The containers are made up of a number of 'buckets', each of which can contain
any number of elements. For example, the following diagram shows an
unordered_set with 7 buckets containing
(this is just for illustration, containers will typically have more buckets).
In order to decide which bucket to place an element in, the container applies
the hash function,
the element's key (for
key is the whole element, but is referred to as the key so that the same terminology
can be used for sets and maps). This returns a value of type
std::size_t has a much greater range of values
then the number of buckets, so that container applies another transformation
to that value to choose a bucket to place the element in.
Retrieving the elements for a given key is simple. The same process is applied
to the key to find the correct bucket. Then the key is compared with the elements
in the bucket to find any elements that match (using the equality predicate
Pred). If the hash function
has worked well the elements will be evenly distributed amongst the buckets
so only a small number of elements will need to be examined.
You can see in the diagram that
D have been placed in
the same bucket. When looking for elements in this bucket up to 2 comparisons
are made, making the search slower. This is known as a collision. To keep things
fast we try to keep collisions to a minimum.
Table 25.1. Methods for Accessing Buckets
||The number of buckets.|
||An upper bound on the number of buckets.|
number of elements in bucket
||Returns the index of the bucket which would contain k|
||Return begin and end iterators for bucket
As more elements are added to an unordered associative container, the number
of elements in the buckets will increase causing performance to degrade. To
combat this the containers increase the bucket count as elements are inserted.
You can also tell the container to change the bucket count (if required) by
The standard leaves a lot of freedom to the implementer to decide how the number of buckets are chosen, but it does make some requirements based on the container's 'load factor', the average number of elements per bucket. Containers also have a 'maximum load factor' which they should try to keep the load factor below.
You can't control the bucket count directly but there are two ways to influence it:
max_load_factor doesn't let
you set the maximum load factor yourself, it just lets you give a hint.
And even then, the draft standard doesn't actually require the container to
pay much attention to this value. The only time the load factor is required
to be less than the maximum is following a call to
But most implementations will try to keep the number of elements below the
max load factor, and set the maximum load factor to be the same as or close
to the hint - unless your hint is unreasonably small or large.
Table 25.2. Methods for Controlling Bucket Size
The average number of elements per bucket.
Returns the current maximum load factor.
Changes the container's maximum load factor, using
Changes the number of buckets so that there at least n buckets, and so that the load factor is less than the maximum load factor.
It is not specified how member functions other than
affect the bucket count, although
is only allowed to invalidate iterators when the insertion causes the load
factor to be greater than or equal to the maximum load factor. For most implementations
this means that insert will only change the number of buckets when this happens.
While iterators can be invalidated by calls to
rehash, pointers and references
to the container's elements are never invalidated.
In a similar manner to using
vectors, it can be a good
idea to call
inserting a large number of elements. This will get the expensive rehashing
out of the way and let you store iterators, safe in the knowledge that they
won't be invalidated. If you are inserting
elements into container
you could first call:
x.rehash((x.size() + n) / x.max_load_factor() + 1);