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# Common Caveats

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This section lists a few constructs to watch out for.

## Operator `++`

The `++` operator copies its left-hand side operand. That is clearly seen if we do our own implementation in Erlang:

    my_plus_plus([H|T], Tail) ->
        [H|my_plus_plus(T, Tail)];
    my_plus_plus([], Tail) ->
        Tail.

We must be careful how we use `++` in a loop. First is how not to use it:

DO NOT

    naive_reverse([H|T]) ->
        naive_reverse(T) ++ [H];
    naive_reverse([]) ->
        [].

As the `++` operator copies its left-hand side operand, the growing result is copied repeatedly, leading to quadratic complexity.

On the other hand, using `++` in loop like this is perfectly fine:

OK

    naive_but_ok_reverse(List) ->
        naive_but_ok_reverse(List, []).

    naive_but_ok_reverse([H|T], Acc) ->
        naive_but_ok_reverse(T, [H] ++ Acc);
    naive_but_ok_reverse([], Acc) ->
        Acc.

Each list element is copied only once. The growing result `Acc` is the right-hand side operand, which it is not copied.

Experienced Erlang programmers would probably write as follows:

DO

    vanilla_reverse([H|T], Acc) ->
        vanilla_reverse(T, [H|Acc]);
    vanilla_reverse([], Acc) ->
        Acc.

In principle, this is slightly more efficient because the list element `[H]` is not built before being copied and discarded. In practice, the compiler rewrites `[H] ++ Acc` to `[H|Acc]`.

## Timer Module

Creating timers using `erlang:send_after/3` and `erlang:start_timer/3`, is more efficient than using the timers provided by the `timer` module in STDLIB.

The `timer` module uses a separate process to manage the timers. Before Erlang/OTP 25, this management overhead was substantial and increasing with the number of timers, especially when they were short-lived, so the timer server process could easily become overloaded and unresponsive. In Erlang/OTP 25, the timer module was improved by removing most of the management overhead and the resulting performance penalty. Still, the timer server remains a single process, and it may at some point become a bottleneck of an application.

The functions in the `timer` module that do not manage timers (such as `timer:tc/3` or `timer:sleep/1`), do not call the timer-server process and are therefore harmless.

## Accidental Copying and Loss of Sharing

When spawning a new process using a fun, one can accidentally copy more data to the process than intended. For example:

DO NOT

    accidental1(State) ->
        spawn(fun() ->
                      io:format("~p\n", [State#state.info])
              end).

The code in the fun will extract one element from the record and print it. The rest of the `state` record is not used. However, when the `spawn/1` function is executed, the entire record is copied to the newly created process.

The same kind of problem can happen with a map:

DO NOT

    accidental2(State) ->
        spawn(fun() ->
                      io:format("~p\n", [map_get(info, State)])
              end).

In the following example (part of a module implementing the `gen_server` behavior) the created fun is sent to another process:

DO NOT

    handle_call(give_me_a_fun, _From, State) ->
        Fun = fun() -> State#state.size =:= 42 end,
        {reply, Fun, State}.

How bad that unnecessary copy is depends on the contents of the record or the map.

For example, if the `state` record is initialized like this:

    init1() ->
        #state{data=lists:seq(1, 10000)}.

a list with 10000 elements (or about 20000 heap words) will be copied to the newly created process.

An unnecessary copy of 10000 element list can be bad enough, but it can get even worse if the `state` record contains shared subterms. Here is a simple example of a term with a shared subterm:

    {SubTerm, SubTerm}

When a term is copied to another process, sharing of subterms will be lost and the copied term can be many times larger than the original term. For example:

    init2() ->
        SharedSubTerms = lists:foldl(fun(_, A) -> [A|A] end, [0], lists:seq(1, 15)),
        #state{data=Shared}.

In the process that calls `init2/0`, the size of the `data` field in the `state` record will be 32 heap words. When the record is copied to the newly created process, sharing will be lost and the size of the copied `data` field will be 131070 heap words. More details about loss off sharing are found in a later section.

To avoid the problem, outside of the fun extract only the fields of the record that are actually used:

DO

    fixed_accidental1(State) ->
        Info = State#state.info,
        spawn(fun() ->
                      io:format("~p\n", [Info])
              end).

Similarly, outside of the fun extract only the map elements that are actually used:

DO

    fixed_accidental2(State) ->
        Info = map_get(info, State),
        spawn(fun() ->
                      io:format("~p\n", [Info])
              end).

## list_to_atom/1

Atoms are not garbage-collected. Once an atom is created, it is never removed. The emulator terminates if the limit for the number of atoms (1,048,576 by default) is reached.

Therefore, converting arbitrary input strings to atoms can be dangerous in a system that runs continuously. If only certain well-defined atoms are allowed as input, `list_to_existing_atom/1` or `binary_to_existing_atom/1` can be used to guard against a denial-of-service attack. (All atoms that are allowed must have been created earlier, for example, by using all of them in a module and loading that module.)

Using `list_to_atom/1` to construct an atom that is passed to `apply/3` is quite expensive.

DO NOT

    apply(list_to_atom("some_prefix"++Var), foo, Args)

## length/1

The time for calculating the length of a list is proportional to the length of the list, as opposed to `tuple_size/1`, `byte_size/1`, and `bit_size/1`, which all execute in constant time.

Normally, there is no need to worry about the speed of `length/1`, because it is efficiently implemented in C. In time-critical code, you might want to avoid it if the input list could potentially be very long.

Some uses of `length/1` can be replaced by matching. For example, the following code:

    foo(L) when length(L) >= 3 ->
        ...

can be rewritten to:

    foo([_,_,_|_]=L) ->
       ...

One slight difference is that `length(L)` fails if `L` is an improper list, while the pattern in the second code fragment accepts an improper list.

## setelement/3

`setelement/3` copies the tuple it modifies. Therefore, updating a tuple in a loop using `setelement/3` creates a new copy of the tuple on each iteration.

### Compiler optimizations of setelement/3

Under certain conditions, the compiler can coalesce multiple calls to `setelement/3` into a single operation, avoiding the cost of copying the tuple for each call.

For example:

    multiple_setelement(T0) when tuple_size(T0) =:= 9 ->
        T1 = setelement(5, T0, new_value),
        T2 = setelement(7, T1, foobar),
        setelement(9, T2, bar).

The compiler will replace the three `setelement/3` calls with code that copies the tuple once and updates the elements at positions 5, 7, and 9.

Starting with Erlang/OTP 26, the following conditions must be met for `setelement/3` calls to be coalesced into a single operation:

- The tuple argument must be known at compile time to be a tuple of a specific size.

- The element indices must be integer literals, not variables or expressions.

- There must be no intervening expressions between the calls to `setelement/3`.

- The tuple returned from one `setelement/3` call must be used only in the subsequent `setelement/3` call.

Before Erlang/OTP 26, an additional condition was that `setelement/3` calls had to be made in descending order of indices.

## size/1

`size/1` returns the size for both tuples and binaries.

Using the BIFs `tuple_size/1` and `byte_size/1` gives the compiler and the runtime system more opportunities for optimization. Another advantage is that those BIFs give Dialyzer more type information.

## Using NIFs

Rewriting Erlang code to a NIF to make it faster should be seen as a last resort.

Doing too much work in each NIF call will degrade responsiveness of the VM. Doing too little work can mean that the gain of the faster processing in the NIF is eaten up by the overhead of calling the NIF and checking the arguments.

Be sure to read about Long-running NIFs before writing a NIF.

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