This wires `auto_date_histogram` into the rounding optimization that I
built in #55559. This is should significantly speed up any
`auto_date_histogram`s with `time_zone`s on them.
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.
* [DOCS] Align with ILM changes.
* Apply suggestions from code review
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: Lee Hinman <dakrone@users.noreply.github.com>
* Incorporated review comments.
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
When an index spans a daylight savings time transition we can't use our
optimization that rewrites the requested time zone to a fixed time zone
and instead we used to fall back to a java.util.time based rounding
implementation. In #55559 we optimized "time unit" rounding. This
optimizes "time interval" rounding.
The java.util.time based implementation is about 1650% slower than the
rounding implementation for a fixed time zone. This replaces it with a
similar optimization that is only about 30% slower than the fixed time
zone. The java.util.time implementation allocates a ton of short lived
objects but the optimized implementation doesn't. So it *might* end up
being faster than the microbenchmarks imply.
Use proper facility for creating temporary index service for the simulation
that does not add itself to the `IndicesService` unnecessarily (breaking an assertion about the
internal consistency of the cluster state and the `IndicesService`).
Closes#56298
Backport of: #56413
Allow cluster health api to resolve data streams and
automatically remove data streams after each test in
test cases extending from `ESIntegTestCase`
Relates to #53100
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
A bug in InternalGeoCentroid#reduce existed that summed up
the aggregation's long-valued counts into a local integer variable.
Since it is definitely possible to reduce more than Integer.MAX points,
this change simply updates that variable to be a long-valued number.
Closes#55992.
Currently, the logging around the SniffConnectionStrategy is limited.
The log messages are inconsistent and sometimes wrong. This commit
cleans up these log message to describe when connections are happening
and what failed if a step fails.
Additionally, this commit enables TRACE logging for a problematic test
(testEnsureWeReconnect).
Currently when a connection closes a new sniff round begins. The
testCollectNodes test closes four transports before triggering the
method to collect the remote nodes. This leads to a race where there are
a number of reasons the collect nodes call might fail. This commit fixes
that issue by changing the test assertion to include a potential failure
condition.
Fixes#55292.
We made a small mistake when breaking out the `ESIntegTestCase`
subclasses that confused eclipse. This makes it happy again. Poor
eclipse!
Relates #55896
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
A recent AWS SDK upgrade has introduced a new source of spurious `WARN` logs
when the security manager prevents access to the user's home directory and
therefore to their shared client configuration. This is actually the behaviour
we want, and it's harmless and handled by the SDK as if the profile config
doesn't exist, so this log message is unnecessary noise. This commit suppresses
this noisy logging by default.
Relates #20313Closes#56333
Changes:
* Moves the document request body parameters for the search API
from the Request body search page to the Search API reference page.
* Relocates a search request body example from the Request body search
page to the Search API reference page.
* Adds a note to any duplicated query and request body parameters.
* 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>
`auto_date_histogram` was returning the incorrect `interval` because
of a combination of two things:
1. When pipeline aggregations rewrote `auto_date_histogram` we reset the
interval to 1. Oops. Fixed that.
2. *Every* bucket aggregation was rewriting its buckets as though there
was a pipeline aggregation even if there aren't any. This is a bit
silly so we skip that too.
Closes#56116
We fail to unregister the child node in registerAndExecute if the parent
task is being canceled. This leads to a bug where a cancel request never
completes.
Closes#55875
Relates #54312
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.
We were logging the cleanup of the snap- and meta- blobs for every snapshot delete
which is needlessly noisy and confusing to users. We should only log actual stale/unexpected
blobs here.
Moving from `5s` to `10s` here because of #56095.
This adds `10s` to the overall runtime of the test which should be
a reasonable tradeoff for stability.
Closes#56095
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.
This commit creates a new gradle plugin to provide a separate task name
and source set for running ESIntegTestCase tests. The only project
converted to use the new plugin in this PR is server, as an example. The
remaining cases in x-pack will be handled in followups.
backport of #55896
* 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.)
Nik Everett
Mark Vieira
debadair
Armin Braun
Jake Landis
Théophile Helleboid - chtitux
Dimitris Athanasiou
Nicole Albee
David Roberts
Martijn van Groningen
Tanguy Leroux
Hendrik Muhs
István Zoltán Szabó
Ryan Ernst
Tal Levy
Tim Brooks
David Turner
James Rodewig
William Brafford
Nhat Nguyen
Andrei Stefan
Jason Tedor
Przemysław Witek
Julie Tibshirani