VictoriaMetrics/lib/storage/index_db_timing_test.go

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Go
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2019-05-22 21:16:55 +00:00
package storage
import (
"fmt"
"os"
"regexp"
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"strconv"
"testing"
"time"
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)
func BenchmarkRegexpFilterMatch(b *testing.B) {
b.ReportAllocs()
b.RunParallel(func(pb *testing.PB) {
re := regexp.MustCompile(`.*foo-bar-baz.*`)
b := []byte("fdsffd foo-bar-baz assd fdsfad dasf dsa")
for pb.Next() {
if !re.Match(b) {
panic("BUG: regexp must match!")
}
b[0]++
}
})
}
func BenchmarkRegexpFilterMismatch(b *testing.B) {
b.ReportAllocs()
b.RunParallel(func(pb *testing.PB) {
re := regexp.MustCompile(`.*foo-bar-baz.*`)
b := []byte("fdsffd foo-bar sfddsf assd nmn,mfdsdsakj")
for pb.Next() {
if re.Match(b) {
panic("BUG: regexp mustn't match!")
}
b[0]++
}
})
}
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func BenchmarkIndexDBAddTSIDs(b *testing.B) {
const recordsPerLoop = 1e3
s := newTestStorage()
defer stopTestStorage(s)
lib/index: reduce read/write load after indexDB rotation (#2177) * lib/index: reduce read/write load after indexDB rotation IndexDB in VM is responsible for storing TSID - ID's used for identifying time series. The index is stored on disk and used by both ingestion and read path. IndexDB is stored separately to data parts and is global for all stored data. It can't be deleted partially as VM deletes data parts. Instead, indexDB is rotated once in `retention` interval. The rotation procedure means that `current` indexDB becomes `previous`, and new freshly created indexDB struct becomes `current`. So in any time, VM holds indexDB for current and previous retention periods. When time series is ingested or queried, VM checks if its TSID is present in `current` indexDB. If it is missing, it checks the `previous` indexDB. If TSID was found, it gets copied to the `current` indexDB. In this way `current` indexDB stores only series which were active during the retention period. To improve indexDB lookups, VM uses a cache layer called `tsidCache`. Both write and read path consult `tsidCache` and on miss the relad lookup happens. When rotation happens, VM resets the `tsidCache`. This is needed for ingestion path to trigger `current` indexDB re-population. Since index re-population requires additional resources, every index rotation event may cause some extra load on CPU and disk. While it may be unnoticeable for most of the cases, for systems with very high number of unique series each rotation may lead to performance degradation for some period of time. This PR makes an attempt to smooth out resource usage after the rotation. The changes are following: 1. `tsidCache` is no longer reset after the rotation; 2. Instead, each entry in `tsidCache` gains a notion of indexDB to which they belong; 3. On ingestion path after the rotation we check if requested TSID was found in `tsidCache`. Then we have 3 branches: 3.1 Fast path. It was found, and belongs to the `current` indexDB. Return TSID. 3.2 Slow path. It wasn't found, so we generate it from scratch, add to `current` indexDB, add it to `tsidCache`. 3.3 Smooth path. It was found but does not belong to the `current` indexDB. In this case, we add it to the `current` indexDB with some probability. The probability is based on time passed since the last rotation with some threshold. The more time has passed since rotation the higher is chance to re-populate `current` indexDB. The default re-population interval in this PR is set to `1h`, during which entries from `previous` index supposed to slowly re-populate `current` index. The new metric `vm_timeseries_repopulated_total` was added to identify how many TSIDs were moved from `previous` indexDB to the `current` indexDB. This metric supposed to grow only during the first `1h` after the last rotation. https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 Signed-off-by: hagen1778 <roman@victoriametrics.com> * wip * wip Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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dbName := nextIndexDBTableName()
var isReadOnly uint32
db := mustOpenIndexDB(dbName, s, 0, &isReadOnly)
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defer func() {
db.MustClose()
if err := os.RemoveAll(dbName); err != nil {
b.Fatalf("cannot remove indexDB: %s", err)
}
}()
b.ReportAllocs()
b.SetBytes(recordsPerLoop)
b.ResetTimer()
b.RunParallel(func(pb *testing.PB) {
var mn MetricName
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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var tsid TSID
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// The most common tags.
mn.Tags = []Tag{
{
Key: []byte("job"),
},
{
Key: []byte("instance"),
},
}
startOffset := 0
for pb.Next() {
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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benchmarkIndexDBAddTSIDs(db, &tsid, &mn, startOffset, recordsPerLoop)
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startOffset += recordsPerLoop
}
})
b.StopTimer()
}
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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func benchmarkIndexDBAddTSIDs(db *indexDB, tsid *TSID, mn *MetricName, startOffset, recordsPerLoop int) {
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var metricName []byte
var metricNameRaw []byte
is := db.getIndexSearch(noDeadline)
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defer db.putIndexSearch(is)
for i := 0; i < recordsPerLoop; i++ {
mn.MetricGroup = strconv.AppendUint(mn.MetricGroup[:0], uint64(i+startOffset), 10)
for j := range mn.Tags {
mn.Tags[j].Value = strconv.AppendUint(mn.Tags[j].Value[:0], uint64(i*j), 16)
}
mn.sortTags()
metricName = mn.Marshal(metricName[:0])
metricNameRaw = mn.marshalRaw(metricNameRaw[:0])
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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if err := is.GetOrCreateTSIDByName(tsid, metricName, metricNameRaw, 0); err != nil {
panic(fmt.Errorf("cannot insert record: %w", err))
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}
}
}
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
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func BenchmarkHeadPostingForMatchers(b *testing.B) {
// This benchmark is equivalent to https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52
// See https://www.robustperception.io/evaluating-performance-and-correctness for more details.
s := newTestStorage()
defer stopTestStorage(s)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
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lib/index: reduce read/write load after indexDB rotation (#2177) * lib/index: reduce read/write load after indexDB rotation IndexDB in VM is responsible for storing TSID - ID's used for identifying time series. The index is stored on disk and used by both ingestion and read path. IndexDB is stored separately to data parts and is global for all stored data. It can't be deleted partially as VM deletes data parts. Instead, indexDB is rotated once in `retention` interval. The rotation procedure means that `current` indexDB becomes `previous`, and new freshly created indexDB struct becomes `current`. So in any time, VM holds indexDB for current and previous retention periods. When time series is ingested or queried, VM checks if its TSID is present in `current` indexDB. If it is missing, it checks the `previous` indexDB. If TSID was found, it gets copied to the `current` indexDB. In this way `current` indexDB stores only series which were active during the retention period. To improve indexDB lookups, VM uses a cache layer called `tsidCache`. Both write and read path consult `tsidCache` and on miss the relad lookup happens. When rotation happens, VM resets the `tsidCache`. This is needed for ingestion path to trigger `current` indexDB re-population. Since index re-population requires additional resources, every index rotation event may cause some extra load on CPU and disk. While it may be unnoticeable for most of the cases, for systems with very high number of unique series each rotation may lead to performance degradation for some period of time. This PR makes an attempt to smooth out resource usage after the rotation. The changes are following: 1. `tsidCache` is no longer reset after the rotation; 2. Instead, each entry in `tsidCache` gains a notion of indexDB to which they belong; 3. On ingestion path after the rotation we check if requested TSID was found in `tsidCache`. Then we have 3 branches: 3.1 Fast path. It was found, and belongs to the `current` indexDB. Return TSID. 3.2 Slow path. It wasn't found, so we generate it from scratch, add to `current` indexDB, add it to `tsidCache`. 3.3 Smooth path. It was found but does not belong to the `current` indexDB. In this case, we add it to the `current` indexDB with some probability. The probability is based on time passed since the last rotation with some threshold. The more time has passed since rotation the higher is chance to re-populate `current` indexDB. The default re-population interval in this PR is set to `1h`, during which entries from `previous` index supposed to slowly re-populate `current` index. The new metric `vm_timeseries_repopulated_total` was added to identify how many TSIDs were moved from `previous` indexDB to the `current` indexDB. This metric supposed to grow only during the first `1h` after the last rotation. https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 Signed-off-by: hagen1778 <roman@victoriametrics.com> * wip * wip Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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dbName := nextIndexDBTableName()
var isReadOnly uint32
db := mustOpenIndexDB(dbName, s, 0, &isReadOnly)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
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defer func() {
db.MustClose()
if err := os.RemoveAll(dbName); err != nil {
b.Fatalf("cannot remove indexDB: %s", err)
}
}()
// Fill the db with data as in https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L66
var mn MetricName
var metricName []byte
var metricNameRaw []byte
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
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var tsid TSID
is := db.getIndexSearch(noDeadline)
defer db.putIndexSearch(is)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
addSeries := func(kvs ...string) {
mn.Reset()
for i := 0; i < len(kvs); i += 2 {
mn.AddTag(kvs[i], kvs[i+1])
}
mn.sortTags()
metricName = mn.Marshal(metricName[:0])
metricNameRaw = mn.marshalRaw(metricNameRaw[:0])
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
2023-05-18 18:11:42 +00:00
if err := is.createTSIDByName(&tsid, metricName, metricNameRaw, 0); err != nil {
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
b.Fatalf("cannot insert record: %s", err)
}
}
for n := 0; n < 10; n++ {
ns := strconv.Itoa(n)
lib/storage: increase the number of created time series in BenchmarkHeadPostingForMatchers in order to be on par with Promethues The previous commit was accidentally creating 10x smaller number of time series than Prometheus and this led to invalid benchmark results. The updated benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 6194893 -97.73% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 10781372 -92.19% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 10632834 -92.11% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 10679975 -94.55% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 100118510 -98.74% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 154955671 -97.96% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 258003769 -77.42% BenchmarkHeadPostingForMatchers/i!="" 9964150263 159783895 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 10937895 -94.96% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 10990027 -94.57% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 87004349 -82.11% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 53342793 -84.79% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 54256156 -85.76% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 21823279 -75.62% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 46671359 -87.70% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 53915842 -87.30% VictoriaMetrics uses 1GB of RAM during the benchmark (vs 3.5GB of RAM for Prometheus)
2019-11-18 17:48:24 +00:00
for i := 0; i < 100000; i++ {
ix := strconv.Itoa(i)
addSeries("i", ix, "n", ns, "j", "foo")
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
// Have some series that won't be matched, to properly test inverted matches.
addSeries("i", ix, "n", ns, "j", "bar")
addSeries("i", ix, "n", "0_"+ns, "j", "bar")
addSeries("i", ix, "n", "1_"+ns, "j", "bar")
addSeries("i", ix, "n", "2_"+ns, "j", "foo")
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
}
}
// Make sure all the items can be searched.
db.tb.DebugFlush()
b.ResetTimer()
benchSearch := func(b *testing.B, tfs *TagFilters, expectedMetricIDs int) {
is := db.getIndexSearch(noDeadline)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
defer db.putIndexSearch(is)
tfss := []*TagFilters{tfs}
tr := TimeRange{
MinTimestamp: 0,
MaxTimestamp: timestampFromTime(time.Now()),
}
for i := 0; i < b.N; i++ {
metricIDs, err := is.searchMetricIDs(nil, tfss, tr, 2e9)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
if err != nil {
b.Fatalf("unexpected error in searchMetricIDs: %s", err)
}
if len(metricIDs) != expectedMetricIDs {
b.Fatalf("unexpected metricIDs found; got %d; want %d", len(metricIDs), expectedMetricIDs)
}
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
}
}
addTagFilter := func(tfs *TagFilters, key, value string, isNegative, isRegexp bool) {
if err := tfs.Add([]byte(key), []byte(value), isNegative, isRegexp); err != nil {
b.Fatalf("cannot add tag filter %q=%q, isNegative=%v, isRegexp=%v", key, value, isNegative, isRegexp)
}
}
b.Run(`n="1"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
benchSearch(b, tfs, 2e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`j="foo",n="1"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "j", "foo", false, false)
addTagFilter(tfs, "n", "1", false, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",j!="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "j", "foo", true, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`i=~".*"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "i", ".*", false, true)
benchSearch(b, tfs, 0)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`i=~".+"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "i", ".+", false, true)
benchSearch(b, tfs, 5e6)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`i=~""`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "i", "", false, true)
benchSearch(b, tfs, 0)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`i!=""`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "i", "", true, false)
benchSearch(b, tfs, 5e6)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~".*",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", ".*", false, true)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~".*",i!="2",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", ".*", false, true)
addTagFilter(tfs, "i", "2", true, false)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5-1)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i!=""`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", "", true, false)
benchSearch(b, tfs, 2e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i!="",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", "", true, false)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~".+",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", ".+", false, true)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~"1.+",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", "1.+", false, true)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 11110)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~".+",i!="2",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", ".+", false, true)
addTagFilter(tfs, "i", "2", true, false)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 1e5-1)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
b.Run(`n="1",i=~".+",i!~"2.*",j="foo"`, func(b *testing.B) {
tfs := NewTagFilters()
addTagFilter(tfs, "n", "1", false, false)
addTagFilter(tfs, "i", ".+", false, true)
addTagFilter(tfs, "i", "2.*", true, true)
addTagFilter(tfs, "j", "foo", false, false)
benchSearch(b, tfs, 88889)
lib/storage: add BenchmarkHeadPostingForMatchers similar to the benchmark from Prometheus See the corresponding benchmark in Prometheus - https://github.com/prometheus/prometheus/blob/23c0299d85bfeb5d9b59e994861553a25ca578e5/tsdb/head_bench_test.go#L52 The benchmark allows performing apples-to-apples comparison of time series search in Prometheus and VictoriaMetrics. The following article - https://www.robustperception.io/evaluating-performance-and-correctness - contains incorrect numbers for VictoriaMetrics, since there wasn't this benchmark yet. Fix this. Benchmarks can be repeated with the following commands from Prometheus and VictoriaMetrics source code roots: - Prometheus: GOMAXPROCS=1 go test ./tsdb/ -run=111 -bench=BenchmarkHeadPostingForMatchers - VictoriaMetrics: GOMAXPROCS=1 go test ./lib/storage/ -run=111 -bench=BenchmarkHeadPostingForMatchers Benchmark results: benchmark old ns/op new ns/op delta BenchmarkHeadPostingForMatchers/n="1" 272756688 364977 -99.87% BenchmarkHeadPostingForMatchers/n="1",j="foo" 138132923 1181636 -99.14% BenchmarkHeadPostingForMatchers/j="foo",n="1" 134723762 1141578 -99.15% BenchmarkHeadPostingForMatchers/n="1",j!="foo" 195823953 1148056 -99.41% BenchmarkHeadPostingForMatchers/i=~".*" 7962582919 8716755 -99.89% BenchmarkHeadPostingForMatchers/i=~".+" 7589543864 12096587 -99.84% BenchmarkHeadPostingForMatchers/i=~"" 1142371741 16164560 -98.59% BenchmarkHeadPostingForMatchers/i!="" 9964150263 12230021 -99.88% BenchmarkHeadPostingForMatchers/n="1",i=~".*",j="foo" 216995884 1173476 -99.46% BenchmarkHeadPostingForMatchers/n="1",i=~".*",i!="2",j="foo" 202541348 1299743 -99.36% BenchmarkHeadPostingForMatchers/n="1",i!="" 486285711 11555193 -97.62% BenchmarkHeadPostingForMatchers/n="1",i!="",j="foo" 350776931 5607506 -98.40% BenchmarkHeadPostingForMatchers/n="1",i=~".+",j="foo" 380888565 6380335 -98.32% BenchmarkHeadPostingForMatchers/n="1",i=~"1.+",j="foo" 89500296 2078970 -97.68% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!="2",j="foo" 379529654 6561368 -98.27% BenchmarkHeadPostingForMatchers/n="1",i=~".+",i!~"2.*",j="foo" 424563825 6757132 -98.41% The first column (old) is for Prometheus, the second column (new) is for VictoriaMetrics. As you can see, VictoriaMetrics outperforms Prometheus by more than 100x in almost all the test cases of this benchmark. Prometheus was using 3.5GB of RAM during the benchmark, while VictoriaMetrics was using 400MB of RAM.
2019-11-18 16:21:27 +00:00
})
}
2019-05-22 21:16:55 +00:00
func BenchmarkIndexDBGetTSIDs(b *testing.B) {
s := newTestStorage()
defer stopTestStorage(s)
lib/index: reduce read/write load after indexDB rotation (#2177) * lib/index: reduce read/write load after indexDB rotation IndexDB in VM is responsible for storing TSID - ID's used for identifying time series. The index is stored on disk and used by both ingestion and read path. IndexDB is stored separately to data parts and is global for all stored data. It can't be deleted partially as VM deletes data parts. Instead, indexDB is rotated once in `retention` interval. The rotation procedure means that `current` indexDB becomes `previous`, and new freshly created indexDB struct becomes `current`. So in any time, VM holds indexDB for current and previous retention periods. When time series is ingested or queried, VM checks if its TSID is present in `current` indexDB. If it is missing, it checks the `previous` indexDB. If TSID was found, it gets copied to the `current` indexDB. In this way `current` indexDB stores only series which were active during the retention period. To improve indexDB lookups, VM uses a cache layer called `tsidCache`. Both write and read path consult `tsidCache` and on miss the relad lookup happens. When rotation happens, VM resets the `tsidCache`. This is needed for ingestion path to trigger `current` indexDB re-population. Since index re-population requires additional resources, every index rotation event may cause some extra load on CPU and disk. While it may be unnoticeable for most of the cases, for systems with very high number of unique series each rotation may lead to performance degradation for some period of time. This PR makes an attempt to smooth out resource usage after the rotation. The changes are following: 1. `tsidCache` is no longer reset after the rotation; 2. Instead, each entry in `tsidCache` gains a notion of indexDB to which they belong; 3. On ingestion path after the rotation we check if requested TSID was found in `tsidCache`. Then we have 3 branches: 3.1 Fast path. It was found, and belongs to the `current` indexDB. Return TSID. 3.2 Slow path. It wasn't found, so we generate it from scratch, add to `current` indexDB, add it to `tsidCache`. 3.3 Smooth path. It was found but does not belong to the `current` indexDB. In this case, we add it to the `current` indexDB with some probability. The probability is based on time passed since the last rotation with some threshold. The more time has passed since rotation the higher is chance to re-populate `current` indexDB. The default re-population interval in this PR is set to `1h`, during which entries from `previous` index supposed to slowly re-populate `current` index. The new metric `vm_timeseries_repopulated_total` was added to identify how many TSIDs were moved from `previous` indexDB to the `current` indexDB. This metric supposed to grow only during the first `1h` after the last rotation. https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 Signed-off-by: hagen1778 <roman@victoriametrics.com> * wip * wip Co-authored-by: Aliaksandr Valialkin <valyala@victoriametrics.com>
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dbName := nextIndexDBTableName()
var isReadOnly uint32
db := mustOpenIndexDB(dbName, s, 0, &isReadOnly)
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defer func() {
db.MustClose()
if err := os.RemoveAll(dbName); err != nil {
b.Fatalf("cannot remove indexDB: %s", err)
}
}()
const recordsPerLoop = 1000
const recordsCount = 1e5
// Fill the db with recordsCount records.
var mn MetricName
mn.MetricGroup = []byte("rps")
for i := 0; i < 2; i++ {
key := fmt.Sprintf("key_%d", i)
value := fmt.Sprintf("value_%d", i)
mn.AddTag(key, value)
}
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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var tsid TSID
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var metricName []byte
var metricNameRaw []byte
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is := db.getIndexSearch(noDeadline)
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defer db.putIndexSearch(is)
for i := 0; i < recordsCount; i++ {
mn.sortTags()
metricName = mn.Marshal(metricName[:0])
metricNameRaw = mn.marshalRaw(metricName[:0])
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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if err := is.GetOrCreateTSIDByName(&tsid, metricName, metricNameRaw, 0); err != nil {
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b.Fatalf("cannot insert record: %s", err)
}
}
b.SetBytes(recordsPerLoop)
b.ReportAllocs()
b.ResetTimer()
b.RunParallel(func(pb *testing.PB) {
lib/storage: revert the migration from global to per-day index for (MetricName -> TSID) This reverts the following commits: - e0e16a2d36d3ad916ad0df2f681e7ff250111d98 - 2ce02a7fe65e20eb9bf3102919dede415a469672 The reason for revert: the updated logic breaks assumptions made when fixing https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 . For example, if a time series stop receiving new samples during the first day after the indexdb rotation, there are chances that the time series won't be registered in the new indexdb. This is OK until the next indexdb rotation, since the time series is registered in the previous indexdb, so it can be found during queries. But the time series will become invisible for search after the next indexdb rotation, while its data is still there. There is also incompletely solved issue with the increased CPU and disk IO resource usage just after the indexdb rotation. There was an attempt to fix it, but it didn't fix it in full, while introducing the issue mentioned above. See https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 TODO: to find out the solution, which simultaneously solves the following issues: - increased memory usage for setups high churn rate and long retention (e.g. what the reverted commit does) - increased CPU and disk IO usage during indexdb rotation ( https://github.com/VictoriaMetrics/VictoriaMetrics/issues/1401 ) - https://github.com/VictoriaMetrics/VictoriaMetrics/issues/2698 Possible solution - to create the new indexdb in one hour before the indexdb rotation and to gradually pre-populate it with the needed index data during the last hour before indexdb rotation. Then the new indexdb will contain all the needed data just after the rotation, so it won't trigger increased CPU and disk IO.
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var tsidLocal TSID
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var metricNameLocal []byte
var metricNameLocalRaw []byte
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mnLocal := mn
is := db.getIndexSearch(noDeadline)
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defer db.putIndexSearch(is)
for pb.Next() {
for i := 0; i < recordsPerLoop; i++ {
mnLocal.sortTags()
metricNameLocal = mnLocal.Marshal(metricNameLocal[:0])
metricNameLocalRaw = mnLocal.marshalRaw(metricNameLocalRaw[:0])
if err := is.GetOrCreateTSIDByName(&tsidLocal, metricNameLocal, metricNameLocalRaw, 0); err != nil {
panic(fmt.Errorf("cannot obtain tsid: %w", err))
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}
}
}
})
b.StopTimer()
}