work (or at least, would be
difficult) in the new VS framework.
This refactor consolidates both factories together and introduces
a PercentilesConfig object to act as a standardized way to pass
algo-specific parameters through the factory. This object
is then used when deciding which kind of aggregator to create
Note: CoreValuesSourceType.HISTOGRAM still lives in core, and will
be moved in a subsequent PR.
* Remove generics and target value type from MultiVSAB (#51647)
* fix checkstyle after merge (#52008)
* Plumb ValuesSourceRegistry through to QuerySearchContext (#51710)
* Convert RareTerms to new VS registry (#52166)
* Wire up Value Count (#52225)
* Wire up Max & Min aggregations (#52219)
* ValuesSource refactoring: Wire up Sum aggregation (#52571)
* ValuesSource refactoring: Wire up SigTerms aggregation (#52590)
* Soft immutability for VSConfig (#52729)
* Unmute testSupportedFieldTypes, fix Percentiles/Ranks/Terms tests (#52734)
Also fixes Percentiles which was incorrectly specified to only accept
numeric, but in fact also accepts Boolean and Date (because those are
numeric on master - thanks `testSupportedFieldTypes` for catching it!)
* VS refactoring: Wire up stats aggregation (#52891)
* ValuesSource refactoring: Wire up string_stats aggregation (#52875)
* VS refactoring: Wire up median (MAD) aggregation (#52945)
* fix valuesourcetype issue with constant_keyword field (#53041)x-pack/plugin/rollup/src/main/java/org/elasticsearch/xpack/rollup/job/RollupIndexer.java
this commit implements `getValuesSourceType` for
the ConstantKeyword field type.
master was merged into feature/extensible-values-source
introducing a new field type that was not implementing
`getValuesSourceType`.
* ValuesSource refactoring: Wire up Avg aggregation (#52752)
* Wire PercentileRanks aggregator into new VS framework (#51693)
* Add a VSConfig resolver for aggregations not using the registry (#53038)
* Vs refactor wire up ranges and date ranges (#52918)
* Wire up geo_bounds aggregation to ValuesSourceRegistry (#53034)
This commit updates the geo_bounds aggregation to depend
on registering itself in the ValuesSourceRegistry
relates #42949.
* VS refactoring: convert Boxplot to new registry (#53132)
* Wire-up geotile_grid and geohash_grid to ValuesSourceRegistry (#53037)
This commit updates the geo*_grid aggregations to depend
on registering itself in the ValuesSourceRegistry
relates to the values-source refactoring meta issue #42949.
* Wire-up geo_centroid agg to ValuesSourceRegistry (#53040)
This commit updates the geo_centroid aggregation to depend
on registering itself in the ValuesSourceRegistry.
relates to the values-source refactoring meta issue #42949.
* Fix type tests for Missing aggregation (#53501)
* ValuesSource Refactor: move histo VSType into XPack module (#53298)
- Introduces a new API (`getBareAggregatorRegistrar()`) which allows plugins to register aggregations against existing agg definitions defined in Core.
- This moves the histogram VSType over to XPack where it belongs. `getHistogramValues()` still remains as a Core concept
- Moves the histo-specific bits over to xpack (e.g. the actual aggregator logic). This requires extra boilerplate since we need to create a new "Analytics" Percentile/Rank aggregators to deal with the histo field. Doubly-so since percentiles/ranks are extra boiler-plate'y... should be much lighter for other aggs
* Wire up DateHistogram to the ValuesSourceRegistry (#53484)
* Vs refactor parser cleanup (#53198)
Co-authored-by: Zachary Tong <polyfractal@elastic.co>
Co-authored-by: Zachary Tong <zach@elastic.co>
Co-authored-by: Christos Soulios <1561376+csoulios@users.noreply.github.com>
Co-authored-by: Tal Levy <JubBoy333@gmail.com>
* First batch of easy fixes
* Remove List.of from ValuesSourceRegistry
Note that we intend to have a follow up PR dealing with the mutability
of the registry, so I didn't even try to address that here.
* More compiler fixes
* More compiler fixes
* More compiler fixes
* Precommit is happy and so am I
* Add new Core VSTs to tests
* Disabled supported type test on SigTerms until we can backport it's fix
* fix checkstyle
* Fix test failure from semantic merge issue
* Fix some metaData->metadata replacements that got lost
* Fix list of supported types for MinAggregator
* Fix list of supported types for Avg
* remove unused import
Co-authored-by: Zachary Tong <polyfractal@elastic.co>
Co-authored-by: Zachary Tong <zach@elastic.co>
Co-authored-by: Christos Soulios <1561376+csoulios@users.noreply.github.com>
Co-authored-by: Tal Levy <JubBoy333@gmail.com>
Today we pass the `RepositoriesService` to the searchable snapshots plugin
during the initialization of the `RepositoryModule`, forcing the plugin to be a
`RepositoryPlugin` even though it does not implement any repositories.
After discussion we decided it best for now to pass this in via
`Plugin#createComponents` instead, pending some future work in which plugins
can depend on services more dynamically.
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
A small follow-up to #54910. Now that we can generated consistent set of
internal aggs to reduce, we no longer need to keep agg parameters as class
variables.
Related to #54910
We added a fancy method to provide random realistic test data to the
reduction tests in #54910. This uses that to remove some of the more
esoteric machinations in the agg tests. This will marginally increase
the coverage of the serialiation tests and, more importantly, remove
some mysterious value generation code that only really made sense for
random reduction tests but was used all over the place. It doesn't, on
the other hand, make the tests shorter. Just *hopefully* more clear.
I only cleaned up a few tests this way. If we like this it'd probably be
worth grabbing others.
This removes pipeline aggregators from the aggregation result tree
except for a single field used for backwards compatibility with pre-7.8
versions of Elasticsearch. That field isn't populated unless we are
serializing to pre-7.8 Elasticsearch. So, good news! We no longer build
pipeline aggregators on the data node. Most of the time.
This allows subclasses of `InternalAggregationTestCase` to make a `List`
of values to reduce so that it can make values that are realistic
*together*. The first use of this is with `InternalTTest` which uses it
to make results that don't cause their `sum` field to wrap. It'd likely
be useful for a ton of other aggs but just one for now.
`testReduceRandom` was bumping up against the serialization that I added
in #54776. This makes it use random values that reduce in ways that
don't cause the randomized serialization to fail.
`scripted_metric` did not work with cross cluster search because it
assumed that you'd never perform a partial reduction, serialize the
results, and then perform a final reduction. That
serialized-after-partial-reduction step was broken.
This is also required to support #54758.
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 pipeline aggregations from the aggregation result tree as they
are no longer used. This stops us from building the pipeline aggregators
at all on data nodes except for backwards compatibility serialization.
This will save a tiny bit of space in the aggregation tree which is
lovely, but the biggest benefit is that it is a step towards simplifying
pipeline aggregators.
This only does about half of the work to remove the pipeline aggs from
the tree. Removing all of it would, well, double the size of the change
and make it harder to review.
- Consolidates HDR/TDigest factories into a single factory
- Consolidates most HDR/TDigest builder into an abstract builder
- Deprecates method(), compression(), numSigFig() in favor of a new
unified PercentileConfig object
- Disallows setting algo options that don't apply to current algo
The unified config method carries both the method and algo-specific
setting. This provides a mechanism to reject settings that apply
to the wrong algorithm. For BWC the old methods are retained
but marked as deprecated, and can be removed in future versions.
Co-authored-by: Mark Tozzi <mark.tozzi@gmail.com>
Co-authored-by: Mark Tozzi <mark.tozzi@gmail.com>
This is a follow up to a previous commit that renamed MetaData to
Metadata in all of the places. In that commit in master, we renamed
META_DATA to METADATA, but lost this on the backport. This commit
addresses that.
This is a simple naming change PR, to fix the fact that "metadata" is a
single English word, and for too long we have not followed general
naming conventions for it. We are also not consistent about it, for
example, METADATA instead of META_DATA if we were trying to be
consistent with MetaData (although METADATA is correct when considered
in the context of "metadata"). This was a simple find and replace across
the code base, only taking a few minutes to fix this naming issue
forever.
* Comprehensively test supported/unsupported field type:agg combinations (#52493)
This adds a test to AggregatorTestCase that allows us to programmatically
verify that an aggregator supports or does not support a particular
field type. It fetches the list of registered field type parsers,
creates a MappedFieldType from the parser and then attempts to run
a basic agg against the field.
A supplied list of supported VSTypes are then compared against the
output (success or exception) and suceeds or fails the test accordingly.
Co-Authored-By: Mark Tozzi <mark.tozzi@gmail.com>
* Skip fields that are not aggregatable
* Use newIndexSearcher() to avoid incompatible readers (#52723)
Lucene's `newSearcher()` can generate readers like ParallelCompositeReader
which we can't use. We need to instead use our helper `newIndexSearcher`
Pipeline aggregations like `stats_bucket`, `sum_bucket`, and
`percentiles_bucket` only operate on buckets that have multiple buckets.
This adds support for those aggregations to `geo_distance`, `ip_range`,
`auto_date_histogram`, and `rare_terms`.
This all happened because we used a marker interface to mark compatible
aggs, `MultiBucketAggregationBuilder` and it was fairly easy to forget
to implement the interface.
This replaces the marker interface with an abstract method in
`AggregationBuilder`, `bucketCardinality` which makes you return `NONE`,
`ONE`, or `MANY`. The `bucket` aggregations can check for `MANY`. At
this point `ONE` and `NONE` amount to about the same thing, but I
suspect that'll be a useful distinction when validating bucket sorts.
Closes#53215
This moves the pipeline aggregation validation from the data node to the
coordinating node so that we, eventually, can stop sending pipeline
aggregations to the data nodes entirely. In fact, it moves it into the
"request validation" stage so multiple errors can be accumulated and
sent back to the requester for the entire request. We can't always take
advantage of that, but it'll be nice for folks not to have to play
whack-a-mole with validation.
This is implemented by replacing `PipelineAggretionBuilder#validate`
with:
```
protected abstract void validate(ValidationContext context);
```
The `ValidationContext` handles the accumulation of validation failures,
provides access to the aggregation's siblings, and implements a few
validation utility methods.
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.
This moves the usage statistics gathering from the `AnalyticsPlugin`
into an `AnalyicsUsage`, removing the static state. It also checks the
license level when parsing all analytics aggregations. This is how we
were checking them before but we did it in an easy to forget way. This
way is slightly simpler, I think.
We've pretty well settled on `ContextParser` for a generic interface to
`ObjectParser`-like-things. This switches the interface used for
building parsing pipeline aggregations to `ContextParser` which saves a
couple of little wrappers around `ObjectParser`.
The top_metrics test assumed that it'd never end up *only* reducing
unmapped results. But, rarely, it does. This handles that case in the
test.
Closes#52462
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>
Mark Tozzi
Zachary Tong
Tal Levy
Alan Woodward
Ignacio Vera
Nik Everett
Julie Tibshirani
Christos Soulios
Ryan Ernst
Igor Motov
Tanguy Leroux
David Turner
Jason Tedor
Alan Woodward