Today a user can create an index without setting the
index.number_of_replicas setting even though the index metadata requires
that the setting has a value. We do this when creating an index by
explicitly settings index.number_of_replicas to a default value if one
is not provided. However, if a user updates the number of replicas, and
then let wants to return to the default value, they are naturally
inclined to try setting this setting to null, as the agreed upon way to
return a setting to its default. Since the index metadata requires that
this setting has a non-null value, we blow up when a user attempts to
make this change. This is because we are not taking the same action when
updating a setting on an index that we take when create an
index. Namely, we are not explicitly setting index.number_of_replicas if
the request does not carry a value for this setting. This would happen
when nulling the setting, which we want to support. This commit
addresses this by setting index.number_of_replicas to the default if the
value for this setting is null when updating the settings for an index.
Currently the `time_zone` parameter in `query_string` queries gets applied
correctly only when using the range syntax, e.g "date:[2020-01-02 TO
2020-01-05]. When a date field gets searched without explicit range syntax, e.g.
"date:"2020-01-01" we internally create a range query than uses the specified
date as start date and rounds up to the next underspecified units for the end
date (e.g. here 2020-01-01T23:59:59) without considering the `time_zone`
settings. This change adds a check in QueryStringQueryParser to detect this
scenario early where we have access to the time zone information and directly
create a range query using it.
Closes#55813
In a race condition, a search context could remain enlisted in
SearchService when an index is deleted, potentially causing the index
folder to not be cleaned up (for either lengthy searches or scrolls with
timeouts > 30 minutes or if the scroll is kept active).
If a conditional is added to a processor, and that processor fails, and
that processor has an on_failure handler, the full trace of all of the
executed processors may not be displayed in simulate verbose. The
information is correct, but misses displaying some of the steps used
to get there.
This happens because a processor that is conditional processor is a
wrapper around the real processor and a processor with an on_failure
handler is also a wrapper around the processor(s). When decorating for
simulation we treat compound processor specially, but if a compound processor
is wrapped by a conditional processor that compound processor's processors
can be missed for decoration resulting in the missing displayed steps.
The fix to this is to treat the conditional processor specially and
explicitly seperate it from the processor it is wrapping. This requires
us to keep track of 2 processors a possible conditional processor and
the actual processor it may be wrapping.
related: #56004
Two spots that allow for some optimization:
* We are often creating a composite reference of just a single item in
the transport layer => special cased via static constructor to make sure we never do that
* Also removed the pointless case of an empty composite bytes ref
* `ByteBufferReference` is practically always created from a heap buffer these days so there
is no point of dealing with all the bounds checks and extra references to sliced buffers from that
and we can just use the underlying array directly
Today the heap size check warns the user about two issues why they might
care about the heap size check: resize pauses, and if memory locking is
enabled. Yet, we unconditionally make mention of the memory locking
reason, even if memory locking is not enabled. This can confuse some
users, so we adjust the warning about memory locking to only display if
memory locking is enabled.
Backport: #55377
This commit adds the ability to auto create data streams using index templates v2.
Index templates (v2) now have a data_steam field that includes a timestamp field,
if provided and index name matches with that template then a data stream
(plus first backing index) is auto created.
Relates to #53100
Similar to what the moving function aggregation does, except merging windows of percentiles
sketches together instead of cumulatively merging final metrics
Move data stream resolvability test from IndicesOptionsIntegrationIT to DataStreamIT class.
Whether a transport action supports data streams is no longer controlled via indices options.
This adds support for parsing numbers as range keys. They get converted
into a string, but we allow numbers.
While I was there I replaced the parser for `Range` with a
`ConstructingObjectParser` which will automatically add support for "did
you mean" style corrections on errors.
Closes#56402
This commit refactors the following:
* GeoPointFieldMapper and PointFieldMapper to
AbstractPointGeometryFieldMapper derived from AbstractGeometryFieldMapper.
* .setupFieldType moved up to AbstractGeometryFieldMapper
* lucene indexing moved up to AbstractGeometryFieldMapper.parse
* new addStoredFields, addDocValuesFields abstract methods for implementing
stored field and doc values field indexing in the concrete field mappers
This refactor is the next phase for setting up a framework for extending
spatial field mapper functionality in x-pack.
This commit removes the `prefer_v2_templates` flag and setting. This was a brief setting that
allowed specifying whether V1 or V2 template should be used when an index is created. It has been
removed in favor of V2 templates always having priority.
Relates to #53101Resolves#56528
This is not a breaking change because this flag was never in a released version.
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
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.
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.
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.
`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.
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
`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.)
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.
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.
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 arose after a backport where we do not have the nicities of the
Java 11 diamond operator. This commit fixes it by adding the proper type
parameter.
When the index we are validating a query does not exist, we try to send
back a response letting the client know that the index does not
exist. Yet, we accidentally fallthrough into the case that the
validation failed for some other reason. This means that we end up
notifying the channel twice. Sometimes the notification occurs after the
failure has been written out and the channel closed (so the second
invocation leads to a silent failed to write to a closed channel issue),
and sometimes the response does end up in the channel, creating garbled
responses to the client. This commit fixes that issue by avoiding the
fallthrough.
Bogdan Pintea
Ignacio Vera
Jason Tedor
Christoph Büscher
Henning Andersen
Jake Landis
Armin Braun
Martijn van Groningen
Dan Hermann
Ryan Ernst
Nik Everett
Nick Knize
Benjamin Trent
Lee Hinman
zhenxianyimeng
Tanguy Leroux
Tal Levy
Tim Brooks
Nhat Nguyen
Mark Vieira
Julie Tibshirani
Rory Hunter
Mark Tozzi
Igor Motov