Until now we had a cloud-azure plugin which is providing 3 distinct features:
* discovery on Azure
* snapshot/restore on Aure
* SMB store
This commit splits the plugin by feature so people can use either one or the other or both features.
Doc is updated accordingly.
With 2.0, we now bind to `localhost` by default instead of binding to the network card and use its IP address.
When the discovery plugin gets from Azure API the list of nodes that should form the cluster, this list is pinged then. But as each node is bound to `localhost`, ping does not get an answer and the node elects itself as the master node.
Closes#13591
Requesting a million hits, or page 100,000 is always a bad idea, but users
may not be aware of this. This adds a per-index limit on the maximum size +
from that can be requested which defaults to 10,000.
This should not interfere with deep-scrolling.
Closes#9311
* Dropped ScoreType in favour of Lucene's ScoreMode
* Removed `score_type` option from `has_child` and `has_parent` queries in favour for the already existing `score_mode` option.
* Removed the score mode `sum` in favour for the already existing `total` score mode. (`sum` doesn't exist in Lucene's ScoreMode class)
* If `max_children` is set to `0` it now really means that zero children are allowed to match.
Allocation filtering by IP only works today using the node host address. But in some cases, you might want to filter using the publish address which could be different.
Previously the parser could take any Term Vectors request, but this would be
not the case of the builder which would still use MultiGetRequest.Item. This
introduces a new Item class which is used by both the builder and parser.
Beyond that the rest is mostly cleanups such as:
1) Deprecating the ignoreLike methods, in favor to using unlike.
2) Deprecating and renaming MoreLikeThisBuilder#addItem to addLikeItem.
3) Ordering the methods of MoreLikeThisBuilder more logically.
This change is needed for the upcoming query refactoring of MLT.
Closes#13372
This pipeline will calculate percentiles over a set of sibling buckets. This is an exact
implementation, meaning it needs to cache a copy of the series in memory and sort it to determine
the percentiles.
This comes with a few limitations: to prevent serializing data around, only the requested percentiles
are calculated (unlike the TDigest version, which allows the java API to ask for any percentile).
It also needs to store the data in-memory, resulting in some overhead if the requested series is
very large.
David Pilato
Clinton Gormley
Mikko Nylén
Nik Everett
Boaz Leskes
Robert Berg
Thomas Cucchietti
Scott Kingsley Clark
Mark Walkom
Britta Weber
Shaunak Kashyap
Lee Hinman
Shane Connelly
Alexander Pepper
Adrien Grand
Masaru Hasegawa
Paul Echeverri
Alexander Reelsen
Martijn van Groningen
Paul Echeverri
Alex Ksikes
Nicholas Knize
Ryan Ernst
xuzha
Zachary Tong
Robert Muir