Silences: Optimize incremental mutes queries via a silence version index by ultrotter · Pull Request #4723 · prometheus/alertmanager

ultrotter · 2025-11-10T19:33:48Z

Currently Mutes is optimized for when the global silences haven't changed at all, and either our label set had zero or a few silences. When even a single silence has been added to the state though, the entire state needs to be scanned to find any match.

Since we know that existing silences cannot start matching without being re-added to the state, and we have a state version at which we are "caught up" available, we can optimize the code path when a few silences have been added to the state by:

Creating an ordered index of versions, and silences which were added at that version
Allowing querying for silences matching filters that were added after a certain moment (QSince(version))
During Mutes performing a query for the old known silences, and one scanning just the newly added ones

This trades some memory, with a greatly increased performance for the situation when we have a lot of silences, and only a few have been added to the state since we last queried.

The GC is able to keep the state and indices up to date, in place, and reallocate the index to shed memory load when the set of silences has stably halved.

goos: linux
goarch: amd64
pkg: github.com/prometheus/alertmanager/silence
cpu: AMD EPYC Processor (with IBPB)
                                                              │ 0-qsince.txt  │            1-qsince.txt             │
                                                              │    sec/op     │    sec/op     vs base               │
Mutes/0_total,_0_matching                                        298.7n ± ∞ ¹   289.4n ± ∞ ¹        ~ (p=0.651 n=5)
Mutes/1_total,_1_matching                                        1.365µ ± ∞ ¹   1.406µ ± ∞ ¹   +3.00% (p=0.008 n=5)
Mutes/100_total,_10_matching                                     3.527µ ± ∞ ¹   3.132µ ± ∞ ¹  -11.20% (p=0.008 n=5)
Mutes/1000_total,_1_matching                                     1.513µ ± ∞ ¹   1.543µ ± ∞ ¹   +1.98% (p=0.008 n=5)
Mutes/1000_total,_10_matching                                    3.942µ ± ∞ ¹   3.479µ ± ∞ ¹  -11.75% (p=0.008 n=5)
Mutes/1000_total,_100_matching                                   24.53µ ± ∞ ¹   22.06µ ± ∞ ¹  -10.07% (p=0.008 n=5)
Mutes/10000_total,_0_matching                                    4.737µ ± ∞ ¹   4.202µ ± ∞ ¹  -11.29% (p=0.008 n=5)
Mutes/10000_total,_10_matching                                   4.839µ ± ∞ ¹   4.200µ ± ∞ ¹  -13.21% (p=0.008 n=5)
Mutes/10000_total,_1000_matching                                 353.9µ ± ∞ ¹   324.9µ ± ∞ ¹   -8.20% (p=0.008 n=5)
MutesIncremental/1000_base_silences                              331.8µ ± ∞ ¹   319.2µ ± ∞ ¹   -3.80% (p=0.008 n=5)
MutesIncremental/3000_base_silences                              512.3µ ± ∞ ¹   307.5µ ± ∞ ¹  -39.98% (p=0.008 n=5)
MutesIncremental/7000_base_silences                              690.8µ ± ∞ ¹   296.9µ ± ∞ ¹  -57.03% (p=0.008 n=5)
MutesIncremental/10000_base_silences                             950.6µ ± ∞ ¹   283.3µ ± ∞ ¹  -70.20% (p=0.008 n=5)
Query/100_silences                                               24.80µ ± ∞ ¹   24.19µ ± ∞ ¹   -2.46% (p=0.008 n=5)
Query/1000_silences                                              356.5µ ± ∞ ¹   336.0µ ± ∞ ¹   -5.73% (p=0.008 n=5)
Query/10000_silences                                            10.441m ± ∞ ¹   6.283m ± ∞ ¹  -39.82% (p=0.008 n=5)
QueryParallel/100_silences                                       23.12µ ± ∞ ¹   22.83µ ± ∞ ¹        ~ (p=0.310 n=5)
QueryParallel/1000_silences                                      362.1µ ± ∞ ¹   344.6µ ± ∞ ¹   -4.85% (p=0.008 n=5)
QueryParallel/10000_silences                                    10.047m ± ∞ ¹   6.256m ± ∞ ¹  -37.73% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_10%_add_rate      971.6µ ± ∞ ¹   843.5µ ± ∞ ¹  -13.18% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_1%_add_rate       419.5µ ± ∞ ¹   396.9µ ± ∞ ¹   -5.38% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_0.1%_add_rate     380.2µ ± ∞ ¹   354.8µ ± ∞ ¹   -6.68% (p=0.008 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_1%_add_rate     10.767m ± ∞ ¹   6.532m ± ∞ ¹  -39.34% (p=0.008 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_0.1%_add_rate   11.072m ± ∞ ¹   6.214m ± ∞ ¹  -43.87% (p=0.008 n=5)**
MutesParallel/100_silences                                       21.22µ ± ∞ ¹   21.53µ ± ∞ ¹   +1.48% (p=0.008 n=5)
MutesParallel/1000_silences                                      242.8µ ± ∞ ¹   236.6µ ± ∞ ¹   -2.56% (p=0.008 n=5)
MutesParallel/10000_silences                                     3.883m ± ∞ ¹   3.429m ± ∞ ¹  -11.67% (p=0.008 n=5)
geomean                                                          126.3µ         101.5µ        -19.60%
¹ need >= 6 samples for confidence interval at level 0.95


                                                              │ 0-qsince.txt  │               1-qsince.txt               │
                                                              │     B/op      │      B/op       vs base                  │
Mutes/0_total,_0_matching                                         376.0 ± ∞ ¹      376.0 ± ∞ ¹         ~ (p=1.000 n=5) ²
Mutes/1_total,_1_matching                                       1.219Ki ± ∞ ¹    1.250Ki ± ∞ ¹    +2.56% (p=0.008 n=5)
Mutes/100_total,_10_matching                                    4.047Ki ± ∞ ¹    3.727Ki ± ∞ ¹    -7.92% (p=0.008 n=5)
Mutes/1000_total,_1_matching                                    1.219Ki ± ∞ ¹    1.250Ki ± ∞ ¹    +2.56% (p=0.008 n=5)
Mutes/1000_total,_10_matching                                   4.047Ki ± ∞ ¹    3.727Ki ± ∞ ¹    -7.92% (p=0.008 n=5)
Mutes/1000_total,_100_matching                                  31.27Ki ± ∞ ¹    29.18Ki ± ∞ ¹    -6.67% (p=0.008 n=5)
Mutes/10000_total,_0_matching                                   4.047Ki ± ∞ ¹    3.727Ki ± ∞ ¹    -7.92% (p=0.008 n=5)
Mutes/10000_total,_10_matching                                  4.047Ki ± ∞ ¹    3.727Ki ± ∞ ¹    -7.92% (p=0.008 n=5)
Mutes/10000_total,_1000_matching                                287.6Ki ± ∞ ¹    276.1Ki ± ∞ ¹    -3.98% (p=0.008 n=5)
MutesIncremental/1000_base_silences                             72.03Ki ± ∞ ¹   230.60Ki ± ∞ ¹  +220.15% (p=0.008 n=5)
MutesIncremental/3000_base_silences                             72.30Ki ± ∞ ¹   213.23Ki ± ∞ ¹  +194.91% (p=0.008 n=5)
MutesIncremental/7000_base_silences                             36.60Ki ± ∞ ¹   190.82Ki ± ∞ ¹  +421.37% (p=0.008 n=5)
MutesIncremental/10000_base_silences                            24.44Ki ± ∞ ¹   169.77Ki ± ∞ ¹  +594.66% (p=0.008 n=5)
Query/100_silences                                              2.711Ki ± ∞ ¹    2.984Ki ± ∞ ¹   +10.09% (p=0.008 n=5)
Query/1000_silences                                             22.87Ki ± ∞ ¹    22.39Ki ± ∞ ¹    -2.08% (p=0.008 n=5)
Query/10000_silences                                            220.7Ki ± ∞ ¹    220.2Ki ± ∞ ¹    -0.22% (p=0.008 n=5)
QueryParallel/100_silences                                      2.641Ki ± ∞ ¹    2.914Ki ± ∞ ¹   +10.36% (p=0.008 n=5)
QueryParallel/1000_silences                                     22.80Ki ± ∞ ¹    22.32Ki ± ∞ ¹    -2.09% (p=0.008 n=5)
QueryParallel/10000_silences                                    220.6Ki ± ∞ ¹    220.1Ki ± ∞ ¹    -0.22% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_10%_add_rate     161.1Ki ± ∞ ¹    176.0Ki ± ∞ ¹    +9.28% (p=0.032 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_1%_add_rate      38.07Ki ± ∞ ¹    39.01Ki ± ∞ ¹    +2.46% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_0.1%_add_rate    24.22Ki ± ∞ ¹    23.94Ki ± ∞ ¹    -1.13% (p=0.008 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_1%_add_rate     218.6Ki ± ∞ ¹    218.7Ki ± ∞ ¹         ~ (p=0.310 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_0.1%_add_rate   219.9Ki ± ∞ ¹    219.8Ki ± ∞ ¹    -0.06% (p=0.008 n=5)
MutesParallel/100_silences                                      31.27Ki ± ∞ ¹    29.18Ki ± ∞ ¹    -6.67% (p=0.008 n=5)
MutesParallel/1000_silences                                     287.6Ki ± ∞ ¹    276.1Ki ± ∞ ¹    -3.98% (p=0.008 n=5)
MutesParallel/10000_silences                                    3.228Mi ± ∞ ¹    2.688Mi ± ∞ ¹   -16.73% (p=0.008 n=5)
geomean                                                         26.38Ki          32.22Ki         +22.15%
¹ need >= 6 samples for confidence interval at level 0.95
² all samples are equal

There is an increase in memory usage in MutesIncremental, which we could potentially mitigate in the future allowing QSince and QIDS queries in the same call, or allowing query types which return IDs instead of silence lists. The cost is still worth it as it's only in the case of massive silence loads that would make alertmanager nowadays fail, and is minimal compared to the current size of servers.

                                                              │ 0-qsince.txt │              1-qsince.txt              │
                                                              │  allocs/op   │  allocs/op    vs base                  │
Mutes/0_total,_0_matching                                        4.000 ± ∞ ¹    4.000 ± ∞ ¹         ~ (p=1.000 n=5) ²
Mutes/1_total,_1_matching                                        19.00 ± ∞ ¹    20.00 ± ∞ ¹    +5.26% (p=0.008 n=5)
Mutes/100_total,_10_matching                                     37.00 ± ∞ ¹    30.00 ± ∞ ¹   -18.92% (p=0.008 n=5)
Mutes/1000_total,_1_matching                                     19.00 ± ∞ ¹    20.00 ± ∞ ¹    +5.26% (p=0.008 n=5)
Mutes/1000_total,_10_matching                                    37.00 ± ∞ ¹    30.00 ± ∞ ¹   -18.92% (p=0.008 n=5)
Mutes/1000_total,_100_matching                                   133.0 ± ∞ ¹    120.0 ± ∞ ¹    -9.77% (p=0.008 n=5)
Mutes/10000_total,_0_matching                                    37.00 ± ∞ ¹    30.00 ± ∞ ¹   -18.92% (p=0.008 n=5)
Mutes/10000_total,_10_matching                                   37.00 ± ∞ ¹    30.00 ± ∞ ¹   -18.92% (p=0.008 n=5)
Mutes/10000_total,_1000_matching                                1.041k ± ∞ ¹   1.022k ± ∞ ¹    -1.83% (p=0.008 n=5)
MutesIncremental/1000_base_silences                              283.0 ± ∞ ¹    853.0 ± ∞ ¹  +201.41% (p=0.008 n=5)
MutesIncremental/3000_base_silences                              284.0 ± ∞ ¹    791.0 ± ∞ ¹  +178.52% (p=0.008 n=5)
MutesIncremental/7000_base_silences                              156.0 ± ∞ ¹    710.0 ± ∞ ¹  +355.13% (p=0.008 n=5)
MutesIncremental/10000_base_silences                             112.0 ± ∞ ¹    633.0 ± ∞ ¹  +465.18% (p=0.008 n=5)
Query/100_silences                                               27.00 ± ∞ ¹    23.00 ± ∞ ¹   -14.81% (p=0.008 n=5)
Query/1000_silences                                              120.0 ± ∞ ¹    114.0 ± ∞ ¹    -5.00% (p=0.008 n=5)
Query/10000_silences                                            1.023k ± ∞ ¹   1.017k ± ∞ ¹    -0.59% (p=0.008 n=5)
QueryParallel/100_silences                                       24.00 ± ∞ ¹    20.00 ± ∞ ¹   -16.67% (p=0.008 n=5)
QueryParallel/1000_silences                                      117.0 ± ∞ ¹    111.0 ± ∞ ¹    -5.13% (p=0.008 n=5)
QueryParallel/10000_silences                                    1.020k ± ∞ ¹   1.014k ± ∞ ¹    -0.59% (p=0.008 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_10%_add_rate      748.0 ± ∞ ¹    818.0 ± ∞ ¹    +9.36% (p=0.032 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_1%_add_rate       183.0 ± ∞ ¹    184.0 ± ∞ ¹         ~ (p=0.405 n=5)
QueryWithConcurrentAdds/1000_initial_silences,_0.1%_add_rate     124.0 ± ∞ ¹    119.0 ± ∞ ¹    -4.03% (p=0.008 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_1%_add_rate     1.012k ± ∞ ¹   1.009k ± ∞ ¹    -0.30% (p=0.008 n=5)
QueryWithConcurrentAdds/10000_initial_silences,_0.1%_add_rate   1.017k ± ∞ ¹   1.013k ± ∞ ¹    -0.39% (p=0.008 n=5)
MutesParallel/100_silences                                       133.0 ± ∞ ¹    120.0 ± ∞ ¹    -9.77% (p=0.008 n=5)
MutesParallel/1000_silences                                     1.041k ± ∞ ¹   1.022k ± ∞ ¹    -1.83% (p=0.008 n=5)
MutesParallel/10000_silences                                    10.05k ± ∞ ¹   10.02k ± ∞ ¹    -0.33% (p=0.008 n=5)
geomean                                                          154.3          178.8         +15.89%
¹ need >= 6 samples for confidence interval at level 0.95
² all samples are equal

GC is not substantially slower, and only spends more memory when it needs to free up memory after a downsize of more than 50%, doing one more allocation then.

goos: linux
goarch: amd64
pkg: github.com/prometheus/alertmanager/silence
cpu: AMD EPYC Processor (with IBPB)
                               │   gc0.txt    │                gc1.txt                 │
                               │    sec/op    │    sec/op      vs base                 │
GC/100_silences,_0%_expired      10.10µ ± ∞ ¹    11.52µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/100_silences,_10%_expired     11.48µ ± ∞ ¹    12.60µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/100_silences,_50%_expired     15.09µ ± ∞ ¹    15.89µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/100_silences,_90%_expired     18.08µ ± ∞ ¹    18.78µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/1000_silences,_0%_expired     66.35µ ± ∞ ¹    81.53µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/1000_silences,_10%_expired    79.73µ ± ∞ ¹    92.18µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/1000_silences,_50%_expired    121.3µ ± ∞ ¹    129.0µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/1000_silences,_90%_expired    156.6µ ± ∞ ¹    166.2µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/10000_silences,_0%_expired    649.0µ ± ∞ ¹   1037.9µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/10000_silences,_10%_expired   823.0µ ± ∞ ¹   1193.3µ ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/10000_silences,_50%_expired   1.383m ± ∞ ¹    1.623m ± ∞ ¹        ~ (p=0.333 n=2) ²
GC/10000_silences,_90%_expired   1.817m ± ∞ ¹    2.103m ± ∞ ¹        ~ (p=0.333 n=2) ²
geomean                          112.8µ          132.6µ        +17.49%
¹ need >= 6 samples for confidence interval at level 0.95
² need >= 4 samples to detect a difference at alpha level 0.05

                               │    gc0.txt    │                 gc1.txt                  │
                               │     B/op      │      B/op        vs base                 │
GC/100_silences,_0%_expired      1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/100_silences,_10%_expired     1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/100_silences,_50%_expired     1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/100_silences,_90%_expired     1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/1000_silences,_0%_expired     1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/1000_silences,_10%_expired    1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/1000_silences,_50%_expired    1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/1000_silences,_90%_expired    1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/10000_silences,_0%_expired    1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/10000_silences,_10%_expired   1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/10000_silences,_50%_expired   1.109Ki ± ∞ ¹     1.109Ki ± ∞ ¹        ~ (p=1.000 n=2) ²
GC/10000_silences,_90%_expired   1.109Ki ± ∞ ¹   121.109Ki ± ∞ ¹        ~ (p=0.333 n=2) ³
geomean                          1.109Ki           1.640Ki        +47.86%
¹ need >= 6 samples for confidence interval at level 0.95
² all samples are equal
³ need >= 4 samples to detect a difference at alpha level 0.05


                               │   gc0.txt   │               gc1.txt               │
                               │  allocs/op  │  allocs/op   vs base                │
GC/100_silences,_0%_expired      18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/100_silences,_10%_expired     18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/100_silences,_50%_expired     18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/100_silences,_90%_expired     18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/1000_silences,_0%_expired     18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/1000_silences,_10%_expired    18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/1000_silences,_50%_expired    18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/1000_silences,_90%_expired    18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/10000_silences,_0%_expired    18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/10000_silences,_10%_expired   18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/10000_silences,_50%_expired   18.00 ± ∞ ¹   18.00 ± ∞ ¹       ~ (p=1.000 n=2) ²
GC/10000_silences,_90%_expired   18.00 ± ∞ ¹   19.00 ± ∞ ¹       ~ (p=0.333 n=2) ³
geomean                          18.00         18.08        +0.45%
¹ need >= 6 samples for confidence interval at level 0.95
² all samples are equal
³ need >= 4 samples to detect a difference at alpha level 0.05

siavashs

LGTM, left a minor comment.

silence/silence.go

SuperQ · 2025-11-12T13:37:13Z

This needs a rebase.

rajagopalanand · 2025-11-12T15:53:47Z

silence/silence.go

+			newVersionIndex.add(s.version+1, e.Silence.Id)
 		}
 	}
 	s.mtx.Lock()


This is unrelated to your change. However, I'm wondering if this lock is necessary because it appears loadSnapshot is only called during construction of new Silences type.

Indeed, it's probably unnecessary. But also harmless as at construction there's no contention.

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

…new silences that have been added since the last version we saw Signed-off-by: Guido Trotter <guido@hudson-trading.com>

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

rajagopalanand

LGTM. Had one question about lock acquisition in the loadSnapshot method

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

ultrotter mentioned this pull request Nov 10, 2025

GC: report errors, but remove erroneous silences and continue #4724

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siavashs approved these changes Nov 11, 2025

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siavashs added the component/silences label Nov 11, 2025

SuperQ approved these changes Nov 12, 2025

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rajagopalanand reviewed Nov 12, 2025

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Guido Trotter added 4 commits November 12, 2025 10:55

Add silence version index an QSince query

57ee4e9

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

Add scanned total, skipped total, and indices/state sizes metrics

b413f93

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

Use QSince to optimize Mutes: we can now only scan old silences, and …

3d86ff2

…new silences that have been added since the last version we saw Signed-off-by: Guido Trotter <guido@hudson-trading.com>

query: pre-guess the allocation result size

efca6d3

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

rajagopalanand reviewed Nov 12, 2025

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Rename versionIndex.find to versionIndex.findVersionGreaterThan

00dedba

Signed-off-by: Guido Trotter <guido@hudson-trading.com>

ultrotter force-pushed the versionidx4 branch from c4cecac to 00dedba Compare November 12, 2025 15:58

rajagopalanand approved these changes Nov 12, 2025

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SuperQ merged commit 888fb29 into prometheus:main Nov 12, 2025
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ultrotter deleted the versionidx4 branch November 13, 2025 07:13

SoloJacobs mentioned this pull request Nov 24, 2025

Release v0.30.0-rc.0 #4770

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Silences: Optimize incremental mutes queries via a silence version index#4723

Silences: Optimize incremental mutes queries via a silence version index#4723
SuperQ merged 5 commits intoprometheus:mainfrom
ultrotter:versionidx4

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