Use a worker-specific random source to remove lock contention.

statsd/worker.go

@@ -3,23 +3,31 @@ package statsd
 import (
 	"math/rand"
 	"sync"
+	"time"
 )
 
 type worker struct {
 	pool   *bufferPool
 	buffer *statsdBuffer
 	sender *sender
+	random *rand.Rand
 	sync.Mutex
 
 	inputMetrics chan metric
 	stop         chan struct{}
 }
 
 func newWorker(pool *bufferPool, sender *sender) *worker {
+	// Note that calling time.Now().UnixNano() repeatedly quickly may return
+	// very similar values. That's fine for seeding the worker-specific random
+	// source because we just need an evenly distributed stream of float values.
+	// Do not use this random source for cryptographic randomness.
+	random := rand.New(rand.NewSource(time.Now().UnixNano()))
 	return &worker{
 		pool:   pool,
 		sender: sender,
 		buffer: pool.borrowBuffer(),
+		random: random,
 		stop:   make(chan struct{}),
 	}
 }
@@ -59,7 +67,7 @@ func (w *worker) processMetric(m metric) error {
 }
 
 func (w *worker) shouldSample(rate float64) bool {
-	if rate < 1 && rand.Float64() > rate {
+	if rate < 1 && w.random.Float64() > rate {
 		return false
 	}
 	return true

statsd/worker_test.go

@@ -0,0 +1,35 @@
+package statsd
+
+import (
+	"fmt"
+	"testing"
+
+	"github.com/stretchr/testify/assert"
+)
+
+func TestShouldSample(t *testing.T) {
+	rates := []float64{0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0}
+	iterations := 50_000
+	worker := newWorker(newBufferPool(1, 1, 1), nil)
+	for _, rate := range rates {
+		rate := rate // Capture range variable.
+		t.Run(fmt.Sprintf("Rate %0.2f", rate), func(t *testing.T) {
+			count := 0
+			for i := 0; i < iterations; i++ {
+				if worker.shouldSample(rate) {
+					count++
+				}
+			}
+			assert.InDelta(t, rate, float64(count)/float64(iterations), 0.01)
+		})
+	}
+}
+
+func BenchmarkShouldSample(b *testing.B) {
+	b.RunParallel(func(pb *testing.PB) {
+		worker := newWorker(newBufferPool(1, 1, 1), nil)
+		for pb.Next() {
+			worker.shouldSample(0.1)
+		}
+	})
+}