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Merge pull request #25 from ipfs/feat/dup-blocks-test-enhancement
feat(Benchmarks): Add real world dup blocks test
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,63 @@ | ||
| package bitswap | ||
|
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| import ( | ||
| "math/rand" | ||
| "time" | ||
|
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| "github.com/ipfs/go-ipfs-delay" | ||
| ) | ||
|
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| var sharedRNG = rand.New(rand.NewSource(time.Now().UnixNano())) | ||
|
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| // InternetLatencyDelayGenerator generates three clusters of delays, | ||
| // typical of the type of peers you would encounter on the interenet | ||
| // Given a base delay time T, the wait time generated will be either: | ||
| // 1. A normalized distribution around the base time | ||
| // 2. A normalized distribution around the base time plus a "medium" delay | ||
| // 3. A normalized distribution around the base time plus a "large" delay | ||
| // The size of the medium & large delays are determined when the generator | ||
| // is constructed, as well as the relative percentages with which delays fall | ||
| // into each of the three different clusters, and the standard deviation for | ||
| // the normalized distribution | ||
| // This can be used to generate a number of scenarios typical of latency | ||
| // distribution among peers on the internet | ||
| func InternetLatencyDelayGenerator( | ||
| mediumDelay time.Duration, | ||
| largeDelay time.Duration, | ||
| percentMedium float64, | ||
| percentLarge float64, | ||
| std time.Duration, | ||
| rng *rand.Rand) delay.Generator { | ||
| if rng == nil { | ||
| rng = sharedRNG | ||
| } | ||
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| return &internetLatencyDelayGenerator{ | ||
| mediumDelay: mediumDelay, | ||
| largeDelay: largeDelay, | ||
| percentLarge: percentLarge, | ||
| percentMedium: percentMedium, | ||
| std: std, | ||
| rng: rng, | ||
| } | ||
| } | ||
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| type internetLatencyDelayGenerator struct { | ||
| mediumDelay time.Duration | ||
| largeDelay time.Duration | ||
| percentLarge float64 | ||
| percentMedium float64 | ||
| std time.Duration | ||
| rng *rand.Rand | ||
| } | ||
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| func (d *internetLatencyDelayGenerator) NextWaitTime(t time.Duration) time.Duration { | ||
| clusterDistribution := d.rng.Float64() | ||
| baseDelay := time.Duration(d.rng.NormFloat64()*float64(d.std)) + t | ||
| if clusterDistribution < d.percentLarge { | ||
| return baseDelay + d.largeDelay | ||
| } else if clusterDistribution < d.percentMedium+d.percentLarge { | ||
| return baseDelay + d.mediumDelay | ||
| } | ||
| return baseDelay | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,69 @@ | ||
| package bitswap | ||
|
|
||
| import ( | ||
| "math" | ||
| "math/rand" | ||
| "testing" | ||
| "time" | ||
| ) | ||
|
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| const testSeed = 99 | ||
|
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| func TestInternetLatencyDelayNextWaitTimeDistribution(t *testing.T) { | ||
| initialValue := 1000 * time.Millisecond | ||
| deviation := 100 * time.Millisecond | ||
| mediumDelay := 1000 * time.Millisecond | ||
| largeDelay := 3000 * time.Millisecond | ||
| percentMedium := 0.2 | ||
| percentLarge := 0.4 | ||
| buckets := make(map[string]int) | ||
| internetLatencyDistributionDelay := InternetLatencyDelayGenerator( | ||
| mediumDelay, | ||
| largeDelay, | ||
| percentMedium, | ||
| percentLarge, | ||
| deviation, | ||
| rand.New(rand.NewSource(testSeed))) | ||
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| buckets["fast"] = 0 | ||
| buckets["medium"] = 0 | ||
| buckets["slow"] = 0 | ||
| buckets["outside_1_deviation"] = 0 | ||
|
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| // strategy here is rather than mock randomness, just use enough samples to | ||
| // get approximately the distribution you'd expect | ||
| for i := 0; i < 10000; i++ { | ||
| next := internetLatencyDistributionDelay.NextWaitTime(initialValue) | ||
| if math.Abs((next - initialValue).Seconds()) <= deviation.Seconds() { | ||
| buckets["fast"]++ | ||
| } else if math.Abs((next - initialValue - mediumDelay).Seconds()) <= deviation.Seconds() { | ||
| buckets["medium"]++ | ||
| } else if math.Abs((next - initialValue - largeDelay).Seconds()) <= deviation.Seconds() { | ||
| buckets["slow"]++ | ||
| } else { | ||
| buckets["outside_1_deviation"]++ | ||
| } | ||
| } | ||
| totalInOneDeviation := float64(10000 - buckets["outside_1_deviation"]) | ||
| oneDeviationPercentage := totalInOneDeviation / 10000 | ||
| fastPercentageResult := float64(buckets["fast"]) / totalInOneDeviation | ||
| mediumPercentageResult := float64(buckets["medium"]) / totalInOneDeviation | ||
| slowPercentageResult := float64(buckets["slow"]) / totalInOneDeviation | ||
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| // see 68-95-99 rule for normal distributions | ||
| if math.Abs(oneDeviationPercentage-0.6827) >= 0.1 { | ||
| t.Fatal("Failed to distribute values normally based on standard deviation") | ||
| } | ||
|
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| if math.Abs(fastPercentageResult+percentMedium+percentLarge-1) >= 0.1 { | ||
| t.Fatal("Incorrect percentage of values distributed around fast delay time") | ||
| } | ||
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| if math.Abs(mediumPercentageResult-percentMedium) >= 0.1 { | ||
| t.Fatal("Incorrect percentage of values distributed around medium delay time") | ||
| } | ||
|
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| if math.Abs(slowPercentageResult-percentLarge) >= 0.1 { | ||
| t.Fatal("Incorrect percentage of values distributed around slow delay time") | ||
| } | ||
| } |
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