implement dispatch_as

[xlc]

Closes #6428

I am not going to add any additional types to frame-system until paritytech/polkadot-sdk#367 is addressed.

I am go for the most generalized approach so root can not only dispatch as signed origin, but also all other kinds of origin including collectives etc, which may or may not be useful.

Let me know if you think I should just have a dispatch_as_signed instead of this generalized approach.

polkadot address: 14DsLzVyTUTDMm2eP3czwPbH53KgqnQRp3CJJZS9GR7yxGDP

skip check-dependent-cumulus

polkadot companion: paritytech/polkadot#4075
cumulus companion: paritytech/cumulus#715

[xlc]

Let me know if this makes sense and then I can do the polkadot companion.

[athei]

Let me know if this makes sense and then I can do the polkadot companion.

To me it looks fine. But I don't feel comfortable signing off on this. I requested reviews from the FRAME experts.

[emostov]

On the surface this seems reasonable but I think it makes more sense to have this in its own minimal pallet.

[shawntabrizi]

I see no issues with this.

[gui1117]

shouldn't we have the new call have a proper benchmark ?
I think even if it straightforward it a better practice than having some hardcoded weight in the pallet.

EDIT: otherwise code is good to me

[xlc]

/benchmark runtime pallet pallet_utility

[parity-benchapp]

Benchmark Runtime Pallet for branch "dispatch-as" with command cargo run --quiet --release --features=runtime-benchmarks --manifest-path=bin/node/cli/Cargo.toml -- benchmark --chain=dev --steps=50 --repeat=20 --pallet=pallet_utility --extrinsic="*" --execution=wasm --wasm-execution=compiled --heap-pages=4096 --output=./frame/utility/src/weights.rs --template=./.maintain/frame-weight-template.hbs

Results

Pallet: "pallet_utility", Extrinsic: "batch", Lowest values: [], Highest values: [], Steps: 50, Repeat: 20
Raw Storage Info
========

Median Slopes Analysis
========
-- Extrinsic Time --

Model:
Time ~=    17.98
    + c    5.525
              µs

Reads = 0 + (0 * c)
Writes = 0 + (0 * c)

Min Squares Analysis
========
-- Extrinsic Time --

Data points distribution:
    c   mean µs  sigma µs       %
    0     13.08      0.12    0.9%
   20     128.8     0.554    0.4%
   40     240.7     1.572    0.6%
   60       345     2.352    0.6%
   80     455.7     1.523    0.3%
  100     568.4     9.892    1.7%
  120     670.8     3.679    0.5%
  140       787     10.59    1.3%
  160     914.5     12.84    1.4%
  180      1003     9.558    0.9%
  200      1136     8.234    0.7%
  220      1245     13.13    1.0%
  240      1336     10.54    0.7%
  260      1465     15.89    1.0%
  280      1565     13.05    0.8%
  300      1673     12.14    0.7%
  320      1776     6.616    0.3%
  340      1909     7.749    0.4%
  360      2012     3.416    0.1%
  380      2120     11.47    0.5%
  400      2232     7.756    0.3%
  420      2323     10.12    0.4%
  440      2451     18.16    0.7%
  460      2549     14.27    0.5%
  480      2653     16.85    0.6%
  500      2780     23.15    0.8%
  520      2880     14.59    0.5%
  540      3033     11.88    0.3%
  560      3120     12.84    0.4%
  580      3244     7.704    0.2%
  600      3360     10.26    0.3%
  620      3473      5.49    0.1%
  640      3568     16.31    0.4%
  660      3740     14.45    0.3%
  680      3758     15.83    0.4%
  700      3858     10.55    0.2%
  720      3972     14.02    0.3%
  740      4110     12.46    0.3%
  760      4218     14.74    0.3%
  780      4316     20.17    0.4%
  800      4466     22.39    0.5%
  820      4568     17.36    0.3%
  840      4653     11.05    0.2%
  860      4758     27.05    0.5%
  880      4871     18.92    0.3%
  900      4999     12.78    0.2%
  920      5054     13.02    0.2%
  940      5222     27.29    0.5%
  960      5315     18.36    0.3%
  980      5451     18.81    0.3%
 1000      5558     11.82    0.2%

Quality and confidence:
param     error
c         0.003

Model:
Time ~=    18.29
    + c     5.53
              µs

Reads = 0 + (0 * c)
Writes = 0 + (0 * c)

Pallet: "pallet_utility", Extrinsic: "as_derivative", Lowest values: [], Highest values: [], Steps: 50, Repeat: 20
Raw Storage Info
========

Median Slopes Analysis
========
-- Extrinsic Time --

Model:
Time ~=    3.387
              µs

Reads = 0
Writes = 0

Min Squares Analysis
========
-- Extrinsic Time --

Model:
Time ~=    3.387
              µs

Reads = 0
Writes = 0

Pallet: "pallet_utility", Extrinsic: "batch_all", Lowest values: [], Highest values: [], Steps: 50, Repeat: 20
Raw Storage Info
========

Median Slopes Analysis
========
-- Extrinsic Time --

Model:
Time ~=    21.14
    + c    5.989
              µs

Reads = 0 + (0 * c)
Writes = 0 + (0 * c)

Min Squares Analysis
========
-- Extrinsic Time --

Data points distribution:
    c   mean µs  sigma µs       %
    0     13.91      0.06    0.4%
   20     139.7     0.435    0.3%
   40     260.9     1.563    0.5%
   60     376.9     3.471    0.9%
   80     498.2     2.046    0.4%
  100     619.9     4.076    0.6%
  120     737.9     3.743    0.5%
  140     859.1     4.176    0.4%
  160     975.9      11.7    1.1%
  180      1098     15.95    1.4%
  200      1237     12.05    0.9%
  220      1358     11.24    0.8%
  240      1469     13.99    0.9%
  260      1607     8.548    0.5%
  280      1694     14.04    0.8%
  300      1823     5.823    0.3%
  320      1939      8.34    0.4%
  340      2069     11.12    0.5%
  360      2179     11.63    0.5%
  380      2295     11.85    0.5%
  400      2415     16.02    0.6%
  420      2549      8.38    0.3%
  440      2658     15.01    0.5%
  460      2770     11.18    0.4%
  480      2905     7.623    0.2%
  500      3003      8.42    0.2%
  520      3145     9.052    0.2%
  540      3259     10.96    0.3%
  560      3371     13.72    0.4%
  580      3502     8.609    0.2%
  600      3608     13.11    0.3%
  620      3747     13.63    0.3%
  640      3837     13.05    0.3%
  660      3943      16.6    0.4%
  680      4097     18.12    0.4%
  700      4202     9.469    0.2%
  720      4332     10.87    0.2%
  740      4436     16.24    0.3%
  760      4560     19.56    0.4%
  780      4700     15.78    0.3%
  800      4795     10.82    0.2%
  820      4919      19.8    0.4%
  840      5020     15.65    0.3%
  860      5151     17.74    0.3%
  880      5260     11.56    0.2%
  900      5377     19.11    0.3%
  920      5510     21.39    0.3%
  940      5709     14.45    0.2%
  960      5848      21.5    0.3%
  980      5982     17.01    0.2%
 1000      6060     22.65    0.3%

Quality and confidence:
param     error
c         0.004

Model:
Time ~=    19.22
    + c    5.998
              µs

Reads = 0 + (0 * c)
Writes = 0 + (0 * c)

Pallet: "pallet_utility", Extrinsic: "dispatch_as", Lowest values: [], Highest values: [], Steps: 50, Repeat: 20
Raw Storage Info
========

Median Slopes Analysis
========
-- Extrinsic Time --

Model:
Time ~=    14.34
              µs

Reads = 0
Writes = 0

Min Squares Analysis
========
-- Extrinsic Time --

Model:
Time ~=    14.34
              µs

Reads = 0
Writes = 0

[athei]

Why do we need to box it? It wasn't boxed before you added the benchmark. We should solve it there when possible.

[xlc]

it is generally good idea to box big data structure in call parameter to avoid increase the Call enum size. the same reason box is used elsewhere.

[athei]

I see. I thought it was related to the benchmarks. Didn't know that this is a big argument, though.

[azerella]

bot merge

[paritytech-processbot]

Error: Response error (status 404 Not Found):

{"documentation_url":"https://docs.github.com/rest/reference/pulls#get-a-pull-request","message":"Not Found"}

[azerella]

bot merge

[paritytech-processbot]

Error: Response error (status 404 Not Found):

{"documentation_url":"https://docs.github.com/rest/reference/pulls#get-a-pull-request","message":"Not Found"}

[azerella]

bot merge

[paritytech-processbot]

Error: Response error (status 404 Not Found):

{"documentation_url":"https://docs.github.com/rest/reference/pulls#get-a-pull-request","message":"Not Found"}

[xlc]

Ummm... Is there some bots end up in an infinite loop or something?

[shawntabrizi]

@xlc we are looking into it. We can merge manually if we need to, but better to let the bot engineers figure out what is going on here.

[xlc]

No problems. This isn’t a blocking issue for us so take your time.

[azerella]

bot merge

[paritytech-processbot]

Error: Github API says paritytech/polkadot#4075 is not mergeable

[TriplEight]

bot merge

[paritytech-processbot]

Error: Github API says paritytech/polkadot#4075 is not mergeable