* Default include_type_name to false for get and put mappings.
* Default include_type_name to false for get field mappings.
* Add a constant for the default include_type_name value.
* Default include_type_name to false for get and put index templates.
* Default include_type_name to false for create index.
* Update create index calls in REST documentation to use include_type_name=true.
* Some minor clean-ups around the get index API.
* In REST tests, use include_type_name=true by default for index creation.
* Make sure to use 'expression == false'.
* Clarify the different IndexTemplateMetaData toXContent methods.
* Fix FullClusterRestartIT#testSnapshotRestore.
* Fix the ml_anomalies_default_mappings test.
* Fix GetFieldMappingsResponseTests and GetIndexTemplateResponseTests.
We make sure to specify include_type_name=true during xContent parsing,
so we continue to test the legacy typed responses. XContent generation
for the typeless responses is currently only covered by REST tests,
but we will be adding unit test coverage for these as we implement
each typeless API in the Java HLRC.
This commit also refactors GetMappingsResponse to follow the same appraoch
as the other mappings-related responses, where we read include_type_name
out of the xContent params, instead of creating a second toXContent method.
This gives better consistency in the response parsing code.
* Fix more REST tests.
* Improve some wording in the create index documentation.
* Add a note about types removal in the create index docs.
* Fix SmokeTestMonitoringWithSecurityIT#testHTTPExporterWithSSL.
* Make sure to mention include_type_name in the REST docs for affected APIs.
* Make sure to use 'expression == false' in FullClusterRestartIT.
* Mention include_type_name in the REST templates docs.
This commit changes the format of the `hits.total` in the search response to be an object with
a `value` and a `relation`. The `value` indicates the number of hits that match the query and the
`relation` indicates whether the number is accurate (in which case the relation is equals to `eq`)
or a lower bound of the total (in which case it is equals to `gte`).
This change also adds a parameter called `rest_total_hits_as_int` that can be used in the
search APIs to opt out from this change (retrieve the total hits as a number in the rest response).
Note that currently all search responses are accurate (`track_total_hits: true`) or they don't contain
`hits.total` (`track_total_hits: true`). We'll add a way to get a lower bound of the total hits in a
follow up (to allow numbers to be passed to `track_total_hits`).
Relates #33028
This commit changes the default out-of-the-box configuration for the
number of shards from five to one. We think this will help address a
common problem of oversharding. For users with time-based indices that
need a different default, this can be managed with index templates. For
users with non-time-based indices that find they need to re-shard with
the split API in place they no longer need to resort only to
reindexing.
Since this has the impact of changing the default number of shards used
in REST tests, we want to ensure that we still have coverage for issues
that could arise from multiple shards. As such, we randomize (rarely)
the default number of shards in REST tests to two. This is managed via a
global index template. However, some tests check the templates that are
in the cluster state during the test. Since this template is randomly
there, we need a way for tests to skip adding the template used to set
the number of shards to two. For this we add the default_shards feature
skip. To avoid having to write our docs in a complicated way because
sometimes they might be behind one shard, and sometimes they might be
behind two shards we apply the default_shards feature skip to all docs
tests. That is, these tests will always run with the default number of
shards (one).
Allowing `_doc` as a type will enable users to make the transition to 7.0
smoother since the index APIs will be `PUT index/_doc/id` and `POST index/_doc`.
This also moves most of the documentation to `_doc` as a type name.
Closes#27750Closes#27751
Today we require users to prepare their indices for split operations.
Yet, we can do this automatically when an index is created which would
make the split feature a much more appealing option since it doesn't have
any 3rd party prerequisites anymore.
This change automatically sets the number of routinng shards such that
an index is guaranteed to be able to split once into twice as many shards.
The number of routing shards is scaled towards the default shard limit per index
such that indices with a smaller amount of shards can be split more often than
larger ones. For instance an index with 1 or 2 shards can be split 10x
(until it approaches 1024 shards) while an index created with 128 shards can only
be split 3x by a factor of 2. Please note this is just a default value and users
can still prepare their indices with `index.number_of_routing_shards` for custom
splitting.
NOTE: this change has an impact on the document distribution since we are changing
the hash space. Documents are still uniformly distributed across all shards but since
we are artificually changing the number of buckets in the consistent hashign space
document might be hashed into different shards compared to previous versions.
This is a 7.0 only change.
Removing several occurrences of this typo in the docs and javadocs, seems to be
a common mistake. Corrections turn up once in a while in PRs, better to correct
some of this in one sweep.
The percolator will add a `_percolator_document_slot` field to all percolator
hits to indicate with what document it has matched. This number matches with
the order in which the documents have been specified in the percolate query.
Also improved the support for multiple percolate queries in a search request.
Today if we search across a large amount of shards we hit every shard. Yet, it's quite
common to search across an index pattern for time based indices but filtering will exclude
all results outside a certain time range ie. `now-3d`. While the search can potentially hit
hundreds of shards the majority of the shards might yield 0 results since there is not document
that is within this date range. Kibana for instance does this regularly but used `_field_stats`
to optimize the indexes they need to query. Now with the deprecation of `_field_stats` and it's upcoming removal a single dashboard in kibana can potentially turn into searches hitting hundreds or thousands of shards and that can easily cause search rejections even though the most of the requests are very likely super cheap and only need a query rewriting to early terminate with 0 results.
This change adds a pre-filter phase for searches that can, if the number of shards are higher than a the `pre_filter_shard_size` threshold (defaults to 128 shards), fan out to the shards
and check if the query can potentially match any documents at all. While false positives are possible, a negative response means that no matches are possible. These requests are not subject to rejection and can greatly reduce the number of shards a request needs to hit. The approach here is preferable to the kibana approach with field stats since it correctly handles aliases and uses the correct threadpools to execute these requests. Further it's completely transparent to the user and improves scalability of elasticsearch in general on large clusters.
The created and found fields in index and delete responses became obsolete after the introduction of the result field in index, update and delete responses (#19566).
After deprecating the created and found fields in 5.x (#19633), now they are removed.
Fixes#19630
The `document_type` parameter is no longer required to be specified,
because by default from 6.0 only a single type is allowed. (`index.mapping.single_type` defaults to `true`)
This snapshot has faster range queries on range fields (LUCENE-7828), more
accurate norms (LUCENE-7730) and the ability to use fake term frequencies
(LUCENE-7854).
This adds the `index.mapping.single_type` setting, which enforces that indices
have at most one type when it is true. The default value is true for 6.0+ indices
and false for old indices.
Relates #15613
Hi all,
I was trying to run the percolate examples, but I figured that because of the "type":"keyword" , the code wasn't working.
In the saerch query the "message" : "A new bonsai tree in the office" is a pure string.
I changed it to "text".
Adds `warnings` syntax to the yaml test that allows you to expect
a `Warning` header that looks like:
```
- do:
warnings:
- '[index] is deprecated'
- quotes are not required because yaml
- but this argument is always a list, never a single string
- no matter how many warnings you expect
get:
index: test
type: test
id: 1
```
These are accessible from the docs with:
```
// TEST[warning:some warning]
```
This should help to force you to update the docs if you deprecate
something. You *must* add the warnings marker to the docs or the build
will fail. While you are there you *should* update the docs to add
deprecation warnings visible in the rendered results.
Before the query extraction would have been aborted and the percolator query would be marked as unknown.
This resulted in a situation that these queries always need to be evaluated by the memory index at search time.
By adding support for this query many more percolator query candidate hits can skip the expensive memory index verification step. For example the `match` query parser returns a MatchNoDocsQuery if the query terms are removed by text analysis (lets query text only contained stop words).
Before 5.0 for it was required that the percolator queries were cached in jvm heap as Lucene queries for two reasons:
1) Performance. The percolator evaluated all percolator queries all the time. There was no pre-selecting queries that are likely to match like we have today.
2) Updates made to percolator queries were visible in realtime, Today these changes are visible in near realtime. So updating no longer requires the percolator to have the queries in jvm heap.
So having the percolator queries in jvm heap via the percolator cache is now less attractive. Especially when there are many percolator queries then these queries can consume many GBs of jvm heap.
Removing the percolator cache does make the percolate query slower compared to how the execution time in 5.0.0-alpha1 and alpha2, but it is still faster compared to 2.x and before.
Christoph Büscher
Julie Tibshirani
Jim Ferenczi
Alan Woodward
Jason Tedor
tnsatish
Adrien Grand
Martijn van Groningen
Simon Willnauer
Christoph Büscher
olcbean
dkimdon
Matias Anaya
Clinton Gormley
Nik Everett
antonisppn
Isabel Drost-Fromm
Isabel Drost-Fromm