* Move range logic to 'eval'
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make aggregegate range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* PromQL is statically typed, so don't eval to find the type.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Extend rangewrapper to multiple exprs
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Start making function evaluation ranged
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make instant queries a special case of range queries
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Eliminate evalString
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Evaluate range vector functions one series at a time
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make unary operators range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make binops range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Pass time to range-aware functions.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make simple _over_time functions range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reduce allocs when working with matrix selectors
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Add basic benchmark for range evaluation
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reuse objects for function arguments
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Do dropmetricname and allocating output vector only once.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Add range-aware support for range vector functions with params
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Optimise holt_winters, cut cpu and allocs by ~25%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make rate&friends range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make more functions range aware. Document calling convention.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make date functions range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make simple math functions range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Convert more functions to be range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make more functions range aware
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Specialcase timestamp() with vector selector arg for range awareness
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove transition code for functions
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove the rest of the engine transition code
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove more obselete code
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove the last uses of the eval* functions
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove engine finalizers to prevent corruption
The finalizers set by matrixSelector were being called
just before the value they were retruning to the pool
was then being provided to the caller. Thus a concurrent query
could corrupt the data that the user has just been returned.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Add new benchmark suite for range functinos
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Migrate existing benchmarks to new system
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Expand promql benchmarks
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Simply test by removing unused range code
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* When testing instant queries, check range queries too.
To protect against subsequent steps in a range query being
affected by the previous steps, add a test that evaluates
an instant query that we know works again as a range query
with the tiimestamp we care about not being the first step.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reuse ring for matrix iters. Put query results back in pool.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reuse buffer when iterating over matrix selectors
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Unary minus should remove metric name
Cut down benchmarks for faster runs.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reduce repetition in benchmark test cases
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Work series by series when doing normal vectorSelectors
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Optimise benchmark setup, cuts time by 60%
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Have rangeWrapper use an evalNodeHelper to cache across steps
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Use evalNodeHelper with functions
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Cache dropMetricName within a node evaluation.
This saves both the calculations and allocs done by dropMetricName
across steps.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reuse input vectors in rangewrapper
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Reuse the point slices in the matrixes input/output by rangeWrapper
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make benchmark setup faster using AddFast
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Simplify benchmark code.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Add caching in VectorBinop
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Use xor to have one-level resultMetric hash key
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Add more benchmarks
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Call Query.Close in apiv1
This allows point slices allocated for the response data
to be reused by later queries, saving allocations.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Optimise histogram_quantile
It's now 5-10% faster with 97% less garbage generated for 1k steps
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make the input collection in rangeVector linear rather than quadratic
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Optimise label_replace, for 1k steps 15x fewer allocs and 3x faster
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Optimise label_join, 1.8x faster and 11x less memory for 1k steps
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Expand benchmarks, cleanup comments, simplify numSteps logic.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Address Fabian's comments
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Comments from Alin.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Address jrv's comments
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Remove dead code
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Address Simon's comments.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Rename populateIterators, pre-init some sizes
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Handle case where function has non-matrix args first
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Split rangeWrapper out to rangeEval function, improve comments
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Cleanup and make things more consistent
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Make EvalNodeHelper public
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Fabian's comments.
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
With the squaring of the timestamp, we run into the
limitations of the 53bit mantissa for a 64bit float.
By subtracting away a timestamp of one of the samples (which is how the
intercept is used) we avoid this issue in practice as it's unlikely
that it is used over a very long time range.
Fixes#2674
Make the timestamp of instant vectors be the timestamp of the sample
rather than the evaluation. We were not using this anywhere, so this is
safe.
Add a function to return the timestamp of samples in an instant vector.
Fixes#1557
This was only relevant so far for the benchmark suite as it would
recycle Expr for repetitions. However, the append is unnecessary as
each node is only inspected once when populating iterators, and
population must always start from scratch.
This also introduces error checking during benchmarks and fixes the so
far undetected test errors during benchmarking.
Also, remove a style nit (two golint warnings less…).
Fixes https://github.com/prometheus/prometheus/issues/1401
This remove the last (and in fact bogus) use of BoundaryValues.
Thus, a whole lot of unused (and arguably sub-optimal / ugly) code can
be removed here, too.
This will fix issue #1035 and will also help to make issue #1264 less
bad.
The fundamental problem in the current code:
In the preload phase, we quite accurately determine which chunks will
be used for the query being executed. However, in the subsequent step
of creating series iterators, the created iterators are referencing
_all_ in-memory chunks in their series, even the un-pinned ones. In
iterator creation, we copy a pointer to each in-memory chunk of a
series into the iterator. While this creates a certain amount of
allocation churn, the worst thing about it is that copying the chunk
pointer out of the chunkDesc requires a mutex acquisition. (Remember
that the iterator will also reference un-pinned chunks, so we need to
acquire the mutex to protect against concurrent eviction.) The worst
case happens if a series doesn't even contain any relevant samples for
the query time range. We notice that during preloading but then we
will still create a series iterator for it. But even for series that
do contain relevant samples, the overhead is quite bad for instant
queries that retrieve a single sample from each series, but still go
through all the effort of series iterator creation. All of that is
particularly bad if a series has many in-memory chunks.
This commit addresses the problem from two sides:
First, it merges preloading and iterator creation into one step,
i.e. the preload call returns an iterator for exactly the preloaded
chunks.
Second, the required mutex acquisition in chunkDesc has been greatly
reduced. That was enabled by a side effect of the first step, which is
that the iterator is only referencing pinned chunks, so there is no
risk of concurrent eviction anymore, and chunks can be accessed
without mutex acquisition.
To simplify the code changes for the above, the long-planned change of
ValueAtTime to ValueAtOrBefore time was performed at the same
time. (It should have been done first, but it kind of accidentally
happened while I was in the middle of writing the series iterator
changes. Sorry for that.) So far, we actively filtered the up to two
values that were returned by ValueAtTime, i.e. we invested work to
retrieve up to two values, and then we invested more work to throw one
of them away.
The SeriesIterator.BoundaryValues method can be removed once #1401 is
fixed. But I really didn't want to load even more changes into this
PR.
Benchmarks:
The BenchmarkFuzz.* benchmarks run 83% faster (i.e. about six times
faster) and allocate 95% fewer bytes. The reason for that is that the
benchmark reads one sample after another from the time series and
creates a new series iterator for each sample read.
To find out how much these improvements matter in practice, I have
mirrored a beefy Prometheus server at SoundCloud that suffers from
both issues #1035 and #1264. To reach steady state that would be
comparable, the server needs to run for 15d. So far, it has run for
1d. The test server currently has only half as many memory time series
and 60% of the memory chunks the main server has. The 90th percentile
rule evaluation cycle time is ~11s on the main server and only ~3s on
the test server. However, these numbers might get much closer over
time.
In addition to performance improvements, this commit removes about 150
LOC.
The new implementation detects the start and end of a series by
looking at the average sample interval within the range. If the first
(last) sample in the range is more than 1.1*interval distant from the
beginning (end) of the range, it is considered the first (last) sample
of the series as a whole, and extrapolation is limited to half the
interval (rather than all the way to the beginning (end) of the
range). In addition, if the extrapolated starting point of a counter
(where it is zero) is within the range, it is used as the starting
point of the series.
Fixes#581
This change is breaking, use increase() instead.
I'm not cleaning up the function in this PR, as my solution to #581 will
rewrite and simplify increase/rate/delta.
irate is a rate function that only looks at the most
recent two data points, and calucaltes a per-second value
from that. This produces much more granular graphs for
fast moving data, and works sanely across many scrape intervals.
It doesn't do so well for slowly moving data.
Currently the only way to convert a scalar to a vector is to
use absent(), which isn't very clean. This adds a vector()
function that's the inverse of scalar() and lets your optionally
set labels.
Example usage would be
vector(time() % 86400) < 3600
to filter to only the first hour of the day.
This calculates how much a counter increases over
a given period of time, which is the area under the curve
of it's rate.
increase(x[5m]) is equivilent to rate(x[5m]) * 300.
This copies the evaluation logic from the current rules/ package.
The new engine handles the execution process from query string to final result.
It provides query timeout and cancellation and general flexibility for
future changes.
functions.go: Add evaluation implementation. Slight changes to in/out data but
not to the processing logic.
quantile.go: No changes.
analyzer.go: No changes.
engine.go: Actually new part. Mainly consists of evaluation methods
which were not changed.
setup_test.go: Copy of rules/helpers_test.go to setup test storage.
promql_test.go: Copy of rules/rules_test.go.
This commit creates a (so far unused) package. It contains the a custom
lexer/parser for the query language.
ast.go: New AST that interacts well with the parser.
lex.go: Custom lexer (new).
lex_test.go: Lexer tests (new).
parse.go: Custom parser (new).
parse_test.go: Parser tests (new).
functions.go: Changed function type, dummies for parser testing (barely changed/dummies).
printer.go: Adapted from rules/ and adjusted to new AST (mostly unchanged, few additions).
