## Comparing Performance: Loops vs. Iterators

To determine whether to use loops or iterators, you need to know which
implementation is faster: the version of the `search` function with an explicit
`for` loop or the version with iterators.

We ran a benchmark by loading the entire contents of *The Adventures of
Sherlock Holmes* by Sir Arthur Conan Doyle into a `String` and looking for the
word *the* in the contents. Here are the results of the benchmark on the
version of `search` using the `for` loop and the version using iterators:

```text
test bench_search_for  ... bench:  19,620,300 ns/iter (+/- 915,700)
test bench_search_iter ... bench:  19,234,900 ns/iter (+/- 657,200)
```

The iterator version was slightly faster! We won’t explain the benchmark code
here, because the point is not to prove that the two versions are equivalent
but to get a general sense of how these two implementations compare
performance-wise.

For a more comprehensive benchmark, you should check using various texts of
various sizes as the `contents`, different words and words of different lengths
as the `query`, and all kinds of other variations. The point is this:
iterators, although a high-level abstraction, get compiled down to roughly the
same code as if you’d written the lower-level code yourself. Iterators are one
of Rust’s *zero-cost abstractions*, by which we mean using the abstraction
imposes no additional runtime overhead. This is analogous to how Bjarne
Stroustrup, the original designer and implementor of C++, defines
*zero-overhead* in “Foundations of C++” (2012):

> In general, C++ implementations obey the zero-overhead principle: What you
> don’t use, you don’t pay for. And further: What you do use, you couldn’t hand
> code any better.

As another example, the following code is taken from an audio decoder. The
decoding algorithm uses the linear prediction mathematical operation to
estimate future values based on a linear function of the previous samples. This
code uses an iterator chain to do some math on three variables in scope: a
`buffer` slice of data, an array of 12 `coefficients`, and an amount by which
to shift data in `qlp_shift`. We’ve declared the variables within this example
but not given them any values; although this code doesn’t have much meaning
outside of its context, it’s still a concise, real-world example of how Rust
translates high-level ideas to low-level code.

```rust,ignore
let buffer: &mut [i32];
let coefficients: [i64; 12];
let qlp_shift: i16;

for i in 12..buffer.len() {
    let prediction = coefficients.iter()
                                 .zip(&buffer[i - 12..i])
                                 .map(|(&c, &s)| c * s as i64)
                                 .sum::<i64>() >> qlp_shift;
    let delta = buffer[i];
    buffer[i] = prediction as i32 + delta;
}
```

To calculate the value of `prediction`, this code iterates through each of the
12 values in `coefficients` and uses the `zip` method to pair the coefficient
values with the previous 12 values in `buffer`. Then, for each pair, we
multiply the values together, sum all the results, and shift the bits in the
sum `qlp_shift` bits to the right.

Calculations in applications like audio decoders often prioritize performance
most highly. Here, we’re creating an iterator, using two adaptors, and then
consuming the value. What assembly code would this Rust code compile to? Well,
as of this writing, it compiles down to the same assembly you’d write by hand.
There’s no loop at all corresponding to the iteration over the values in
`coefficients`: Rust knows that there are 12 iterations, so it “unrolls” the
loop. *Unrolling* is an optimization that removes the overhead of the loop
controlling code and instead generates repetitive code for each iteration of
the loop.

All of the coefficients get stored in registers, which means accessing the
values is very fast. There are no bounds checks on the array access at runtime.
All these optimizations that Rust is able to apply make the resulting code
extremely efficient. Now that you know this, you can use iterators and closures
without fear! They make code seem like it’s higher level but don’t impose a
runtime performance penalty for doing so.

## Summary

Closures and iterators are Rust features inspired by functional programming
language ideas. They contribute to Rust’s capability to clearly express
high-level ideas at low-level performance. The implementations of closures and
iterators are such that runtime performance is not affected. This is part of
Rust’s goal to strive to provide zero-cost abstractions.

Now that we’ve improved the expressiveness of our I/O project, let’s look at
some more features of `cargo` that will help us share the project with the
world.
