For conventional histograms, we need to gather all the individual
bucket timeseries at a data point to do the quantile calculation. The
code so far mirrored this behavior for the new native
histograms. However, since a single data point contains all the
buckets alreade, that's actually not needed. This PR simplifies the
code while still detecting a mix of conventional and native
histograms.
The weird signature calculation for the conventional histograms is
getting even weirder because of that. If this PR turns out to do the
right thing, I will implement a proper fix for the signature
calculation upstream.
Signed-off-by: beorn7 <beorn@grafana.com>
- Simplify the code a bit.
- Cover more corner cases.
- Remove TODO for negative buckets. (I think they are handled. Tests
will reveal if not.)
Signed-off-by: beorn7 <beorn@grafana.com>
* MergeFloatBucketIterator for []FloatBucketIterator
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* histogram_quantile for histograms
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Fix histogram_quantile
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Unit test and enhancements
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Iterators to iterate buckets in reverse and all buckets together including zero bucket
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Consider all buckets for histogram_quantile and fix the implementation
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Remove unneeded code
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
* Fix lint
Signed-off-by: Ganesh Vernekar <ganeshvern@gmail.com>
This creates a new `model` directory and moves all data-model related
packages over there:
exemplar labels relabel rulefmt textparse timestamp value
All the others are more or less utilities and have been moved to `util`:
gate logging modetimevfs pool runtime
Signed-off-by: beorn7 <beorn@grafana.com>
This makes things generally more resilient, and will
help with OpenMetrics transitions (and inconsistencies).
Signed-off-by: Brian Brazil <brian.brazil@robustperception.io>
* Force buckets in a histogram to be monotonic for quantile estimation
The assumption that bucket counts increase monotonically with increasing
upperBound may be violated during:
* Recording rule evaluation of histogram_quantile, especially when rate()
has been applied to the underlying bucket timeseries.
* Evaluation of histogram_quantile computed over federated bucket
timeseries, especially when rate() has been applied
This is because scraped data is not made available to RR evalution or
federation atomically, so some buckets are computed with data from the N
most recent scrapes, but the other buckets are missing the most recent
observations.
Monotonicity is usually guaranteed because if a bucket with upper bound
u1 has count c1, then any bucket with a higher upper bound u > u1 must
have counted all c1 observations and perhaps more, so that c >= c1.
Randomly interspersed partial sampling breaks that guarantee, and rate()
exacerbates it. Specifically, suppose bucket le=1000 has a count of 10 from
4 samples but the bucket with le=2000 has a count of 7, from 3 samples. The
monotonicity is broken. It is exacerbated by rate() because under normal
operation, cumulative counting of buckets will cause the bucket counts to
diverge such that small differences from missing samples are not a problem.
rate() removes this divergence.)
bucketQuantile depends on that monotonicity to do a binary search for the
bucket with the qth percentile count, so breaking the monotonicity
guarantee causes bucketQuantile() to return undefined (nonsense) results.
As a somewhat hacky solution until the Prometheus project is ready to
accept the changes required to make scrapes atomic, we calculate the
"envelope" of the histogram buckets, essentially removing any decreases
in the count between successive buckets.
* Fix up comment docs for ensureMonotonic
* ensureMonotonic: Use switch statement
Use switch statement rather than if/else for better readability.
Process the most frequent cases first.
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.
Since we are now getting really deep into floating point calculation,
the tests had to take into account the precision loss. Since the rule
tests are based on direct line matching in the output, implementing
the "almost equal" semantics was pretty cumbersome, but here we are.
beorn7