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${ noResults }
14 Commits (4addee2bee406ac7a47480279d2ae34061a645ca)
Author | SHA1 | Message | Date |
---|---|---|---|
Fabian Reinartz | 73b8ff0ddc |
Merge branch 'master' into dev-2.0
|
8 years ago |
Jack Neely | 896f951e68 |
Force buckets in a histogram to be monotonic for quantile estimation (#2610)
* 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. |
8 years ago |
Fabian Reinartz | 9ea10d5265 |
promql: use labels.Builder to modify labels
|
8 years ago |
Fabian Reinartz | c6cd998905 |
promql: use local labels, add conversion
|
8 years ago |
Fabian Reinartz | ff504af2aa |
promql: undo accidental exports
|
8 years ago |
Fabian Reinartz | ac5d3bc05e |
promql: scalar T/V and Point
|
8 years ago |
Fabian Reinartz | a62df87022 |
promql: rename vector
|
8 years ago |
Fabian Reinartz | 15a931dbdb |
promql: migrate model types, use tsdb interfaces
|
8 years ago |
Brian Brazil | 0303ccc6a7 |
Add quantile aggregator.
|
8 years ago |
Brian Brazil | 15f9fe0a45 |
Factor out quantile fucntion.
|
8 years ago |
Fabian Reinartz | d6b8da8d43 |
Switch promql types to common/model
|
9 years ago |
Fabian Reinartz | 306e8468a0 |
Switch from client_golang/model to common/model
|
9 years ago |
Brian Brazil | f34de493d5 |
Add increase() function, to replace delta(..., 1).
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. |
10 years ago |
Fabian Reinartz | 5602328c7c |
Refactor query evaluation.
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. |
10 years ago |