This package provides micro-benchmarks to measure and compare the performance of various streaming implementations in Haskell.
We have taken due to care to make sure that we are benchmarking correctly and fairly. See the notes on correct benchmarking.
DISCLAIMER: This package is a result of benchmarking effort done during the development of streamly by the authors of streamly.
The benchmark names are obvious, some of them are described below. Single operation benchmarks:
Name | Description |
---|---|
drain |
Just discards all the elements in the stream |
drop-all |
drops all element using the drop operation |
last |
extract the last element of the stream |
fold |
sum all the numbers in the stream |
map |
increments each number in the stream by 1 |
take-all |
Use take to retain all the elements in the stream |
filter-even |
Keep even numbers, discard odd |
scan |
scan the stream using + operation |
mapM |
transform the stream using a monadic action |
zip |
combines corresponding elements of the two streams together |
Composite operation benchmarks:
Name | Description |
---|---|
map x 4 |
perform map operation 4 times |
take-map |
take followed by a map |
For more details on how each benchmark is implemented see this benchmark file.
Each benchmark is run in a separate process to avoid any effects of GC interference and sharing across benchmarks.
Below we present some results comparing streamly with other streaming implementations. Due care has been taken to keep the comparisons fair. We have optimized each library's code to the best of our knowledge, please point out if you find any measurement issues.
Commands to reproduce the benchmark results are provided in each section below. But before you run those commands you need to build the reporting tool once using the following command. Note that this command works with only ghc-8.8.4 or lower. However, after building this tool you can run the benchmarks with any GHC version.
$ bin/bench.sh --with-compiler ghc-8.8.4 --no-measure
Nix users can use --use-nix
option. It uses an older version of
nixpkgs that contains the required dependencies. For example:
$ bin/bench.sh --use-nix --quick
Streamly, when used with Identity
monad, is almost the same as Haskell lists
(in the base
package).
See this
for more details.
The following table compares the timing of several operations for streamly with lists using a one million element stream. For brevity only those operations where the performance of the two packages differ by more than 10% are shown in the table below. The last column shows how many times slower list is compared to streamly.
Benchmark | streamly(μs) | list(μs) | list/streamly |
---|---|---|---|
drop-map x 4 | 375.09 | 76925.32 | 205.08 |
filter-drop x 4 | 382.03 | 54848.54 | 143.57 |
drop-scan x 4 | 795.81 | 76716.79 | 96.40 |
filter-scan x 4 | 795.60 | 44559.15 | 56.01 |
scan-map x 4 | 1192.19 | 48838.22 | 40.97 |
take-map x 4 | 1500.99 | 60126.58 | 40.06 |
filter-take x 4 | 1502.01 | 48766.87 | 32.47 |
take-drop x 4 | 1499.62 | 41720.03 | 27.82 |
take-scan x 4 | 1874.94 | 51283.30 | 27.35 |
drop-one x 4 | 375.33 | 8993.87 | 23.96 |
dropWhile-false x 4 | 374.61 | 8957.79 | 23.91 |
dropWhile-false | 374.83 | 8670.05 | 23.13 |
drop-one | 390.77 | 8681.85 | 22.22 |
dropWhile-true | 571.60 | 12237.48 | 21.41 |
drop-all | 562.94 | 8262.38 | 14.68 |
take-all | 624.83 | 564.34 | 1/1.11 |
scan x 4 | 795.83 | 385.85 | 1/2.06 |
appendR[10000] | 360.75 | 126.95 | 1/2.84 |
concatMap | 34957.71 | 1124.85 | 1/31.08 |
- streamly-0.8.0, base-4.14.1.0, ghc-8.10.4, Linux
To reproduce these results use the following commands:
$ bin/bench.sh --benchmarks "StreamlyPure List" --compare --diff-style absolute --diff-cutoff-percent 10 --quick
$ bin/bench.sh --benchmarks "StreamlyPure List" --compare --diff-style multiples --diff-cutoff-percent 10 --no-measure
The following table compares the timing of several operations for streamly with streaming using a million element stream.
Benchmark | streamly(μs) | streaming(μs) | streaming/streamly |
---|---|---|---|
appendR[10000] | 326.56 | 1301176.69 | 3984.54 |
mapM x 4 | 374.42 | 223591.08 | 597.17 |
filter-map x 4 | 381.07 | 194903.88 | 511.47 |
filter-scan x 4 | 795.66 | 233527.90 | 293.50 |
filter-all-in x 4 | 375.40 | 102629.64 | 273.38 |
filter-drop x 4 | 387.15 | 99096.98 | 255.96 |
map x 4 | 386.49 | 94944.87 | 245.66 |
drop-map x 4 | 375.62 | 89669.37 | 238.73 |
scan x 4 | 797.00 | 166332.40 | 208.70 |
scan-map x 4 | 1194.30 | 238804.48 | 199.95 |
filter-even x 4 | 396.37 | 77865.47 | 196.45 |
drop-scan x 4 | 796.98 | 156063.52 | 195.82 |
takeWhile-true x 4 | 562.49 | 90183.53 | 160.33 |
scan | 375.24 | 47520.57 | 126.64 |
filter-take x 4 | 1498.55 | 189635.34 | 126.55 |
mapM | 388.10 | 46689.61 | 120.30 |
take-map x 4 | 1500.71 | 178954.50 | 119.25 |
zip | 656.65 | 66689.73 | 101.56 |
take-scan x 4 | 2380.35 | 241675.75 | 101.53 |
filter-all-in | 375.97 | 33590.14 | 89.34 |
map | 375.02 | 33081.13 | 88.21 |
filter-even | 393.26 | 30458.46 | 77.45 |
filter-all-out | 382.87 | 26826.21 | 70.07 |
take-all x 4 | 1499.71 | 101332.53 | 67.57 |
take-drop x 4 | 1498.53 | 98281.99 | 65.59 |
takeWhile-true | 562.62 | 31863.25 | 56.63 |
foldl' | 388.22 | 18503.15 | 47.66 |
drop-all | 562.08 | 25200.32 | 44.83 |
take-all | 768.65 | 33247.97 | 43.26 |
dropWhile-true | 564.87 | 24431.50 | 43.25 |
last | 385.53 | 15240.85 | 39.53 |
dropWhile-false | 374.83 | 14566.70 | 38.86 |
drop-one | 374.80 | 14565.01 | 38.86 |
drop-one x 4 | 375.88 | 14448.67 | 38.44 |
dropWhile-false x 4 | 390.12 | 14619.42 | 37.47 |
drain | 375.06 | 13702.29 | 36.53 |
toList | 117708.83 | 201444.81 | 1.71 |
- streamly-0.8.0, streaming-0.2.3.0, ghc-8.10.4, Linux
To reproduce these results use the following commands:
$ bin/bench.sh --benchmarks "Streamly Streaming" --compare --diff-style absolute --diff-cutoff-percent 10 --quick
$ bin/bench.sh --benchmarks "Streamly Streaming" --compare --diff-style multiples --diff-cutoff-percent 10 --no-measure
The following table compares the timing of several operations for streamly with pipes using a million element stream.
Benchmark | streamly(μs) | pipes(μs) | pipes/streamly |
---|---|---|---|
appendR[10000] | 327.90 | 901135.92 | 2748.21 |
mapM x 4 | 375.20 | 407184.39 | 1085.23 |
filter-map x 4 | 381.52 | 366759.70 | 961.31 |
drop-map x 4 | 375.48 | 281296.82 | 749.16 |
filter-all-in x 4 | 375.60 | 222331.68 | 591.93 |
filter-drop x 4 | 387.44 | 222830.71 | 575.14 |
drop-scan x 4 | 797.23 | 336737.89 | 422.39 |
filter-even x 4 | 389.87 | 152688.91 | 391.64 |
filter-scan x 4 | 797.38 | 309733.91 | 388.44 |
drop-one x 4 | 375.48 | 139851.13 | 372.46 |
map x 4 | 386.56 | 136289.32 | 352.57 |
dropWhile-false x 4 | 390.72 | 137395.44 | 351.65 |
scan-map x 4 | 1194.38 | 381286.88 | 319.23 |
takeWhile-true x 4 | 562.86 | 165143.23 | 293.40 |
scan x 4 | 796.68 | 222986.17 | 279.90 |
mapM | 388.19 | 95576.97 | 246.21 |
filter-all-in | 375.21 | 71297.42 | 190.02 |
take-map x 4 | 1502.76 | 275887.24 | 183.59 |
scan | 374.81 | 65549.13 | 174.89 |
take-drop x 4 | 1503.43 | 256448.45 | 170.58 |
filter-even | 390.29 | 66183.72 | 169.57 |
filter-all-out | 376.99 | 59074.54 | 156.70 |
drop-one | 375.19 | 58395.24 | 155.64 |
dropWhile-false | 375.35 | 58223.03 | 155.12 |
map | 375.05 | 57736.43 | 153.94 |
filter-take x 4 | 1503.00 | 227925.71 | 151.65 |
take-scan x 4 | 2455.91 | 354284.33 | 144.26 |
zip | 657.07 | 86011.93 | 130.90 |
takeWhile-true | 564.14 | 61390.21 | 108.82 |
take-all x 4 | 1502.32 | 139730.70 | 93.01 |
dropWhile-true | 564.03 | 49227.19 | 87.28 |
drop-all | 562.05 | 46505.37 | 82.74 |
take-all | 824.09 | 60511.34 | 73.43 |
drain | 375.29 | 26390.59 | 70.32 |
foldl' | 397.34 | 19064.05 | 47.98 |
last | 387.11 | 17364.44 | 44.86 |
toList | 117257.09 | 207405.94 | 1.77 |
- streamly-0.8.0, pipes-4.3.16, ghc-8.10.4, Linux
To reproduce these results use the following commands:
$ bin/bench.sh --benchmarks "Streamly Pipes" --compare --diff-style absolute --diff-cutoff-percent 10 --quick
$ bin/bench.sh --benchmarks "Streamly Pipes" --compare --diff-style multiples --diff-cutoff-percent 10 --no-measure
The following table compares the timing of several operations for streamly with conduit using a million element stream.
Benchmark | streamly(μs) | conduit(μs) | conduit/streamly |
---|---|---|---|
mapM x 4 | 375.46 | 297002.31 | 791.04 |
filter-map x 4 | 380.79 | 267543.81 | 702.60 |
drop-map x 4 | 375.66 | 232307.84 | 618.39 |
filter-drop x 4 | 386.05 | 235029.15 | 608.81 |
filter-scan x 4 | 796.56 | 306556.67 | 384.85 |
drop-scan x 4 | 797.19 | 300789.06 | 377.31 |
zip | 657.29 | 210069.05 | 319.60 |
filter-all-in x 4 | 375.24 | 118506.68 | 315.82 |
scan-map x 4 | 1194.67 | 360671.18 | 301.90 |
map x 4 | 387.00 | 113497.14 | 293.27 |
drop-one x 4 | 375.49 | 101842.95 | 271.23 |
dropWhile-false x 4 | 389.44 | 102051.22 | 262.04 |
scan x 4 | 796.72 | 190479.35 | 239.08 |
takeWhile-true x 4 | 564.58 | 114459.57 | 202.73 |
filter-even x 4 | 391.76 | 72369.30 | 184.73 |
filter-take x 4 | 1502.04 | 267921.27 | 178.37 |
take-map x 4 | 1502.88 | 238875.95 | 158.95 |
take-drop x 4 | 1500.34 | 232606.19 | 155.04 |
take-scan x 4 | 2443.83 | 309738.86 | 126.74 |
mapM | 389.15 | 41897.48 | 107.66 |
scan | 375.40 | 38137.85 | 101.59 |
take-all x 4 | 1502.32 | 110682.74 | 73.67 |
filter-all-in | 375.31 | 26024.21 | 69.34 |
dropWhile-false | 375.10 | 25307.13 | 67.47 |
map | 375.18 | 23088.09 | 61.54 |
drop-one | 375.43 | 22020.65 | 58.65 |
filter-even | 392.28 | 21504.28 | 54.82 |
takeWhile-true | 562.79 | 29012.68 | 51.55 |
filter-all-out | 378.76 | 15736.05 | 41.55 |
drop-all | 562.89 | 19916.48 | 35.38 |
foldl' | 388.88 | 12499.03 | 32.14 |
dropWhile-true | 564.43 | 17983.35 | 31.86 |
take-all | 784.67 | 24425.36 | 31.13 |
last | 385.75 | 10974.84 | 28.45 |
drain | 375.18 | 4272.15 | 11.39 |
appendR[10000] | 326.93 | 1207.88 | 3.69 |
toList | 116441.26 | 199138.09 | 1.71 |
- streamly-0.8.0, conduit-1.3.4.1, ghc-8.10.4, Linux
To reproduce these results use the following commands:
$ bin/bench.sh --benchmarks "Streamly Conduit" --compare --diff-style absolute --diff-cutoff-percent 10 --quick
$ bin/bench.sh --benchmarks "Streamly Conduit" --compare --diff-style multiples --diff-cutoff-percent 10 --no-measure
To report heap utilization by individual benchmarks you can include
maxrss
in the --fields
option.
To know about stack and heap utilization by the libraries you can also take a look at the RTS heap and stack limits used to run the benchmarks of various libraries in bench-config.sh.
This package supports many streaming libraries. Use the following command to see all available benchmarks:
$ ./bench.sh --help
You can then select the libraries you want to compare:
$ ./bench.sh --benchmarks "streaming,pipes" --measure
It is trivial to add a new package. This is how a benchmark file for a streaming package looks like. Pull requests are welcome, we will be happy to help, just join the gitter chat and ask!