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Stop Guessing and Start Measuring - Benchmarkin...

Stop Guessing and Start Measuring - Benchmarking Practice (Poly Version)

This is the Polyconf version of the talk, including a little MJIT vs. GraalVM rebuttal, JavaScript, SQL, Ruby and Elixir to be truly Poly.

“What’s the fastest way of doing this?” - you might ask yourself during development. Sure, you can guess, your intuition might be correct - but how do you know? Benchmarking is here to give you the answers, but there are many pitfalls in setting up a good benchmark and analyzing the results. This talk will guide you through, introduce best practices, and surprise you with some unexpected benchmarking results. You didn’t think that the order of arguments could influence its performance...or did you?

Tobias Pfeiffer

July 09, 2017
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  1. $ ruby -v ruby 2.4.1p111 (2017-03-22 revision 58053) [x86_64-linux] $

    time ruby scripts/sort.rb real 0m0.151s user 0m0.052s sys 0m0.016s $ asdf local ruby jruby-9.1.8.0 $ ruby -v jruby 9.1.8.0 (2.3.1) 2017-03-06 90fc7ab OpenJDK 64-Bit Server VM 25.121-b13 on 1.8.0_121-8u121-b13- 0ubuntu1.16.04.2-b13 +jit [linux-x86_64] $ time ruby scripts/sort.rb real 0m3.468s user 0m8.384s sys 0m0.232s CRuby vs JRuby
  2. • Way too few samples • Realistic data/multiple inputs? •

    No warmup • Non production environment • Does creating the array matter? • Is reverse sorting really the bottle neck? • Setup information • Running on battery • Lots of applications running Numerous Failures!
  3. Benchmark.avg do |benchmark| benchmark.config time: 60, warmup: 95 benchmark.report "pent"

    do mkpieces mkboard $p[4] = [$p[4][0]] $pnum = (0...$p.length).to_a setpiece([],0) end end Proper Benchmark
  4. We just went from Truffle is 3.5 times slower all

    the way to Truffle is 3.7 times faster
  5. • (Ruby versions mentioned when used) • OpenJDK 8 •

    GraalVM 25 • MJIT master as of July 6th (last commit mid of June) • Elixir 1.3.4 • Erlang 19.1 • i5-7200U – 2 x 2.5GHz (Up to 3.10GHz) • 8GB RAM • Linux Mint 18.1 - 64 bit (Ubuntu 16.04 base) • Linux Kernel 4.8.0 • Firefox 54.0 System Specification
  6. “We should forget about small efficiencies, say about 97% of

    the time: premature optimization is the root of all evil.” Donald Knuth, 1974 (Computing Surveys, Vol 6, No 4, December 1974)
  7. “Yet we should not pass up our opportunities in that

    critical 3%” Donald Knuth, 1974 (Computing Surveys, Vol 6, No 4, December 1974) The very next sentence
  8. “(...) a 12 % improvement, easily obtained, is never considered

    marginal.” Donald Knuth, 1974 (Computing Surveys, Vol 6, No 4, December 1974) Prior Paragraph
  9. getColor = function(x, y, board) { if (isOutOfBounds(x, y, board))

    { return NEUTRAL; } else { return board[y][x]; } }; isOutOfBounds = function(x, y, board) { var board_size = board.length; if ((x < 0) || (y < 0) || (x >= board_size) || (y >= board_size)) { return true; } else { return false; } }; Checks...
  10. getColor = function(x, y, board) { if (isOutOfBounds(x, y, board))

    { return NEUTRAL; } else { return board[y][x]; } }; isOutOfBounds = function(x, y, board) { var board_size = board.length; if ((x < 0) || (y < 0) || (x >= board_size) || (y >= board_size)) { return true; } else { return false; } }; Checks...
  11. getColor = function(x, y, board) { if (isOutOfBounds(x, y, board))

    { return NEUTRAL; } else { return board[y][x]; } }; isOutOfBounds = function(x, y, board) { var board_size = board.length; if ((x < 0) || (y < 0) || (x >= board_size) || (y >= board_size)) { return true; } else { return false; } }; Checks...
  12. getColor = function(x, y, board) { if (isOutOfBounds(x, y, board))

    { return NEUTRAL; } else { return board[y][x]; } }; isOutOfBounds = function(x, y, board) { var board_size = board.length; if ((x < 0) || (y < 0) || (x >= board_size) || (y >= board_size)) { return true; } else { return false; } }; Checks...
  13. Benchmark.js var board = initBoard(3); var suite = new Benchmark.Suite;

    suite.add('getColor', function() { getColor(1, 2, board); }).on('cycle', function(event) { // log result }).on('complete', function() { // done }).run({ async: true });
  14. Benchmark function var board = initBoard(3); var suite = new

    Benchmark.Suite; suite.add('getColor', function() { getColor(1, 2, board); }).on('cycle', function(event) { // log result }).on('complete', function() { // done }).run({ async: true });
  15. getColor x 278,959,094 ops/sec ±0.39% (98 runs sampled) getColor x

    1,816,254,291 ops/sec ±0.17% (94 runs sampled) Run it! New Old
  16. getColor x ~279 Million ops/sec ±0.39% (98 runs sampled) getColor

    x ~1 816 Million ops/sec ±0.17% (94 runs sampled) Run it! New Old
  17. getColor x ~279 Million ops/sec ±0.39% (98 runs sampled) getColor

    x ~1 816 Million ops/sec ±0.17% (94 runs sampled) Over 6 times faster!!! New Old
  18. var suite = new Benchmark.Suite; suite.add('playing a random 9x9 game',

    function() { playoutForBoard(initBoard(9)); }).add('playing a random 13x13 game', function() { playoutForBoard(initBoard(13)); }).add('playing a random 19x19 game', function() { playoutForBoard(initBoard(19)); }).on('cycle', function(event) { // log result here }).on('complete', function() { // done }).run({ async: true }); A bigger benchmark
  19. var suite = new Benchmark.Suite; suite.add('playing a random 9x9 game',

    function() { playoutForBoard(initBoard(9)); }).add('playing a random 13x13 game', function() { playoutForBoard(initBoard(13)); }).add('playing a random 19x19 game', function() { playoutForBoard(initBoard(19)); }).on('cycle', function(event) { // log result here }).on('complete', function() { // done }).run({ async: true }); A bigger benchmark
  20. 9x9 game x 252 ops/sec ±2.44% 13x13 game x 80.50

    ops/sec ±4.14% 19x19 game x 25.19 ops/sec ±6.33% 9x9 game x 199 ops/sec ±18.09% 13x13 game x 67.11 ops/sec ±3.24% 19x19 game x 20.97 ops/sec ±7.56% Run it! New Old
  21. 9x9 game x 252 ops/sec ±2.44% 13x13 game x 80.50

    ops/sec ±4.14% 19x19 game x 25.19 ops/sec ±6.33% 9x9 game x 199 ops/sec ±18.09% 13x13 game x 67.11 ops/sec ±3.24% 19x19 game x 20.97 ops/sec ±7.56% New Old ~20% slower?!?!
  22. 9x9 game x 252 ops/sec ±2.44% 13x13 game x 80.50

    ops/sec ±4.14% 19x19 game x 25.19 ops/sec ±6.33% 9x9 game x 199 ops/sec ±18.09% 13x13 game x 67.11 ops/sec ±3.24% 19x19 game x 20.97 ops/sec ±7.56% New Old Initializing & Copying Boards
  23. Benchmark.ips do |benchmark| benchmark.report '19x19 playout' do game = Rubykon::Game.new(19)

    game_state = Rubykon::GameState.new(game) mcts = MCTS::Playout.new(game_state) mcts.playout end end macro: playout
  24. Benchmark.avg do |benchmark| game_19 = Rubykon::Game.new(19) game_state_19 = Rubykon::GameState.new game_19

    mcts = MCTS::MCTS.new benchmark.config warmup: 180, time: 180 benchmark.report "19x19 1_000 iterations" do mcts.start game_state_19, 1_000 end end Application: tree search
  25. Micro Macro Setup Complexity Execution Time Confidence of Real Impact

    Components involved Chance of Interference Application
  26. Micro Macro Setup Complexity Execution Time Confidence of Real Impact

    Components involved Chance of Interference Golden Middle Application
  27. Benchmark.avg do |benchmark| game_19 = Rubykon::Game.new(19) game_state_19 = Rubykon::GameState.new game_19

    mcts = MCTS::MCTS.new benchmark.config warmup: 180, time: 180 benchmark.report "19x19 1_000 iterations" do mcts.start game_state_19, 1_000 end end Application: tree search
  28. Benchee.run %{ "Using LatestCourierLocation" => fn(courier_id) -> LatestCourierLocation |> CourierLocation.with_courier_ids(courier_id)

    |> Repo.one end, "with_courier_ids + order" => fn(courier_id) -> CourierLocation.with_courier_ids(courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end, "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end } A real case
  29. Benchee.run %{ "Using LatestCourierLocation" => fn(courier_id) -> LatestCourierLocation |> CourierLocation.with_courier_ids(courier_id)

    |> Repo.one end, "with_courier_ids + order" => fn(courier_id) -> CourierLocation.with_courier_ids(courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end, "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end } Database View
  30. Benchee.run %{ "Using LatestCourierLocation" => fn(courier_id) -> LatestCourierLocation |> CourierLocation.with_courier_ids(courier_id)

    |> Repo.one end, "with_courier_ids + order" => fn(courier_id) -> CourierLocation.with_courier_ids(courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end, "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end } Only difference
  31. Name ips average deviation median with_courier_ids + order 1.19 K

    841.44 μs ±67.64% 675.00 μs full custom 1.16 K 862.36 μs ±56.06% 737.00 μs Using LatestCourierLocation 0.00603 K 165897.47 μs ±2.33% 165570.00 μs Comparison: with_courier_ids + order 1.19 K full custom 1.16 K - 1.02x slower Using LatestCourierLocation 0.00603 K - 197.16x slower Another job well done?
  32. inputs = %{ "Big 2.3 Million locations" => 3799, "No

    locations" => 8901, "~200k locations" => 4238, "~20k locations" => 4201 } Benchee.run %{ ... "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end }, inputs: inputs, time: 25, warmup: 5 Inputs to the rescue!
  33. inputs = %{ "Big 2.3 Million locations" => 3799, "No

    locations" => 8901, "~200k locations" => 4238, "~20k locations" => 4201 } Benchee.run %{ ... "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end }, inputs: inputs, time: 25, warmup: 5 Inputs to the rescue!
  34. inputs = %{ "Big 2.3 Million locations" => 3799, "No

    locations" => 8901, "~200k locations" => 4238, "~20k locations" => 4201 } Benchee.run %{ ... "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end }, inputs: inputs, time: 25, warmup: 5 Inputs to the rescue!
  35. inputs = %{ "Big 2.3 Million locations" => 3799, "No

    locations" => 8901, "~200k locations" => 4238, "~20k locations" => 4201 } Benchee.run %{ ... "full custom" => fn(courier_id) -> CourierLocation |> Ecto.Query.where(courier_id: ^courier_id) |> Ecto.Query.order_by(desc: :time) |> Ecto.Query.limit(1) |> Repo.one end }, inputs: inputs, time: 25, warmup: 5 Inputs to the rescue!
  36. ##### With input Big 2.3 Million locations ##### Comparison: with_courier_ids

    + order 1.19 K full custom 1.16 K - 1.02x slower Using LatestCourierLocation 0.00603 K - 197.16x slower ##### With input ~200k locations ##### Comparison: Using LatestCourierLocation 3.66 full custom 0.133 - 27.57x slower with_courier_ids + order 0.132 - 27.63x slower ##### With input ~20k locations ##### Comparison: Using LatestCourierLocation 38.12 full custom 0.122 - 312.44x slower with_courier_ids + order 0.122 - 313.33x slower ##### With input No locations ##### Comparison: Using LatestCourierLocation 2967.48 full custom 0.114 - 25970.57x slower with_courier_ids + order 0.114 - 26046.06x slower Old
  37. ##### With input Big 2.3 Million locations ##### Comparison: with_courier_ids

    + order 1.19 K full custom 1.16 K - 1.02x slower Using LatestCourierLocation 0.00603 K - 197.16x slower ##### With input ~200k locations ##### Comparison: Using LatestCourierLocation 3.66 full custom 0.133 - 27.57x slower with_courier_ids + order 0.132 - 27.63x slower ##### With input ~20k locations ##### Comparison: Using LatestCourierLocation 38.12 full custom 0.122 - 312.44x slower with_courier_ids + order 0.122 - 313.33x slower ##### With input No locations ##### Comparison: Using LatestCourierLocation 2967.48 full custom 0.114 - 25970.57x slower with_courier_ids + order 0.114 - 26046.06x slower Old
  38. ##### With input Big 2.3 Million locations ##### Comparison: with_courier_ids

    + order 1.19 K full custom 1.16 K - 1.02x slower Using LatestCourierLocation 0.00603 K - 197.16x slower ##### With input ~200k locations ##### Comparison: Using LatestCourierLocation 3.66 full custom 0.133 - 27.57x slower with_courier_ids + order 0.132 - 27.63x slower ##### With input ~20k locations ##### Comparison: Using LatestCourierLocation 38.12 full custom 0.122 - 312.44x slower with_courier_ids + order 0.122 - 313.33x slower ##### With input No locations ##### Comparison: Using LatestCourierLocation 2967.48 full custom 0.114 - 25970.57x slower with_courier_ids + order 0.114 - 26046.06x slower Old
  39. ##### With input Big 2.3 Million locations ##### Comparison: full

    custom 3921.12 with_courier_ids + order 23.05 - 170.09x slower Using LatestCourierLocation 5.98 - 655.74x slower ##### With input ~200k locations ##### Comparison: full custom 4272.84 with_courier_ids + order 14.20 - 300.91x slower Using LatestCourierLocation 3.80 - 1125.59x slower ##### With input ~20k locations ##### Comparison: full custom 3792.97 with_courier_ids + order 78.93 - 48.06x slower Using LatestCourierLocation 35.62 - 106.47x slower ##### With input No locations ##### Comparison: full custom 5.14 K with_courier_ids + order 3.87 K - 1.33x slower Using LatestCourierLocation 3.29 K - 1.56x slower Combined Index
  40. ##### With input Big 2.3 Million locations ##### Comparison: full

    custom 3921.12 with_courier_ids + order 23.05 - 170.09x slower Using LatestCourierLocation 5.98 - 655.74x slower ##### With input ~200k locations ##### Comparison: full custom 4272.84 with_courier_ids + order 14.20 - 300.91x slower Using LatestCourierLocation 3.80 - 1125.59x slower ##### With input ~20k locations ##### Comparison: full custom 3792.97 with_courier_ids + order 78.93 - 48.06x slower Using LatestCourierLocation 35.62 - 106.47x slower ##### With input No locations ##### Comparison: full custom 5.14 K with_courier_ids + order 3.87 K - 1.33x slower Using LatestCourierLocation 3.29 K - 1.56x slower Combined Index
  41. # with an index on courier_id and one on time

    Name ips average deviation median Updating a location 366.90 2.73 ms ±36.35% 2.29 ms # with a combined index on courier_id and time Name ips average deviation median Updating a location 283.41 3.53 ms ±52.18% 2.77 ms Insertion Time
  42. [info] GET / [debug] Processing by Rumbl.PageController.index/2 Parameters: %{} Pipelines:

    [:browser] [info] Sent 200 in 46ms [info] GET /sessions/new [debug] Processing by Rumbl.SessionController.new/2 Parameters: %{} Pipelines: [:browser] [info] Sent 200 in 5ms [info] GET /users/new [debug] Processing by Rumbl.UserController.new/2 Parameters: %{} Pipelines: [:browser] [info] Sent 200 in 7ms [info] POST /users [debug] Processing by Rumbl.UserController.create/2 Parameters: %{"_csrf_token" => "NUEUdRMNAiBfIHEeNwZkfA05PgAOJgAAf0ACXJqCjl7YojW+trdjdg==", "_utf8" => " ", "user" => ✓ %{"name" => "asdasd", "password" => "[FILTERED]", "username" => "Homer"}} Pipelines: [:browser] [debug] QUERY OK db=0.1ms begin [] [debug] QUERY OK db=0.9ms INSERT INTO "users" ("name","password_hash","username","inserted_at","updated_at") VALUES ($1,$2,$3,$4,$5) RETURNING "id" ["asdasd", "$2b$12$.qY/kpo0Dec7vMK1ClJoC.Lw77c3oGllX7uieZILMlFh2hFpJ3F.C", "Homer", {{2016, 12, 2}, {14, 10, 28, 0}}, {{2016, 12, 2}, {14, 10, 28, 0}}] Logging & Friends
  43. defmodule MyMap do def map_tco(list, function) do Enum.reverse do_map_tco([], list,

    function) end defp do_map_tco(acc, [], _function) do acc end defp do_map_tco(acc, [head | tail], func) do do_map_tco([func.(head) | acc], tail, func) end def map_body([], _func), do: [] def map_body([head | tail], func) do [func.(head) | map_body(tail, func)] end end TCO
  44. defmodule MyMap do def map_tco(list, function) do Enum.reverse do_map_tco([], list,

    function) end defp do_map_tco(acc, [], _function) do acc end defp do_map_tco(acc, [head | tail], func) do do_map_tco([func.(head) | acc], tail, func) end def map_body([], _func), do: [] def map_body([head | tail], func) do [func.(head) | map_body(tail, func)] end end TCO
  45. defmodule MyMap do def map_tco(list, function) do Enum.reverse do_map_tco([], list,

    function) end defp do_map_tco(acc, [], _function) do acc end defp do_map_tco(acc, [head | tail], func) do do_map_tco([func.(head) | acc], tail, func) end def map_body([], _func), do: [] def map_body([head | tail], func) do [func.(head) | map_body(tail, func)] end end TCO
  46. map_fun = fn(i) -> i + 1 end inputs =

    %{ "Small (10 Thousand)" => Enum.to_list(1..10_000), "Middle (100 Thousand)" => Enum.to_list(1..100_000), "Big (1 Million)" => Enum.to_list(1..1_000_000), "Bigger (5 Million)" => Enum.to_list(1..5_000_000) } Benchee.run %{ "tail-recursive" => fn(list) -> MyMap.map_tco(list, map_fun) end, "stdlib map" => fn(list) -> Enum.map(list, map_fun) end, "body-recursive" => fn(list) -> MyMap.map_body(list, map_fun) end }, inputs: inputs TCO
  47. map_fun = fn(i) -> i + 1 end inputs =

    %{ "Small (10 Thousand)" => Enum.to_list(1..10_000), "Middle (100 Thousand)" => Enum.to_list(1..100_000), "Big (1 Million)" => Enum.to_list(1..1_000_000), "Bigger (5 Million)" => Enum.to_list(1..5_000_000) } Benchee.run %{ "tail-recursive" => fn(list) -> MyMap.map_tco(list, map_fun) end, "stdlib map" => fn(list) -> Enum.map(list, map_fun) end, "body-recursive" => fn(list) -> MyMap.map_body(list, map_fun) end }, inputs: inputs TCO
  48. map_fun = fn(i) -> i + 1 end inputs =

    %{ "Small (10 Thousand)" => Enum.to_list(1..10_000), "Middle (100 Thousand)" => Enum.to_list(1..100_000), "Big (1 Million)" => Enum.to_list(1..1_000_000), "Bigger (5 Million)" => Enum.to_list(1..5_000_000) } Benchee.run %{ "tail-recursive" => fn(list) -> MyMap.map_tco(list, map_fun) end, "stdlib map" => fn(list) -> Enum.map(list, map_fun) end, "body-recursive" => fn(list) -> MyMap.map_body(list, map_fun) end }, inputs: inputs TCO
  49. ##### With input Small (10 Thousand) ##### Comparison: body-recursive 5.12

    K stdlib map 5.07 K - 1.01x slower tail-recursive 4.38 K - 1.17x slower ##### With input Middle (100 Thousand) ##### Comparison: body-recursive 491.16 stdlib map 488.45 - 1.01x slower tail-recursive 399.08 - 1.23x slower ##### With input Big (1 Million) ##### Comparison: tail-recursive 35.36 body-recursive 25.69 - 1.38x slower stdlib map 24.85 - 1.42x slower ##### With input Bigger (5 Million) ##### Comparison: tail-recursive 6.93 body-recursive 4.92 - 1.41x slower stdlib map 4.87 - 1.42x slower TCO
  50. ##### With input Small (10 Thousand) ##### Comparison: body-recursive 5.12

    K stdlib map 5.07 K - 1.01x slower tail-recursive 4.38 K - 1.17x slower ##### With input Middle (100 Thousand) ##### Comparison: body-recursive 491.16 stdlib map 488.45 - 1.01x slower tail-recursive 399.08 - 1.23x slower ##### With input Big (1 Million) ##### Comparison: tail-recursive 35.36 body-recursive 25.69 - 1.38x slower stdlib map 24.85 - 1.42x slower ##### With input Bigger (5 Million) ##### Comparison: tail-recursive 6.93 body-recursive 4.92 - 1.41x slower stdlib map 4.87 - 1.42x slower TCO
  51. TCO

  52. config |> Benchee.init |> Benchee.system |> Benchee.benchmark("job", fn -> magic

    end) |> Benchee.measure |> Benchee.statistics |> Benchee.Formatters.Console.output |> Benchee.Formatters.HTML.output A transformation of inputs