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lib/model: Optimize jobQueue performance and memory use (#8023)

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By truncating time.Time to an int64 nanosecond count, we lose the
ability to precisely order timestamps before 1678 or after 2262, but we
gain (linux/amd64, Go 1.17.1):

name                      old time/op    new time/op    delta
JobQueuePushPopDone10k-8    2.85ms ± 5%    2.29ms ± 2%  -19.80%  (p=0.000 n=20+18)
JobQueueBump-8              34.0µs ± 1%    29.8µs ± 1%  -12.35%  (p=0.000 n=19+19)

name                      old alloc/op   new alloc/op   delta
JobQueuePushPopDone10k-8    2.56MB ± 0%    1.76MB ± 0%  -31.31%  (p=0.000 n=18+13)

name                      old allocs/op  new allocs/op  delta
JobQueuePushPopDone10k-8      23.0 ± 0%      23.0 ± 0%     ~     (all equal)

Results for BenchmarkJobQueueBump are with the fixed version, which no
longer depends on b.N for the amount of work performed. rand.Rand.Intn
is cheap at ~10ns per iteration.
This commit is contained in:
greatroar
2021-10-29 20:20:46 +02:00
committed by GitHub
parent 296cc1bca2
commit 807a6b1022
2 changed files with 10 additions and 5 deletions
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lib/model/queue.go
+4 -3
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@@ -23,7 +23,7 @@ type jobQueue struct {
type jobQueueEntry struct {
name string
size int64
modified time.Time
modified int64
}
func newJobQueue() *jobQueue {
@@ -34,7 +34,8 @@ func newJobQueue() *jobQueue {
func (q *jobQueue) Push(file string, size int64, modified time.Time) {
q.mut.Lock()
q.queued = append(q.queued, jobQueueEntry{file, size, modified})
// The range of UnixNano covers a range of reasonable timestamps.
q.queued = append(q.queued, jobQueueEntry{file, size, modified.UnixNano()})
q.mut.Unlock()
}
@@ -191,5 +192,5 @@ func (q smallestFirst) Swap(a, b int) { q[a], q[b] = q[b], q[a] }
type oldestFirst []jobQueueEntry
func (q oldestFirst) Len() int { return len(q) }
func (q oldestFirst) Less(a, b int) bool { return q[a].modified.Before(q[b].modified) }
func (q oldestFirst) Less(a, b int) bool { return q[a].modified < q[b].modified }
func (q oldestFirst) Swap(a, b int) { q[a], q[b] = q[b], q[a] }