Removal of the pattern node.addShard() -> calculate weight -> node.removeShard() which is expensive as, beside map lookups, it invalidates caching of precomputed values in ModelNode and ModelIndex. Replaced by adding an additional parameter to the weight function which accounts for the added / removed shard.
Today when dynamically mapping a field that is already defined in another type,
we use the regular dynamic mapping logic and try to copy some settings to avoid
introducing conflicts. However this is quite fragile as we don't deal with every
existing setting. This proposes a different approach that will just reuse the
shared field type.
Close#15568
FunctionScoreQuery should do two things that it doesn't do today:
- propagate the two-phase iterator from the wrapped scorer so that things are
still executed efficiently eg. if a phrase or geo-distance query is wrapped
- filter out docs that don't have a high enough score using two-phase
iteration: this way the score is only checked when everything else matches
While doing these changes, I noticed that minScore was ignored when scores were
not needed and that explain did not take it into account, so I fixed these
issues as well.
This changes a couple of things:
Mappings are truly immutable. Before, each field mapper stored a
MappedFieldTypeReference that was shared across fields that have the same name
across types. This means that a mapping update could have the side-effect of
changing the field type in other types when updateAllTypes is true. This works
differently now: after a mapping update, a new copy of the mappings is created
in such a way that fields across different types have the same MappedFieldType.
See the new Mapper.updateFieldType API which replaces MappedFieldTypeReference.
DocumentMapper is now immutable and MapperService.merge has been refactored in
such a way that if an exception is thrown while eg. lookup structures are being
updated, then the whole mapping update will be aborted. As a consequence,
FieldTypeLookup's checkCompatibility has been folded into copyAndAddAll.
Synchronization was simplified: given that mappings are truly immutable, we
don't need the read/write lock so that no documents can be parsed while a
mapping update is being processed. Document parsing is not performed under a
lock anymore, and mapping merging uses a simple synchronized block.
This adds the required changes/checks so that the build can run on
FreeBSD.
There are a few things that differ between FreeBSD and Linux:
- CPU probes return -1 for CPU usage
- `hot_threads` cannot be supported on FreeBSD
From OpenJDK's `os_bsd.cpp`:
```c++
bool os::is_thread_cpu_time_supported() {
#ifdef __APPLE__
return true;
#else
return false;
#endif
}
```
So this API now returns (for each FreeBSD node):
```
curl -s localhost:9200/_nodes/hot_threads
::: {Devil Hunter Gabriel}{q8OJnKCcQS6EB9fygU4R4g}{127.0.0.1}{127.0.0.1:9300}
hot_threads is not supported on FreeBSD
```
- multicast fails in native `join` method - known bug:
https://bugs.freebsd.org/bugzilla/show_bug.cgi?id=193246
Which causes:
```
1> Caused by: java.net.SocketException: Invalid argument
1> at java.net.PlainDatagramSocketImpl.join(Native Method)
1> at java.net.AbstractPlainDatagramSocketImpl.join(AbstractPlainDatagramSocketImpl.java:179)
1> at java.net.MulticastSocket.joinGroup(MulticastSocket.java:323)
1> at org.elasticsearch.plugin.discovery.multicast.MulticastChannel$Plain.buildMulticastSocket(MulticastChannel.java:309)
```
So these tests are skipped on FreeBSD.
Resolves#15562
In this commit we increase the queue size of the bulk pool in
BulkProcessorRetryIT to make it less sensitive.
As this test case should stress the pool so bulk processor needs to
back off but not so much that the backoff policy will give up at
some point (which is a valid condition), we still keep it below the
default queue size of 50.
Simon Willnauer
Yannick Welsch
Adrien Grand
Simon Willnauer
Jason Tedor
socurites
Robert Muir
Robert Muir
Alexander Reelsen
Lee Hinman
Daniel Mitterdorfer