The order of series keys is in ascending alphabetical order, not
descending alphabetical order, when it is ordered by descending time.
This fixes the ordering so points are returned in descending order. The
emitter also had the conditions for choosing which iterator to use in
the wrong direction (which only affects aggregates with `FILL(none)`).
When using `non_negative_derivative()` and `last()` in a math aggregate
with each other, the math would not be matched with each other because
one of those aggregates would emit one fewer point than the others. The
math iterators have been modified so they now track the name and tags of
a point and match based on those.
This isn't necessarily ideal and may come to bite us in the future. We
don't necessarily have a defined structure for all iterators so it can
be difficult to know which of two points is supposed to come first in
the ordering. This uses the common ordering that usually makes sense,
but the query engine is getting complicated enough where I am not 100%
certain that this is correct in all circumstances.
Fixes#7906
In an attempt to reduce the overhead of using regex for exact matches,
the query parser will replace `=~ /^thing$/` with `== 'thing'`, but the
conditions being checked would ignore if any flags were set on the
expression, so `=~ /(?i)^THING$/` was replaced with `== 'THING'`, which
will fail unless the case was already exact. This change ensures that no
flags have been changed from those defaulted by the parser.
Fixes#7906
In an attempt to reduce the overhead of using regex for exact matches,
the query parser will replace `=~ /^thing$/` with `== 'thing'`, but the
conditions being checked would ignore if any flags were set on the
expression, so `=~ /(?i)^THING$/` was replaced with `== 'THING'`, which
will fail unless the case was already exact. This change ensures that no
flags have been changed from those defaulted by the parser.
With the new shard mapper implementation, regexes were just ignored so
it attempted to look up the field type inside of a measurement with no
name (which cannot possibly exist) so it would think the field didn't
exist and map it as the unknown type.
With the new shard mapper implementation, regexes were just ignored so
it attempted to look up the field type inside of a measurement with no
name (which cannot possibly exist) so it would think the field didn't
exist and map it as the unknown type.
Previously, only time expressions got propagated inwards. The reason for
this was simple. If the outer query was going to filter to a specific
time range, then it would be unnecessary for the inner query to output
points within that time frame. It started as an optimization, but became
a feature because there was no reason to have the user repeat the same
time clause for the inner query as the outer query. So we allowed an
aggregate query with an interval to pass validation in the subquery if
the outer query had a time range. But `GROUP BY` clauses were not
propagated because that same logic didn't apply to them. It's not an
optimization there. So while grouping by a tag in the outer query
without grouping by it in the inner query was useless, there wasn't any
particular reason to care.
Then a bug was found where wildcards would propagate the dimensions
correctly, but the outer query containing a group by with the inner
query omitting it wouldn't correctly filter out the outer group by. We
could fix that filtering, but on further review, I had been seeing
people make that same mistake a lot. People seem to just believe that
the grouping should be propagated inwards. Instead of trying to fight
what the user wanted and explicitly erase groupings that weren't
propagated manually, we might as well just propagate them for the user
to make their lives easier. There is no useful situation where you would
want to group into buckets that can't physically exist so we might as
well do _something_ useful.
This will also now propagate time intervals to inner queries since the
same applies there. But, while the interval propagates, the following
query will not pass validation since it is still not possible to use a
grouping interval with a raw query (even if the inner query is an
aggregate):
SELECT * FROM (SELECT mean(value) FROM cpu) WHERE time > now() - 5m GROUP BY time(1m)
This also means wildcards will behave a bit differently. They will
retrieve dimensions from the sources in the inner query rather than just
using the dimensions in the group by.
Fixing top() and bottom() to return the correct auxiliary fields.
Unfortunately, we were not copying the buffer with the auxiliary fields
so those values would be overwritten by a later point.
Previously, only time expressions got propagated inwards. The reason for
this was simple. If the outer query was going to filter to a specific
time range, then it would be unnecessary for the inner query to output
points within that time frame. It started as an optimization, but became
a feature because there was no reason to have the user repeat the same
time clause for the inner query as the outer query. So we allowed an
aggregate query with an interval to pass validation in the subquery if
the outer query had a time range. But `GROUP BY` clauses were not
propagated because that same logic didn't apply to them. It's not an
optimization there. So while grouping by a tag in the outer query
without grouping by it in the inner query was useless, there wasn't any
particular reason to care.
Then a bug was found where wildcards would propagate the dimensions
correctly, but the outer query containing a group by with the inner
query omitting it wouldn't correctly filter out the outer group by. We
could fix that filtering, but on further review, I had been seeing
people make that same mistake a lot. People seem to just believe that
the grouping should be propagated inwards. Instead of trying to fight
what the user wanted and explicitly erase groupings that weren't
propagated manually, we might as well just propagate them for the user
to make their lives easier. There is no useful situation where you would
want to group into buckets that can't physically exist so we might as
well do _something_ useful.
This will also now propagate time intervals to inner queries since the
same applies there. But, while the interval propagates, the following
query will not pass validation since it is still not possible to use a
grouping interval with a raw query (even if the inner query is an
aggregate):
SELECT * FROM (SELECT mean(value) FROM cpu) WHERE time > now() - 5m GROUP BY time(1m)
This also means wildcards will behave a bit differently. They will
retrieve dimensions from the sources in the inner query rather than just
using the dimensions in the group by.
Fixing top() and bottom() to return the correct auxiliary fields.
Unfortunately, we were not copying the buffer with the auxiliary fields
so those values would be overwritten by a later point.
When an error that appears to be an SSL error happens without SSL
enabled, the client will attempt to reconnect with SSL just to see if
that works. If it works, it exits with an error message telling the user
to add `-ssl`. It will also do the same if the SSL connection is unsafe
although it will warn that this is insecure.
The backup command can fail if a snapshot is running which silently
closes the connection. This causes the backup shard command to continue
on as if nothing failed.
This adds query syntax support for subqueries and adds support to the
query engine to execute queries on subqueries.
Subqueries act as a source for another query. It is the equivalent of
writing the results of a query to a temporary database, executing
a query on that temporary database, and then deleting the database
(except this is all performed in-memory).
The syntax is like this:
SELECT sum(derivative) FROM (SELECT derivative(mean(value)) FROM cpu GROUP BY *)
This will execute derivative and then sum the result of those derivatives.
Another example:
SELECT max(min) FROM (SELECT min(value) FROM cpu GROUP BY host)
This would let you find the maximum minimum value of each host.
There is complete freedom to mix subqueries with auxiliary fields. The only
caveat is that the following two queries:
SELECT mean(value) FROM cpu
SELECT mean(value) FROM (SELECT value FROM cpu)
Have different performance characteristics. The first will calculate
`mean(value)` at the shard level and will be faster, especially when it comes to
clustered setups. The second will process the mean at the top level and will not
include that optimization.
Benchmark improvements with this change:
benchmark old ns/op new ns/op delta
BenchmarkExportTSMFloats_100s_250vps-4 23206480 10279106 -55.71%
BenchmarkExportTSMInts_100s_250vps-4 17995000 5762310 -67.98%
BenchmarkExportTSMBools_100s_250vps-4 17067605 4235467 -75.18%
BenchmarkExportTSMStrings_100s_250vps-4 54846997 34682568 -36.76%
BenchmarkExportWALFloats_100s_250vps-4 23459937 10436297 -55.51%
BenchmarkExportWALInts_100s_250vps-4 18747150 6236062 -66.74%
BenchmarkExportWALBools_100s_250vps-4 17988273 4814358 -73.24%
BenchmarkExportWALStrings_100s_250vps-4 59700802 35815739 -40.01%
benchmark old allocs new allocs delta
BenchmarkExportTSMFloats_100s_250vps-4 201442 51738 -74.32%
BenchmarkExportTSMInts_100s_250vps-4 201442 51728 -74.32%
BenchmarkExportTSMBools_100s_250vps-4 201441 51638 -74.37%
BenchmarkExportTSMStrings_100s_250vps-4 404092 201584 -50.11%
BenchmarkExportWALFloats_100s_250vps-4 250322 75627 -69.79%
BenchmarkExportWALInts_100s_250vps-4 250323 75617 -69.79%
BenchmarkExportWALBools_100s_250vps-4 250321 75527 -69.83%
BenchmarkExportWALStrings_100s_250vps-4 452868 225291 -50.25%
benchmark old bytes new bytes delta
BenchmarkExportTSMFloats_100s_250vps-4 5170539 2351789 -54.52%
BenchmarkExportTSMInts_100s_250vps-4 5143189 2331276 -54.67%
BenchmarkExportTSMBools_100s_250vps-4 3724951 2143780 -42.45%
BenchmarkExportTSMStrings_100s_250vps-4 17131400 10796281 -36.98%
BenchmarkExportWALFloats_100s_250vps-4 4487868 1468109 -67.29%
BenchmarkExportWALInts_100s_250vps-4 4458395 1452359 -67.42%
BenchmarkExportWALBools_100s_250vps-4 2838719 1258755 -55.66%
BenchmarkExportWALStrings_100s_250vps-4 16787201 10010700 -40.37%
Also, after improving those benchmarks, I did a time-filtered export on
a 450MB TSM file to a 21GB plain text output, with and without the
bufio.BufferedWriter.
Without buffering, it took about 263s, and with buffering, it took about
60s, for a delta of about -77%.
It looks like the real import path to the project is go.uber.org/zap
instead of github.com/uber-go/zap since the example in the project
references that path.
Jason Wilder
Jonathan A. Sternberg
Joe LeGasse
Cory LaNou
Paul Dix
Edd Robinson
gunnaraasen
Mark Rushakoff
Mark Rushakoff
Michael Nikitochkin
Gustav Westling