The code that checked if a query was authorized did not account for
sources that were subqueries. Now, the check for the required privileges
will descend into the subquery and add the subqueries required
privileges to the list of required privileges for the entire query.
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.
During development, I, at some point, decided that the dimensions should
be expanded based on what was available rather than what was present in
the subquery. I don't really know the rationale for this because I
forgot, but it doesn't make sense or seem to be particularly useful.
Expanding dimensions now just uses the values specified in the subquery
rather than expanding to all available dimensions of the measurement in
the subquery.
During development, I, at some point, decided that the dimensions should
be expanded based on what was available rather than what was present in
the subquery. I don't really know the rationale for this because I
forgot, but it doesn't make sense or seem to be particularly useful.
Expanding dimensions now just uses the values specified in the subquery
rather than expanding to all available dimensions of the measurement in
the subquery.
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.
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.
This commit adds support for replacing regexes with non-regex conditions
when possible. Currently the following regexes are supported:
- host =~ /^foo$/ will be converted into host = 'foo'
- host !~ /^foo$/ will be converted into host != 'foo'
Note: if the regex expression contains character classes, grouping,
repetition or similar, it may not be rewritten.
For example, the condition: name =~ /^foo|bar$/ will not be rewritten.
Support for this may arrive in the future.
Regexes that can be converted into simpler expression will be able to
take advantage of the tsdb index, making them significantly faster.
Previously, calling derivative with a non-duration second argument was
allowed during parsing but would panic during execution due to a failed
type conversion. This change ensures the second argument is a duration
literal.
The functionality works the same as wildcards, but this time, you can
specify a regular expression.
One limitation is that you can't specify whether you only want to select
fields or tags. Since the regex can be changed to suit the person's
needs, I don't currently think this is an issue.
Strings would always return an empty string and stddev is meaningless
when it comes to strings. This removes that functionality so strings
don't automatically get picked up when using a wildcard.
The `cumulative_sum()` function can be used to sum each new point and
output the current total. For the following points:
cpu value=2 0
cpu value=4 10
cpu value=6 20
This would output the following points:
> SELECT cumulative_sum(value) FROM cpu
time value
---- -----
0 2
10 6
20 12
As can be seen, each new point adds to the sum of the previous point and
outputs the value with the same timestamp.
The function can also be used with an aggregate like `derivative()`.
> SELECT cumulative_sum(mean(value) FROM cpu WHERE time >= now() - 10m GROUP BY time(1m)
First Pass at implementing sample
Add sample iterators for all types
Remove size from sample struct
Fix off by one error when generating random number
Add benchmarks for sample iterator
Add test and associated fixes for off by one error
Add test for sample function
Remove NumericLiteral from sample function call
Make clear that the counter is incr w/ each call
Rename IsRandom to AllSamplesSeen
Add a rng for each reducer that is created
The default rng that comes with math/rand has a global lock. To avoid
having to worry about any contention on the lock, each reducer now has
its own time seeded rng.
Add sample function to changelog
Clean up template for fill average
Change fill(average) to fill(linear)
Update average to linear in infuxql spec
Add Integer Tests and associated fixes
Update CHANGELOG for fill(linear)
Manual use of system queries could result in a user using the query
incorrect. Rather than check to make sure the query was used correctly,
we're just going to prevent users from using those sources so they can't
use them incorrectly.
Normalize all of the SHOW commands so they allow both using ON to
specify the database and using the default database. Some commands would
require one and some would require the other and it was confusing when
using the query language.
Affected commands:
* SHOW RETENTION POLICIES
* SHOW MEASUREMENTS
* SHOW SERIES
* SHOW TAG KEYS
* SHOW TAG VALUES
* SHOW FIELD KEYS
When attempting to reduce the WHERE clause, the time literals had not
been converted from string literals yet. This adds the functionality to
have it handle the same time math when the time literal is still a
string literal.
Instead of having the parser set the defaults, the command will set the
defaults so that the constants for that are actually used. This way we
can also identify which things the user provided and which ones we are
filling with default values.
This allows the meta client to be able to make smarter decisions when
determining if the user requested a conflict or if the requested
capabilities match with what is currently available. If you just say
`CREATE DATABASE WITH NAME myrp`, the user doesn't really care what the
duration of the retention policy is and just wants to use the default.
Now, we can use that information to determine if an existing retention
policy would conflict with what the user requested rather than returning
an error if a default value ever gets changed since the meta client
command can communicate intent more easily.
Negative timestamps are now supported. We also now refuse two
nanoseconds that are at the edge of the minimum time window. One of the
nanoseconds we do not accept is because we need MinInt64 to be used for
some internal comparisons in the TSM engine and it was causing an
underflow when we subtracted one from the minimum time. The second is so
we can have one minimum time that signifies the default minimum that
nobody can write to (so we can implicitly rewrite the timestamp on
aggregate queries) but still use the explicit timestamp if it is given
to us by the user. We aren't able to tell the difference between if the
user provided it or if it was implicit without those values being
different.
If the default minimum time is used with an aggregate query, we rewrite
the time to be the epoch for backwards compatibility since we believe
that's more important than supporting that extra nanosecond.
Instead of having the parser set the defaults, the command will set the
defaults so that the constants for that are actually used. This way we
can also identify which things the user provided and which ones we are
filling with default values.
This allows the meta client to be able to make smarter decisions when
determining if the user requested a conflict or if the requested
capabilities match with what is currently available. If you just say
`CREATE DATABASE WITH NAME myrp`, the user doesn't really care what the
duration of the retention policy is and just wants to use the default.
Now, we can use that information to determine if an existing retention
policy would conflict with what the user requested rather than returning
an error if a default value ever gets changed since the meta client
command can communicate intent more easily.
Truncate the time interval output of the monitor service to be on even
time intervals rather than on every minute based on the start time. This
normalizes the output from the monitor service.
Previously, it encoded the text representation of the regex literal
which included the surrounding slashes used in the query language. The
binary encoding should only include the exact string used to create the
regular expression.
This adds support for using regex expressions in SHOW TAG VALUES when
selecting the key. Also supporting the `!=` operation for the
comparison. Now you can do any of the following:
SHOW TAG VALUES WITH KEY != "region"
SHOW TAG VALUES WITH KEY =~ /region/
SHOW TAG VALUES WITH KEY !~ /region/
It also adds a new SetLiteral AST node that will potentially be used in
the future to allow set operations for other comparisons in the future.
Fixes#4532.
The current code would compare every string literal it crossed and tried
to coerce them to time literals if the _looked_ like date/time strings.
The only time the TimeLiteral was used is when comparing to the the
'time' value in a where clause. This change moves the string parsing
code until we attempt to compare 'time' to a string, at which point we
know we need/want a TimeLiteral, and not just an ordinary string.
Fixes#6727
Jonathan A. Sternberg
Jason Wilder
Joe LeGasse
Paul Dix
Edd Robinson
Mark Rushakoff
Tom Young
Michael Desa
Ashish Gaurav
Cory LaNou