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
This change converts the module and plugin parameters
for testClusters to be lazy. Meaning that the values
are not resolved until they are actually used. This
removes the requirement to use project.afterEvaluate to
be able to resolve the bundle artifact.
Note - this does not completely remove the need for afterEvaluate
since it is still needed for the custom resource extension.
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
* [ML] Start gathering and storing inference stats (#53429)
This PR enables stats on inference to be gathered and stored in the `.ml-stats-*` indices.
Each node + model_id will have its own running stats document and these will later be summed together when returning _stats to the user.
`.ml-stats-*` is ILM managed (when possible). So, at any point the underlying index could change. This means that a stats document that is read in and then later updated will actually be a new doc in a new index. This complicates matters as this means that having a running knowledge of seq_no and primary_term is complicated and almost impossible. This is because we don't know the latest index name.
We should also strive for throughput, as this code sits in the middle of an ingest pipeline (or even a query).
The secure_settings_password was never taken into consideration in
the ReloadSecureSettings API. This commit fixes that and adds
necessary REST layer testing. Doing so, it also:
- Allows TestClusters to have a password protected keystore
so that it can be set for tests.
- Adds a parameter to the run task so that elastisearch can
be run with a password protected keystore from source.
The usage of local parameter for GetFieldMappingRequest has been removed from the underlying transport action since v2.0.
This PR deprecates the parameter from rest layer. It will be removed in next major version.
This commits adds a timeout when moving ILM back on to a failed step. In
case the master is struggling with processing the cluster update requests
these ones will expire (as we'll send them again anyway on the next ILM
loop run)
ILM more descriptive source messages for cluster updates
Use the configured ILM step master timeout setting
(cherry picked from commit ff6c5ed16616eadfcddd9c95317d370f0d126583)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
* ILM use Priority.IMMEDIATE for stop ILM cluster update (#54909)
This changes the priority of the cluster state update that stops ILM
altogether to `IMMEDIATE`. We've chosen to change this as it can be useful to
temporarily stop ILM if a cluster is overwhelmed, but a `NORMAL`
priority can see the "stop ILM update" not make it up the tasks queue.
On the same note, we're keeping the `start ILM` cluster update priority
to `NORMAL` on purpose such that we only start `ILM` if the cluster can
handle it.
(cherry picked from commit d67df3a7cd2a8619c2c9efac4dde3ba83271f2fa)
Signed-off-by: Andrei Dan <andrei.dan@elastic.co>
Changes boilerplate sentence of "If using a field as the argument, this
parameter only supports..." to "...this parameter supports only...".
The latter is a bit more clear and readable.
This change makes sure that all internal client requests spawned by the
data frame analytics persistent task executor and that use the end user
security credentials, have the parent task id assigned. The objective here
is to permit auditing (as well as tracking for debugging purposes) of all
the end-user requests executed on its behalf by persistent tasks.
Because data frame analytics taks already implements graceful shutdown
of child tasks, this change does not interfere with it by opting out of
the persistent task cancellation of child tasks.
Relates #54943#52314
Some of these characters are special to Asciidoctor and they ruin the
rendering on this page. Instead, we use a macro to passthrough these
characters without Asciidoctor applying any subtitutions to them. This
commit then addresses some rendering issues in the thread pool docs.
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
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.
We found some problems during the test.
Data: 200Million docs, 1 shard, 0 replica
hits | avg | sum | value_count |
----------- | ------- | ------- | ----------- |
20,000 | .038s | .033s | .063s |
200,000 | .127s | .125s | .334s |
2,000,000 | .789s | .729s | 3.176s |
20,000,000 | 4.200s | 3.239s | 22.787s |
200,000,000 | 21.000s | 22.000s | 154.917s |
The performance of `avg`, `sum` and other is very close when performing
statistics, but the performance of `value_count` has always been poor,
even not on an order of magnitude. Based on some common-sense knowledge,
we think that `value_count` and sum are similar operations, and the time
consumed should be the same. Therefore, we have discussed the agg
of `value_count`.
The principle of counting in es is to traverse the field of each
document. If the field is an ordinary value, the count value is
increased by 1. If it is an array type, the count value is increased
by n. However, the problem lies in traversing each document and taking
out the field, which changes from disk to an object in the Java
language. We summarize its current problems with Elasticsearch as:
- Number cast to string overhead, and GC problems caused by a large
number of strings
- After the number type is converted to string, sorting and other
unnecessary operations are performed
Here is the proof of type conversion overhead.
```
// Java long to string source code, getChars is very time-consuming.
public static String toString(long i) {
int size = stringSize(i);
if (COMPACT_STRINGS) {
byte[] buf = new byte[size];
getChars(i, size, buf);
return new String(buf, LATIN1);
} else {
byte[] buf = new byte[size * 2];
StringUTF16.getChars(i, size, buf);
return new String(buf, UTF16);
}
}
```
test type | average | min | max | sum
------------ | ------- | ---- | ----------- | -------
double->long | 32.2ns | 28ns | 0.024ms | 3.22s
long->double | 31.9ns | 28ns | 0.036ms | 3.19s
long->String | 163.8ns | 93ns | 1921 ms | 16.3s
particularly serious.
Our optimization code is actually very simple. It is to manage different
types separately, instead of uniformly converting to string unified
processing. We added type identification in ValueCountAggregator, and
made special treatment for number and geopoint types to cancel their
type conversion. Because the string type is reduced and the string
constant is reduced, the improvement effect is very obvious.
hits | avg | sum | value_count | value_count | value_count | value_count | value_count | value_count |
| | | double | double | keyword | keyword | geo_point | geo_point |
| | | before | after | before | after | before | after |
----------- | ------- | ------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
20,000 | 38s | .033s | .063s | .026s | .030s | .030s | .038s | .015s |
200,000 | 127s | .125s | .334s | .078s | .116s | .099s | .278s | .031s |
2,000,000 | 789s | .729s | 3.176s | .439s | .348s | .386s | 3.365s | .178s |
20,000,000 | 4.200s | 3.239s | 22.787s | 2.700s | 2.500s | 2.600s | 25.192s | 1.278s |
200,000,000 | 21.000s | 22.000s | 154.917s | 18.990s | 19.000s | 20.000s | 168.971s | 9.093s |
- The results are more in line with common sense. `value_count` is about
the same as `avg`, `sum`, etc., or even lower than these. Previously,
`value_count` was much larger than avg and sum, and it was not even an
order of magnitude when the amount of data was large.
- When calculating numeric types such as `double` and `long`, the
performance is improved by about 8 to 9 times; when calculating the
`geo_point` type, the performance is improved by 18 to 20 times.
We currently create the .async-search index if necessary before performing any action (index, update or delete). Truth is that this is needed only before storing the initial response. The other operations are either update or delete, which will anyways not find the document to update/delete even if the index gets created when missing. This also caused `testCancellation` failures as we were trying to delete the document twice from the .async-search index, once from `TransportDeleteAsyncSearchAction` and once as a consequence of the search task being completed. The latter may be called after the test is completed, but before the cluster is shut down and causing problems to the after test checks, for instance if it happens after all the indices have been cleaned up. It is totally fine to try to delete a response that is no longer found, but not quite so if such call will also trigger an index creation.
With this commit we remove all the calls to createIndexIfNecessary from the update/delete operation, and we leave one call only from storeInitialResponse which is where the index is expected to be created.
Closes#54180
Currently the remote cluster sniff connection process can succeed even
if no connections are opened. This commit fixes this by failing the
connection process if no connections are successfully opened.
This commit adds an explicit test of time zone rewrite on date nanos
field. Today this is working but we need tests to ensure that we don't
break it unintentionally.
Makes query result serialization more robust by propagating possible
IOExceptions that can occur during shard level result serialization to the
caller instead of throwing AssertionError that is not intercepted.
Fixes#54665
We recently cleaned up the use of the word "metadata" across the
codebase. Even more additional uses have trickled in, likely from
in-progress work. This commit cleans up these last few additional
instances.
Relates #54519
The use of available processors, the terminology, and the settings
around it have evolved over time. This commit cleans up some places in
the codes and in the docs to adjust to the current terminology.
Creates a reusable template for token filter reference documentation.
Contributors can make a copy of this template and customize it when
documenting new token filters.
* Prevent putting V2 index template when overlapping with existing template
This change prevents putting V2 index template when it would overlap with existing V2 template
of the same priority
Relates to #53101
Deprecate alternative sequence parameter declaration (with then by)
Disallow lack of time units inside maxspan
Fix#55023
Relate #54680
(cherry picked from commit 201adafba9def1de4bf843760defb9def3394f63)
Implement DATETIME_PARSE(<datetime_str>, <pattern_str>) function
which allows to parse a datetime string according to the specified
pattern into a datetime object. The patterns allowed are those of
java.time.format.DateTimeFormatter.
Relates to #53714
(cherry picked from commit 3febcd8f3cdf9fdda4faf01f23a5f139f38b57e0)
This changes the behavior of aggregations when search is performed
against enough shards to enable "batch reduce" mode. In this case we
force always store aggregations in serialized form rather than a
traditional java reference. This should shrink the memory usage of large
aggregations at the cost of slightly slowing down aggregations where the
coordinating node is also a data node. Because we're only doing this
when there are many shards this is likely to be fairly rare.
As a side effect this lets us add logs for the memory usage of the aggs
buffer:
```
[2020-04-03T17:03:57,052][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [1320->448] max [1320]
[2020-04-03T17:03:57,089][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [1328->448] max [1328]
[2020-04-03T17:03:57,102][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [1328->448] max [1328]
[2020-04-03T17:03:57,103][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [1328->448] max [1328]
[2020-04-03T17:03:57,105][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs final reduction [888] max [1328]
```
These are useful, but you need to keep some things in mind before
trusting them:
1. The buffers are oversized ala Lucene's ArrayUtils. This means that we
are using more space than we need, but probably not much more.
2. Before they are merged the aggregations are inflated into their
traditional Java objects which *probably* take up a lot more space
than the serialized form. That is, after all, the reason why we store
them in serialized form in the first place.
And, just because I can, here is another example of the log:
```
[2020-04-03T17:06:18,731][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [147528->49176] max [147528]
[2020-04-03T17:06:18,750][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [147528->49176] max [147528]
[2020-04-03T17:06:18,809][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [147528->49176] max [147528]
[2020-04-03T17:06:18,827][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs partial reduction [147528->49176] max [147528]
[2020-04-03T17:06:18,829][TRACE][o.e.a.s.SearchPhaseController] [runTask-0] aggs final reduction [98352] max [147528]
```
I got that last one by building a ten shard index with a million docs in
it and running a `sum` in three layers of `terms` aggregations, all on
`long` fields, and with a `batched_reduce_size` of `3`.
Today, we do not clear the recent errors in AutoFollowCoordinator when
we successfully auto-follow indices. This can lead to confusion for the
operators.
`PipelineAggregator`s are only sent across the wire for backwards
compatibility with 7.7.0. `PipelineAggregator` needs to continue to
implement `NamedWriteable` for backwards compatibility but pipeline
aggregations created after 7.7.0 need not implement any of the methods
in that interface because we'll never attempt to call them. So this
creates implementations in `PipelineAggregator` (the base class) that
just throw exceptions.
Instead delete the data streams manually, until client yaml test runners
have been updated to also delete all data streams after each yaml test.
Relates to #53100
This change preserves the task id for internal requests for the `StartDatafeedPersistentTask`.
Task ids are a way to express a relationship between related internal requests.
In this particular case, the task ids are used for debugging and (soon) security auditing,
but not for task cancellation, because there is already a graceful-shutdown of child
internal requests (given a task id) in place.
The ranges in HTTP headers are using inclusive values for start and end of the range.
The math we used was off in so far that start equals end for the range resulted in length `0`
instead of the correct value of `1`.
Closes#54981Closes#54995
* HLRC support for Index Templates V2 (#54838)
* HLRC support for Index Templates V2
This change adds High Level Rest Client support for Index Templates V2.
Relates to #53101
* fixed compilation error
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
This commit includes a number of changes to reduce overall build
configuration time. These optimizations include:
- Removing the usage of the 'nebula.info-scm' plugin. This plugin
leverages jgit to load read various pieces of VCS information. This
is mostly overkill and we have our own minimal implementation for
determining the current commit id.
- Removing unnecessary build dependencies such as perforce and jgit
now that we don't need them. This reduces our classpath considerably.
- Expanding the usage lazy task creation, particularly in our
distribution projects. The archives and packages projects create
lots of tasks with very complex configuration. Avoiding the creation
of these tasks at configuration time gives us a nice boost.
Igor Motov
Jake Landis
James Rodewig
Peter Dyson
Vishal Patel
Benjamin Trent
Ioannis Kakavas
Yang Wang
Andrei Dan
Albert Zaharovits
Jason Tedor
Mark Vieira
Nik Everett
Luca Cavanna
Tim Brooks
Ross Wolf
Jim Ferenczi
Przemysław Witek
oneoneonepig
Dimitrios Liappis
Przemko Robakowski
Costin Leau
Marios Trivyzas
Julie Tibshirani
Nhat Nguyen
István Zoltán Szabó
Martijn van Groningen
Armin Braun
Hendrik Muhs
Andrei Stefan