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author | Trygve Laugstøl <trygvis@inamo.no> | 2024-02-23 07:08:18 +0100 |
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committer | Trygve Laugstøl <trygvis@inamo.no> | 2024-02-23 07:08:18 +0100 |
commit | 5a9cdd3cc89507d4d74f8bded56ce5e037b3b56e (patch) | |
tree | 982ca2e7f9ac4e8c350dfb5c4f60bcfdfff5afaf /learn-you-some-erlang/keyval_benchmark.erl | |
parent | 05ae56e5e89abf2993f84e6d52b250131f247c35 (diff) | |
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Diffstat (limited to 'learn-you-some-erlang/keyval_benchmark.erl')
-rw-r--r-- | learn-you-some-erlang/keyval_benchmark.erl | 192 |
1 files changed, 192 insertions, 0 deletions
diff --git a/learn-you-some-erlang/keyval_benchmark.erl b/learn-you-some-erlang/keyval_benchmark.erl new file mode 100644 index 0000000..45c83be --- /dev/null +++ b/learn-you-some-erlang/keyval_benchmark.erl @@ -0,0 +1,192 @@ +-module(keyval_benchmark). +-compile(export_all). + +%% Runs all benchmarks with Reps number of elements. +bench(Reps) -> + io:format("Base Case:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(base_case(Reps)), + io:format("~nNaive Orddict:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(naive_orddict(Reps)), + io:format("~nSmart Orddict:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(smart_orddict(Reps)), + io:format("~nNaive Dict:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(naive_dict(Reps)), + io:format("~nSmart Dict:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(smart_dict(Reps)), + io:format("~nNaive gb_trees:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(naive_gb_trees(Reps)), + io:format("~nSmart gb_trees:~n"), + io:format("Operation\tTotal (µs)\tAverage (µs)~n"), + print(smart_gb_trees(Reps)). + +%% formats the benchmark results cleanly. +print([]) -> ok; +print([{Op, Total, Avg} | Rest]) -> + io:format("~8s\t~10B\t~.6f~n", [Op, Total, Avg]), + print(Rest). + +%% Example of a base benchmark function. This one actually does +%% nothing and can be used as a control for all the benchmark - it +%% will measure how much time it takes just to loop over data and +%% apply functions. +base_case(Reps) -> + benchmark(Reps, % N repetitions + [], % Empty data structure + fun ?MODULE:null/3, % Create + fun ?MODULE:null/2, % Read + fun ?MODULE:null/3, % Update + fun ?MODULE:null/2). % Delete + +%% Ordered dictionaries. Assumes that the value is present on reads. +smart_orddict(Reps) -> + benchmark(Reps, + orddict:new(), + fun orddict:store/3, + fun orddict:fetch/2, + fun orddict:store/3, + fun orddict:erase/2). + +%% Ordered dictionaries. Doesn't know whether a key is there or not on +%% reads. +naive_orddict(Reps) -> + benchmark(Reps, + orddict:new(), + fun orddict:store/3, + fun orddict:find/2, + fun orddict:store/3, + fun orddict:erase/2). + +%% Dictionary benchmark. Assumes that the value is present on reads. +smart_dict(Reps) -> + benchmark(Reps, + dict:new(), + fun dict:store/3, + fun dict:fetch/2, + fun dict:store/3, + fun dict:erase/2). + +%% Dictionary benchmark. Doesn't know if the value exisst at read time. +naive_dict(Reps) -> + benchmark(Reps, + dict:new(), + fun dict:store/3, + fun dict:find/2, + fun dict:store/3, + fun dict:erase/2). + +%% gb_trees benchmark. Uses the most general functions -- i.e.: it never +%% assumes that the value is not in a tree when inserting and never assumes +%% it is there when updating or deleting. +naive_gb_trees(Reps) -> + benchmark(Reps, + gb_trees:empty(), + fun gb_trees:enter/3, + fun gb_trees:lookup/2, + fun gb_trees:enter/3, + fun gb_trees:delete_any/2). + +%% gb_trees benchmark. Uses specific function: it assumes that the values +%% are not there when inserting and assumes it exists when updating or +%% deleting. +smart_gb_trees(Reps) -> + benchmark(Reps, + gb_trees:empty(), + fun gb_trees:insert/3, + fun gb_trees:get/2, + fun gb_trees:update/3, + fun gb_trees:delete/2). + +%% Empty functions used for the 'base_case/1' benchmark. They must do +%% nothing interesting. +null(_, _) -> ok. +null(_, _, _) -> ok. + +%% Runs all the functions of 4 formats: Create, Read, Update, Delete. +%% Create: it's a regular insertion, but it goes from an empty structure +%% to a filled one. Requires an empty data structure, +%% Read: reads every key from a complete data structure. +%% Update: usually, this is the same as the insertion from 'create', +%% except that it's run on full data structures. In some cases, +%% like gb_trees, there also exist operations for updates when +%% the keys are known that act differently from regular inserts. +%% Delete: removes a key from a tree. Because we want to test the +%% efficiency of it all, we will always delete from a complete +%% structure. +%% The function returns a list of all times averaged over the number +%% of repetitions (Reps) needed. +benchmark(Reps, Empty, CreateFun, ReadFun, UpdateFun, DeleteFun) -> + Keys = make_keys(Reps), + {TimeC, Struct} = timer:tc(?MODULE, create, [Keys, CreateFun, Empty]), + {TimeR, _} = timer:tc(?MODULE, read, [Keys, Struct, ReadFun]), + {TimeU, _} = timer:tc(?MODULE, update, [Keys, Struct, UpdateFun]), + {TimeD, _} = timer:tc(?MODULE, delete, [Keys, Struct, DeleteFun]), + [{create, TimeC, TimeC/Reps}, + {read, TimeR, TimeR/Reps}, + {update, TimeU, TimeU/Reps}, + {delete, TimeD, TimeD/Reps}]. + +%% Generate unique random numbers. No repetition allowed +make_keys(N) -> + %% The trick is to generate all numbers as usual, but match them + %% with a random value in a tuple of the form {Random, Number}. + %% The idea is to then sort the list generated that way; done in + %% this manner, we know all values will be unique and the randomness + %% will be done by the sorting. + Random = lists:sort([{random:uniform(N), X} || X <- lists:seq(1, N)]), + %% it's a good idea to then filter out the index (the random index) + %% to only return the real numbers we want. This is simple to do + %% with a list comprehension where '_' removes the extraneous data. + %% The keys are then fit into a tuple to make the test a bit more + %% realistic for comparison. + [{some, key, X} || {_, X} <- Random]. + +%% Loop function to apply the construction of a data structure. +%% The parameters passed are a list of all keys to use and then the +%% higher order function responsible of the creation of a data +%% structure. This is usually a function of the form +%% F(Key, Value, Structure). +%% In the first call, the structure has to be the empty data structure +%% that will progressively be filled. +%% The only value inserted for each key is 'some_data'; we only care +%% about the keys when dealing with key/value stuff. +create([], _, Acc) -> Acc; +create([Key|Rest], Fun, Acc) -> + create(Rest, Fun, Fun(Key, some_data, Acc)). + +%% Loop function to apply successive readings to a data structure. +%% The parameters passed are a list of all keys, the complete data +%% structure and then a higher order function responsible for +%% fetching the data. Such functions are usually of the form +%% F(Key, Structure). +read([], _, _) -> ok; +read([Key|Rest], Struct, Fun) -> + Fun(Key, Struct), + read(Rest, Struct, Fun). + +%% Loop function to apply updates to a data structure. +%% Takes a list of keys, a full data structure and a higher order +%% function responsible for the updating, usually of the form +%% F(Key, NewValue, Structure). +%% All values for a given key are replaced by 'newval', again because +%% we don't care about the values, but merely the operations with +%% the keys. +update([], _, _) -> ok; +update([Key|Rest], Struct, Fun) -> + Fun(Key, newval, Struct), + update(Rest, Struct, Fun). + +%% Loop function to apply deletions to a data structure. +%% Each deletion is made on a full data structure. +%% Takes a list of keys, a data structure and a higher order function +%% to do the deletion. Usually of the form +%% F(Key, Structure). +delete([], _, _) -> ok; +delete([Key|Rest], Struct, Fun) -> + Fun(Key, Struct), + delete(Rest, Struct, Fun). |