Most of our field types have the same implementation for their `existsQuery` method which relies on doc_values if present, otherwise it queries norms if available or uses a term query against the _field_names meta field. This standard implementation is repeated in many different mappers.
There are field types that only query doc_values, because they always have them, and field types that always query _field_names, because they never have norms nor doc_values. We could apply the same standard logic to all of these field types as `MappedFieldType` has the knowledge about what data structures are available.
This commit introduces a standard implementation that does the right thing depending on the data structure that is available. With that only field types that require a different behaviour need to override the existsQuery method.
At the same time, this no longer forces subclasses to override `existsQuery`, which could be forgotten when needed. To address this we introduced a new test method in `MapperTestCase` that verifies the `existsQuery` being generated and its consistency with the available data structures.
This PR adds a new 'version' field type that allows indexing string values
representing software versions similar to the ones defined in the Semantic
Versioning definition (semver.org). The field behaves very similar to a
'keyword' field but allows efficient sorting and range queries that take into
accound the special ordering needed for version strings. For example, the main
version parts are sorted numerically (ie 2.0.0 < 11.0.0) whereas this wouldn't
be possible with 'keyword' fields today.
Valid version values are similar to the Semantic Versioning definition, with the
notable exception that in addition to the "main" version consiting of
major.minor.patch, we allow less or more than three numeric identifiers, i.e.
"1.2" or "1.4.6.123.12" are treated as valid too.
Relates to #48878
This implements the `fields` API in `_search` for runtime fields using
doc values. Most of that implementation is stolen from the
`docvalue_fields` fetch sub-phase, just moved into the same API that the
`fields` API uses. At this point the `docvalue_fields` fetch phase looks
like a special case of the `fields` API.
While I was at it I moved the "which doc values sub-implementation
should I use for fetching?" question from a bunch of `instanceof`s to a
method on `LeafFieldData` so we can be much more flexible with what is
returned and we're not forced to extend certain classes just to make the
fetch phase happy.
Relates to #59332
This commit removes `integTest` task from all es-plugins.
Most relevant projects have been converted to use yamlRestTest, javaRestTest,
or internalClusterTest in prior PRs.
A few projects needed to be adjusted to allow complete removal of this task
* x-pack/plugin - converted to use yamlRestTest and javaRestTest
* plugins/repository-hdfs - kept the integTest task, but use `rest-test` plugin to define the task
* qa/die-with-dignity - convert to javaRestTest
* x-pack/qa/security-example-spi-extension - convert to javaRestTest
* multiple projects - remove the integTest.enabled = false (yay!)
related: #61802
related: #60630
related: #59444
related: #59089
related: #56841
related: #59939
related: #55896
We frequently use `long`s with `BitArray` in aggs and right now we have
to assert that the `long` fits in an `int`. This adds support for `long`
to `BitArray` so we don't need those assertions.
Runtime fields need to have a SearchLookup available, when building their fielddata implementations, so that they can look up other fields, runtime or not.
To achieve that, we add a Supplier<SearchLookup> argument to the existing MappedFieldType#fielddataBuilder method.
As we introduce the ability to look up other fields while building fielddata for mapped fields, we implicitly add the ability for a field to require other fields. This requires some protection mechanism that detects dependency cycles to prevent stack overflow errors.
With this commit we also introduce detection for cycles, as well as a limit on the depth of the references for a runtime field. Note that we also plan on introducing cycles detection at compile time, so the runtime cycles detection is a last resort to prevent stack overflow errors but we hope that we can reject runtime fields from being registered in the mappings when they create a cycle in their definition.
Note that this commit does not introduce any production implementation of runtime fields, but is rather a pre-requisite to merge the runtime fields feature branch.
This is a breaking change for MapperPlugins that plug in a mapper, as the signature of MappedFieldType#fielddataBuilder changes from taking a single argument (the index name), to also accept a Supplier<SearchLookup>.
Relates to #59332
Co-authored-by: Nik Everett <nik9000@gmail.com>
Replaces the superclass of the test for `HistogramFieldMapperTests` with
one that doesn't extend `ESSingleNodeTestCase` so we don't depend on the
entire world to test the field mapper.
Continues #61301.
Before when a value was copied to a field through a parent field or `copy_to`,
we parsed it using the `FieldMapper` from the source field. Instead we should
parse it using the target `FieldMapper`. This ensures that we apply the
appropriate mapping type and options to the copied value.
To implement the fix cleanly, this PR refactors the value parsing strategy. Now
instead of looking up values directly, field mappers produce a helper object
`ValueFetcher`. The value fetchers are responsible for almost all aspects of
fetching, including looking up the right paths in the _source.
The PR is fairly big but each commit can be reviewed individually.
Fixes#61033.
This commit removes the ability to test the top level result of an aggregator
before it runs the final reduce. All aggregator tests that use AggregatorTestCase#search
are rewritten with AggregatorTestCase#searchAndReduce in order to ensure that we test
the final output (the one sent to the end user) rather than an intermediary result
that could be different.
This change also removes spurious commits triggered on top of a random index writer.
These commits slow down the tests and are redundant with the commits that the
random index writer performs.
This commit does three things:
* Removes all Copyright/license headers for the build.gradle files under x-pack. (implicit Apache license)
* Removes evaluationDependsOn(xpackModule('core')) from build.gradle files under x-pack
* Removes a place holder test in favor of disabling the test task (in the async plugin)
This feature adds a new `fields` parameter to the search request, which
consults both the document `_source` and the mappings to fetch fields in a
consistent way. The PR merges the `field-retrieval` feature branch.
Addresses #49028 and #55363.
We never used the `IndexSettings` parameter and we only used the
`MappedFieldType` parameter to get the name of the field which we
already know everywhere where we build the `IFD.Builder`. This allows us
to drop a fair bit of ceremony from a couple of tests.
Adds a hard_bounds parameter to explicitly limit the buckets that a histogram
can generate. This is especially useful in case of open ended ranges that can
produce a very large number of buckets.
With the removal of mapping types and the immutability of FieldTypeLookup in #58162, we no longer
have any cause to compare MappedFieldType instances. This means that we can remove all equals
and hashCode implementations, and in addition we no longer need the clone implementations which
were required for equals/hashcode testing. This greatly simplifies implementing new MappedFieldTypes,
which will be particularly useful for the runtime fields project.
The FieldMapper infrastructure currently has a bunch of shared parameters, many of which
are only applicable to a subset of the 41 mapper implementations we ship with. Merging,
parsing and serialization of these parameters are spread around the class hierarchy, with
much repetitive boilerplate code required. It would be much easier to reason about these
things if we could declare the parameter set of each FieldMapper directly in the implementing
class, and share the parsing, merging and serialization logic instead.
This commit is a first effort at introducing a declarative parameter style. It adds a new FieldMapper
subclass, ParametrizedFieldMapper, and refactors two mappers, Boolean and Binary, to use it.
Parameters are declared on Builder classes, with the declaration including the parameter name,
whether or not it is updateable, a default value, how to parse it from mappings, and how to
extract it from another mapper at merge time. Builders have a getParameters method, which
returns a list of the declared parameters; this is then used for parsing, merging and serialization.
Merging is achieved by constructing a new Builder from the existing Mapper, and merging in
values from the merging Mapper; conflicts are all caught at this point, and if none exist then a new,
merged, Mapper can be built from the Builder. This allows all values on the Mapper to be final.
Other mappers can be gradually migrated to this new style, and once they have all been refactored
we can merge ParametrizedFieldMapper and FieldMapper entirely.
This makes a `parentCardinality` available to every `Aggregator`'s ctor
so it can make intelligent choices about how it collects bucket values.
This replaces `collectsFromSingleBucket` and is similar to it but:
1. It supports `NONE`, `ONE`, and `MANY` values and is generally
extensible if we decide we can use more precise counts.
2. It is more accurate. `collectsFromSingleBucket` assumed that all
sub-aggregations live under multi-bucket aggregations. This is
normally true but `parentCardinality` is properly carried forward
for single bucket aggregations like `filter` and for multi-bucket
aggregations configured in single-bucket for like `range` with a
single range.
While I was touching every aggregation I renamed `doCreateInternal` to
`createMapped` because that seemed like a much better name and it was
right there, next to the change I was already making.
Relates to #56487
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Working through a heap dump for an unrelated issue I found that we can easily rack up
tens of MBs of duplicate empty instances in some cases.
I moved to a static constructor to guard against that in all cases.
The checks on the license state have a singular method, isAllowed, that
returns whether the given feature is allowed by the current license.
However, there are two classes of usages, one which intends to actually
use a feature, and another that intends to return in telemetry whether
the feature is allowed. When feature usage tracking is added, the latter
case should not count as a "usage", so this commit reworks the calls to
isAllowed into 2 methods, checkFeature, which will (eventually) both
check whether a feature is allowed, and keep track of the last usage
time, and isAllowed, which simply determines whether the feature is
allowed.
Note that I considered having a boolean flag on the current method, but
wanted the additional clarity that a different method name provides,
versus a boolean flag which is more easily copied without realizing what
the flag means since it is nameless in call sites.
* Replace compile configuration usage with api (#58451)
- Use java-library instead of plugin to allow api configuration usage
- Remove explicit references to runtime configurations in dependency declarations
- Make test runtime classpath input for testing convention
- required as java library will by default not have build jar file
- jar file is now explicit input of the task and gradle will ensure its properly build
* Fix compile usages in 7.x branch
Now that MappedFieldType no longer extends lucene's FieldType, we need to have a
way of getting the index information about a field necessary for building text queries,
building term vectors, highlighting, etc. This commit introduces a new TextSearchInfo
abstraction that holds this information, and a getTextSearchInfo() method to
MappedFieldType to make it available. Field types that do not support text search can
just return null here.
This allows us to remove the MapperService.getLuceneFieldType() shim method.
Fixes a bug in TextFieldMapper serialization when index is false, and adds a
base-class test to ensure that all field mappers are tested against all variations
with defaults both included and excluded.
Fixes#58188
This is currently used to set the indexVersionCreated parameter on FieldMapper.
However, this parameter is only actually used by two implementations, and clutters
the API considerably. We should just remove it, and use it directly in the
implementations that require it.
MappedFieldType is a combination of two concerns:
* an extension of lucene's FieldType, defining how a field should be indexed
* a set of query factory methods, defining how a field should be searched
We want to break these two concerns apart. This commit is a first step to doing this, breaking
the inheritance relationship between MappedFieldType and FieldType. MappedFieldType
instead has a series of boolean flags defining whether or not the field is searchable or
aggregatable, and FieldMapper has a separate FieldType passed to its constructor defining
how indexing should be done.
Relates to #56814
* Remove usage of deprecated testCompile configuration
* Replace testCompile usage by testImplementation
* Make testImplementation non transitive by default (as we did for testCompile)
* Update CONTRIBUTING about using testImplementation for test dependencies
* Fail on testCompile configuration usage
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.
Luca Cavanna
Christoph Büscher
Christos Soulios
Ignacio Vera
Nik Everett
Jake Landis
Igor Motov
Julie Tibshirani
Mark Tozzi
Jim Ferenczi
Alan Woodward
Armin Braun
Ryan Ernst
Rene Groeschke
Zachary Tong
Tal Levy