Change TransportBroadcastByNodeAction and TransportBroadcastReplicationAction
to be able to resolve data streams by default. Implementations can change this ability.
This change allows to following APIs to resolve data streams: flush,
refresh (already supported data streams), force merge, clear indices cache,
indices stats (already supported data streams), segments, upgrade stats,
upgrade, validate query, searchable snapshots stats, clear searchable snapshots cache and
reload analyzers APIs.
Relates to #53100
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.
Even with changes from #48854 we're still seeing significant (as in tens and hundreds of MB)
buffer usage for bulk exports in some cases which destabilizes master nodes.
Since we need to know the serialized length of the bulk body we can't do the serialization
in a streaming manner. (also it's not easily doable with the HTTP client API we're using anyway).
=> let's at least serialize on heap in compressed form and decompress as we're streaming to the
HTTP connection. For small requests this adds negligible overhead but for large requests this reduces
the size of the payload field by about an order of magnitude (empirically determined) which is a massive reduction in size when considering O(100MB) bulk requests.
We have been using a zero timeout in the case that DF analytics
is stopped. This may cause a timeout when we cancel, for example,
the reindex task.
This commit fixes this by using the default timeout instead.
Backport of #56423
While investigating possible optimizations to speed up searchable
snapshots shard restores, we noticed that Elasticsearch builds the
list of shard files on local disk in order to compare it with the list of
files contained in the snapshot to restore. This list of files is
materialized with a MetadataSnapshot object whose construction
involves to read the footer checksum of every files of the shard
using Store.checksumFromLuceneFile() method.
Further investigation shows that a MetadataSnapshot object is
also created for other types of operations like building the list of
files to recover in a peer recovery (and primary shard relocation)
or in order to assign a shard to a node. These operations use the
Store.getMetadata(IndexCommit) method to build the list of files
and checksums.
In the case of searchable snapshots building the MetadataSnapshot
object can potentially trigger cache misses, which in turn can
cause the download and the writing in cache of the last range of
the file in order to check the 16 bytes footer. This in turn can
cause more evictions.
Since searchable snapshots already contains the footer information
of every file in BlobStoreIndexShardSnapshot it can directly read the
checksum from it and avoid to use the cache at all to create a
MetadataSnapshot for the operations mentioned above.
This commit adds a shortcut to the
SearchableSnapshotDirectory.openInput() method - similarly to what
already exists for segment infos - so that it creates a specific
IndexInput for checksum reading operation.
It is possible that the config document for a data frame
analytics job is deleted from the config index. If that is
the case the user is unable to stop a running job because
we attempt to retrieve the config and that will throw.
This commit changes that. When the request is forced,
we do not expand the requested ids based on the existing
configs but from the list of running tasks instead.
Backport of #56360
Due to multi-threading it is possible that phase progress
updates written from the c++ process arrive reordered.
We can address this by ensuring that progress may only increase.
Closes#56282
Backport of #56339
* Add xpack setting deprecations to deprecation API
The deprecated settings showed up in the deprecation log file by
default, but I did not add them to the deprecation API. This commit
fixes that. Now if you use one of the deprecated basic feature
enablement settings, calling _monitoring/deprecations will inform you of
that fact.
* Remove incorrectly backported settings documents
It seems that I backported these docs to the wrong place in #56061,
in #55980, and in #56167. I hope they're in the right place now.
Co-authored-by: debadair <debadair@elastic.co>
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.
This test sometimes fails when prewarming is enabled because
it's possible that some files are cached in background while the
test tries to clear the cache. This commit disables prewarming
for this test.
* Simplify equals/not-equals TRUE/FALSE expressions, by returning them
as is (TRUE variant) or negating them (FALSE variant)
(cherry picked from commit 17858afbe6da5fa0b3ecfc537cabb337e4baaffe)
Another Jackson release is available. There are some CVEs addressed,
none of which impact us, but since we can now bump Jackson easily, let
us move along with the train to avoid the false positives from security
scanners.
`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.)
Fixes#56164. A minor update in the documentation, API key name is required when creating API key. If the API key name is not provided then the request will fail.
Async search allows users to retrieve partial results for a running search. For partial results, the number of successful shards does not include the skipped shards, while the response returned to users should.
Also, we recently had a bug where async search would miss tracking shard failures, which would have been caught if we had assertions in place that verified that whenever we get the last response, the number of failures included in it is the same as the failures that were tracked through the listener notifications.
A FilterBlobContainer class was introduced in #55952 and it delegates
its behavior to a given BlobContainer while allowing to override
only necessary methods.
This commit replaces the existing BlobContainerWrapper class from
the test framework with the new FilterBlobContainer from core.
If an exception occurs while flushing a bulk the cause of the exception
can be lost. This commit ensures that cause of the exception is carried
forward and gets logged.
* SQL: Add BigDecimal support to JDBC (#56015)
* Introduce BigDecimal support to JDBC -- fetching
This commit adds support for the getBigDecimal() methods.
* Allow BigDecimal params in double range
A prepared statement will now accept a BigDecimal parameter as a proxy
for a double, if the conversion is lossless.
(cherry picked from commit e9a873ad7f387682e3472110b1d7c0514bd347c9)
* Fix compilation error
Dimond notation with anonymous inner classes not avail in Java8.
The incomatible client version test is changed to:
- iterate on all versions prior to the allowed one_s;
- format the exception message just as the server does it.
The defect stemed from the fact that the clients will not send a
version's qualifier, but just major.minor.revision, so the raised
error/exception_message won't contain it, while the test expected it.
(cherry picked from commit 4a81c8f7a1f4573e3be95f346d9fb18772b297ee)
* [ML] lay ground work for handling >1 result indices (#55892)
This commit removes all but one reference to `getInitialResultsIndexName`.
This is to support more than one result index for a single job.
* Introduce a query builder for the rest tests
The new BaseRestSqlTestCase.RequestObjectBuilder class is a helper class
to build REST request objects for the tests. Consequently, "manual" string
concatenation to form JSON is done away with.
The class mimics SqlQueryRequestBuilder API.
(cherry picked from commit c8363f04c029542c233a758e9286d33c51d9c0c4)
this commit adds aggregation support for the geo_shape field
type on geo*_grid aggregations.
it introduces a Tiler for both tiles and hashes that enables a new type of
ValuesSource to replace the GeoPoint's CellIdSource. This makes it possible
for the existing Aggregator to be re-used, so no new implementations of
the grid aggregators are added.
Transforms should propagate up the search execution exception if one is returned when it does the test query.
this allows transforms to return a `4xx` when the aggs are malformed but parseable.
closes https://github.com/elastic/elasticsearch/issues/55994
* Relax version lock between ES/SQL and clients
Allow older-than-server clients to connect, if these are past or on a
certain min release.
(cherry picked from commit 108f907297542ce649aa7304060aaf0a504eb699)
The following settings are now no-ops:
* xpack.flattened.enabled
* xpack.logstash.enabled
* xpack.rollup.enabled
* xpack.slm.enabled
* xpack.sql.enabled
* xpack.transform.enabled
* xpack.vectors.enabled
Since these settings no longer need to be checked, we can remove settings
parameters from a number of constructors and methods, and do so in this
commit.
We also update documentation to remove references to these settings.
This commit changes searchable snapshots so that it now respects the
repository's max_restore_bytes_per_sec setting when it downloads blobs.
Backport of #55952 for 7.x
This PR implements the following changes to make ML model snapshot
retention more flexible in advance of adding a UI for the feature in
an upcoming release.
- The default for `model_snapshot_retention_days` for new jobs is now
10 instead of 1
- There is a new job setting, `daily_model_snapshot_retention_after_days`,
that defaults to 1 for new jobs and `model_snapshot_retention_days`
for pre-7.8 jobs
- For days that are older than `model_snapshot_retention_days`, all
model snapshots are deleted as before
- For days that are in between `daily_model_snapshot_retention_after_days`
and `model_snapshot_retention_days` all but the first model snapshot
for that day are deleted
- The `retain` setting of model snapshots is still respected to allow
selected model snapshots to be retained indefinitely
Backport of #56125
This commit strengthens the assertion about which threads may access a blob
store to exclude the cluster applier thread, since we no longer need to do so.
Relates #50999
As of elastic/ml-cpp#1179, the analytics process reports phases
depending on the analysis type. This commit adjusts the phases
of current analyses from `analyzing` to the following:
- outlier_detection: [`computing_outlier`]
- regression/classification: [`feature_selection`, `coarse_parameter_search`, `fine_tuning_parameters`, `final_training`]
Backport of #56107
Martijn van Groningen
Rene Groeschke
Nik Everett
Mark Vieira
Armin Braun
Dimitris Athanasiou
David Roberts
Tanguy Leroux
Hendrik Muhs
William Brafford
Andrei Stefan
Jason Tedor
Przemysław Witek
Julie Tibshirani
Navneet Kumar
Luca Cavanna
Jake Landis
Bogdan Pintea
Dan Hermann
Benjamin Trent
Ross Wolf
Tal Levy
David Turner