Decouple Staking and Election - Part 2.1: Unleash Multi Phase

[kianenigma]

This is called part 2.1, because it can be applied after part 2 is merged, and there's no need to have part 3 (signed phase).

• Original PR: Decouple Staking and Election #7319
• Part 1 Decouple Stkaing and Election - Part1: Support traits #7908
• Part 2 Decouple Staking and Election - Part 2 Unsigned Phase #7909 --> Enabling it as dry-run: Enable two phase election provider in runtimes in 'DryRun' mode polkadot#2476
• Part 2.1 Decouple Staking and Election - Part 2.1: Unleash Multi Phase #8113, and polkadot companion: Companion for substrate/pull/8113 (Unleash multi phase) polkadot#2432
• Part 3 Decouple Staking and Election - Part 3: Signed Phase #7910

So, this is building on top of the part 2 for now. We can rebase it later.

By the end of #7909, If we merge it with its companion, the pallet will be deployed, and it will compute all the elections, but staking will still use its own election mechanism. This PR will wipe a lot of code from staking, and henceforth staking will use the new multi-phase election (again, regardless of having a signed phase or not).

[parity-benchapp]

Finished benchmark for branch: kiz-election-provider-21-enable-multi-phase

Benchmark: Benchmark Runtime Pallet

cargo run --release --features=runtime-benchmarks --manifest-path=bin/node/cli/Cargo.toml -- benchmark --chain=dev --steps=50 --repeat=20 --pallet=pallet_election_provider_multi_phase --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --output=./frame/election-provider-multi-phase/src/weights.rs --template=./.maintain/frame-weight-template.hbs

Results

Pallet: "pallet_election_provider_multi_phase", Extrinsic: "on_initialize_nothing", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 22.73
µs

Reads = 7
Writes = 0
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 22.73
µs

Reads = 7
Writes = 0
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "on_initialize_open_signed", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 112
µs

Reads = 8
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 112
µs

Reads = 8
Writes = 4
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "on_initialize_open_unsigned_with_snapshot", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 112.1
µs

Reads = 8
Writes = 4
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 112.1
µs

Reads = 8
Writes = 4
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "on_initialize_open_unsigned_without_snapshot", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 21.03
µs

Reads = 1
Writes = 1
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 21.03
µs

Reads = 1
Writes = 1
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "elect_queued", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 7362
µs

Reads = 2
Writes = 6
Min Squares Analysis

-- Extrinsic Time --

Model:
Time ~= 7362
µs

Reads = 2
Writes = 6
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "submit_unsigned", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 3.939
+ t 0.202
+ a 12.89
+ d 5.45
µs

Reads = 6 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Writes = 1 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v t a d mean µs sigma µs %
4000 1600 3000 800 56300 180.7 0.3%
4040 1600 3000 800 56460 93.96 0.1%
4080 1600 3000 800 56750 85.64 0.1%
4120 1600 3000 800 56770 116.7 0.2%
4160 1600 3000 800 56940 92.19 0.1%
4200 1600 3000 800 57000 104.8 0.1%
4240 1600 3000 800 57120 89.72 0.1%
4280 1600 3000 800 57290 67.76 0.1%
4320 1600 3000 800 57420 102.1 0.1%
4360 1600 3000 800 57640 120.2 0.2%
4400 1600 3000 800 57680 91.76 0.1%
4440 1600 3000 800 57940 202.9 0.3%
4480 1600 3000 800 58040 133.7 0.2%
4520 1600 3000 800 58170 69.43 0.1%
4560 1600 3000 800 58340 76.2 0.1%
4600 1600 3000 800 58600 123 0.2%
4640 1600 3000 800 58780 95.43 0.1%
4680 1600 3000 800 58800 101.8 0.1%
4720 1600 3000 800 59010 118.7 0.2%
4760 1600 3000 800 59160 84.54 0.1%
4800 1600 3000 800 59350 112.3 0.1%
4840 1600 3000 800 59520 116.1 0.1%
4880 1600 3000 800 59570 117.1 0.1%
4920 1600 3000 800 59740 136.2 0.2%
4960 1600 3000 800 59940 108.4 0.1%
5000 1600 3000 800 60110 95.68 0.1%
5040 1600 3000 800 60260 92.22 0.1%
5080 1600 3000 800 60320 154.8 0.2%
5120 1600 3000 800 60650 84.72 0.1%
5160 1600 3000 800 60660 150.1 0.2%
5200 1600 3000 800 60990 141.8 0.2%
5240 1600 3000 800 60900 110.1 0.1%
5280 1600 3000 800 61290 83.9 0.1%
5320 1600 3000 800 61280 96.6 0.1%
5360 1600 3000 800 61500 124 0.2%
5400 1600 3000 800 61760 113.4 0.1%
5440 1600 3000 800 61870 112.1 0.1%
5480 1600 3000 800 62070 50.5 0.0%
5520 1600 3000 800 62270 118.4 0.1%
5560 1600 3000 800 62380 76.2 0.1%
5600 1600 3000 800 62490 146.2 0.2%
5640 1600 3000 800 62640 128.3 0.2%
5680 1600 3000 800 62760 65.23 0.1%
5720 1600 3000 800 62960 146.4 0.2%
5760 1600 3000 800 63110 101.5 0.1%
5800 1600 3000 800 63350 142 0.2%
5840 1600 3000 800 63470 147.6 0.2%
5880 1600 3000 800 63680 219.2 0.3%
5920 1600 3000 800 63770 118.2 0.1%
5960 1600 3000 800 63940 74.94 0.1%
6000 1000 3000 800 64040 134.3 0.2%
6000 1012 3000 800 64080 105.4 0.1%
6000 1024 3000 800 64170 135.4 0.2%
6000 1036 3000 800 64020 97.26 0.1%
6000 1048 3000 800 63990 49.49 0.0%
6000 1060 3000 800 63990 148.9 0.2%
6000 1072 3000 800 64010 105 0.1%
6000 1084 3000 800 64100 89.03 0.1%
6000 1096 3000 800 64090 89.04 0.1%
6000 1108 3000 800 64040 76.08 0.1%
6000 1120 3000 800 64090 20.04 0.0%
6000 1132 3000 800 64080 36.9 0.0%
6000 1144 3000 800 64070 103.9 0.1%
6000 1156 3000 800 64100 82.6 0.1%
6000 1168 3000 800 64120 153 0.2%
6000 1180 3000 800 64060 126.5 0.1%
6000 1192 3000 800 64020 117.3 0.1%
6000 1204 3000 800 64140 112.8 0.1%
6000 1216 3000 800 64210 135.7 0.2%
6000 1228 3000 800 64000 136.9 0.2%
6000 1240 3000 800 63990 56.06 0.0%
6000 1252 3000 800 64180 123.5 0.1%
6000 1264 3000 800 64080 101.6 0.1%
6000 1276 3000 800 64260 123.8 0.1%
6000 1288 3000 800 63990 191.8 0.2%
6000 1300 3000 800 64110 133.1 0.2%
6000 1312 3000 800 64190 127.1 0.1%
6000 1324 3000 800 64100 113.9 0.1%
6000 1336 3000 800 64090 102.2 0.1%
6000 1348 3000 800 64020 62.88 0.0%
6000 1360 3000 800 64490 82.21 0.1%
6000 1372 3000 800 64070 132 0.2%
6000 1384 3000 800 64070 110.3 0.1%
6000 1396 3000 800 64120 60.12 0.0%
6000 1408 3000 800 64280 107.1 0.1%
6000 1420 3000 800 64100 107.9 0.1%
6000 1432 3000 800 64180 81.89 0.1%
6000 1444 3000 800 64510 75.05 0.1%
6000 1456 3000 800 64080 106.3 0.1%
6000 1468 3000 800 64100 135.1 0.2%
6000 1480 3000 800 64040 105.7 0.1%
6000 1492 3000 800 64110 152.8 0.2%
6000 1504 3000 800 64240 161.3 0.2%
6000 1516 3000 800 64070 97.2 0.1%
6000 1528 3000 800 64160 123 0.1%
6000 1540 3000 800 64070 104.5 0.1%
6000 1552 3000 800 64290 125.5 0.1%
6000 1564 3000 800 64130 111.4 0.1%
6000 1576 3000 800 64210 124.5 0.1%
6000 1588 3000 800 64140 86.21 0.1%
6000 1600 1000 800 37260 74.11 0.1%
6000 1600 1040 800 37740 97.05 0.2%
6000 1600 1080 800 38200 105.3 0.2%
6000 1600 1120 800 38770 47.52 0.1%
6000 1600 1160 800 39130 97.42 0.2%
6000 1600 1200 800 39710 60.3 0.1%
6000 1600 1240 800 40070 93.5 0.2%
6000 1600 1280 800 40700 69.92 0.1%
6000 1600 1320 800 41110 55.67 0.1%
6000 1600 1360 800 41650 125 0.3%
6000 1600 1400 800 43020 118.8 0.2%
6000 1600 1440 800 43510 87.34 0.2%
6000 1600 1480 800 44070 74.26 0.1%
6000 1600 1520 800 44530 53.44 0.1%
6000 1600 1560 800 44980 100.3 0.2%
6000 1600 1600 800 45590 104.4 0.2%
6000 1600 1640 800 45990 100.2 0.2%
6000 1600 1680 800 46660 53.73 0.1%
6000 1600 1720 800 47140 49.79 0.1%
6000 1600 1760 800 47710 34.57 0.0%
6000 1600 1800 800 48190 62.87 0.1%
6000 1600 1840 800 48680 48.37 0.0%
6000 1600 1880 800 49180 73.2 0.1%
6000 1600 1920 800 49670 58.85 0.1%
6000 1600 1960 800 50110 62.14 0.1%
6000 1600 2000 800 50530 54.17 0.1%
6000 1600 2040 800 51040 110.5 0.2%
6000 1600 2080 800 51510 93.18 0.1%
6000 1600 2120 800 52020 74.43 0.1%
6000 1600 2160 800 52390 31.35 0.0%
6000 1600 2200 800 52910 102.9 0.1%
6000 1600 2240 800 53400 67.42 0.1%
6000 1600 2280 800 53830 83.77 0.1%
6000 1600 2320 800 54220 77.61 0.1%
6000 1600 2360 800 54680 87.76 0.1%
6000 1600 2400 800 55330 89.48 0.1%
6000 1600 2440 800 55670 57.89 0.1%
6000 1600 2480 800 56100 84.6 0.1%
6000 1600 2520 800 56530 76.5 0.1%
6000 1600 2560 800 57030 99.79 0.1%
6000 1600 2600 800 57500 43.34 0.0%
6000 1600 2640 800 57960 103.5 0.1%
6000 1600 2680 800 58410 78.82 0.1%
6000 1600 2720 800 58880 96.18 0.1%
6000 1600 2760 800 59420 112.9 0.1%
6000 1600 2800 800 59890 106.4 0.1%
6000 1600 2840 800 60310 116.4 0.1%
6000 1600 2880 800 62640 76.02 0.1%
6000 1600 2920 800 63140 76.18 0.1%
6000 1600 2960 800 63750 48.17 0.0%
6000 1600 3000 400 62440 52.68 0.0%
6000 1600 3000 408 62420 94.23 0.1%
6000 1600 3000 416 62370 70.88 0.1%
6000 1600 3000 424 62490 115.1 0.1%
6000 1600 3000 432 62410 60.82 0.0%
6000 1600 3000 440 62480 57.26 0.0%
6000 1600 3000 448 62520 62.01 0.0%
6000 1600 3000 456 62730 80.23 0.1%
6000 1600 3000 464 62700 92.01 0.1%
6000 1600 3000 472 62670 91.27 0.1%
6000 1600 3000 480 62870 87.87 0.1%
6000 1600 3000 488 62820 115.6 0.1%
6000 1600 3000 496 62910 54.68 0.0%
6000 1600 3000 504 62820 31.79 0.0%
6000 1600 3000 512 63250 75.12 0.1%
6000 1600 3000 520 63330 77.46 0.1%
6000 1600 3000 528 63330 79.86 0.1%
6000 1600 3000 536 63450 75.04 0.1%
6000 1600 3000 544 63530 67.32 0.1%
6000 1600 3000 552 63550 70.23 0.1%
6000 1600 3000 560 63750 88.95 0.1%
6000 1600 3000 568 63770 75.09 0.1%
6000 1600 3000 576 63910 89.64 0.1%
6000 1600 3000 584 63980 37.06 0.0%
6000 1600 3000 592 63890 103.7 0.1%
6000 1600 3000 600 64040 70.47 0.1%
6000 1600 3000 608 64100 85 0.1%
6000 1600 3000 616 64150 79.8 0.1%
6000 1600 3000 624 64140 125.8 0.1%
6000 1600 3000 632 64230 133.2 0.2%
6000 1600 3000 640 64380 74.26 0.1%
6000 1600 3000 648 64290 112.4 0.1%
6000 1600 3000 656 64300 54.07 0.0%
6000 1600 3000 664 64310 69.44 0.1%
6000 1600 3000 672 64440 49.25 0.0%
6000 1600 3000 680 64500 68.54 0.1%
6000 1600 3000 688 64440 62.9 0.0%
6000 1600 3000 696 64450 58.43 0.0%
6000 1600 3000 704 64440 167.2 0.2%
6000 1600 3000 712 64310 64.21 0.0%
6000 1600 3000 720 64510 92.05 0.1%
6000 1600 3000 728 64500 64.64 0.1%
6000 1600 3000 736 64330 103.5 0.1%
6000 1600 3000 744 64430 54.84 0.0%
6000 1600 3000 752 64320 83.2 0.1%
6000 1600 3000 760 64330 43.84 0.0%
6000 1600 3000 768 64120 71.06 0.1%
6000 1600 3000 776 64250 82.34 0.1%
6000 1600 3000 784 64340 93.17 0.1%
6000 1600 3000 792 64390 83.2 0.1%
6000 1600 3000 800 64130 94.1 0.1%

Quality and confidence:
param error
v 0.021
t 0.071
a 0.021
d 0.107

Model:
Time ~= 0
+ v 3.933
+ t 0
+ a 13.52
+ d 2.88
µs

Reads = 6 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Writes = 1 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Pallet: "pallet_election_provider_multi_phase", Extrinsic: "feasibility_check", Lowest values: [], Highest values: [], Steps: [50], Repeat: 20
Median Slopes Analysis

-- Extrinsic Time --

Model:
Time ~= 0
+ v 3.94
+ t 0.212
+ a 9.889
+ d 5.084
µs

Reads = 3 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Writes = 0 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Min Squares Analysis

-- Extrinsic Time --

Data points distribution:
v t a d mean µs sigma µs %
4000 1600 3000 800 46010 55.12 0.1%
4040 1600 3000 800 46080 79.08 0.1%
4080 1600 3000 800 46220 65.37 0.1%
4120 1600 3000 800 46380 100.5 0.2%
4160 1600 3000 800 46550 72.02 0.1%
4200 1600 3000 800 46660 46.89 0.1%
4240 1600 3000 800 46800 62.49 0.1%
4280 1600 3000 800 46980 97.5 0.2%
4320 1600 3000 800 47240 95.88 0.2%
4360 1600 3000 800 47350 95.98 0.2%
4400 1600 3000 800 47330 50.7 0.1%
4440 1600 3000 800 47700 49.29 0.1%
4480 1600 3000 800 47730 70.09 0.1%
4520 1600 3000 800 47930 43.82 0.0%
4560 1600 3000 800 48050 53.45 0.1%
4600 1600 3000 800 48210 30.06 0.0%
4640 1600 3000 800 48340 82.97 0.1%
4680 1600 3000 800 48600 59.63 0.1%
4720 1600 3000 800 48670 86.58 0.1%
4760 1600 3000 800 48850 95.2 0.1%
4800 1600 3000 800 49010 52.35 0.1%
4840 1600 3000 800 49190 74.18 0.1%
4880 1600 3000 800 49390 43.36 0.0%
4920 1600 3000 800 49540 28.49 0.0%
4960 1600 3000 800 49730 47.7 0.0%
5000 1600 3000 800 49830 78.39 0.1%
5040 1600 3000 800 50000 120.5 0.2%
5080 1600 3000 800 50150 38.09 0.0%
5120 1600 3000 800 50280 54.68 0.1%
5160 1600 3000 800 50390 63.92 0.1%
5200 1600 3000 800 50590 59.02 0.1%
5240 1600 3000 800 50830 72.88 0.1%
5280 1600 3000 800 50890 64.1 0.1%
5320 1600 3000 800 51090 53.64 0.1%
5360 1600 3000 800 51270 48.9 0.0%
5400 1600 3000 800 51450 33.71 0.0%
5440 1600 3000 800 51400 81.33 0.1%
5480 1600 3000 800 51810 66.42 0.1%
5520 1600 3000 800 51840 65.39 0.1%
5560 1600 3000 800 52070 105.8 0.2%
5600 1600 3000 800 52120 71.57 0.1%
5640 1600 3000 800 52320 64.29 0.1%
5680 1600 3000 800 52510 82.46 0.1%
5720 1600 3000 800 52750 46.12 0.0%
5760 1600 3000 800 52860 59.94 0.1%
5800 1600 3000 800 52970 78.89 0.1%
5840 1600 3000 800 53190 59.49 0.1%
5880 1600 3000 800 53330 81.09 0.1%
5920 1600 3000 800 53430 57.01 0.1%
5960 1600 3000 800 53510 85.38 0.1%
6000 1000 3000 800 53680 111.2 0.2%
6000 1012 3000 800 53750 48.85 0.0%
6000 1024 3000 800 53810 48.87 0.0%
6000 1036 3000 800 53620 71.09 0.1%
6000 1048 3000 800 53740 100.1 0.1%
6000 1060 3000 800 53810 68.05 0.1%
6000 1072 3000 800 53780 45.91 0.0%
6000 1084 3000 800 53740 109.9 0.2%
6000 1096 3000 800 53580 41.74 0.0%
6000 1108 3000 800 53650 102.5 0.1%
6000 1120 3000 800 53810 74.7 0.1%
6000 1132 3000 800 53720 35.56 0.0%
6000 1144 3000 800 53640 66.44 0.1%
6000 1156 3000 800 53810 50.04 0.0%
6000 1168 3000 800 53620 77.28 0.1%
6000 1180 3000 800 53750 66.57 0.1%
6000 1192 3000 800 53690 71.43 0.1%
6000 1204 3000 800 53820 83.57 0.1%
6000 1216 3000 800 53740 58.23 0.1%
6000 1228 3000 800 53560 21.99 0.0%
6000 1240 3000 800 53680 23.4 0.0%
6000 1252 3000 800 53760 72.17 0.1%
6000 1264 3000 800 53730 64.52 0.1%
6000 1276 3000 800 53870 28.65 0.0%
6000 1288 3000 800 53680 63.04 0.1%
6000 1300 3000 800 53670 123.3 0.2%
6000 1312 3000 800 53780 76.14 0.1%
6000 1324 3000 800 53820 73.94 0.1%
6000 1336 3000 800 53760 66.32 0.1%
6000 1348 3000 800 53760 85.63 0.1%
6000 1360 3000 800 53960 94.09 0.1%
6000 1372 3000 800 53780 81.52 0.1%
6000 1384 3000 800 53650 54.63 0.1%
6000 1396 3000 800 53660 70.36 0.1%
6000 1408 3000 800 53890 69.18 0.1%
6000 1420 3000 800 53850 42.88 0.0%
6000 1432 3000 800 53860 61.09 0.1%
6000 1444 3000 800 54000 103.7 0.1%
6000 1456 3000 800 53850 69.99 0.1%
6000 1468 3000 800 53740 81.62 0.1%
6000 1480 3000 800 53740 81.43 0.1%
6000 1492 3000 800 53700 78.98 0.1%
6000 1504 3000 800 53860 45.94 0.0%
6000 1516 3000 800 53840 65.37 0.1%
6000 1528 3000 800 53810 55.35 0.1%
6000 1540 3000 800 53750 84.74 0.1%
6000 1552 3000 800 53880 49.8 0.0%
6000 1564 3000 800 53880 75.93 0.1%
6000 1576 3000 800 53830 92.51 0.1%
6000 1588 3000 800 53820 97.3 0.1%
6000 1600 1000 800 33850 88.65 0.2%
6000 1600 1040 800 34410 76.93 0.2%
6000 1600 1080 800 34730 70.59 0.2%
6000 1600 1120 800 35060 84.13 0.2%
6000 1600 1160 800 35560 45.66 0.1%
6000 1600 1200 800 35850 60.74 0.1%
6000 1600 1240 800 36310 66.4 0.1%
6000 1600 1280 800 36510 39.11 0.1%
6000 1600 1320 800 36970 86.15 0.2%
6000 1600 1360 800 37470 40.45 0.1%
6000 1600 1400 800 37940 47.66 0.1%
6000 1600 1440 800 38350 57.34 0.1%
6000 1600 1480 800 38680 89.48 0.2%
6000 1600 1520 800 39080 84.31 0.2%
6000 1600 1560 800 39490 98.63 0.2%
6000 1600 1600 800 40020 57.66 0.1%
6000 1600 1640 800 40340 71.53 0.1%
6000 1600 1680 800 40880 100 0.2%
6000 1600 1720 800 41270 65.72 0.1%
6000 1600 1760 800 41680 71.53 0.1%
6000 1600 1800 800 42080 81.88 0.1%
6000 1600 1840 800 42540 83.19 0.1%
6000 1600 1880 800 42910 88.83 0.2%
6000 1600 1920 800 43380 95.72 0.2%
6000 1600 1960 800 43720 55.64 0.1%
6000 1600 2000 800 44080 97.68 0.2%
6000 1600 2040 800 44510 102 0.2%
6000 1600 2080 800 44990 101.9 0.2%
6000 1600 2120 800 45410 78.57 0.1%
6000 1600 2160 800 45540 48.68 0.1%
6000 1600 2200 800 46060 82.49 0.1%
6000 1600 2240 800 46400 74.61 0.1%
6000 1600 2280 800 46790 65.76 0.1%
6000 1600 2320 800 47100 82.51 0.1%
6000 1600 2360 800 47480 60.57 0.1%
6000 1600 2400 800 47810 101.3 0.2%
6000 1600 2440 800 48210 73.2 0.1%
6000 1600 2480 800 48650 93.91 0.1%
6000 1600 2520 800 49000 67.68 0.1%
6000 1600 2560 800 49290 58.43 0.1%
6000 1600 2600 800 49690 112.2 0.2%
6000 1600 2640 800 50130 37.39 0.0%
6000 1600 2680 800 50550 34.81 0.0%
6000 1600 2720 800 50920 66.06 0.1%
6000 1600 2760 800 51130 52.39 0.1%
6000 1600 2800 800 51670 97.08 0.1%
6000 1600 2840 800 52170 72 0.1%
6000 1600 2880 800 52410 98.28 0.1%
6000 1600 2920 800 52980 52.12 0.0%
6000 1600 2960 800 53300 72.04 0.1%
6000 1600 3000 400 52100 78.52 0.1%
6000 1600 3000 408 52140 42.42 0.0%
6000 1600 3000 416 52060 76.03 0.1%
6000 1600 3000 424 52220 82.11 0.1%
6000 1600 3000 432 52150 60.08 0.1%
6000 1600 3000 440 52250 44.87 0.0%
6000 1600 3000 448 52180 64.76 0.1%
6000 1600 3000 456 52380 79.05 0.1%
6000 1600 3000 464 52230 71.02 0.1%
6000 1600 3000 472 52280 70.64 0.1%
6000 1600 3000 480 52490 67.39 0.1%
6000 1600 3000 488 52580 114.3 0.2%
6000 1600 3000 496 52680 130 0.2%
6000 1600 3000 504 52760 81.65 0.1%
6000 1600 3000 512 52800 90.52 0.1%
6000 1600 3000 520 52860 49.09 0.0%
6000 1600 3000 528 53060 92.24 0.1%
6000 1600 3000 536 53110 67.37 0.1%
6000 1600 3000 544 53110 108.8 0.2%
6000 1600 3000 552 53180 66.46 0.1%
6000 1600 3000 560 53250 121.9 0.2%
6000 1600 3000 568 53370 80.24 0.1%
6000 1600 3000 576 53400 108.5 0.2%
6000 1600 3000 584 53490 97.24 0.1%
6000 1600 3000 592 53590 99.42 0.1%
6000 1600 3000 600 53640 102.7 0.1%
6000 1600 3000 608 53770 70.69 0.1%
6000 1600 3000 616 53750 83.3 0.1%
6000 1600 3000 624 53790 117.9 0.2%
6000 1600 3000 632 53820 123.6 0.2%
6000 1600 3000 640 53950 102.4 0.1%
6000 1600 3000 648 53870 67.98 0.1%
6000 1600 3000 656 53880 62.14 0.1%
6000 1600 3000 664 53920 73.5 0.1%
6000 1600 3000 672 53930 59.6 0.1%
6000 1600 3000 680 53950 76.87 0.1%
6000 1600 3000 688 54030 101 0.1%
6000 1600 3000 696 53950 80.9 0.1%
6000 1600 3000 704 53930 73.33 0.1%
6000 1600 3000 712 54020 41.42 0.0%
6000 1600 3000 720 54070 50.65 0.0%
6000 1600 3000 728 54010 60.25 0.1%
6000 1600 3000 736 54000 65.96 0.1%
6000 1600 3000 744 53990 42.36 0.0%
6000 1600 3000 752 53830 100.5 0.1%
6000 1600 3000 760 53900 53.56 0.0%
6000 1600 3000 768 53860 53.72 0.0%
6000 1600 3000 776 53790 78.54 0.1%
6000 1600 3000 784 53840 42.99 0.0%
6000 1600 3000 792 53830 86.03 0.1%
6000 1600 3000 800 53810 81.71 0.1%

Quality and confidence:
param error
v 0.01
t 0.036
a 0.01
d 0.054

Model:
Time ~= 0
+ v 4.069
+ t 0.503
+ a 10
+ d 3.734
µs

Reads = 3 + (0 * v) + (0 * t) + (0 * a) + (0 * d)
Writes = 0 + (0 * v) + (0 * t) + (0 * a) + (0 * d)

[kianenigma]

I'll do one last review of this in the weekend and then merge it @shawntabrizi @thiolliere @coriolinus

Thank you for your reviews.

[shawntabrizi]

🚀

[kianenigma]

bot merge

[ghost]

Waiting for commit status.

[gabrieljaegerde]

is the runtime update v30 or the motion for it scheduled already?

[kianenigma]

is the runtime update v30 or the motion for it scheduled already?

not yet, Polkadot will move a bit slower, v29 is not yet enacted there.

[dominicpalarchio]

Is there an ETA on v30? I've been closely following the thread on minimum staking amount, because I've lost 'exposure' and it led me here

[kianenigma]

There's a release-candidate already made for v30. But testing is still on going. I hesitated to give any time estimates as it is almost impossible to know it accurately; a release needs to be tested thoroughly first, shipped to kusama and only then is ready for polkadot.