The Prometheus monitoring system and time series database.
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// Copyright 2017 The Prometheus Authors
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
package storage
import (
"fmt"
"math"
Style cleanup of all the changes in sparsehistogram so far A lot of this code was hacked together, literally during a hackathon. This commit intends not to change the code substantially, but just make the code obey the usual style practices. A (possibly incomplete) list of areas: * Generally address linter warnings. * The `pgk` directory is deprecated as per dev-summit. No new packages should be added to it. I moved the new `pkg/histogram` package to `model` anticipating what's proposed in #9478. * Make the naming of the Sparse Histogram more consistent. Including abbreviations, there were just too many names for it: SparseHistogram, Histogram, Histo, hist, his, shs, h. The idea is to call it "Histogram" in general. Only add "Sparse" if it is needed to avoid confusion with conventional Histograms (which is rare because the TSDB really has no notion of conventional Histograms). Use abbreviations only in local scope, and then really abbreviate (not just removing three out of seven letters like in "Histo"). This is in the spirit of https://github.com/golang/go/wiki/CodeReviewComments#variable-names * Several other minor name changes. * A lot of formatting of doc comments. For one, following https://github.com/golang/go/wiki/CodeReviewComments#comment-sentences , but also layout question, anticipating how things will look like when rendered by `godoc` (even where `godoc` doesn't render them right now because they are for unexported types or not a doc comment at all but just a normal code comment - consistency is queen!). * Re-enabled `TestQueryLog` and `TestEndopints` (they pass now, leaving them disabled was presumably an oversight). * Bucket iterator for histogram.Histogram is now created with a method. * HistogramChunk.iterator now allows iterator recycling. (I think @dieterbe only commented it out because he was confused by the question in the comment.) * HistogramAppender.Append panics now because we decided to treat staleness marker differently. Signed-off-by: beorn7 <beorn@grafana.com>
3 years ago
"github.com/prometheus/prometheus/model/histogram"
"github.com/prometheus/prometheus/tsdb/chunkenc"
"github.com/prometheus/prometheus/tsdb/chunks"
)
// BufferedSeriesIterator wraps an iterator with a look-back buffer.
type BufferedSeriesIterator struct {
hReader histogram.Histogram
fhReader histogram.FloatHistogram
it chunkenc.Iterator
buf *sampleRing
delta int64
lastTime int64
valueType chunkenc.ValueType
}
// NewBuffer returns a new iterator that buffers the values within the time range
// of the current element and the duration of delta before, initialized with an
// empty iterator. Use Reset() to set an actual iterator to be buffered.
func NewBuffer(delta int64) *BufferedSeriesIterator {
return NewBufferIterator(chunkenc.NewNopIterator(), delta)
}
// NewBufferIterator returns a new iterator that buffers the values within the
// time range of the current element and the duration of delta before.
func NewBufferIterator(it chunkenc.Iterator, delta int64) *BufferedSeriesIterator {
bit := &BufferedSeriesIterator{
buf: newSampleRing(delta, 0, chunkenc.ValNone),
delta: delta,
}
Optimise PromQL (#3966) * 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>
7 years ago
bit.Reset(it)
return bit
}
// Reset re-uses the buffer with a new iterator, resetting the buffered time
// delta to its original value.
func (b *BufferedSeriesIterator) Reset(it chunkenc.Iterator) {
Optimise PromQL (#3966) * 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>
7 years ago
b.it = it
b.lastTime = math.MinInt64
b.buf.reset()
b.buf.delta = b.delta
b.valueType = it.Next()
Optimise PromQL (#3966) * 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>
7 years ago
}
// ReduceDelta lowers the buffered time delta, for the current SeriesIterator only.
func (b *BufferedSeriesIterator) ReduceDelta(delta int64) bool {
return b.buf.reduceDelta(delta)
}
// PeekBack returns the nth previous element of the iterator. If there is none buffered,
// ok is false.
func (b *BufferedSeriesIterator) PeekBack(n int) (sample chunks.Sample, ok bool) {
return b.buf.nthLast(n)
}
Optimise PromQL (#3966) * 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>
7 years ago
// Buffer returns an iterator over the buffered data. Invalidates previously
// returned iterators.
func (b *BufferedSeriesIterator) Buffer() *SampleRingIterator {
return b.buf.iterator()
}
// Seek advances the iterator to the element at time t or greater.
func (b *BufferedSeriesIterator) Seek(t int64) chunkenc.ValueType {
t0 := t - b.buf.delta
// If the delta would cause us to seek backwards, preserve the buffer
// and just continue regular advancement while filling the buffer on the way.
if b.valueType != chunkenc.ValNone && t0 > b.lastTime {
b.buf.reset()
b.valueType = b.it.Seek(t0)
switch b.valueType {
case chunkenc.ValNone:
return chunkenc.ValNone
case chunkenc.ValFloat, chunkenc.ValHistogram, chunkenc.ValFloatHistogram:
b.lastTime = b.AtT()
default:
panic(fmt.Errorf("BufferedSeriesIterator: unknown value type %v", b.valueType))
}
}
if b.lastTime >= t {
return b.valueType
}
for {
if b.valueType = b.Next(); b.valueType == chunkenc.ValNone || b.lastTime >= t {
return b.valueType
}
}
}
// Next advances the iterator to the next element.
func (b *BufferedSeriesIterator) Next() chunkenc.ValueType {
// Add current element to buffer before advancing.
switch b.valueType {
case chunkenc.ValNone:
return chunkenc.ValNone
case chunkenc.ValFloat:
t, f := b.it.At()
b.buf.addF(fSample{t: t, f: f})
case chunkenc.ValHistogram:
t, h := b.it.AtHistogram(&b.hReader)
b.buf.addH(hSample{t: t, h: h})
case chunkenc.ValFloatHistogram:
t, fh := b.it.AtFloatHistogram(&b.fhReader)
b.buf.addFH(fhSample{t: t, fh: fh})
default:
panic(fmt.Errorf("BufferedSeriesIterator: unknown value type %v", b.valueType))
}
b.valueType = b.it.Next()
if b.valueType != chunkenc.ValNone {
b.lastTime = b.AtT()
}
return b.valueType
}
// At returns the current float element of the iterator.
func (b *BufferedSeriesIterator) At() (int64, float64) {
return b.it.At()
}
// AtHistogram returns the current histogram element of the iterator.
func (b *BufferedSeriesIterator) AtHistogram(fh *histogram.Histogram) (int64, *histogram.Histogram) {
return b.it.AtHistogram(fh)
}
// AtFloatHistogram returns the current float-histogram element of the iterator.
func (b *BufferedSeriesIterator) AtFloatHistogram(fh *histogram.FloatHistogram) (int64, *histogram.FloatHistogram) {
return b.it.AtFloatHistogram(fh)
}
// AtT returns the current timestamp of the iterator.
func (b *BufferedSeriesIterator) AtT() int64 {
return b.it.AtT()
}
// Err returns the last encountered error.
func (b *BufferedSeriesIterator) Err() error {
return b.it.Err()
}
type fSample struct {
t int64
f float64
}
func (s fSample) T() int64 {
return s.t
}
func (s fSample) F() float64 {
return s.f
}
func (s fSample) H() *histogram.Histogram {
panic("H() called for fSample")
}
func (s fSample) FH() *histogram.FloatHistogram {
panic("FH() called for fSample")
}
func (s fSample) Type() chunkenc.ValueType {
return chunkenc.ValFloat
}
func (s fSample) Copy() chunks.Sample {
return s
}
type hSample struct {
t int64
h *histogram.Histogram
}
func (s hSample) T() int64 {
return s.t
}
func (s hSample) F() float64 {
panic("F() called for hSample")
}
func (s hSample) H() *histogram.Histogram {
return s.h
}
func (s hSample) FH() *histogram.FloatHistogram {
return s.h.ToFloat(nil)
}
func (s hSample) Type() chunkenc.ValueType {
return chunkenc.ValHistogram
}
func (s hSample) Copy() chunks.Sample {
return hSample{t: s.t, h: s.h.Copy()}
}
type fhSample struct {
t int64
fh *histogram.FloatHistogram
}
func (s fhSample) T() int64 {
storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. (#7005) * storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. ## Rationales: In many places (e.g. chunk Remote read, Thanos Receive fetching chunk from TSDB), we operate on encoded chunks not samples. This means that we unnecessary decode/encode, wasting CPU, time and memory. This PR adds chunk iterator interfaces and makes the merge code to be reused between both seriesSets I will make the use of it in following PR inside tsdb itself. For now fanout implements it and mergers. All merges now also allows passing series mergers. This opens doors for custom deduplications other than TSDB vertical ones (e.g. offline one we have in Thanos). ## Changes * Added Chunk versions of all iterating methods. It all starts in Querier/ChunkQuerier. The plan is that Storage will implement both chunked and samples. * Added Seek to chunks.Iterator interface for iterating over chunks. * NewMergeChunkQuerier was added; Both this and NewMergeQuerier are now using generigMergeQuerier to share the code. Generic code was added. * Improved tests. * Added some TODO for further simplifications in next PRs. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Brian's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Moved s/Labeled/SeriesLabels as per Krasi suggestion. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Krasi's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Second iteration of Krasi comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Another round of comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
5 years ago
return s.t
}
func (s fhSample) F() float64 {
panic("F() called for fhSample")
storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. (#7005) * storage: Added Chunks{Queryable/Querier/SeriesSet/Series/Iteratable. Added generic Merge{SeriesSet/Querier} implementation. ## Rationales: In many places (e.g. chunk Remote read, Thanos Receive fetching chunk from TSDB), we operate on encoded chunks not samples. This means that we unnecessary decode/encode, wasting CPU, time and memory. This PR adds chunk iterator interfaces and makes the merge code to be reused between both seriesSets I will make the use of it in following PR inside tsdb itself. For now fanout implements it and mergers. All merges now also allows passing series mergers. This opens doors for custom deduplications other than TSDB vertical ones (e.g. offline one we have in Thanos). ## Changes * Added Chunk versions of all iterating methods. It all starts in Querier/ChunkQuerier. The plan is that Storage will implement both chunked and samples. * Added Seek to chunks.Iterator interface for iterating over chunks. * NewMergeChunkQuerier was added; Both this and NewMergeQuerier are now using generigMergeQuerier to share the code. Generic code was added. * Improved tests. * Added some TODO for further simplifications in next PRs. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Brian's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Moved s/Labeled/SeriesLabels as per Krasi suggestion. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Addressed Krasi's comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Second iteration of Krasi comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com> * Another round of comments. Signed-off-by: Bartlomiej Plotka <bwplotka@gmail.com>
5 years ago
}
func (s fhSample) H() *histogram.Histogram {
panic("H() called for fhSample")
}
func (s fhSample) FH() *histogram.FloatHistogram {
return s.fh
}
func (s fhSample) Type() chunkenc.ValueType {
return chunkenc.ValFloatHistogram
}
func (s fhSample) Copy() chunks.Sample {
return fhSample{t: s.t, fh: s.fh.Copy()}
}
type sampleRing struct {
delta int64
// Lookback buffers. We use iBuf for mixed samples, but one of the three
// concrete ones for homogeneous samples. (Only one of the four bufs is
// allowed to be populated!) This avoids the overhead of the interface
// wrapper for the happy (and by far most common) case of homogeneous
// samples.
iBuf []chunks.Sample
fBuf []fSample
hBuf []hSample
fhBuf []fhSample
bufInUse bufType
i int // Position of most recent element in ring buffer.
f int // Position of first element in ring buffer.
l int // Number of elements in buffer.
Optimise PromQL (#3966) * 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>
7 years ago
it SampleRingIterator
}
type bufType int
const (
noBuf bufType = iota // Nothing yet stored in sampleRing.
iBuf
fBuf
hBuf
fhBuf
)
// newSampleRing creates a new sampleRing. If you do not know the preferred
// value type yet, use a size of 0 (in which case the provided typ doesn't
// matter). On the first add, a buffer of size 16 will be allocated with the
// preferred type being the type of the first added sample.
func newSampleRing(delta int64, size int, typ chunkenc.ValueType) *sampleRing {
r := &sampleRing{delta: delta}
r.reset()
if size <= 0 {
// Will initialize on first add.
return r
}
switch typ {
case chunkenc.ValFloat:
r.fBuf = make([]fSample, size)
case chunkenc.ValHistogram:
r.hBuf = make([]hSample, size)
case chunkenc.ValFloatHistogram:
r.fhBuf = make([]fhSample, size)
default:
// Do not initialize anything because the 1st sample will be
// added to one of the other bufs anyway.
}
return r
}
func (r *sampleRing) reset() {
r.l = 0
r.i = -1
r.f = 0
r.bufInUse = noBuf
// The first sample after the reset will always go to a specialized
// buffer. If we later need to change to the interface buffer, we'll
// copy from the specialized buffer to the interface buffer. For that to
// work properly, we have to reset the interface buffer here, too.
r.iBuf = r.iBuf[:0]
}
// Resets and returns the iterator. Invalidates previously returned iterators.
func (r *sampleRing) iterator() *SampleRingIterator {
r.it.reset(r)
Optimise PromQL (#3966) * 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>
7 years ago
return &r.it
}
// SampleRingIterator is returned by BufferedSeriesIterator.Buffer() and can be
// used to iterate samples buffered in the lookback window.
type SampleRingIterator struct {
r *sampleRing
i int
t int64
f float64
h *histogram.Histogram
fh *histogram.FloatHistogram
}
func (it *SampleRingIterator) reset(r *sampleRing) {
it.r = r
it.i = -1
it.h = nil
it.fh = nil
}
func (it *SampleRingIterator) Next() chunkenc.ValueType {
it.i++
if it.i >= it.r.l {
return chunkenc.ValNone
}
switch it.r.bufInUse {
case fBuf:
s := it.r.atF(it.i)
it.t = s.t
it.f = s.f
return chunkenc.ValFloat
case hBuf:
s := it.r.atH(it.i)
it.t = s.t
it.h = s.h
return chunkenc.ValHistogram
case fhBuf:
s := it.r.atFH(it.i)
it.t = s.t
it.fh = s.fh
return chunkenc.ValFloatHistogram
}
s := it.r.at(it.i)
it.t = s.T()
switch s.Type() {
case chunkenc.ValHistogram:
it.h = s.H()
it.fh = nil
return chunkenc.ValHistogram
case chunkenc.ValFloatHistogram:
it.fh = s.FH()
it.h = nil
return chunkenc.ValFloatHistogram
default:
it.f = s.F()
return chunkenc.ValFloat
}
}
// At returns the current float element of the iterator.
func (it *SampleRingIterator) At() (int64, float64) {
return it.t, it.f
}
// AtHistogram returns the current histogram element of the iterator.
func (it *SampleRingIterator) AtHistogram() (int64, *histogram.Histogram) {
return it.t, it.h
}
// AtFloatHistogram returns the current histogram element of the iterator. If the
// current sample is an integer histogram, it will be converted to a float histogram.
// An optional histogram.FloatHistogram can be provided to avoid allocating a new
// object for the conversion.
func (it *SampleRingIterator) AtFloatHistogram(fh *histogram.FloatHistogram) (int64, *histogram.FloatHistogram) {
if it.fh == nil {
return it.t, it.h.ToFloat(fh)
}
if fh != nil {
it.fh.CopyTo(fh)
return it.t, fh
}
return it.t, it.fh.Copy()
}
func (it *SampleRingIterator) AtT() int64 {
return it.t
}
func (r *sampleRing) at(i int) chunks.Sample {
j := (r.f + i) % len(r.iBuf)
return r.iBuf[j]
}
func (r *sampleRing) atF(i int) fSample {
j := (r.f + i) % len(r.fBuf)
return r.fBuf[j]
}
func (r *sampleRing) atH(i int) hSample {
j := (r.f + i) % len(r.hBuf)
return r.hBuf[j]
}
func (r *sampleRing) atFH(i int) fhSample {
j := (r.f + i) % len(r.fhBuf)
return r.fhBuf[j]
}
// add adds a sample to the ring buffer and frees all samples that fall out of
// the delta range. Note that this method works for any sample
// implementation. If you know you are dealing with one of the implementations
// from this package (fSample, hSample, fhSample), call one of the specialized
// methods addF, addH, or addFH for better performance.
func (r *sampleRing) add(s chunks.Sample) {
if r.bufInUse == noBuf {
// First sample.
switch s := s.(type) {
case fSample:
r.bufInUse = fBuf
r.fBuf = addF(s, r.fBuf, r)
case hSample:
r.bufInUse = hBuf
r.hBuf = addH(s, r.hBuf, r)
case fhSample:
r.bufInUse = fhBuf
r.fhBuf = addFH(s, r.fhBuf, r)
}
return
}
if r.bufInUse != iBuf {
// Nothing added to the interface buf yet. Let's check if we can
// stay specialized.
switch s := s.(type) {
case fSample:
if r.bufInUse == fBuf {
r.fBuf = addF(s, r.fBuf, r)
return
}
case hSample:
if r.bufInUse == hBuf {
r.hBuf = addH(s, r.hBuf, r)
return
}
case fhSample:
if r.bufInUse == fhBuf {
r.fhBuf = addFH(s, r.fhBuf, r)
return
}
}
// The new sample isn't a fit for the already existing
// ones. Copy the latter into the interface buffer where needed.
// The interface buffer is assumed to be of length zero at this point.
switch r.bufInUse {
case fBuf:
for _, s := range r.fBuf {
r.iBuf = append(r.iBuf, s)
}
r.fBuf = nil
case hBuf:
for _, s := range r.hBuf {
r.iBuf = append(r.iBuf, s)
}
r.hBuf = nil
case fhBuf:
for _, s := range r.fhBuf {
r.iBuf = append(r.iBuf, s)
}
r.fhBuf = nil
}
r.bufInUse = iBuf
}
r.iBuf = addSample(s, r.iBuf, r)
}
// addF is a version of the add method specialized for fSample.
func (r *sampleRing) addF(s fSample) {
switch r.bufInUse {
case fBuf: // Add to existing fSamples.
r.fBuf = addF(s, r.fBuf, r)
case noBuf: // Add first sample.
r.fBuf = addF(s, r.fBuf, r)
r.bufInUse = fBuf
case iBuf: // Already have interface samples. Add to the interface buf.
r.iBuf = addSample(s, r.iBuf, r)
default:
// Already have specialized samples that are not fSamples.
// Need to call the checked add method for conversion.
r.add(s)
}
}
// addH is a version of the add method specialized for hSample.
func (r *sampleRing) addH(s hSample) {
switch r.bufInUse {
case hBuf: // Add to existing hSamples.
r.hBuf = addH(s, r.hBuf, r)
case noBuf: // Add first sample.
r.hBuf = addH(s, r.hBuf, r)
r.bufInUse = hBuf
case iBuf: // Already have interface samples. Add to the interface buf.
r.iBuf = addSample(s, r.iBuf, r)
default:
// Already have specialized samples that are not hSamples.
// Need to call the checked add method for conversion.
r.add(s)
}
}
// addFH is a version of the add method specialized for fhSample.
func (r *sampleRing) addFH(s fhSample) {
switch r.bufInUse {
case fhBuf: // Add to existing fhSamples.
r.fhBuf = addFH(s, r.fhBuf, r)
case noBuf: // Add first sample.
r.fhBuf = addFH(s, r.fhBuf, r)
r.bufInUse = fhBuf
case iBuf: // Already have interface samples. Add to the interface buf.
r.iBuf = addSample(s, r.iBuf, r)
default:
// Already have specialized samples that are not fhSamples.
// Need to call the checked add method for conversion.
r.add(s)
}
}
// addSample adds a sample to a buffer of chunks.Sample, i.e. the general case
// using an interface as the type.
func addSample(s chunks.Sample, buf []chunks.Sample, r *sampleRing) []chunks.Sample {
l := len(buf)
// Grow the ring buffer if it fits no more elements.
if l == 0 {
buf = make([]chunks.Sample, 16)
l = 16
}
if l == r.l {
newBuf := make([]chunks.Sample, 2*l)
copy(newBuf[l+r.f:], buf[r.f:])
copy(newBuf, buf[:r.f])
buf = newBuf
r.i = r.f
r.f += l
l = 2 * l
} else {
r.i++
if r.i >= l {
r.i -= l
}
}
buf[r.i] = s.Copy()
r.l++
// Free head of the buffer of samples that just fell out of the range.
tmin := s.T() - r.delta
for buf[r.f].T() < tmin {
r.f++
if r.f >= l {
r.f -= l
}
r.l--
}
return buf
}
// addF adds an fSample to a (specialized) fSample buffer.
func addF(s fSample, buf []fSample, r *sampleRing) []fSample {
l := len(buf)
// Grow the ring buffer if it fits no more elements.
if l == 0 {
buf = make([]fSample, 16)
l = 16
}
if l == r.l {
newBuf := make([]fSample, 2*l)
copy(newBuf[l+r.f:], buf[r.f:])
copy(newBuf, buf[:r.f])
buf = newBuf
r.i = r.f
r.f += l
l = 2 * l
} else {
r.i++
if r.i >= l {
r.i -= l
}
}
buf[r.i] = s
r.l++
// Free head of the buffer of samples that just fell out of the range.
tmin := s.T() - r.delta
for buf[r.f].T() < tmin {
r.f++
if r.f >= l {
r.f -= l
}
r.l--
}
return buf
}
// addH adds an hSample to a (specialized) hSample buffer.
func addH(s hSample, buf []hSample, r *sampleRing) []hSample {
l := len(buf)
// Grow the ring buffer if it fits no more elements.
if l == 0 {
buf = make([]hSample, 16)
l = 16
}
if l == r.l {
newBuf := make([]hSample, 2*l)
copy(newBuf[l+r.f:], buf[r.f:])
copy(newBuf, buf[:r.f])
buf = newBuf
r.i = r.f
r.f += l
l = 2 * l
} else {
r.i++
if r.i >= l {
r.i -= l
}
}
buf[r.i].t = s.t
if buf[r.i].h == nil {
buf[r.i].h = s.h.Copy()
} else {
s.h.CopyTo(buf[r.i].h)
}
r.l++
// Free head of the buffer of samples that just fell out of the range.
tmin := s.T() - r.delta
for buf[r.f].T() < tmin {
r.f++
if r.f >= l {
r.f -= l
}
r.l--
}
return buf
}
// addFH adds an fhSample to a (specialized) fhSample buffer.
func addFH(s fhSample, buf []fhSample, r *sampleRing) []fhSample {
l := len(buf)
// Grow the ring buffer if it fits no more elements.
if l == 0 {
buf = make([]fhSample, 16)
l = 16
}
if l == r.l {
newBuf := make([]fhSample, 2*l)
copy(newBuf[l+r.f:], buf[r.f:])
copy(newBuf, buf[:r.f])
buf = newBuf
r.i = r.f
r.f += l
l = 2 * l
} else {
r.i++
if r.i >= l {
r.i -= l
}
}
buf[r.i].t = s.t
if buf[r.i].fh == nil {
buf[r.i].fh = s.fh.Copy()
} else {
s.fh.CopyTo(buf[r.i].fh)
}
r.l++
// Free head of the buffer of samples that just fell out of the range.
tmin := s.T() - r.delta
for buf[r.f].T() < tmin {
r.f++
if r.f >= l {
r.f -= l
}
r.l--
}
return buf
}
// reduceDelta lowers the buffered time delta, dropping any samples that are
// out of the new delta range.
func (r *sampleRing) reduceDelta(delta int64) bool {
if delta > r.delta {
return false
}
r.delta = delta
if r.l == 0 {
return true
}
switch r.bufInUse {
case fBuf:
genericReduceDelta(r.fBuf, r)
case hBuf:
genericReduceDelta(r.hBuf, r)
case fhBuf:
genericReduceDelta(r.fhBuf, r)
default:
genericReduceDelta(r.iBuf, r)
}
return true
}
func genericReduceDelta[T chunks.Sample](buf []T, r *sampleRing) {
// Free head of the buffer of samples that just fell out of the range.
l := len(buf)
tmin := buf[r.i].T() - r.delta
for buf[r.f].T() < tmin {
r.f++
if r.f >= l {
r.f -= l
}
r.l--
}
}
// nthLast returns the nth most recent element added to the ring.
func (r *sampleRing) nthLast(n int) (chunks.Sample, bool) {
if n > r.l {
return fSample{}, false
}
i := r.l - n
switch r.bufInUse {
case fBuf:
return r.atF(i), true
case hBuf:
return r.atH(i), true
case fhBuf:
return r.atFH(i), true
default:
return r.at(i), true
}
}
func (r *sampleRing) samples() []chunks.Sample {
res := make([]chunks.Sample, r.l)
k := r.f + r.l
var j int
switch r.bufInUse {
case iBuf:
if k > len(r.iBuf) {
k = len(r.iBuf)
j = r.l - k + r.f
}
n := copy(res, r.iBuf[r.f:k])
copy(res[n:], r.iBuf[:j])
case fBuf:
if k > len(r.fBuf) {
k = len(r.fBuf)
j = r.l - k + r.f
}
resF := make([]fSample, r.l)
n := copy(resF, r.fBuf[r.f:k])
copy(resF[n:], r.fBuf[:j])
for i, s := range resF {
res[i] = s
}
case hBuf:
if k > len(r.hBuf) {
k = len(r.hBuf)
j = r.l - k + r.f
}
resH := make([]hSample, r.l)
n := copy(resH, r.hBuf[r.f:k])
copy(resH[n:], r.hBuf[:j])
for i, s := range resH {
res[i] = s
}
case fhBuf:
if k > len(r.fhBuf) {
k = len(r.fhBuf)
j = r.l - k + r.f
}
resFH := make([]fhSample, r.l)
n := copy(resFH, r.fhBuf[r.f:k])
copy(resFH[n:], r.fhBuf[:j])
for i, s := range resFH {
res[i] = s
}
}
return res
}