umgeher/erlware_commons
0
Fork 0
Code Issues Pull requests Releases Activity

add suport and documentation for signatures for dictionaries

Browse source 
Signed-off-by: Jordan Wilberding <jwilberding@gmail.com>
This commit is contained in:
Eric Merritt 2011-10-14 12:29:28 -05:00 • committed by Jordan Wilberding
parent cea6de7c84
commit ff3557d883
11 changed files with 2082 additions and 3 deletions
Download patch file Download diff file Expand all files Collapse all files

363
doc/property_based_testing.md Normal file
View file

@ -0,0 +1,363 @@
Property based testing for unit testers
=======================================
Main contributors: Torben Hoffmann, Raghav Karol, Eric Merritt
The purpose of the short document is to help people who are familiar
with unit testing understand how property based testing (PBT) differs,
but also where the thinking is the same.
This document focusses on the PBT tool
[`PropEr`](https://github.com/manopapad/proper) for Erlang since that is
what I am familiar with, but the general principles applies to all PBT
tools regardless of which language they are written in.
The approach taken here is that we hear from people who are used to
working with unit testing regarding how they think when designing
their tests and how a concrete test might look.
These descriptions are then "converted" into the way it works with
PBT, with a clear focus on what stays the same and what is different.
## Testing philosophies
### A quote from Martin Logan:
> For me unit testing is about contracts. I think about the same things
> I think about when I write statements like {ok, Resp} =
> Mod:Func(Args). Unit testing and writing specs are very close for me.
> Hypothetically speaking lets say a function should return return {ok,
> string()} | {error, term()} for all given input parameters then my
> unit tests should be able to show that for a representative set of
> input parameters that those contracts are honored. The art comes in
> thinking about what that set is.
The trap in writing all your own tests can often be that we think
about the set in terms of what we coded for and not what may indeed be
asked of our function. As the code is tried in further exploratory
testing and in production new input parameter sets for which the given
function does not meet the stated contract are discovered and added to
the test case once a fix has been put into place.
This is a very good description of what the ground rules for unit
testing are:
* Checking that contracts are obeyed.
* Creating a representative set of input parameters.
The former is very much part of PBT - each property you write will
check a contract, so that thinking is the same.
## xUnit vs PBT
Unit testing has become popular for software testing with the advent
of xUnit tools like jUnit for Java. xUnit like tools typically
provide a testing framework with the following functionality
* test fixture setup
* test case execution
* test fixture teardown
* test suite management
* test status reporting and management
While xUnit tools provide a lot of functionality to execute and manage
test cases and suites, reporting results there is no focus on test
case execution step, while this is the main focus area of
property-based testing (PBT).
Consider the following function specification
:::erlang
sort(list::integer()) ---> list::integer() | error
A verbal specification of this function is,
> For all input lists of integers, the sort function returns a sorted
> list of integers.
For any other kind of argument the function returns the atom error.
The specification above may be a requirement of how the function
should behave or even how the function does behave. This distinction
is important; the former is the requirement for the function, the
latter is the actual API. Both should be the same and that is what our
testing should confirm. Test cases for this function might look like
:::erlang
assertEqual(sort([5,4,3,2,1]), [1,2,3,4,5])
assertEqual(sort([1,2,3,4,5]), [1,2,3,4,5])
assertEqual(sort([] ), [] )
assertEqual(sort([-1,0, 1] ), [-1, 0, 1] )
How many tests cases should we write to be convinced that the actual
behaviour of the function is the same as its specification? Clearly,
it is impossible to write tests cases for all possible input values,
here all lists of integers, the art of testing is finding individual
input values that are representative of a large part of the input
space. We hope that the test cases are exhaustive to cover the
specification. xUnit tools offer no support for this and this is where
PBT and PBT Tools like `PropEr` and `QuickCheck` come in.
PBT introduces testing with a large set of random input values and
verifying that the specification holds for each input value
selected. Functions used to generate input values, generators, are
specified using rules and can be simply composed together to construct
complicated values. So, a property based test for the function above
may look like:
:::erlang
FOREACH({I, J, InputList}, {nat(), nat(), integer_list()},
SUCHTHAT(I < J andalso J < length(InputList),
SortedList = sort(InputList)
length(SortedList) == length(InputList)
andalso
lists:get(SortedList, I) =< lists:get(SortedList, J))
The property above works as follows
* Generate a random list of integers `InputList` and two natural numbers
I, J, such that I < J < size of `InputList`
* Check that size of sorted and input lists is the same.
* Check that element with smaller index I is less than or equal to
element with larger index J in `SortedList`.
Notice in the property above, we *specify* property. Verification of
the property based on random input values will be done by the property
based tool, therefore we can generated a large number of tests cases
with random input values and have a higher level of confidence that
the function when using unit tests alone.
But it does not stop at generation of input parameters. If you have
more complex tests where you have to generate a series of events and
keep track of some state then your PBT tool will generate random
sequences of events which corresponds to legal sequences of events and
test that your system behaves correctly for all sequences.
So when you have written a property with associated generators you
have in fact created something that can create numerous test cases -
you just have to tell your PBT tool how many test cases you want to
check the property on.
## Shrinking the bar
At this point you might still have the feeling that introducing the
notion of some sort of generators to your unit testing tool of choice
would bring you on par with PBT tools, but wait there is more to
come.
When a PBT tool creates a test case that fails there is real chance
that it has created a long test case or some big input parameters -
trying to debug that is very much like receiving a humongous log from
a system in the field and try to figure out what cause the system to
fail.
Enter shrinking...
When a test case fails the PBT tool will try to shrink the failing
test case down to the essentials by stripping out input elements or
events that does not cause the failure. In most cases this results in
a very short counterexample that clearly states which events and
inputs are required to break a property.
As we go through some concrete examples later the effects of shrinking
will be shown.
Shrinking makes it a lot easier to debug problems and is as key to the
strength of PBT as the generators.
## Converting a unit test
We will now take a look at one possible way of translating a unit
test into a PBT setting.
The example comes from Eric Merritt and is about the `add/2` function in
the `ec_dictionary` instance `ec_gb_trees`.
The add function has the following spec:
:::erlang
-spec add(ec_dictionary:key(), ec_dictionary:value(), Object::dictionary()) ->
dictionary().
and it is supposed to do the obvious: add the key and value pair to
the dictionary and return a new dictionary.
Eric states his basic expectations as follows:
1. I can put arbitrary terms into the dictionary as keys
2. I can put arbitrary terms into the dictionary as values
3. When I put a value in the dictionary by a key, I can retrieve that same value
4. When I put a different value in the dictionary by key it does not change other key value pairs.
5. When I update a value the new value in available by the new key
6. When a value does not exist a not found exception is created
The first two expectations regarding being able to use arbritrary
terms as keys and values is a job for generators.
The latter four are prime candidates for properties and we will create
one for each of them.
### Generators
:::erlang
key() -> any().
value() -> any().
For `PropEr` this approach has the drawback that creation and shrinking
becomes rather time consuming, so it might be better to narrow to
something like this:
:::erlang
key() -> union([integer(),atom()]).
value() -> union([integer(),atom(),binary(),boolean(),string()]).
What is best depends on the situation and intended usage.
Now, being able to generate keys and values is not enough. You also
have to tell `PropEr` how to create a dictionary and in this case we
will use a symbolic generator (detail to be explained later).
:::erlang
sym_dict() ->
?SIZED(N,sym_dict(N)).
sym_dict(0) ->
{'$call',ec_dictionary,new,[ec_gb_trees]};
sym_dict(N) ->
?LAZY(
frequency([
{1, {'$call',ec_dictionary,remove,[key(),sym_dict(N-1)]}},
{2, {'$call',ec_dictionary,add,[value(),value(),sym_dict(N-1)]}}
])).
`sym_dict/0` uses the `?SIZED` macro to control the size of the
generated dictionary. `PropEr` will start out with small numbers and
gradually raise it.
`sym_dict/1` is building a dictionary by randomly adding key/value
pairs and removing keys. Eventually the base case is reached which
will create an empty dictionary.
The `?LAZY` macro is used to defer the calculation of the
`sym_dict(N-1)` until they are needed and `frequency/1` is used
to ensure that twice as many adds compared to removes are done. This
should give rather more interesting dictionaries in the long run, if
not one can alter the frequencies accondingly.
But does it really work?
That is a good question and one that should always be asked when
looking at genetors. Fortunately there is a way to see what a
generator produces provided that the generator functions are exported.
Hint: in most cases it will not hurt to throw in a
`-compile(export_all).` in the module used to specify the
properties. And here we actually have a sub-hint: specify the
properties in a separate file to avoid peeking inside the
implementation! Base the test on the published API as this is what the
users of the code will be restricted to.
When the test module has been loaded you can test the generators by
starting up an Erlang shell (this example uses the erlware_commons
code so get yourself a clone to play with):
:::sh
$ erl -pz ebin -pz test
1> proper_gen:pick(ec_dictionary_proper:key()).
{ok,4}
2> proper_gen:pick(ec_dictionary_proper:key()).
{ok,35}
3> proper_gen:pick(ec_dictionary_proper:key()).
{ok,-5}
4> proper_gen:pick(ec_dictionary_proper:key()).
{ok,48}
5> proper_gen:pick(ec_dictionary_proper:key()).
{ok,'\036\207_là´?\nc'}
6> proper_gen:pick(ec_dictionary_proper:value()).
{ok,2}
7> proper_gen:pick(ec_dictionary_proper:value()).
{ok,-14}
8> proper_gen:pick(ec_dictionary_proper:value()).
{ok,-3}
9> proper_gen:pick(ec_dictionary_proper:value()).
{ok,27}
10> proper_gen:pick(ec_dictionary_proper:value()).
{ok,-8}
11> proper_gen:pick(ec_dictionary_proper:value()).
{ok,[472765,17121]}
12> proper_gen:pick(ec_dictionary_proper:value()).
{ok,true}
13> proper_gen:pick(ec_dictionary_proper:value()).
{ok,<<>>}
14> proper_gen:pick(ec_dictionary_proper:value()).
{ok,<<89,69,18,148,32,42,238,101>>}
15> proper_gen:pick(ec_dictionary_proper:sym_dict()).
{ok,{'$call',ec_dictionary,add,
[[114776,1053475],
'fª\020\227\215',
{'$call',ec_dictionary,add,
['',true,
{'$call',ec_dictionary,add,
['2^Ø¡',
[900408,886056],
{'$call',ec_dictionary,add,[[48618|...],<<...>>|...]}]}]}]}}
16> proper_gen:pick(ec_dictionary_proper:sym_dict()).
{ok,{'$call',ec_dictionary,add,
[10,'a¯\214\031fõC',
{'$call',ec_dictionary,add,
[false,-1,
{'$call',ec_dictionary,remove,
['d·ÉV÷[',
{'$call',ec_dictionary,remove,[12,{'$call',...}]}]}]}]}}
That does not look too bad, so we will continue with that for now.
### Properties of `add/2`
The first expectation Eric had about how the dictionary works was that
if a key had been stored it could be retrieved.
One way of expressing this could be with this property:
:::erlang
prop_get_after_add_returns_correct_value() ->
?FORALL({Dict,K,V}, {sym_dict(),key(),value()},
begin
try ec_dictionary:get(K,ec_dictionary:add(K,V,Dict)) of
V ->
true;
_ ->
false
catch
_:_ ->
false
end
end).
This property reads that for all dictionaries `get/2` using a key
from a key/value pair just inserted using the `add/3` function
will return that value. If that is not the case the property will
evaluate to false.
Running the property is done using `proper:quickcheck/1`:
:::sh
proper:quickcheck(ec_dictionary_proper:prop_get_after_add_returns_correct_value()).
....................................................................................................
OK: Passed 100 test(s).
true
This was as expected, but at this point we will take a little detour
and introduce a mistake in the `ec_gb_trees` implementation and see
how that works.

462
doc/signatures.md Normal file
View file

@ -0,0 +1,462 @@
Signatures
==========
It often occurs in coding that we need a library, a set of
functionaly. Often there are several algorithms that could provide
this functionality. However, the code that uses it, either doesn't
care about the individual algorithm or wishes to delegate choosing
that algorithm to some higher level. Lets take the concrete example of
dictionaries. A dictionary provides the ability to access a value via
a key (other things as well but primarily this). There are may ways to
implement a dictionary. Just a few are:
* [Associative Arrays](http://en.wikipedia.org/wiki/Associative_array)
* [Binary Trees](http://en.wikipedia.org/wiki/Binary_tree)
* [Hash Tables](http://en.wikipedia.org/wiki/Hash_table#Performance_analysis)
* [Skip Lists](http://en.wikipedia.org/wiki/Skip_list)
* Many, many more ....
Each of these approaches has there own performance characteristics,
memory footprints etc. For example, a table of size n with open
addressing has no collisions and holds up to n elements, with a single
comparison for successful lookup, and a table of size n with chaining
and k keys has the minimum max(0, k-n) collisions and O(1 + k/n)
comparisons for lookup. While for skip lists the performance
characteristics are about as good as that of randomly-built binary
search trees - namely (O log n). So the choice of which to select
depends very much on memory available, insert/read characteristics,
etc. So delegating the choice to a single point in your code is a very
good idea. Unfortunately, in Erlang thats ot so easy to do at the moment.
Other languages, have built in support for this
functionality. [Java](http://en.wikipedia.org/wiki/Java_(programming_language))
has
[Interfaces](http://download.oracle.com/javase/tutorial/java/IandI/createinterface.html),
[SML](http://en.wikipedia.org/wiki/Standard_ML) has
[Signatures](http://en.wikipedia.org/wiki/Standard_ML#Module_system).
Erlang, though, doesn't currently support this model, at least not
directly. There are a few ways you can approximate it. One way is to
pass the Module name to the calling functions along with the data that
it is going to be called on.
:::erlang
add(ModuleToUse, Key, Value, DictData) ->
ModuleToUse:add(Key, Value, DictData).
This works, and you can vary how you want to pass the data. For
example, you could easily use a tuple to contain the data. That is,
you could pass in `{ModuleToUse, DictData}` and that would make it a
bit cleaner.
:::erlang
add(Key, Value, {ModuleToUse, DictData}) ->
ModuleToUse:add(Key, Value, DictData).
Either way, there are a few problems with this approach. One of the
biggest is that you lose code locality, by looking at this bit of code
you don't know what `ModuleToUse` is at all. You would need to follow
the call chain up to figure out what it is. Also it may not be obvious
what is actually happening. The fact that `ModuleToUse` is a variable
name obscures the code making it harder to understand. The other big
problem is that the tools provided with Erlang can't help find
mistakes that you might have made. Tools like
[Xref](http://www.erlang.org/doc/man/xref.html) and
[Dialyzer](http://www.erlang.org/doc/man/dialyzer.html) have just as
hard a time figuring out the what `ModuleToUse` is pointing to as you
do. So they can't give you warnings about potential problems. In fact
someone could inadvertantly pass an unexpected function name as
`ModuleToUse` and you would never get any warnings, just an exception
at run time.
Fortunately, Erlang is a pretty flexable language so we can use a
similar approach with a few adjustments to give us the best of both
worlds. Both the flexibiltiy of ignoreing a specific implementation
and keeping all the nice locality we get by using an explicit module
name.
So what we actually want to do is something mole like this:
:::erlang
add(Key, Value, DictData) ->
dictionary:add(Key, Value, DictData).
Doing this we retain the locality. We can easily look up the
`dictionary` Module. We immediately have a good idea what a
`dictionary` actually is and we know what functions we are
calling. Also, all the tools know what a `dictionary` is as well and
how to check that your code is calling it correctly. For all of these
reasons, this is a much better approach to the problem. This is what
*Signatures* are all about.
Signatures
----------
How do we actually do this in Erlang now that Erlang is missing what Java, SML and friends has built in?
The first thing we need to do is to define
a [Behaviour](http://metajack.im/2008/10/29/custom-behaviors-in-erlang/)
for our functionality. To continue our example we will define a
Behaviour for dictionaries. That Behaviour looks like this:
:::erlang
-module(ec_dictionary).
-export([behaviour_info/1]).
behaviour_info(callbacks) ->
[{new, 0},
{has_key, 2},
{get, 2},
{add, 3},
{remove, 2},
{has_value, 2},
{size, 1},
{to_list, 1},
{from_list, 1},
{keys, 1}];
behaviour_info(_) ->
undefined.
So we have our Behaviour now. Unfortunately, this doesn't give us much
yet. It will make sure that any dictionaries we write will have all
the functions they need to have, but it wont help use actually use the
dictionaries in an abstract way in our code. To do that we need to add
a bit of functionality. We do that by actually implementing our own
behaviour, starting with `new/1`.
:::erlang
%% @doc create a new dictionary object from the specified module. The
%% module should implement the dictionary behaviour.
%%
%% @param ModuleName The module name.
-spec new(module()) -> dictionary(_K, _V).
new(ModuleName) when is_atom(ModuleName) ->
#dict_t{callback = ModuleName, data = ModuleName:new()}.
This code creates a new dictionary for us. Or to be more specific it
actually creates a new dictionary Signature record, that will be used
subsequently in other calls. This might look a bit familiar from our
previous less optimal approach. We have both the module name and the
data. here in the record. We call the module name named in
`ModuleName` to create the initial data. We then construct the record
and return that record to the caller and we have a new
dictionary. What about the other functions, the ones that don't create
a dictionary but make use of it. Let's take a look at the
implementations of two kinds of functions, one that updates the
dictionary and another that just retrieves data.
The first we will look at is the one that updates the dictionary by
adding a value.
:::erlang
%% @doc add a new value to the existing dictionary. Return a new
%% dictionary containing the value.
%%
%% @param Dict the dictionary object to add too
%% @param Key the key to add
%% @param Value the value to add
-spec add(key(K), value(V), dictionary(K, V)) -> dictionary(K, V).
add(Key, Value, #dict_t{callback = Mod, data = Data} = Dict) ->
Dict#dict_t{data = Mod:add(Key, Value, Data)}.
There are two key things here.
1. The dictionary is deconstructed so we can get access to the data
and the callback module.
1. We modify the dictionary record we the new data and return that
modified record.
This is the same approach that you will use for any Signature that
updates data. As a side note, notice that we are calling the concrete
implementation to do the work itself.
Now lets do a data retrieval function. In this case, the `get` function
of the dictionary Signature.
:::erlang
%% @doc given a key return that key from the dictionary. If the key is
%% not found throw a 'not_found' exception.
%%
%% @param Dict The dictionary object to return the value from
%% @param Key The key requested
%% @throws not_found when the key does not exist
-spec get(key(K), dictionary(K, V)) -> value(V).
get(Key, #dict_t{callback = Mod, data = Data}) ->
Mod:get(Key, Data).
In this case, you can see a very similar approach to deconstructing
the dict record. We still need to pull out the callback module and the
data itself and call the concrete implementation of the algorithm. In
this case, we return the data returned from the call, not the record
itself.
That is really all you need to define a Signature. There is a complete
implementation in
[erlware_commons/ec_dictionary](https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_dictionary.erl).
Using Signatures
----------------
Its a good idea to work through an example so we have a bit better
idea of how to use these Signatures. If you are like me, you probably
have some questions about what kind of performance burden this places
on the code. At the very least we have an additional function call
along with the record deconstruction. This must add some overhead. So
lets write a little timing test, so we can get a good idea of how much
this is all costing us.
In general, there are two kinds of concrete implementations for
Signatures. The first is a native implementations, the second is a
wrapper.
### Native Signature Implementations
A Native Signature Implementation is just that, a module that
implements the Behaviour defined by a Signature directly. For most
user defined Signatures this is going to be the norm. In our current
example, the
[erlware_commons/ec_rbdict](https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_rbdict.erl)
module is the best example of a Native Signature Implementation. It
implements the ec_dictionary module directly.
### Signature Wrappers
A Signature Wrapper is a module that wraps another module. Its
purpose is to help a preexisting module implement the Behaviour
defined by a Signature. A good example if this in our current example
is the
[erlware_commons/ec_dict](https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_dict.erl)
module. It implements the ec_dictionary Behaviour, but all the
functionality is provided by the
[stdlib/dict](http://www.erlang.org/doc/man/dict.html) module
itself. Lets take a look at one example to see how this is done.
We will take a look at one of the functions we have already seen. The
`get` function an ec_dictionary `get` doesn't have quite the same
semantics as any of the functions in the dict module. So a bit of
translation needs to be done. We do that in the ec_dict module `get` function.
:::erlang
-spec get(ec_dictionary:key(K), Object::dictionary(K, V)) ->
ec_dictionary:value(V).
get(Key, Data) ->
case dict:find(Key, Data) of
{ok, Value} ->
Value;
error ->
throw(not_found)
end.
So the ec_dict module's purpose for existence is to help the
preexisting dict module implement the Behaviour defined by the
Signature.
Why do we bring this up here? Because we are going to be looking at
timings, and Signature Wrappers add an extra level of indirection to
the mix and that adds a bit of additional overhead.
### Creating the Timing Module
We are going to creating timings for both Native Signature
Implementations and Signature Wrappers.
Lets get started by looking at some helper functions. We want
dictionaries to have a bit of data in them. So to that end we are will
create a couple of functions that create dictionaries for each type we
want to test. The first we want to time is the Signature Wrapper, so
`dict` vs `ec_dict` called as a Signature.
:::erlang
create_dict() ->
lists:foldl(fun(El, Dict) ->
dict:store(El, El, Dict)
end, dict:new(),
lists:seq(1,100)).
The only thing we do here is create a sequence of numbers 1 to 100,
and then add each of those to the dict as an entry. We aren't too
worried about replicating real data in the dictionary. We care about
timing the function call overhead of Signatures, not the performance
of the dictionaries themselves.
We need to create a similar function for our Signature based
dictionary `ec_dict`.
:::erlang
create_dictionary(Type) ->
lists:foldl(fun(El, Dict) ->
ec_dictionary:add(El, El, Dict)
end,
ec_dictionary:new(Type),
lists:seq(1,100)).
Here we actually create everything using the Signature. So we don't
need one function for each type. We can have one function that can
create anything that implements the Signature. That is the magic of
Signatures. Otherwise, this does the exact same thing as the dict
`create_dict/1`.
We are going to use two function calls in our timing. One that updates
data and one that returns data, just to get good coverage. For our
dictionaries that we are going to use the `size` function as well as
the `add` function.
:::erlang
time_direct_vs_signature_dict() ->
io:format("Timing dict~n"),
Dict = create_dict(),
test_avg(fun() ->
dict:size(dict:store(some_key, some_value, Dict))
end,
1000000),
io:format("Timing ec_dict implementation of ec_dictionary~n"),
time_dict_type(ec_dict).
The `test_avg` function runs the provided function the number of times
specified in the second argument and collects timing information. We
are going to run these one million times to get a good average (its
fast so it doesn't take long). You can see that in the anonymous
function that we directly call `dict:size/1` and `dict:store/3` to perform
the test. However, because we are in the wonderful world of Signatures
we don't have to hard code the calls for the Signature
implementations. Lets take a look at the `time_dict_type` function.
:::erlang
time_dict_type(Type) ->
io:format("Testing ~p~n", [Type]),
Dict = create_dictionary(Type),
test_avg(fun() ->
ec_dictionary:size(ec_dictionary:add(some_key, some_value, Dict))
end,
1000000).
As you can see we take the type as an argument (we need it for `dict`
creation) and call our create function. Then we run the same timings
that we did for ec dict. In this case though, the type of dictionary
is never specified, we only ever call ec_dictionary, so this test will
work for anything that implements that Signature.
#### `dict` vs `ec_dict` Results
So we have our tests, what was the result. Well on my laptop this is
what it looked like.
:::sh
Erlang R14B01 (erts-5.8.2) [source] [64-bit] [smp:4:4] [rq:4] [async-threads:0] [hipe] [kernel-poll:false]
Eshell V5.8.2 (abort with ^G)
1> ec_timing:time_direct_vs_signature_dict().
Timing dict
Range: 2 - 5621 mics
Median: 3 mics
Average: 3 mics
Timing ec_dict implementation of ec_dictionary
Testing ec_dict
Range: 3 - 6097 mics
Median: 3 mics
Average: 4 mics
2>
So for the direct dict call, we average about 3 mics per call, while
for the Signature Wrapper we average around 4. Thats a 25% cost for
Signature Wrappers in this example, for a very small number of
calls. Depending on what you are doing that is going to be greater or
lesser. In any case, we can see that there is some cost associated
with the Signature Wrapper Implementations.
What about native Signatures though? Lets take a look at
`ec_rbdict`. The `ec_rbdict` also implements the `ec_dictionary`
Signature, but it is not a Signature Wrapper. It is a native
implementation of the Signature. To use `ec_rbdict` directly we have
to create a creation helper just like we did for dict.
:::erlang
create_rbdict() ->
lists:foldl(fun(El, Dict) ->
ec_rbdict:add(El, El, Dict)
end, ec_rbdict:new(),
lists:seq(1,100)).
This is exactly the same as `create_dict` with the exception that dict
is replaced by `ec_rbdict`.
The timing function itself looks very similar as well. Again notice
that we have to hard code the concrete name for the concrete
implementation, but we don't for the ec_dictionary test.
:::erlang
time_direct_vs_signature_rbdict() ->
io:format("Timing rbdict~n"),
Dict = create_rbdict(),
test_avg(fun() ->
ec_rbdict:size(ec_rbdict:add(some_key, some_value, Dict))
end,
1000000),
io:format("Timing ec_dict implementation of ec_dictionary~n"),
time_dict_type(ec_rbdict).
And there we have our test. What do the results look like?
#### `ec_rbdict` vs `ec_rbdict` as an `ec_dictionary` Results
The main thing we are timing here is the additional cost of the
dictionary Signature itself. Keep that in mind as we look at the
results.
:::sh
Erlang R14B01 (erts-5.8.2) [source] [64-bit] [smp:4:4] [rq:4] [async-threads:0] [hipe] [kernel-poll:false]
Eshell V5.8.2 (abort with ^G)
1> ec_timing:time_direct_vs_signature_rbdict().
Timing rbdict
Range: 6 - 15070 mics
Median: 7 mics
Average: 7 mics
Timing ec_dict implementation of ec_dictionary
Testing ec_rbdict
Range: 6 - 6013 mics
Median: 7 mics
Average: 7 mics
2>
So no difference it time. Well the reality is that there is a
difference in timing, there must be, but we don't have enough
resolution in the timing system to be able to figure out what that
difference is. Essentially that means its really, really small - or small
enough not to worry about at the very least.
Conclusion
----------
Signatures are a viable, useful approach to the problem of interfaces
in Erlang. The have little or no over head depending on the type of
implementation, and greatly increase the flexibility of the a library
while retaining testability and locality.
### Terminology
Behaviour
: A normal Erlang Behaviour that defines a contract
Signature
: A combination of an Behaviour and functionality to make the
functions callable in a concrete way
Native Signature Implementation
: A module that implements a signature directly
Signature Wrapper
: A module that does translation between a preexisting module and a
Signature, allowing the preexisting module to be used as a Signature
Implementation.
### Code Referenced
* [ec_dictionary Implementation] (https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_dictionary.erl)
* [ec_dict Signature Wrapper] (https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_dict.erl)
* [ec_rbdict Native Signature Implementation] (https://github.com/ericbmerritt/erlware_commons/blob/types/src/ec_rbdict.erl)
* [ec_timing Signature Use Example and Timing Collector] (https://github.com/ericbmerritt/erlware_commons/blob/types/examples/ec_timing.erl)