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.
Simon Willnauer
Christoph Büscher
Martijn van Groningen
olcbean
dkimdon
Adrien Grand
Jason Tedor
Matias Anaya
Clinton Gormley
Nik Everett
antonisppn
Isabel Drost-Fromm
Isabel Drost-Fromm