rustkas

Property-Based Testing with PropEr, Erlang, and Elixir: implementation without map module restriction is better choise for CSV parser (page 148)

CSV parsing

Dear author, @ferd, I am solely out of positive motives and a desire to improve the books, I would like to suggest that you think about update the section without the help of the maps module functionality in future editions of your very informative and useful book. As shown by the working tests and the implementation that I did without using this module, but using an older ones (lists, proplists) (and as the last test convincingly showed) - the maps module is not the best and not very visual solution for this task, moreover, it has limitations in which there is no need.

%% @doc this counterexample is taken literally from the RFC and cannot
%% work with the current implementation because maps have no dupe keys
dupe_keys_unsupported_test() ->
    CSV = "field_name,field_name,field_name\r\n"
          "aaa,bbb,ccc\r\n"
          "zzz,yyy,xxx\r\n",
    [Map1, Map2] = bday_csv:decode(CSV),
    %?debugFmt("Map1 = ~p~nMap2 = ~p~n", [Map1, Map2]),
    %?debugFmt("Map2 = ~p~n",[Map2]),
    ?assertEqual(1, length(maps:keys(Map1))),
    ?assertEqual(1, length(maps:keys(Map2))),
    ?assertMatch(#{"field_name" := _}, Map1),
    ?assertMatch(#{"field_name" := _}, Map2).

See what we can get by simplifying our CSV parser implementation:

%% @doc this counterexample is taken literally from the RFC
dupe_keys_unsupported_test() ->
    CSV = "field_name,field_name,field_name\r\n"
          "aaa,bbb,ccc\r\n"
          "zzz,yyy,xxx\r\n",
    Result = bday_csv_tuple:decode(CSV),
    List = lists:flatten(Result),
    ?assertEqual(6, length(List)),
    lists:foreach(fun(Elem) -> ?assertMatch({"field_name", _}, Elem) end, List).

Link to source code

2 comments

/book-property-based-testing-with-proper-erlang-and-elixir #suggestion

1 757 2

2021-07-02 19:29:32 UTC

Marked As Solved

ferd

Author of Property-Based Testing with PropEr, LYSE, & Erlang in Anger

That would make the implementation and testing shorter, but do note that the chapter has chosen to use maps as a datastructure for its ease of use to the callers.

That there is a mismatch between the chosen disk format and the useful code format is one of the interesting things that come up and we have to adjust to: either change the spec, or tweak the tests. You are suggesting the former, the book went for the latter.

There is a last gotcha implicit to the implementation of our CSV parser: since it uses maps, duplicate column names are not tolerated. Since our CSV files have to be used to represent a database, it is probably a fine assumption to make about the data set that column names are all unique. All in all, we’re probably good ignoring duplicate columns and single-columns CSV files since it’s unlikely database tables would be that way either, but it’s not fully CSV compliant.

If your CSV parser now supports multiple duplicate columns, there is now a concern that the code that uses the returned lists is able to deal with the edge case of multiple keys being returned, or that a conversion step that removes (or errors on) duplicates is added and also tested. I tend to like narrowing all of this at the edge of the system (when converting from CSV to what is now safe internally).

Your approach is fine and simplifies the CSV testing (your snippets are cleaner), but you should still expect to add specific testing elsewhere in the application that tackles that mismatch between what CSV supports and what the records represented by a database would support somewhere.

Post #2