Sunday, October 20, 2013

Implementation of C++ unordered associative containers

Unordered associative container is a fancy name adopted by C++ for what the rest of the world refers to as a hash table. With the 2011 revision of the standard, the language provides four different unordered associative containers:
  • unordered_set and unordered_map,
  • unordered_multiset and unordered_multimap, allowing for duplicate elements.
Given the particular interface these containers have, their internal representation is universally based on closed addressing, of which the following is a straightforward implementation.
In this data structure, each bucket points to a null-terminated singly linked list of elements. If the provided hash function works as it should, the number of elements in each bucket only rarely exceeds by much the specified load factor, so insertion and erasure of elements is O(1) on average. In the figure above, buckets b1 and b3 have one element each, and bucket b2 holds two elements. Simple and memory-efficient as this structure is, it turns out C++ unordered associative containers cannot use it directly: containers are required to be traversable, i.e. they must provide an associated iterator type which can go through all the elements of a container c as comprised in the range [c.begin(), c.end()). So, how does an iterator go from say bucket b1 to b2? The obvious solution of scanning the bucket array until the next non-empty bucket is found does not work, since the average time this operation takes grows as the bucket array is depleted and more buckets need to be walked through to reach the following element, whereas the standard requires that iterator increment be O(1). As this has been discussed in extenso elsewhere, we'll make a long story short and simply conclude that, in order to meet the requirements of C++ unordered associative containers, all the elements must be linked together.
Let's see the resulting data structures used by some popular implementations of unordered associative containers, plus a new approach adopted by Boost.MultiIndex. We consider containers without duplicate elements fist (unordered_set and unordered_map), and study unordered_multiset and unordered_multimap in a further article. In what follows we call N the number of elements in the container and B the number of its buckets.


These are the providers of the Microsoft Visual Studio C++ standard library implementation. Their hash tables are designed as shown in the figure:
(A note on this and the following diagrams: although seemingly implied by the pictures, bucket entries need not be arranged in the same order as their corresponding buckets are traversed.)
As anticipated, all the elements are linked together, although in this case they are doubly so: this allows for bidirectional iteration at the expense of one more pointer per element, and, more importantly, makes unlinking elements extremely easy. As for the bucket array, each entry holds two pointers to the first and last element of the bucket, respectively (the latter pointer is needed to determine the extent of the bucket). Insertion of a new element is simple enough:
  1. Use the hash function to locate the corresponding bucket entry constant time.
  2. Look for duplicate elements in the bucket and abort insertion if found time linear on the number of elements in the bucket.
  3. Link into the element list at the beginning of the bucket constant time.
  4. Adjust bucket entry pointers as needed constant time.
And erasure goes as follows:
  1. Use the hash function to locate the corresponding bucket entry constant time.
  2. Unlink from the element list constant time.
  3. Adjust bucket entry pointers as needed constant time.
The resulting complexity of these operations is then O(1) (in the case of insertion, under the assumption that the hash function is well behaved and does not overpopulate buckets) and the memory overhead is two pointers per element plus two pointers per bucket entry:
2N + 2B,
All is well then, right? Well, actually not: Dinkumware's scheme has a serious problem with erasure, as the first step of the procedure
  1. Use the hash function to locate the corresponding bucket entry.
will fail if the user-provided hash function throws an exception, and the C++ standard requires that erasure work unconditionally and never throw. Blunt as it sounds, it is my opinion that Dinkuware's implementation of C++ unordered associative containers is broken.

Boost.Unordered, libc++, libstdc++-v3

Boost.Unordered and libc++ (Clang's standard library implementation) use basically the same data structure, whose design is a direct application of three guiding principles:
  1. All elements are linked (here, singly linked) together.
  2. Recognizing the fact that, in order to implement erasure without throwing, we must connect an element with its bucket entry without invoking the hash function, the associated hash value is stored as part of the element node.
  3. This is the trickiest part: in a singly linked list, erasure of an element takes linear time unless we know the preceding element in advance. So, instead of having each bucket entry point to the first element in the bucket, we make it point to the element before that one. The figure can be more easily understood if color codes are taken into account: for instance, b2, which is yellow, points to a fuchsia element (in b1).
The algorithm for insertion is based on the same guidelines as those of Dinkumware (with the complication that the end of the bucket is located by comparing stored hash values), whereas erasure follows these steps:
  1. Locate the associated bucket entry via the stored hash value constant time, never throws.
  2. Locate the preceding element linear on the number of elements in the bucket.
  3. Use the preceding element to unlink from the element list constant time.
  4. Adjust bucket entry pointers as needed constant time.
Insertion and erasure are then O(1) for reasonably good hash distributions, which is the minimum mandated by the C++ standard. The memory overhead is, using the same terminology as before,
2N + 1B,
(here we are assuming that storing a hash value takes the same space as a pointer, namely a word.)
GNU Standard C++ Library v3, also known as libstdc++-v3, provides a memory optimization over this data structure: if the hash function is determined to not throw (by noexcept specification) and is marked as "fast" (via some dedicated type trait), hash values are not stored and the function is safely used instead. The way this optimization is currently implemented looks to me to a bit dangerous, as the __is_fast_hash type trait is true by default except for some basic types, which means that all noexcept user-provided hash functions will be deemed as fast unless the user is aware of this implementation-specific trait and states otherwise.
So, leaving libstdc++-v3 optimizations aside, 2N + 1B seems to be the minimum overhead required to implement C++ unordered associative containers based on closed addressing and meeting the complexity requirements of the standard. In fact, we can do better in the area of complexity without giving up extra memory.

A novel data structure

As of Boost 1.56, Boost.MultiIndex hashed indices internally use a new hash table design with some nice complexity characteristics. The key insight behind the approach taken is this: a circular doubly linked list has redundancies that we can take advantage of to accomodate extra information:
Given a node pointer X, let's call Xn and Xp the pointers to the next and prior node, respectively. A circular doubly-linked list maintains this invariant:
X = Xnp = Xpn for every X in the list.
With some care, we can redirect Xn or Xp (which is detected by the breach of the former invariant) provided we know how to get back to X. This is precisely what we are doing to associate elements to bucket entries in a hash table:
Think of this data structure as a simple circular doubly linked list where some redirections have been applied in the following case:
  • If an element X is the first of its bucket, Xp points to the bucket entry.
Besides, bucket entries point to the element preceding the first one in the bucket, just as Boost.Unordered does. Extending our terminology so that if X points to a bucket entry then Xn denotes the element the entry points to, we have these formal properties:
  • X is the first element of its bucket iff Xpn X,
  • X is the last element of its bucket iff Xnp X,
  • the element following X is always Xn,
  • the element preceding X is Xpn if X is the first of its bucket, Xp otherwise. 
Note that the evaluation of all these properties can be done in constant time, so we can treat our element list as a circular doubly linked list with special operations for accessing the preceding and following nodes and use it for implementing hash table insertion (following the same general steps as with the other data structures) and erasure:
  1. Unlink from the (doubly linked) list constant time.
  2. Adjust bucket entry pointers if the element is the first or the last element of its bucket, or both constant time.
Erasure of an element does not imply traversing the bucket, so unlinking happens in constant time even if the bucket is crowded by a bad behaved hash function; we have then designed a hash table with unconditional O(1) erasure complexity at no extra cost in terms of memory overhead (2N + 1B).


  1. Thank you for your impressing post, it's very helpful!

  2. Hi,

    Thanks for the wonderful explanation. I am really confused about one aspect. If In the boost.unordered or libc++ implementation I want to delete an entry by key, I don't understand how storing a hash value in the linked list node helps? I understand it links the data to the bucket.

    Can you explain a little more this line "we must connect an element with its bucket entry without invoking the hash function".

    Let us say I have a unordered_map and I have stored a key say "AAB" and the associated hash value is stored along with the key and value. IF I say

    boost::unordered_map um;
    um["AAB"] = 1;

    In this case how does storing the "hash value" associated with "AAB" help me delete that entry without using the hash function?

    I am sorry maybe it is something I am missing but I don't understand.Could you please elaborate a little.

    1. In the case um.erase("AAB") the hash function will be invoked regardless of whether nodes store hash values or not. It is the following case when such stored value matters:

      auto it=um.find(...);
      um.erase(it); // guaranteed by the standard not to throw

      When erasing through an iterator, the standard requires that erase not throw, which precludes using the hash function (as it might throw). As the hash value is absolutely needed (in libc++ and Boost.Unordered) to get the bucket in which the element pointed to by it belongs, a non-throwing solution is having the node store the value for readymade consumption.

  3. okay thanks a lot. Much clearer.

  4. Could you please elaborate on this "So, instead of having each bucket entry point to the first element in the bucket, we make it point to the element before that one."? I'm not sure why that would make erasure not linear time, because you still need to search from the beginning to find the element preceding the one you want to erase, no?

    1. This trick (having buckets point to the element before the first one in the bucket) achieves O(1) erasure for good hash distributions:

      "Insertion and erasure are then O(1) for reasonably good hash distributions"

      The explanation is that you don't have to traverse the whole container to erase an element (as would be the case with a regular singly-linked list), but only from the beginning of the bucket.