``````<img width="160" src="https://raw.githubusercontent.com/sabiwara/aja/main/images/logo_large.png" alt="Aja">

[![Hex Version](https://img.shields.io/hexpm/v/aja.svg)](https://hex.pm/packages/aja)

Extension of the Elixir standard library focused on data stuctures, data manipulation and performance.

- [Data structures](#data-structures)
- [Utility functions](#utility-functions)
- [Installation](#installation)
- [FAQ](#faq)

## Data structures

> "there is one aspect of functional programming that no amount of cleverness on the part of the
compiler writer is likely to mitigate — the use of inferior or inappropriate data structures."
> -- [Chris Okasaki](https://www.cs.cmu.edu/~rwh/theses/okasaki.pdf)

#### Persistent vectors: `A.Vector`

A blazing fast, pure Elixir implementation of a persistent vector, meant to offer an efficient alternative to lists.
Supports many operations like appends and random access in effective constant time.

```elixir
iex> vector = A.Vector.new(1..10)
vec([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
iex> A.Vector.append(vector, :foo)
vec([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, :foo])
iex> vector[3]
4
iex> A.Vector.replace_at(vector, -1, :bar)
vec([1, 2, 3, 4, 5, 6, 7, 8, 9, :bar])
iex> 3 in vector
true
```

`A.Vector` reimplements many of the functions from the `Enum` module specifically for vectors,
with efficiency in mind. It should be easier to use from Elixir than Erlang's
[`:array`](http://erlang.org/doc/man/array.html) module and faster in most cases.

The `A.vec/1` and `A.vec_size/1` macros, while being totally optional, can make it easier to work with vectors
and make pattern-matching possible:

```elixir
iex> import A
iex> vec([a, 2, c, _d, e]) = A.Vector.new(1..5); {a, c, e}
{1, 3, 5}
iex> vec(first ||| last) = A.Vector.new(1..1_000_000); {first, last}
{1, 1000000}
iex> match?(v when vec_size(v) > 9, vec(1..10))
true
```

The `A.+++/2` operator provides synctactic sugar for vector concatenation:

```elixir
iex> vec([1, 2, 3]) +++ vec([4, 5])
vec([1, 2, 3, 4, 5])
```

#### Ordered maps: `A.OrdMap`

The standard library does not offer any similar functionality:
- regular maps do not keep track of the insertion order
- keywords do but they only support atoms and do not have the right performance characteristics (plain lists)

```elixir
iex> %{"one" => 1, "two" => 2, "three" => 3}
%{"one" => 1, "three" => 3, "two" => 2}
iex> ord_map = A.OrdMap.new([{"one", 1}, {"two", 2}, {"three", 3}])
ord(%{"one" => 1, "two" => 2, "three" => 3})
iex> ord_map["two"]
2
iex> Enum.to_list(ord_map)
[{"one", 1}, {"two", 2}, {"three", 3}]
```

Ordered maps behave pretty much like regular maps, and the `A.OrdMap` module
offers the same API as `Map`.
The convenience macro `A.ord/1` make them a breeze to instantiate or pattern-match upon:

```elixir
iex> import A
iex> ord_map = ord(%{"一" => 1, "二" => 2, "三" => 3})
ord(%{"一" => 1, "二" => 2, "三" => 3})
iex> ord(%{"三" => three, "一" => one}) = ord_map
iex> {one, three}
{1, 3}
```

All data structures offer:
- great performance characteristics at any size (see [FAQ](#faq))
- well-documented APIs that are consistent with the standard library
- implementation of `Inspect`, `Enumerable` and `Collectable` protocols
- implementation of the `Access` behaviour
- (optional if `Jason` is installed) implemention of the `Jason.Encoder` protocol

#### Optimized `Enum`: `A.Enum`

`A.Enum` mirrors the `Enum` module, but its implementation is highly optimized for
Aja structures such as `A.Vector` or `A.OrdMap`.

`A.Enum` on vectors/ord maps can often be faster than `Enum` on lists/maps,
depending on the function and size of the sequence.

## Utility functions

#### IO data

[IO data](https://hexdocs.pm/elixir/IO.html#module-io-data)
are nested structures based on lists to work more efficiently with binary/text
data without the need for any expensive concatenation.

The `~i` sigil provides a way to build IO data using string interpolation:

```elixir
iex> import A
iex> ~i"atom: #{:foo}, charlist: #{'abc'}, number: #{12 + 2.35}\n"
["atom: ", "foo", ", charlist: ", 'abc', ", number: ", "14.35", 10]
```

The `A.IO` module provides functions to work with IO data:

```elixir
iex> A.IO.to_iodata(:foo)
"foo"
iex> A.IO.to_iodata(["abc", 'def' | "ghi"])
["abc", 'def' | "ghi"]
iex> A.IO.iodata_empty?(["", []])
true
```

## Installation

Aja can be installed by adding `aja` to your list of dependencies in `mix.exs`:

```elixir
def deps do
[
{:aja, "~> 0.5.2"}
]
end
```

Or, if you are using Elixir 1.12, you can just try it out from `iex` or an `.exs` script:

```elixir
iex> Mix.install([:aja])
:ok
iex> A.Vector.new(["Hello", "world!"])
vec(["Hello", "world!"])
```

Documentation can be found at [https://hexdocs.pm/aja](https://hexdocs.pm/aja).

### Inspirations

- the amazingly polished [Elixir standard library](https://hexdocs.pm/elixir): self-consistent,
well-documented and just **delightful** ✨️
- the also amazing [Python standard library](https://docs.python.org/3/library/),
notably its [collections](https://docs.python.org/3/library/collections.html) module
- various work on efficient [persistent data structures](https://en.wikipedia.org/wiki/Persistent_data_structure) spearheaded by Okasaki
- Clojure's persistent vectors, by Rich Hickey and influenced by Phil Bagwell

### Goals

- being consistent with Elixir and with itself (API, quality, documentation)
- no external dependency to help you preserve a decent dependency tree
- performance-conscious (right algorithm, proper benchmarking, fast compile times*)
- mostly dead-simple pure functions: no configuration, no mandatory macro, no statefulness / OTP

(\* while fast compile time is a target, vectors are optimized for fast runtime at the expense of compile time)

### Resources

- Chris Okasaki's [Purely Functional Data Structures](https://www.cs.cmu.edu/~rwh/theses/okasaki.pdf)
- Jean Niklas L'orange's [articles](https://hypirion.com/musings/understanding-persistent-vector-pt-1)
and [thesis](https://hypirion.com/thesis.pdf) about persistent vectors and RRB trees

## FAQ

### How stable is it?

Aja is still pretty early stage and the high-level organisation is still in flux.
Expect some breaking changes until it reaches maturity.

However, most of its APIs are based on the standard library and should therefore remain fairly stable.

Besides, Aja is tested quite thoroughly both with unit tests and property-based testing (especially for
data structures).
This effort is far from perfect, but increases our confidence in the overall reliability.

### How is the performance?

#### Vectors

Most operations from `A.Vector` are much faster than Erlang's `:array` equivalents, and in some cases are even
noticeably faster than equivalent list operations (map, folds, join, sum...). Make sure to read the efficiency
guide from `A.Vector` doc.

#### Ordered maps

Performance for ordered maps has an inevitable though decent overhead over plain maps in terms of creation and
update time (write operations), as well as memory usage, since some extra work is needed to keep track of the order.
It has however very good read performance, with a very minimal overhead in terms of key access, and can be
enumerated much faster than maps using `A.Enum`.

#### Aja 💖️ JIT

Aja's data structures (vectors and ordered maps) are already pretty fast on pre-JIT versions of OTP (`<= 23`).
Benchmarks on OTP 24 suggest however that they are taking great advantage of the
[JIT](https://blog.erlang.org/a-first-look-at-the-jit/), relative to lists/maps, making them
even more interesting performance-wise.

#### Benchmarks

Aja data structures should work fine in most cases, but if you're considering them for
performance-critical sections of your code, make sure to benchmark them.

Benchmarking is still a work in progress, but you can check the
[`bench` folder](https://github.com/sabiwara/aja/blob/main/bench) for more detailed figures.