Today `GET _nodes/stats/fs` includes `{least,most}_usage_estimate`
fields for some nodes. These fields have rather strange semantics. They
are only reported on the elected master and on nodes that have been the
elected master since they were last restarted; when a node stops being
the elected master these stats remain in place but we stop updating them
so they may become arbitrarily stale.
This means that these statistics are pretty meaningless and impossible
to use correctly. Even if they were kept up to date they're never
reported for data-only nodes anyway, despite the fact that data nodes
are the ones where we care most about disk usage. The information needed
to compute the path with the least/most available space is already
provided in the rest the stats output, so we can treat the inclusion of
these stats as a bug and fix it by simply removing them in this commit.
Since these stats were always optional and mostly omitted (for opaque
reasons) this is not considered a breaking change.
Backport of #58898.
Part of #48366. Now that there is a dedicated API for dangling indices, the auto-import
behaviour can default to off. Also add a note to the breaking changes for 7.9.0.
This commit increases the default write queue size to 10000. This is to
allow a greater number of pending indexing requests. This work is safe
as we have added additional memory limits. Relates to #59263.
Restoring from a snapshot (which is a particular form of recovery) does not currently take recovery throttling into account
(i.e. the `indices.recovery.max_bytes_per_sec` setting). While restores are subject to their own throttling (repository
setting `max_restore_bytes_per_sec`), this repository setting does not allow for values to be configured differently on a
per-node basis. As restores are very similar in nature to peer recoveries (streaming bytes to the node), it makes sense to
configure throttling in a single place.
The `max_restore_bytes_per_sec` setting is also changed to default to unlimited now, whereas previously it was set to
`40mb`, which is the current default of `indices.recovery.max_bytes_per_sec`). This means that no behavioral change
will be observed by clusters where the recovery and restore settings were not adapted.
Relates https://github.com/elastic/elasticsearch/issues/57023
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: James Rodewig <james.rodewig@elastic.co>
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
Co-authored-by: Tim Vernum <tim@adjective.org>
Co-authored-by: lcawl <lcawley@elastic.co>
The create index action name (`indices:admin/create`) can no longer be used to grant privileges to auto create indices and instead the `create_index` builtin privilege should be used.
Relates to #55858
Co-authored-by: Jake Landis <jake.landis@elastic.co>
The old description mentions a setting that we ended up not merging.
The periodic real-memory checks are automatic and do not require
the user to configure any setting.
* Add the change log for 7.7
Add the change log for 7.7
* Update rel. notes to latest state (BC5)
Update the release notes to current state (i.e. BC5).
* Update docs/reference/release-notes/7.7.asciidoc
Co-Authored-By: James Rodewig <james.rodewig@elastic.co>
We found some problems during the test.
Data: 200Million docs, 1 shard, 0 replica
hits | avg | sum | value_count |
----------- | ------- | ------- | ----------- |
20,000 | .038s | .033s | .063s |
200,000 | .127s | .125s | .334s |
2,000,000 | .789s | .729s | 3.176s |
20,000,000 | 4.200s | 3.239s | 22.787s |
200,000,000 | 21.000s | 22.000s | 154.917s |
The performance of `avg`, `sum` and other is very close when performing
statistics, but the performance of `value_count` has always been poor,
even not on an order of magnitude. Based on some common-sense knowledge,
we think that `value_count` and sum are similar operations, and the time
consumed should be the same. Therefore, we have discussed the agg
of `value_count`.
The principle of counting in es is to traverse the field of each
document. If the field is an ordinary value, the count value is
increased by 1. If it is an array type, the count value is increased
by n. However, the problem lies in traversing each document and taking
out the field, which changes from disk to an object in the Java
language. We summarize its current problems with Elasticsearch as:
- Number cast to string overhead, and GC problems caused by a large
number of strings
- After the number type is converted to string, sorting and other
unnecessary operations are performed
Here is the proof of type conversion overhead.
```
// Java long to string source code, getChars is very time-consuming.
public static String toString(long i) {
int size = stringSize(i);
if (COMPACT_STRINGS) {
byte[] buf = new byte[size];
getChars(i, size, buf);
return new String(buf, LATIN1);
} else {
byte[] buf = new byte[size * 2];
StringUTF16.getChars(i, size, buf);
return new String(buf, UTF16);
}
}
```
test type | average | min | max | sum
------------ | ------- | ---- | ----------- | -------
double->long | 32.2ns | 28ns | 0.024ms | 3.22s
long->double | 31.9ns | 28ns | 0.036ms | 3.19s
long->String | 163.8ns | 93ns | 1921 ms | 16.3s
particularly serious.
Our optimization code is actually very simple. It is to manage different
types separately, instead of uniformly converting to string unified
processing. We added type identification in ValueCountAggregator, and
made special treatment for number and geopoint types to cancel their
type conversion. Because the string type is reduced and the string
constant is reduced, the improvement effect is very obvious.
hits | avg | sum | value_count | value_count | value_count | value_count | value_count | value_count |
| | | double | double | keyword | keyword | geo_point | geo_point |
| | | before | after | before | after | before | after |
----------- | ------- | ------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
20,000 | 38s | .033s | .063s | .026s | .030s | .030s | .038s | .015s |
200,000 | 127s | .125s | .334s | .078s | .116s | .099s | .278s | .031s |
2,000,000 | 789s | .729s | 3.176s | .439s | .348s | .386s | 3.365s | .178s |
20,000,000 | 4.200s | 3.239s | 22.787s | 2.700s | 2.500s | 2.600s | 25.192s | 1.278s |
200,000,000 | 21.000s | 22.000s | 154.917s | 18.990s | 19.000s | 20.000s | 168.971s | 9.093s |
- The results are more in line with common sense. `value_count` is about
the same as `avg`, `sum`, etc., or even lower than these. Previously,
`value_count` was much larger than avg and sum, and it was not even an
order of magnitude when the amount of data was large.
- When calculating numeric types such as `double` and `long`, the
performance is improved by about 8 to 9 times; when calculating the
`geo_point` type, the performance is improved by 18 to 20 times.
This is a simple naming change PR, to fix the fact that "metadata" is a
single English word, and for too long we have not followed general
naming conventions for it. We are also not consistent about it, for
example, METADATA instead of META_DATA if we were trying to be
consistent with MetaData (although METADATA is correct when considered
in the context of "metadata"). This was a simple find and replace across
the code base, only taking a few minutes to fix this naming issue
forever.
Lisa Cawley
James Rodewig
Jason Tedor
David Roberts
David Turner
James Rodewig
Rory Hunter
Martijn Laarman
Tim Brooks
Yannick Welsch
markharwood
Stuart Tettemer
Przemyslaw Gomulka
debadair
Martijn van Groningen
William Brafford
Zachary Tong
Bogdan Pintea
István Zoltán Szabó
Paul Sanwald
Adrien Grand
Nik Everett
qiye