VictoriaMetrics/app/vmselect/netstorage/netstorage_test.go

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package netstorage
import (
"flag"
"reflect"
"runtime"
"testing"
)
func TestInitStopNodes(t *testing.T) {
if err := flag.Set("vmstorageDialTimeout", "1ms"); err != nil {
t.Fatalf("cannot set vmstorageDialTimeout flag: %s", err)
}
for i := 0; i < 3; i++ {
Init([]string{"host1", "host2"})
runtime.Gosched()
MustStop()
}
// Try initializing the netstorage with bigger number of nodes
for i := 0; i < 3; i++ {
Init([]string{"host1", "host2", "host3"})
runtime.Gosched()
MustStop()
}
// Try initializing the netstorage with smaller number of nodes
for i := 0; i < 3; i++ {
Init([]string{"host1"})
runtime.Gosched()
MustStop()
}
}
func TestMergeSortBlocks(t *testing.T) {
f := func(blocks []*sortBlock, dedupInterval int64, expectedResult *Result) {
t.Helper()
var result Result
sbh := getSortBlocksHeap()
sbh.sbs = append(sbh.sbs[:0], blocks...)
mergeSortBlocks(&result, sbh, dedupInterval)
putSortBlocksHeap(sbh)
if !reflect.DeepEqual(result.Values, expectedResult.Values) {
t.Fatalf("unexpected values;\ngot\n%v\nwant\n%v", result.Values, expectedResult.Values)
}
if !reflect.DeepEqual(result.Timestamps, expectedResult.Timestamps) {
t.Fatalf("unexpected timestamps;\ngot\n%v\nwant\n%v", result.Timestamps, expectedResult.Timestamps)
}
}
// Zero blocks
f(nil, 1, &Result{})
// Single block without samples
f([]*sortBlock{{}}, 1, &Result{})
// Single block with a single samples.
f([]*sortBlock{
{
Timestamps: []int64{1},
Values: []float64{4.2},
},
}, 1, &Result{
Timestamps: []int64{1},
Values: []float64{4.2},
})
// Single block with multiple samples.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 3},
Values: []float64{4.2, 2.1, 10},
},
}, 1, &Result{
Timestamps: []int64{1, 2, 3},
Values: []float64{4.2, 2.1, 10},
})
// Single block with multiple samples with deduplication.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 3},
Values: []float64{4.2, 2.1, 10},
},
}, 2, &Result{
Timestamps: []int64{2, 3},
Values: []float64{2.1, 10},
})
// Multiple blocks without time range intersection.
f([]*sortBlock{
{
Timestamps: []int64{3, 5},
Values: []float64{5.2, 6.1},
},
{
Timestamps: []int64{1, 2},
Values: []float64{4.2, 2.1},
},
}, 1, &Result{
Timestamps: []int64{1, 2, 3, 5},
Values: []float64{4.2, 2.1, 5.2, 6.1},
})
// Multiple blocks with time range intersection.
f([]*sortBlock{
{
Timestamps: []int64{3, 5},
Values: []float64{5.2, 6.1},
},
{
Timestamps: []int64{1, 2, 4},
Values: []float64{4.2, 2.1, 42},
},
}, 1, &Result{
Timestamps: []int64{1, 2, 3, 4, 5},
Values: []float64{4.2, 2.1, 5.2, 42, 6.1},
})
// Multiple blocks with time range inclusion.
f([]*sortBlock{
{
Timestamps: []int64{0, 3, 5},
Values: []float64{9, 5.2, 6.1},
},
{
Timestamps: []int64{1, 2, 4},
Values: []float64{4.2, 2.1, 42},
},
}, 1, &Result{
Timestamps: []int64{0, 1, 2, 3, 4, 5},
Values: []float64{9, 4.2, 2.1, 5.2, 42, 6.1},
})
app/vmselect/netstorage: consistently select the sample with the biggest value out of samples with identical timestamps Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3333 This fix is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3620 , but doesn't slow down the common case with merging replicated data blocks so significantly. Benchmark results: Before the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13968 85643 ns/op 956.53 MB/s 1700 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 10806 109171 ns/op 1500.77 MB/s 2191 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 8887 130623 ns/op 1881.45 MB/s 2660 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 7440 157348 ns/op 2082.52 MB/s 3174 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 6534 184473 ns/op 2220.38 MB/s 3612 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 13419 85205 ns/op 961.44 MB/s 2213 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 579 1894900 ns/op 43.23 MB/s 46760 B/op 1 allocs/op After the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13832 85298 ns/op 960.40 MB/s 1716 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 8833 134222 ns/op 1220.66 MB/s 2675 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 6487 184830 ns/op 1329.65 MB/s 3636 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 4977 236318 ns/op 1386.61 MB/s 4733 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 4088 296734 ns/op 1380.36 MB/s 5761 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 14083 84067 ns/op 974.47 MB/s 2110 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 536 2043534 ns/op 40.09 MB/s 50511 B/op 1 allocs/op
2023-01-09 20:57:43 +00:00
// Multiple blocks with identical timestamps and identical values.
f([]*sortBlock{
app/vmselect/netstorage: consistently select the sample with the biggest value out of samples with identical timestamps Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3333 This fix is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3620 , but doesn't slow down the common case with merging replicated data blocks so significantly. Benchmark results: Before the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13968 85643 ns/op 956.53 MB/s 1700 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 10806 109171 ns/op 1500.77 MB/s 2191 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 8887 130623 ns/op 1881.45 MB/s 2660 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 7440 157348 ns/op 2082.52 MB/s 3174 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 6534 184473 ns/op 2220.38 MB/s 3612 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 13419 85205 ns/op 961.44 MB/s 2213 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 579 1894900 ns/op 43.23 MB/s 46760 B/op 1 allocs/op After the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13832 85298 ns/op 960.40 MB/s 1716 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 8833 134222 ns/op 1220.66 MB/s 2675 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 6487 184830 ns/op 1329.65 MB/s 3636 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 4977 236318 ns/op 1386.61 MB/s 4733 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 4088 296734 ns/op 1380.36 MB/s 5761 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 14083 84067 ns/op 974.47 MB/s 2110 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 536 2043534 ns/op 40.09 MB/s 50511 B/op 1 allocs/op
2023-01-09 20:57:43 +00:00
{
Timestamps: []int64{1, 2, 4, 5},
Values: []float64{9, 5.2, 6.1, 9},
},
{
Timestamps: []int64{1, 2, 4},
Values: []float64{9, 5.2, 6.1},
},
app/vmselect/netstorage: consistently select the sample with the biggest value out of samples with identical timestamps Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3333 This fix is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3620 , but doesn't slow down the common case with merging replicated data blocks so significantly. Benchmark results: Before the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13968 85643 ns/op 956.53 MB/s 1700 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 10806 109171 ns/op 1500.77 MB/s 2191 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 8887 130623 ns/op 1881.45 MB/s 2660 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 7440 157348 ns/op 2082.52 MB/s 3174 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 6534 184473 ns/op 2220.38 MB/s 3612 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 13419 85205 ns/op 961.44 MB/s 2213 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 579 1894900 ns/op 43.23 MB/s 46760 B/op 1 allocs/op After the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13832 85298 ns/op 960.40 MB/s 1716 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 8833 134222 ns/op 1220.66 MB/s 2675 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 6487 184830 ns/op 1329.65 MB/s 3636 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 4977 236318 ns/op 1386.61 MB/s 4733 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 4088 296734 ns/op 1380.36 MB/s 5761 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 14083 84067 ns/op 974.47 MB/s 2110 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 536 2043534 ns/op 40.09 MB/s 50511 B/op 1 allocs/op
2023-01-09 20:57:43 +00:00
}, 1, &Result{
Timestamps: []int64{1, 2, 4, 5},
Values: []float64{9, 5.2, 6.1, 9},
})
// Multiple blocks with identical timestamps.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 4, 5},
Values: []float64{9, 5.2, 6.1, 9},
},
{
Timestamps: []int64{1, 2, 4},
Values: []float64{4.2, 2.1, 42},
},
}, 1, &Result{
app/vmselect/netstorage: consistently select the sample with the biggest value out of samples with identical timestamps Updates https://github.com/VictoriaMetrics/VictoriaMetrics/issues/3333 This fix is based on https://github.com/VictoriaMetrics/VictoriaMetrics/pull/3620 , but doesn't slow down the common case with merging replicated data blocks so significantly. Benchmark results: Before the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13968 85643 ns/op 956.53 MB/s 1700 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 10806 109171 ns/op 1500.77 MB/s 2191 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 8887 130623 ns/op 1881.45 MB/s 2660 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 7440 157348 ns/op 2082.52 MB/s 3174 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 6534 184473 ns/op 2220.38 MB/s 3612 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 13419 85205 ns/op 961.44 MB/s 2213 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 579 1894900 ns/op 43.23 MB/s 46760 B/op 1 allocs/op After the change: BenchmarkMergeSortBlocks/replicationFactor-1-4 13832 85298 ns/op 960.40 MB/s 1716 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-2-4 8833 134222 ns/op 1220.66 MB/s 2675 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-3-4 6487 184830 ns/op 1329.65 MB/s 3636 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-4-4 4977 236318 ns/op 1386.61 MB/s 4733 B/op 1 allocs/op BenchmarkMergeSortBlocks/replicationFactor-5-4 4088 296734 ns/op 1380.36 MB/s 5761 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-bestcase-4 14083 84067 ns/op 974.47 MB/s 2110 B/op 1 allocs/op BenchmarkMergeSortBlocks/overlapped-blocks-worstcase-4 536 2043534 ns/op 40.09 MB/s 50511 B/op 1 allocs/op
2023-01-09 20:57:43 +00:00
Timestamps: []int64{1, 2, 4, 5},
Values: []float64{9, 5.2, 42, 9},
})
// Multiple blocks with identical timestamps, disabled deduplication.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 4},
Values: []float64{9, 5.2, 6.1},
},
{
Timestamps: []int64{1, 2, 4},
Values: []float64{4.2, 2.1, 42},
},
}, 0, &Result{
Timestamps: []int64{1, 1, 2, 2, 4, 4},
Values: []float64{9, 4.2, 2.1, 5.2, 6.1, 42},
})
// Multiple blocks with identical timestamp ranges.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 5, 10, 11},
Values: []float64{9, 8, 7, 6, 5},
},
{
Timestamps: []int64{1, 2, 4, 10, 11, 12},
Values: []float64{21, 22, 23, 24, 25, 26},
},
}, 1, &Result{
Timestamps: []int64{1, 2, 4, 5, 10, 11, 12},
Values: []float64{21, 22, 23, 7, 24, 25, 26},
})
// Multiple blocks with identical timestamp ranges, no deduplication.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 5, 10, 11},
Values: []float64{9, 8, 7, 6, 5},
},
{
Timestamps: []int64{1, 2, 4, 10, 11, 12},
Values: []float64{21, 22, 23, 24, 25, 26},
},
}, 0, &Result{
Timestamps: []int64{1, 1, 2, 2, 4, 5, 10, 10, 11, 11, 12},
Values: []float64{9, 21, 22, 8, 23, 7, 6, 24, 25, 5, 26},
})
// Multiple blocks with identical timestamp ranges with deduplication.
f([]*sortBlock{
{
Timestamps: []int64{1, 2, 5, 10, 11},
Values: []float64{9, 8, 7, 6, 5},
},
{
Timestamps: []int64{1, 2, 4, 10, 11, 12},
Values: []float64{21, 22, 23, 24, 25, 26},
},
}, 5, &Result{
Timestamps: []int64{5, 10, 12},
Values: []float64{7, 24, 26},
})
}