|ReleasedLast Release||Sep 2017|
This project provides an efficient in-memory B-tree implementation in pure Swift, and several useful sorted collection types that use B-trees for their underlying storage.
Map<Key, Value> implements a sorted mapping from unique comparable keys to arbitrary values. It is like
Dictionary in the standard library, but it does not require keys to be hashable, it has strong guarantees on worst-case performance, and it maintains its elements in a well-defined order.
List<Element> implements a random-access collection of arbitrary elements. It is like
Array in the standard library, but lookup, insertion and removal of elements at any index have logarithmic complexity. (
Array has O(1) lookup, but insertion and removal at an arbitrary index costs O(n).) Concatenation of two lists of any size, inserting a list into another list at any position, removal of any subrange of elements, or extraction of an arbitrary sub-list are also operations with O(log(n)) complexity.
SortedSet<Element> implements a sorted collection of unique comparable elements. It is like
Set in the standard library, but lookup, insertion and removal of any element has logarithmic complexity. Elements in an
SortedSet are kept sorted in ascending order. Operations working on full sets (such as taking the union, intersection or difference) can take as little as O(log(n)) time if the elements in the source sets aren’t interleaved.
SortedBag<Element> implements a sorted multiset with comparable elements. This is a generalization of a set that allows multiple instances of the same value. (The standard library does not include such a collection, although you can use a dictionary to emulate one by storing the multiplicities of the keys as values.) The implementation provided in this package stores each duplicate element separately, which may come useful if your elements are reference types with identities or you have some other means to distinguish between equal elements.
SortedBag operations have the same time complexities as the equivalent operations in
BTree<Key, Value> is the underlying primitive collection that serves as base storage for all of the above collections. It is a general sorted key-value store with full support for elements with duplicate keys; it provides a sum of all operations individually provided by the higher-level abstractions above (and more!).
BTree type is public; you may want to use it if you need a collection flavor that isn’t provided by default (such as a multimap) or if you need to use an operation that isn’t exposed by the wrappers.
All of these collections are structs and they implement the same copy-on-write value semantics as standard Swift collection types like
Dictionary. (In fact, copy-on-write works even better with these than standard collections; continue reading to find out why!)
The latest version of
BTree requires Swift 4. (The last release supporting Swift 3 was 4.0.2.)
The project includes a nicely formatted reference document generated from the documentation comments embedded in its source code.
If you want to learn more about how this package works, the book Optimizing Collections includes detailed explanations of many of the algorithms and optimization tricks implemented by this package – and so, so much more. It is written by the same author, and published by the fine folks at objc.io. Buying a copy of the book is not only a nice way to support this project, it also gets you something quite interesting to read. Win-win!
B-trees are search trees that provide a sorted key-value store with excellent performance characteristics. In essence, each node maintains a sorted array of its own elements, and another array for its children. The tree is kept balanced by three constraints:
This module implements a “vanilla” B-tree where every node contains full key-value pairs. (The other popular type is the B+-tree where only leaf nodes contain values; internal nodes contain only copies of keys. This often makes more sense on an external storage device with a fixed block size, but it seems less useful for an in-memory implementation.)
Each node in the tree also maintains the count of all elements under it. This makes the tree an order statistic tree, where efficient positional lookup is possible.
The Swift standard library offers heavily optimized arrays and hash tables, but omits linked lists and tree-based data structures. This is a result of the Swift engineering team spending resources (effort, code size) on the abstractions that provide the biggest bang for the buck.
Indeed, the library lacks even a basic double-ended queue construct – although Cocoa’s
Foundationframework does include one in
However, some problems call for a wider variety of data structures.
In the past, linked lists and low-order search trees such as red-black trees were frequently employed; however, the performance of these constructs on modern hardware is greatly limited by their heavy use of pointers.
B-trees were originally invented in the 1970s as a data structure for slow external storage devices. As such, they are strongly optimized for locality of reference: they prefer to keep data in long contiguous buffers and they keep pointer derefencing to a minimum. (Dereferencing a pointer in a B-tree usually meant reading another block of data from the spinning hard drive, which is a glacially slow device compared to the main memory.)
Today’s computers have multi-tiered memory architectures; they rely on caching to keep the system performant. This means that locality of reference has become a hugely important property for in-memory data structures, too.
Arrays are the epitome of reference locality, so the Swift stdlib’s heavy emphasis on
Array as the universal collection type is well justified.
For example, using a single array to hold a sorted list of items has quite horrible (quadratic) asymptotic complexity when there are many elements. However, up to a certain maximum size, a simple array is in fact the most efficient way to represent a sorted list.
The benchmark above demonstrates this really well: insertion of n elements into a sorted array costs O(n^2) when there are many items, but for many reasonably sized data sets, it is still much faster than creating a red-black tree with its fancypants O(n * log(n)) complexity.
Near the beginning of the curve, up to about eighteen thousand items, a sorted array implementation imported from an external module is very consistently about 6-7 times faster than a red-black tree, with a slope that is indistinguishable from O(n * log(n)).
Even after it catches up to quadratic complexity, in this particular benchmark, it takes about a hundred thousand items for the sorted array to become slower than the red-black tree!
The exact cutoff point depends on the type/size of elements that you work with, and the capabilities of the compiler. This benchmark used tiny 8-byte integer elements, hence the huge number.
The benchmark is based on my own red-black tree implementation that uses a single flat array to store node data. A more typical implementation would store each node in a separately allocated object, so it would likely be even slower.
The chart above is a log-log plot which makes it easy to compare the polynomial exponents of the complexity curves of competing algorithms at a glance. The slope of a quadratic algorithm on a log-log chart (like insertion into a sorted array—the green curves) is twice of that of a linear algorithm (like appending n items to an unsorted array—light blue curve) or a quasilinear one (like inserting into a red-black tree, red curve).
Note that the big gap between collections imported from stdlib and those imported from external modules is caused by a limitation in the current Swift compiler/ABI: when this limitation is lifted, the gap will narrow considerably, which will reduce the element count at which you’ll be able to reap the benefits of lower asymptotic complexity.
(This effect is already visible (albeit in reverse) on the benchmark for the “inlined” sorted array (light green), which is essentially the same code as the regular one (dark green) except it was implemented in the same module as the benchmarking loop, so the compiler has more options to optimize away witness tables and other levels of abstraction. That line starts curving up much sooner, at about 2000 items–imagine having a B-tree implementation that’s equally fast! Or better, try it yourself and report your results. Producing benchmarks like this takes a lot of time and effort.) :-)
This remarkable result is due in large part to the vast number of (to a CPU, random-looking) memory references that are needed to operate on red-black trees. Their intricate ballet of tree rotations looks mighty impressive to us mere humans, but to the delicate caches of your poor CPU, it looks more like a drunken elephant moshing at a thrash metal concert.
Meanwhile, the humble
Array does the only thing it knows: sliding around long contiguous memory regions. It does this over and over, ad nauseum. It doesn’t look impressive, but (up to a point) it fits well with how computers work.
So a small
Array is perfect for maintaining a sorted list. But what if the list gets too long? The B-tree’s answer is to simply cut the array in half, and to create a new index tree node on top to allow it to quickly find its way around this more complex list structure. These internal index nodes can also consist of arrays of elements and node references, creating a nice recursive data structure.
Because their fanout number is so high, B-trees are extremely shallow: for a B-tree with order 100 (which is actually rather on the low end), you can fit a billion items into a tree that’s not more than five levels deep.
Once you accept that small arrays are fast, it is easy to see why B-trees work so well: unless it holds more elements than its order, a B-tree quite literally is just an
Array. So it has the same performance behavior as an
Array for a small number of elements, and when it grows larger it prevents a quadratic upswing by never allowing its arrays to get too large. The yellow curve on the benchmark above demonstrates this behavior well.
Consider that each node in a typical B-tree can hold about ten full levels of a red-black tree (or AVL trees or whatever binary tree you like). Looking up an item in a B-tree node still requires a binary search of the node array, but this search works on a contiguous memory region, while the conventional search tree is fiddling around with loading pointer values and dereferencing them.
So it makes perfect sense to employ B-trees as an in-memory data structure.
Think about this, though: how many times do you need to work with a hundred thousand sorted items in a typical app? Or even twenty thousand? Or even just two thousand? The most interesting benefits of B-trees often occur at element counts well over a hundred thousand. However, B-trees are not much slower than arrays for low element counts (remember, they are arrays in that case), so it makes sense to use them when there’s even a slight chance that the count will get large.
The data structures implemented by
Set are remarkably versatile: a huge class of problems is easily and efficiently solved by simple combinations of these abstractions. However, they aren’t without drawbacks: you have probably run into cases when the standard collections exhibit suboptimal behavior:
Insertion and removal in the middle of an
Array can be slow when there are many items. (Keep the previous section in mind, though.)
The all-or-nothing copy-on-write behavior of
Set can lead to performance problems that are hard to detect and fix. If the underlying storage buffer is being shared by multiple collection instances, the modification of a single element in any of the instances requires creating a full copy of every element.
It is not at all obvious from the code when this happens, and it is even harder to reliably check for. You can’t (easily) write unit tests to check against accidental copying of items with value semantics!
With standard collection types, you often need to think about memory management.
Arrays and dictionaries never release memory until they’re entirely deallocated; a long-lived collection may hold onto a large piece of memory due to an earlier, temporary spike in the number of its elements. This is a form of subtle resource leak that can be hard to detect. On memory-constrained systems, wasting too much space may cause abrupt process termination.
Appending a new element to an array, or inserting a new element into a dictionary or a set are usually constant time operations, but they sometimes take O(n) time when the collection exhausts its allocated capacity. These spikes in execution time are often undesired, but preventing them requires careful size analysis.
If you reserve too little space, you’ll still get spikes; if you reserve too much, you’re wasting memory.
The order of elements in a
Dictionary or a
Set is undefined, and it isn’t even stable: it may change after seemingly simple mutations. Two collections with the exact same set of elements may store them in wildly different order.
Hashing collections require their keys to be
Hashable. If you want to use your own type as the key, you need to write a hash function yourself. It is annoyingly hard to write a good hash function, and it is even harder to test that it doesn’t produce too many collisions for the sets of values your code will typically use.
The possibility of hash collisions make
Set badly suited for tasks which require guaranteed worst-case performance. (E.g. server code may face low-bandwidth denial of service attacks due to artificial hash collisions.)
Array concatenation takes O(n) time, because it needs to put a copy of every element from both arrays into a new contiguous buffer.
Merging dictionaries or taking the union/intersection etc. of two sets are all costly O(n) operations, even if the elements aren’t interleaved at all.
Creating an independently editable sub-dictionary or subset requires elementwise iteration over either the entire collection, or the entire set of potential target items. This is often impractical, especially when the collection is large but sparse.
Getting an independently editable sub-array out of an array takes time that is linear in the size of the result. (
ArraySlice is often helpful, but it is most effective as a short-lived read-only view in temporary local variables.)
These issues don’t always matter. In fact, lots of interesting problems can be solved without running into any of them. When they do occur, the problems they cause are often insignificant. Even when they cause significant problems, it is usually straightforward to work around them by chosing a slightly different algorithm.
But sometimes you run into a case where the standard collection types are too slow, and it would be too painful to work around them.
B-trees solve all of the issues above. (Of course, they come with a set of different issues of their own. Life is hard.)
Insertion or removal from any position in a B-tree-based data structure takes O(log(n)) time, no matter what.
Like standard collection types, B-trees implement full copy-on-write value semantics. Copying a B-tree into another variable takes O(1) time; mutations of a copy do not affect the original instance.
However, B-trees implement a greatly improved version of copy-on-write that is not all-or-nothing: each node in the tree may be independently shared with other trees.
If you need to insert/remove/update a single element, B-trees will copy at most O(log(n)) elements to satisfy value semantics, even if the tree was entirely shared before the mutation.
Storage management in B-trees is granular; you do not need to reserve space for a B-tree in advance, and it never allocates more memory than it needs to store the actual number of elements it contains.
Storage is gradually allocated and released in small increments as the tree grows and shrinks. Storage is only copied when mutating shared elements, and even then it is done in small batches.
The performance of B-trees is extremely stable, with no irregular spikes ever.
(Note that there is a bit of leeway in allocations to make it easy to balance the tree. In the worst case, a B-tree may only fill 50% of the space it allocates. The ratio is typically much higher than that, though.)
B-trees always keep their items sorted in ascending key order, and they provide efficient positional lookups. You can get the ith smallest/largest item in a tree in O(log(n)) time.
Keys of a B-tree need to be
Hashable. It is often significantly easier to write comparison operators than hash functions; it is also much easier to verify that the implementation works correctly. A buggy
< operator will typically lead to obvious issues that are relatively easy to catch; a badly collisioning hash may go undetected for years.
Adversaries (or blind chance) will never produce a set of elements for which B-trees behave especially badly. The performance of B-trees only depends on the size of the tree, not its contents. (Provided that key comparison also behaves uniformly, of course. If you allow multi-megabyte strings as keys, you’re gonna have a bad time.)
Concatenation of any two B-trees takes O(log(n)) time. For trees that aren’t of a trivial size, the result will share some of its nodes with the input trees, deferring most copying until the time the tree needs to be modified. (Which may never happen.) Copy-on-write really shines with B-trees!
Merging the contents of two B-trees into a single tree takes O(n) time in the worst case, but if the elements aren’t too badly interleaved, it can often finish in O(log(n)) time by linking entire subtrees into the result in one go.
Set operations on the keys of a B-tree (such as calculating the intersection set, subtraction set, symmetric difference, etc.) also exploit the same trick for a huge performance boost. If the input trees are mutated versions of the same original tree, these operations are also able to skip elementwise processing of entire subtrees that are shared between the inputs.
SubSequence of a B-tree is also a B-tree. You can slice and dice B-trees any way you like: getting a fully independent copy of any prefix, suffix or subrange in a tree only takes O(log(n)) time. You can then take the subtree you extracted and insert it into another tree; this also costs O(log(n)), no matter where in the tree you want to put it. (You do need to keep the order of keys correct, though.)
BTree is a generic struct with copy-on-write value semantics. Internally, it stores its data in nodes with a fixed maximum size, arranged in a tree.
BTree type provides a full set of hand-tuned high-level operations to work with elements of a B-tree.
Nodes are represented by instances of a reference type that is not exported as public API. (Low-level access to individual tree nodes would be tricky to get right, and it would prevent future optimizations, such as moving node counts up to parent nodes.)
By default, the tree order (a.k.a., the fanout, or the maximum number of children) is set such that each node stores about 16KiB data. Larger node sizes make lookups faster, while insertion/removal becomes slower – 16KiB is a good enough approximation of the optimal node size on most modern systems. (But you can also set a custom node size if you know better. Note though that you cannot mix-n-match trees of different orders.) Thus, on a 64-bit system, a B-tree holding
Int elements will store about 2047 elements per node. Wow!
Individual B-tree nodes may be independently shared between multiple B-trees. When mutating a (partially or fully) shared tree, copy-on-write is restricted to only clone the nodes whose subtree is actually affected by the mutation. This has the following consequences:
Nodes cannot contain a reference to their parent node, because it is not necessarily unique.
Mutations of shared trees are typically much cheaper than copying the entire collection at once, which is what standard collection types do.
The root node is never shared between trees that are not equal.
BTree allows elements with duplicate keys to be stored in the tree. (In fact,
List works by using the same (empty) key for all elements.)
All methods that take a key to find an element let you (optionally) specify if you want to work with the first or last matching element, or if you’re happy with any match. The latter option is sometimes faster as it often allows the search to stop at the topmost matching element. There is also a selector that looks for the element after the specified key – this can be nice to determine the position of the end of a range of matching items.
Each node keeps track of the number of items in its entire subtree, so efficient positional lookup is possible. For any i, you can get, set, remove or insert the ith item in the tree in log(n) time.
There is a
BTreeIterator and a
BTreeIndex that provide the usual generator/indexing semantics. While individual element lookup usually takes O(log(n)) operations, iterating over all elements via these interfaces requires linear time. Using the generator is faster than indexing, so you should prefer using it whenever possible. There are methods to start an iterator from the middle of the tree: from any offset, any index, or any key.
forEach has a specialized recursive implementation, which makes it the fastest way to iterate over B-trees. There is even a variant that allows you to stop the iteration when you had seen enough items and want to get off the carousel.
BTreeCursor is an easy-to-use, general-purpose batch editing facility that allows you to manipulate the elements of a B-tree conveniently and highly efficiently. You can use a cursor to walk over the contents of a tree, modifying/inserting/removing elements as needed without a per-element log(n) lookup overhead. If you need to insert or remove a bunch or consecutive elements, it is better to use the provided bulk removal/insertion methods than to process them individually (Range operations have O(log(n)) complexity vs. elementwise processing takes O(k * log(n)).)
Internally, navigation in a B-Tree is based on abstract primitives that maintain a path to a particular position in the tree, as described by the
BTreePath protocol. The methods directly provided by this protocol are too low-level for convenient use, but the protocol has extension methods built on top of these that support familiar concepts like moving back and forth step by step, jumping to a specific offset in the tree, or looking up a particular key.
Indexes, generators and cursors use their particular implementation of
BTreePath to represent their own path flavors. All three of them maintain a path of nodes from the root of the tree to a particular slot of a particular node, but the details are very different:
BTreeIndex may not hold a strong reference to its tree, because that would interfere with copy-on-write when you want to mutate the tree at a certain index. Thus, indices are wrappers around a
BTreeWeakPath, which uses weak references, and needs to tread very carefully in order to detect when one of its references gets out of date.
BTreeIterator is supposed to support standalone iteration over the contents of the tree, so it must contain strong references. It uses a
BTreeStrongPath to represent the path of its next element. While an iterator only needs to be able to move one step forward,
BTreeStrongPath supports the full tree navigation API, making it very useful elsewhere in the codebase whenever we need a kind of read-only cursor into a tree. For example, the tree merging algorithm uses strong paths to represent its current positions in its input trees.
BTreeCursor needs to maintain a path where each node is uniquely held by the cursor, ready for mutation. (A cursor owns its own copy of the tree, and does not share it with the outside world until it is finished.) This special path flavor is implemented by
BTreeCursorPath. To speed things up, this struct intentionally breaks the node counts on its current path, to allow for super speedy elementwise insertions and removals. The counts are carefully recalculated whenever the path moves off a node’s branch in the tree.
BTreealso provides a set of recursive methods that implement the same sort of lookups and simple mutations. They are faster when you need to retrieve a single item, but they aren’t efficient when called repeatedly.
BTree includes a bulk loading algorithm that efficiently initializes fully loaded trees from any sorted sequence. You can also specify a fill factor that’s less than 100% if you expect to insert data into the middle of the tree later; leaving some space available may reduce work to keep the tree balanced. The bulk loader can optionally filter out duplicate keys for you. It verifies that the elements are in the correct order and traps if they aren’t.
The bulk loader is based on a general
BTreeBuilder struct that specializes on appending elements to a newly created tree. Beside individual elements, it also supports efficiently appending entire B-trees. This comes useful in optimized tree merging algorithms.
Merge operations (such as
BTree.symmetricDifference) are highly tuned to detect when they can skip over entire subtrees on their input, linking them into the result or skipping their contents as required. For input trees that contain long runs of distinct elements, these operations can finish in as little as O(log(n)) time. These algorithms are expressed on top of a general tree merging construct called
Current versions of the Swift compiler are unable to specialize generic types that are imported from external modules other than the standard library. (In fact, it is not entirely incorrect to say that the standard library works as if it was compiled each time anew as part of every Swift module rather than linked in as an opaque external binary.)
This limitation puts a considerable limit on the raw performance achievable by collection types imported from external modules, especially if they are parameterized with simple, extremely optimizable value types such as
Int or even
String. Relying on
import will incur a 10-200x slowdown when your collection is holding these most basic value types. (The effect is much reduced for reference types, though.)
Without access to the full source code of the collection, the compiler is unable to optimize away abstractions like virtual dispatch tables, function calls and the rest of the fluff we’ve learned to mostly ignore inside a module. In cross-module generics, even retrieving a single
Int will necessarily go through at least one lookup to a virtual table. This is because the code that implements the unspecialized generic also executes for type parameters that contain reference types, whose reference count needs to be maintained.
If raw performance is essential, currently the only way out of this pit is to put the collection’s code inside your module. (Other than hacking stdlib to include these extra types, of course – but that is a bad idea for a thousand obvious reasons.) However, having each module maintain its own set of collections would smell horrible, plus it would make it hard or impossible to transfer collection instances across module boundaries. Plus, if this strategy would be used across many modules, it would lead to a C++ templates-style (or worse) code explosion. A better (but still rather unsatisfactory) workaround is to compile the collection code with the single module that benefits most from specialization. The rest of the modules will still have access to it, if in a much slower way.
The Swift compiler team has plans to address this issue in future compiler versions, e.g., by allowing library authors to manually specialize generics for a predetermined set of type parameters.