# Searching

Sometimes you need to find the first element of an array that
satisfies some property. This can be done with a
`map`

-`reduce`

composition, where the `map`

tags each element with its index and a
boolean indicating whether it is a match, and then a reduction
operator `op`

that picks the match with the lowest index. While we
may know that `op`

is
commutative,
the compiler cannot figure this out on its own, so we use
`reduce_comm`

to tell it explicitly. This permits slightly more
efficient code generation for the reduction.

```
def find_index 'a [n] (p: a -> bool) (as: [n]a): i64 =
let op (x, i) (y, j) =
if x && y then if i < j
then (x, i)
else (y, j)
else if y then (y, j)
else (x, i)
in (reduce_comm op (false, -1) (zip (map p as) (iota n))).1
```

Note that if no element is acceptable, we return the index -1. An alternative could be to use a sum type to return the actual element we found:

```
type found 'a = #found a | #not_found
def find_elem 'a [n] (p: a -> bool) (as: [n]a) : found a =
let tag x = if p x then #found x else #not_found
let op x y =
match (x,y)
case (#found _, _) -> x
case (_, #found _) -> y
case _ -> x
in reduce_comm op #not_found (map tag as)
```

Both of these solutions share a potential problem: they will always look at the entire array, even if the element we are looking for is near the beginning. When searching in parallel, some redundant (or “speculative”) work is necessary. But can we constrain it? We can of course always just write a sequential search:

```
def find_elem_seq 'a [n] (p: a -> bool) (as: [n]a) : found a =
loop (_res, i) = (#not_found, 0)
(while i < n do
if p as[i]
then (#found as[i], n)
else (#not_found, i+1)).0
```

No parallelism here. We can combine the sequential and the parallel approaches by writing a function that sequentially iterates through large chunks of the input, searches each chunk in parallel, and stops at the first match.

```
def find_elem_chunked 'a [n] (k: i64) (p: a -> bool) (as: [n]a) : found a =
loop (_res, chunk_offset) = (#not_found, 0)
(while chunk_offset < n do
match find_elem p as[chunk_offset: i64.min n (chunk_offset+k)]
case #found x -> (#found x, n)
case #not_found -> (#not_found, chunk_offset+k)).0
```

In most cases, the original `find_index`

and `find_elem`

will be
fast enough, as these simple `map`

-`reduce`

compositions are quite
efficient. While `find_elem_chunked`

can in some cases be faster,
we now have the chunk size, `k`

, as a tunable parameter that we
have to worry about, and the compiler cannot help us figure out the
best value.