Documents several parameters missing from the bulk API's response body
docs. Also moves several response-related chunks of text to the response
body section.
Relates to #55237
The ML info endpoint returns the max_model_memory_limit setting
if one is configured. However, it is still possible to create
a job that cannot run anywhere in the current cluster because
no node in the cluster has enough memory to accommodate it.
This change adds an extra piece of information,
limits.effective_max_model_memory_limit, to the ML info
response that returns the biggest model memory limit that could
be run in the current cluster assuming no other jobs were
running.
The idea is that the ML UI will be able to warn users who try to
create jobs with higher model memory limits that their jobs will
not be able to start unless they add a bigger ML node to their
cluster.
Backport of #55529
Adds a "node" field to the response from the following endpoints:
1. Open anomaly detection job
2. Start datafeed
3. Start data frame analytics job
If the job or datafeed is assigned to a node immediately then
this field will return the ID of that node.
In the case where a job or datafeed is opened or started lazily
the node field will contain an empty string. Clients that want
to test whether a job or datafeed was opened or started lazily
can therefore check for this.
Backport of #55473
Adds an example for bulk API requests that include failures.
Also documents guidance on use the `filter_path` parameter
to narrow the bulk API response for errors.
Closes#55237
Removes the 'Testing' chapter from the Elasticsearch Reference guide.
This chapter was originally written for so that users using the Java HLRC client could
use the same test classes when testing Elasticsearch in their own applications.
However, this is no longer the case or recommended.
Closes#55257.
This paves the data layer way so that exceptionally large models are partitioned across multiple documents.
This change means that nodes before 7.8.0 will not be able to use trained inference models created on nodes on or after 7.8.0.
I chose the definition document limit to be 100. This *SHOULD* be plenty for any large model. One of the largest models that I have created so far had the following stats:
~314MB of inflated JSON, ~66MB when compressed, ~177MB of heap.
With the chunking sizes of `16 * 1024 * 1024` its compressed string could be partitioned to 5 documents.
Supporting models 20 times this size (compressed) seems adequate for now.
This commit adds a new querystring parameter on the following APIs:
- Index
- Update
- Bulk
- Create Index
- Rollover
These APIs now support a `?prefer_v2_templates=true|false` flag. This flag changes the preference
creation to use either V2 index templates or V1 templates. This flag defaults to `false` and will be
changed to `true` for 8.0+ in subsequent work.
Additionally, setting this flag internally sets the `index.prefer_v2_templates` index-level setting.
This setting is used so that actions that automatically create a new index (things like rollover
initiated by ILM) will inherit the preference from the original index. This setting is dynamic so
that a transition from v1 to v2 templates can occur for long-running indices grouped by an alias
performing periodic rollover.
This also adds support for sending this parameter to the High Level Rest Client.
Relates to #53101
We believe there's no longer a need to be able to disable basic-license
features completely using the "xpack.*.enabled" settings. If users don't
want to use those features, they simply don't need to use them. Having
such features always available lets us build more complex features that
assume basic-license features are present.
This commit deprecates settings of the form "xpack.*.enabled" for
basic-license features, excluding "security", which is a special case.
It also removes deprecated settings from integration tests and unit
tests where they're not directly relevant; e.g. monitoring and ILM are
no longer disabled in many integration tests.
PR #51260 moved usage counts about mapping field types and analysis to
the `_cluster/stats` API.
This documents those stats in the response section of the cluster stats
API docs.
Implement the use of scalar functions inside aggregate functions.
This allows for complex expressions inside aggregations, with or without
GROUBY as well as with or without a HAVING clause. e.g.:
```
SELECT MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) AS max, b
FROM test
GROUP BY b
HAVING MAX(CASE WHEN a IS NULL then -1 ELSE abs(a * 10) + 1 END) > 5
```
Scalar functions are still not allowed for `KURTOSIS` and `SKEWNESS` as
this is currently not implemented on the ElasticSearch side.
Fixes: #29980Fixes: #36865Fixes: #37271
(cherry picked from commit 506d1beea7abb2b45de793bba2e349090a78f2f9)
The main changes are:
1. Throw an error when updating `include_in_parent` or `include_in_root` attribute of nested field dynamically by the PUT mapping API.
2. Add a test for the change.
Closes#53792
Co-authored-by: bellengao <gbl_long@163.com>
* [DOCS] Reformat `flatten_graph` token filter
Makes the following changes to the `flatten_graph` token filter docs:
* Rewrites description and adds Lucene link
* Adds detailed analyze example
* Adds analyzer example
* Add the change log for 7.7
Add the change log for 7.7
* Update rel. notes to latest state (BC5)
Update the release notes to current state (i.e. BC5).
* Update docs/reference/release-notes/7.7.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
* [ML] adding prediction_field_type to inference config (#55128)
Data frame analytics dynamically determines the classification field type. This field type then dictates the encoded JSON that is written to Elasticsearch.
Inference needs to know about this field type so that it may provide the EXACT SAME predicted values as analytics.
Here is added a new field `prediction_field_type` which indicates the desired type. Options are: `string` (DEFAULT), `number`, `boolean` (where close_to(1.0) == true, false otherwise).
Analytics provides the default `prediction_field_type` when the model is created from the process.
Updates the supported upgrade path table in [Upgrade Elasticsearch][0]
to include a new row for maintenance releases. For example, this row
covers upgrading from 7.6.0 to 7.6.2.
The new table row only displays for releases greater than n.x.0. For
example, the new row will display for the 7.7.1 release but not the
7.7.0 release.
[0]: https://www.elastic.co/guide/en/elasticsearch/reference/master/setup-upgrade.html
Provides basic repository-level stats that will allow us to get some insight into how many
requests are actually being made by the underlying SDK. Currently only tracks GET and LIST
calls for S3 repositories. Most of the code is unfortunately boiler plate to add a new endpoint
that will help us better understand some of the low-level dynamics of searchable snapshots.
With this change, when a task is canceled, the task manager will cancel
not only its direct child tasks but all also its descendant tasks.
Closes#50990
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
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.
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.
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 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.
Lisa Cawley
James Rodewig
David Roberts
István Zoltán Szabó
James Baiera
Paul Sanwald
Benjamin Trent
David Turner
Lee Hinman
jmceniery
Ben Skelker
William Brafford
Andrei Dan
Adrien Grand
markharwood
Marios Trivyzas
Julie Tibshirani
Bogdan Pintea
Jake Landis
Yang Wang
Igor Motov
Yannick Welsch
Nhat Nguyen
Peter Dyson
Vishal Patel
Ioannis Kakavas
Jason Tedor
Nik Everett