1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
// This Source Code Form is subject to the terms of the Mozilla Public
// License, v. 2.0. If a copy of the MPL was not distributed with this
// file, You can obtain one at http://mozilla.org/MPL/2.0/.

//! # Immutable Data Structures for Rust
//!
//! This library implements several of the more commonly useful immutable data
//! structures for Rust.
//!
//! ## What are immutable data structures?
//!
//! Immutable data structures are data structures which can be copied and
//! modified efficiently without altering the original. The most uncomplicated
//! example of this is the venerable [cons list][cons-list]. This crate offers a
//! selection of more modern and flexible data structures with similar
//! properties, tuned for the needs of Rust developers.
//!
//! Briefly, the following data structures are provided:
//!
//! * [Vectors][vector::Vector] based on [RRB trees][rrb-tree]
//! * [Hash maps][hashmap::HashMap]/[sets][hashset::HashSet] based on [hash
//!   array mapped tries][hamt]
//! * [Ordered maps][ordmap::OrdMap]/[sets][ordset::OrdSet] based on
//!   [B-trees][b-tree]
//!
//! ## Why Would I Want This?
//!
//! While immutable data structures can be a game changer for other
//! programming languages, the most obvious benefit - avoiding the
//! accidental mutation of data - is already handled so well by Rust's
//! type system that it's just not something a Rust programmer needs
//! to worry about even when using data structures that would send a
//! conscientious Clojure programmer into a panic.
//!
//! Immutable data structures offer other benefits, though, some of
//! which are useful even in a language like Rust. The most prominent
//! is *structural sharing*, which means that if two data structures
//! are mostly copies of each other, most of the memory they take up
//! will be shared between them. This implies that making copies of an
//! immutable data structure is cheap: it's really only a matter of
//! copying a pointer and increasing a reference counter, where in the
//! case of [`Vec`][std::vec::Vec] you have to allocate the same
//! amount of memory all over again and make a copy of every element
//! it contains. For immutable data structures, extra memory isn't
//! allocated until you modify either the copy or the original, and
//! then only the memory needed to record the difference.
//!
//! Another goal of this library has been the idea that you shouldn't
//! even have to think about what data structure to use in any given
//! situation, until the point where you need to start worring about
//! optimisation - which, in practice, often never comes. Beyond the
//! shape of your data (ie. whether to use a list or a map), it should
//! be fine not to think too carefully about data structures - you can
//! just pick the one that has the right shape and it should have
//! acceptable performance characteristics for every operation you
//! might need. Specialised data structures will always be faster at
//! what they've been specialised for, but `im` aims to provide the
//! data structures which deliver the least chance of accidentally
//! using them for the wrong thing.
//!
//! For instance, [`Vec`][std::vec::Vec] beats everything at memory
//! usage, indexing and operations that happen at the back of the
//! list, but is terrible at insertion and removal, and gets worse the
//! closer to the front of the list you get.
//! [`VecDeque`][std::collections::VecDeque] adds a little bit of
//! complexity in order to make operations at the front as efficient
//! as operations at the back, but is still bad at insertion and
//! especially concatenation. [`Vector`][vector::Vector] adds another
//! bit of complexity, and could never match [`Vec`][std::vec::Vec] at
//! what it's best at, but in return every operation you can throw at
//! it can be completed in a reasonable amount of time - even normally
//! expensive operations like copying and especially concatenation are
//! reasonably cheap when using a [`Vector`][vector::Vector].
//!
//! It should be noted, however, that because of its simplicity,
//! [`Vec`][std::vec::Vec] actually beats [`Vector`][vector::Vector] even at its
//! strongest operations at small sizes, just because modern CPUs are
//! hyperoptimised for things like copying small chunks of contiguous memory -
//! you actually need to go past a certain size (usually in the vicinity of
//! several hundred elements) before you get to the point where
//! [`Vec`][std::vec::Vec] isn't always going to be the fastest choice.
//! [`Vector`][vector::Vector] attempts to overcome this by actually just being
//! an array at very small sizes, and being able to switch efficiently to the
//! full data structure when it grows large enough. Thus,
//! [`Vector`][vector::Vector] will actually be equivalent to
//! [Vec][std::vec::Vec] until it grows past the size of a single chunk.
//!
//! The maps - [`HashMap`][hashmap::HashMap] and
//! [`OrdMap`][ordmap::OrdMap] - generally perform similarly to their
//! equivalents in the standard library, but tend to run a bit slower
//! on the basic operations ([`HashMap`][hashmap::HashMap] is almost
//! neck and neck with its counterpart, while
//! [`OrdMap`][ordmap::OrdMap] currently tends to run 2-3x slower). On
//! the other hand, they offer the cheap copy and structural sharing
//! between copies that you'd expect from immutable data structures.
//!
//! In conclusion, the aim of this library is to provide a safe
//! default choice for the most common kinds of data structures,
//! allowing you to defer careful thinking about the right data
//! structure for the job until you need to start looking for
//! optimisations - and you may find, especially for larger data sets,
//! that immutable data structures are still the right choice.
//!
//! ## Values
//!
//! Because we need to make copies of shared nodes in these data structures
//! before updating them, the values you store in them must implement
//! [`Clone`][std::clone::Clone].  For primitive values that implement
//! [`Copy`][std::marker::Copy], such as numbers, everything is fine: this is
//! the case for which the data structures are optimised, and performance is
//! going to be great.
//!
//! On the other hand, if you want to store values for which cloning is
//! expensive, or values that don't implement [`Clone`][std::clone::Clone], you
//! need to wrap them in [`Rc`][std::rc::Rc] or [`Arc`][std::sync::Arc]. Thus,
//! if you have a complex structure `BigBlobOfData` and you want to store a list
//! of them as a `Vector<BigBlobOfData>`, you should instead use a
//! `Vector<Rc<BigBlobOfData>>`, which is going to save you not only the time
//! spent cloning the big blobs of data, but also the memory spent keeping
//! multiple copies of it around, as [`Rc`][std::rc::Rc] keeps a single
//! reference counted copy around instead.
//!
//! If you're storing smaller values that aren't
//! [`Copy`][std::marker::Copy]able, you'll need to exercise judgement: if your
//! values are going to be very cheap to clone, as would be the case for short
//! [`String`][std::string::String]s or small [`Vec`][std::vec::Vec]s, you're
//! probably better off storing them directly without wrapping them in an
//! [`Rc`][std::rc::Rc], because, like the [`Rc`][std::rc::Rc], they're just
//! pointers to some data on the heap, and that data isn't expensive to clone -
//! you might actually lose more performance from the extra redirection of
//! wrapping them in an [`Rc`][std::rc::Rc] than you would from occasionally
//! cloning them.
//!
//! ### When does cloning happen?
//!
//! So when will your values actually be cloned? The easy answer is only if you
//! [`clone`][std::clone::Clone::clone] the data structure itself, and then only
//! lazily as you change it. Values are stored in tree nodes inside the data
//! structure, each node of which contains up to 64 values. When you
//! [`clone`][std::clone::Clone::clone] a data structure, nothing is actually
//! copied - it's just the reference count on the root node that's incremented,
//! to indicate that it's shared between two data structures. It's only when you
//! actually modify one of the shared data structures that nodes are cloned:
//! when you make a change somewhere in the tree, the node containing the change
//! needs to be cloned, and then its parent nodes need to be updated to contain
//! the new child node instead of the old version, and so they're cloned as
//! well.
//!
//! We can call this "lazy" cloning - if you make two copies of a data structure
//! and you never change either of them, there's never any need to clone the
//! data they contain. It's only when you start making changes that cloning
//! starts to happen, and then only on the specific tree nodes that are part of
//! the change. Note that the implications of lazily cloning the data structure
//! extend to memory usage as well as the CPU workload of copying the data
//! around - cloning an immutable data structure means both copies share the
//! same allocated memory, until you start making changes.
//!
//! Most crucially, if you never clone the data structure, the data inside it is
//! also never cloned, and in this case it acts just like a mutable data
//! structure, with minimal performance differences (but still non-zero, as we
//! still have to check for shared nodes).
//!
//! ## Data Structures
//!
//! We'll attempt to provide a comprehensive guide to the available
//! data structures below.
//!
//! ### Performance Notes
//!
//! "Big O notation" is the standard way of talking about the time
//! complexity of data structure operations. If you're not familiar
//! with big O notation, here's a quick cheat sheet:
//!
//! *O(1)* means an operation runs in constant time: it will take the
//! same time to complete regardless of the size of the data
//! structure.
//!
//! *O(n)* means an operation runs in linear time: if you double the
//! size of your data structure, the operation will take twice as long
//! to complete; if you quadruple the size, it will take four times as
//! long, etc.
//!
//! *O(log n)* means an operation runs in logarithmic time: for
//! *log<sub>2</sub>*, if you double the size of your data structure,
//! the operation will take one step longer to complete; if you
//! quadruple the size, it will need two steps more; and so on.
//! However, the data structures in this library generally run in
//! *log<sub>64</sub>* time, meaning you have to make your data
//! structure 64 times bigger to need one extra step, and 4096 times
//! bigger to need two steps. This means that, while they still count
//! as O(log n), operations on all but really large data sets will run
//! at near enough to O(1) that you won't usually notice.
//!
//! *O(n log n)* is the most expensive operation you'll see in this
//! library: it means that for every one of the *n* elements in your
//! data structure, you have to perform *log n* operations. In our
//! case, as noted above, this is often close enough to O(n) that it's
//! not usually as bad as it sounds, but even O(n) isn't cheap and the
//! cost still increases logarithmically, if slowly, as the size of
//! your data increases. O(n log n) basically means "are you sure you
//! need to do this?"
//!
//! *O(1)** means 'amortised O(1),' which means that an operation
//! usually runs in constant time but will occasionally be more
//! expensive: for instance,
//! [`Vector::push_back`][vector::Vector::push_back], if called in
//! sequence, will be O(1) most of the time but every 64th time it
//! will be O(log n), as it fills up its tail chunk and needs to
//! insert it into the tree. Please note that the O(1) with the
//! asterisk attached is not a common notation; it's just a convention
//! I've used in these docs to save myself from having to type
//! 'amortised' everywhere.
//!
//! ### Lists
//!
//! Lists are sequences of single elements which maintain the order in
//! which you inserted them. The only list in this library is
//! [`Vector`][vector::Vector], which offers the best all round
//! performance characteristics: it's pretty good at everything, even
//! if there's always another kind of list that's better at something.
//!
//! | Type | Algorithm | Constraints | Order | Push | Pop | Split | Append | Lookup |
//! | --- | --- | --- | --- | --- | --- | --- |
//! | [`Vector<A>`][vector::Vector] | [RRB tree][rrb-tree] | [`Clone`][std::clone::Clone] | insertion | O(1)* | O(1)* | O(log n) | O(log n) | O(log n) |
//!
//! ### Maps
//!
//! Maps are mappings of keys to values, where the most common read
//! operation is to find the value associated with a given key. Maps
//! may or may not have a defined order. Any given key can only occur
//! once inside a map, and setting a key to a different value will
//! overwrite the previous value.
//!
//! | Type | Algorithm | Key Constraints | Order | Insert | Remove | Lookup |
//! | --- | --- | --- | --- | --- | --- |
//! | [`HashMap<K, V>`][hashmap::HashMap] | [HAMT][hamt] | [`Clone`][std::clone::Clone] + [`Hash`][std::hash::Hash] + [`Eq`][std::cmp::Eq] | undefined | O(log n) | O(log n) | O(log n) |
//! | [`OrdMap<K, V>`][ordmap::OrdMap] | [B-tree][b-tree] | [`Clone`][std::clone::Clone] + [`Ord`][std::cmp::Ord] | sorted | O(log n) | O(log n) | O(log n) |
//!
//! ### Sets
//!
//! Sets are collections of unique values, and may or may not have a
//! defined order. Their crucial property is that any given value can
//! only exist once in a given set.
//!
//! | Type | Algorithm | Constraints | Order | Insert | Remove | Lookup |
//! | --- | --- | --- | --- | --- | --- |
//! | [`HashSet<A>`][hashset::HashSet] | [HAMT][hamt] | [`Clone`][std::clone::Clone] + [`Hash`][std::hash::Hash] + [`Eq`][std::cmp::Eq] | undefined | O(log n) | O(log n) | O(log n) |
//! | [`OrdSet<A>`][ordset::OrdSet] | [B-tree][b-tree] | [`Clone`][std::clone::Clone] + [`Ord`][std::cmp::Ord] | sorted | O(log n) | O(log n) | O(log n) |
//!
//! ## In-place Mutation
//!
//! All of these data structures support in-place copy-on-write
//! mutation, which means that if you're the sole user of a data
//! structure, you can update it in place without taking the
//! performance hit of making a copy of the data structure before
//! modifying it (this is about an order of magnitude faster than
//! immutable operations, almost as fast as
//! [`std::collections`][std::collections]'s mutable data structures).
//!
//! Thanks to [`Rc`][std::rc::Rc]'s reference counting, we are able to
//! determine whether a node in a data structure is being shared with
//! other data structures, or whether it's safe to mutate it in place.
//! When it's shared, we'll automatically make a copy of the node
//! before modifying it. The consequence of this is that cloning a
//! data structure becomes a lazy operation: the initial clone is
//! instant, and as you modify the cloned data structure it will clone
//! chunks only where you change them, so that if you change the
//! entire thing you will eventually have performed a full clone.
//!
//! This also gives us a couple of other optimisations for free:
//! implementations of immutable data structures in other languages
//! often have the idea of local mutation, like Clojure's transients
//! or Haskell's `ST` monad - a managed scope where you can treat an
//! immutable data structure like a mutable one, gaining a
//! considerable amount of performance because you no longer need to
//! copy your changed nodes for every operation, just the first time
//! you hit a node that's sharing structure. In Rust, we don't need to
//! think about this kind of managed scope, it's all taken care of
//! behind the scenes because of our low level access to the garbage
//! collector (which, in our case, is just a simple
//! [`Rc`][std::rc::Rc]).
//!
//! ## Thread Safety
//!
//! The data structures in the `im` crate are thread safe, through
//! [`Arc`][std::sync::Arc]. This comes with a slight performance impact, so
//! that if you prioritise speed over thread safety, you may want to use the
//! `im-rc` crate instead, which is identical to `im` except that it uses
//! [`Rc`][std::rc::Rc] instead of [`Arc`][std::sync::Arc], implying that the
//! data structures in `im-rc` do not implement [`Send`][std::marker::Send] and
//! [`Sync`][std::marker::Sync]. This yields approximately a 20-25% increase in
//! general performance.
//!
//! ## Feature Flags
//!
//! `im` comes with optional support for the following crates through Cargo
//! feature flags. You can enable them in your `Cargo.toml` file like this:
//!
//! ```no_compile
//! [dependencies]
//! im = { version = "*", features = ["proptest", "serde"] }
//! ```
//!
//! | Feature | Description |
//! | ------- | ----------- |
//! | [`proptest`](https://crates.io/crates/proptest) | Strategies for all `im` datatypes under a `proptest` namespace, eg. `im::vector::proptest::vector()` |
//! | [`quickcheck`](https://crates.io/crates/quickcheck) | `Arbitrary` implementations for all `im` datatypes (not available in `im-rc`) |
//! | [`rayon`](https://crates.io/crates/rayon) | parallel iterator implementations for `Vector` (not available in `im-rc`) |
//! | [`serde`](https://crates.io/crates/serde) | `Serialize` and `Deserialize` implementations for all `im` datatypes |
//!
//! [std::collections]: https://doc.rust-lang.org/std/collections/index.html
//! [std::collections::VecDeque]: https://doc.rust-lang.org/std/collections/struct.VecDeque.html
//! [std::vec::Vec]: https://doc.rust-lang.org/std/vec/struct.Vec.html
//! [std::string::String]: https://doc.rust-lang.org/std/string/struct.String.html
//! [std::rc::Rc]: https://doc.rust-lang.org/std/rc/struct.Rc.html
//! [std::sync::Arc]: https://doc.rust-lang.org/std/sync/struct.Arc.html
//! [std::cmp::Eq]: https://doc.rust-lang.org/std/cmp/trait.Eq.html
//! [std::cmp::Ord]: https://doc.rust-lang.org/std/cmp/trait.Ord.html
//! [std::clone::Clone]: https://doc.rust-lang.org/std/clone/trait.Clone.html
//! [std::clone::Clone::clone]: https://doc.rust-lang.org/std/clone/trait.Clone.html#tymethod.clone
//! [std::marker::Copy]: https://doc.rust-lang.org/std/marker/trait.Copy.html
//! [std::hash::Hash]: https://doc.rust-lang.org/std/hash/trait.Hash.html
//! [std::marker::Send]: https://doc.rust-lang.org/std/marker/trait.Send.html
//! [std::marker::Sync]: https://doc.rust-lang.org/std/marker/trait.Sync.html
//! [hashmap::HashMap]: ./hashmap/struct.HashMap.html
//! [hashset::HashSet]: ./hashset/struct.HashSet.html
//! [ordmap::OrdMap]: ./ordmap/struct.OrdMap.html
//! [ordset::OrdSet]: ./ordset/struct.OrdSet.html
//! [vector::Vector]: ./vector/enum.Vector.html
//! [vector::Vector::push_back]: ./vector/enum.Vector.html#method.push_back
//! [rrb-tree]: https://infoscience.epfl.ch/record/213452/files/rrbvector.pdf
//! [hamt]: https://en.wikipedia.org/wiki/Hash_array_mapped_trie
//! [b-tree]: https://en.wikipedia.org/wiki/B-tree
//! [cons-list]: https://en.wikipedia.org/wiki/Cons#Lists

#![deny(unsafe_code)]
#![cfg_attr(has_specialisation, feature(specialization))]

#[cfg(test)]
#[macro_use]
extern crate pretty_assertions;

mod config;
mod nodes;
mod sort;
mod sync;
mod util;

#[macro_use]
mod ord;
pub use crate::ord::map as ordmap;
pub use crate::ord::set as ordset;

#[macro_use]
mod hash;
pub use crate::hash::map as hashmap;
pub use crate::hash::set as hashset;

#[macro_use]
pub mod vector;

pub mod iter;

#[cfg(any(test, feature = "serde"))]
pub mod ser;

pub use crate::hashmap::HashMap;
pub use crate::hashset::HashSet;
pub use crate::ordmap::OrdMap;
pub use crate::ordset::OrdSet;
#[doc(inline)]
pub use crate::vector::Vector;

#[cfg(test)]
mod test;

#[cfg(test)]
mod tests;

/// Update a value inside multiple levels of data structures.
///
/// This macro takes a [`Vector`][Vector], [`OrdMap`][OrdMap] or [`HashMap`][HashMap],
/// a key or a series of keys, and a value, and returns the data structure with the
/// new value at the location described by the keys.
///
/// If one of the keys in the path doesn't exist, the macro will panic.
///
/// # Examples
///
/// ```
/// # #[macro_use] extern crate im_rc as im;
/// # use std::sync::Arc;
/// # fn main() {
/// let vec_inside_vec = vector![vector![1, 2, 3], vector![4, 5, 6]];
///
/// let expected = vector![vector![1, 2, 3], vector![4, 5, 1337]];
///
/// assert_eq!(expected, update_in![vec_inside_vec, 1 => 2, 1337]);
/// # }
/// ```
///
/// [Vector]: ../vector/enum.Vector.html
/// [HashMap]: ../hashmap/struct.HashMap.html
/// [OrdMap]: ../ordmap/struct.OrdMap.html
#[macro_export]
macro_rules! update_in {
    ($target:expr, $path:expr => $($tail:tt) => *, $value:expr ) => {{
        let inner = $target.get($path).expect("update_in! macro: key not found in target");
        $target.update($path, update_in!(inner, $($tail) => *, $value))
    }};

    ($target:expr, $path:expr, $value:expr) => {
        $target.update($path, $value)
    };
}

/// Get a value inside multiple levels of data structures.
///
/// This macro takes a [`Vector`][Vector], [`OrdMap`][OrdMap] or [`HashMap`][HashMap],
/// along with a key or a series of keys, and returns the value at the location inside
/// the data structure described by the key sequence, or `None` if any of the keys didn't
/// exist.
///
/// # Examples
///
/// ```
/// # #[macro_use] extern crate im_rc as im;
/// # use std::sync::Arc;
/// # fn main() {
/// let vec_inside_vec = vector![vector![1, 2, 3], vector![4, 5, 6]];
///
/// assert_eq!(Some(&6), get_in![vec_inside_vec, 1 => 2]);
/// # }
/// ```
///
/// [Vector]: ../vector/enum.Vector.html
/// [HashMap]: ../hashmap/struct.HashMap.html
/// [OrdMap]: ../ordmap/struct.OrdMap.html
#[macro_export]
macro_rules! get_in {
    ($target:expr, $path:expr => $($tail:tt) => * ) => {{
        $target.get($path).and_then(|v| get_in!(v, $($tail) => *))
    }};

    ($target:expr, $path:expr) => {
        $target.get($path)
    };
}

#[cfg(test)]
mod lib_test {
    #[test]
    fn update_in() {
        let vector = vector![1, 2, 3, 4, 5];
        assert_eq!(vector![1, 2, 23, 4, 5], update_in!(vector, 2, 23));
        let hashmap = hashmap![1 => 1, 2 => 2, 3 => 3];
        assert_eq!(
            hashmap![1 => 1, 2 => 23, 3 => 3],
            update_in!(hashmap, 2, 23)
        );
        let ordmap = ordmap![1 => 1, 2 => 2, 3 => 3];
        assert_eq!(ordmap![1 => 1, 2 => 23, 3 => 3], update_in!(ordmap, 2, 23));

        let vecs = vector![vector![1, 2, 3], vector![4, 5, 6], vector![7, 8, 9]];
        let vecs_target = vector![vector![1, 2, 3], vector![4, 5, 23], vector![7, 8, 9]];
        assert_eq!(vecs_target, update_in!(vecs, 1 => 2, 23));
    }

    #[test]
    fn get_in() {
        let vector = vector![1, 2, 3, 4, 5];
        assert_eq!(Some(&3), get_in!(vector, 2));
        let hashmap = hashmap![1 => 1, 2 => 2, 3 => 3];
        assert_eq!(Some(&2), get_in!(hashmap, &2));
        let ordmap = ordmap![1 => 1, 2 => 2, 3 => 3];
        assert_eq!(Some(&2), get_in!(ordmap, &2));

        let vecs = vector![vector![1, 2, 3], vector![4, 5, 6], vector![7, 8, 9]];
        assert_eq!(Some(&6), get_in!(vecs, 1 => 2));
    }
}