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120 lines
4.7 KiB
120 lines
4.7 KiB
# Tests for limitk
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#
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# NB: those many `and http_requests` are to ensure that the series _are_ indeed
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# a subset of the original series.
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load 5m
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http_requests{job="api-server", instance="0", group="production"} 0+10x10
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http_requests{job="api-server", instance="1", group="production"} 0+20x10
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http_requests{job="api-server", instance="0", group="canary"} 0+30x10
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http_requests{job="api-server", instance="1", group="canary"} 0+40x10
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http_requests{job="api-server", instance="2", group="canary"} 0+50x10
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http_requests{job="api-server", instance="3", group="canary"} 0+60x10
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eval instant at 50m count(limitk by (group) (0, http_requests))
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# empty
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eval instant at 50m count(limitk by (group) (-1, http_requests))
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# empty
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# Exercise k==1 special case (as sample is added before the main series loop
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eval instant at 50m count(limitk by (group) (1, http_requests) and http_requests)
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{} 2
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eval instant at 50m count(limitk by (group) (2, http_requests) and http_requests)
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{} 4
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eval instant at 50m count(limitk(100, http_requests) and http_requests)
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{} 6
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# Exercise k==1 special case (as sample is added before the main series loop
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eval instant at 50m count(limitk by (group) (1, http_requests) and http_requests)
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{} 2
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eval instant at 50m count(limitk by (group) (2, http_requests) and http_requests)
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{} 4
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eval instant at 50m count(limitk(100, http_requests) and http_requests)
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{} 6
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# limit_ratio
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eval range from 0 to 50m step 5m count(limit_ratio(0.0, http_requests))
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# empty
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# limitk(2, ...) should always return a 2-count subset of the timeseries (hence the AND'ing)
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eval range from 0 to 50m step 5m count(limitk(2, http_requests) and http_requests)
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{} 2+0x10
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# Tests for limit_ratio
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#
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# NB: below 0.5 ratio will depend on some hashing "luck" (also there's no guarantee that
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# an integer comes from: total number of series * ratio), as it depends on:
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#
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# * ratioLimit = [0.0, 1.0]:
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# float64(sample.Metric.Hash()) / float64MaxUint64 < Ratio ?
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# * ratioLimit = [-1.0, 1.0):
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# float64(sample.Metric.Hash()) / float64MaxUint64 >= (1.0 + Ratio) ?
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#
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# See `AddRatioSample()` in promql/engine.go for more details.
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# Half~ish samples: verify we get "near" 3 (of 0.5 * 6)
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) <= bool (3+1)
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{} 1+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and http_requests) >= bool (3-1)
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{} 1+0x10
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# All samples
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eval range from 0 to 50m step 5m count(limit_ratio(1.0, http_requests) and http_requests)
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{} 6+0x10
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# All samples
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eval range from 0 to 50m step 5m count(limit_ratio(-1.0, http_requests) and http_requests)
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{} 6+0x10
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# Capped to 1.0 -> all samples
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eval_warn range from 0 to 50m step 5m count(limit_ratio(1.1, http_requests) and http_requests)
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{} 6+0x10
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# Capped to -1.0 -> all samples
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eval_warn range from 0 to 50m step 5m count(limit_ratio(-1.1, http_requests) and http_requests)
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{} 6+0x10
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# Verify that limit_ratio(value) and limit_ratio(1.0-value) return the "complement" of each other
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# Complement below for [0.2, -0.8]
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#
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# Complement 1of2: `or` should return all samples
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eval range from 0 to 50m step 5m count(limit_ratio(0.2, http_requests) or limit_ratio(-0.8, http_requests))
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{} 6+0x10
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# Complement 2of2: `and` should return no samples
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eval range from 0 to 50m step 5m count(limit_ratio(0.2, http_requests) and limit_ratio(-0.8, http_requests))
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# empty
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# Complement below for [0.5, -0.5]
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) or limit_ratio(-0.5, http_requests))
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{} 6+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.5, http_requests) and limit_ratio(-0.5, http_requests))
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# empty
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# Complement below for [0.8, -0.2]
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eval range from 0 to 50m step 5m count(limit_ratio(0.8, http_requests) or limit_ratio(-0.2, http_requests))
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{} 6+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(0.8, http_requests) and limit_ratio(-0.2, http_requests))
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# empty
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# Complement below for [some_ratio, 1.0 - some_ratio], some_ratio derived from time(),
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# using a small prime number to avoid rounded ratio values, and a small set of them.
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eval range from 0 to 50m step 5m count(limit_ratio(time() % 17/17, http_requests) or limit_ratio(1.0 - (time() % 17/17), http_requests))
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{} 6+0x10
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eval range from 0 to 50m step 5m count(limit_ratio(time() % 17/17, http_requests) and limit_ratio(1.0 - (time() % 17/17), http_requests))
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# empty
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# Poor man's normality check: ok (loaded samples follow a nice linearity over labels and time)
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# The check giving: 1 (i.e. true)
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eval range from 0 to 50m step 5m abs(avg(limit_ratio(0.5, http_requests)) - avg(limit_ratio(-0.5, http_requests))) <= bool stddev(http_requests)
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{} 1+0x10
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