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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>Design</title><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><meta name="keywords" content="ISO C++, policy, container, data, structure, associated, tree, trie, hash, metaprogramming" /><meta name="keywords" content="ISO C++, library" /><meta name="keywords" content="ISO C++, runtime, library" /><link rel="home" href="../index.html" title="The GNU C++ Library" /><link rel="up" href="policy_data_structures.html" title="Chapter 21. Policy-Based Data Structures" /><link rel="prev" href="policy_data_structures_using.html" title="Using" /><link rel="next" href="policy_based_data_structures_test.html" title="Testing" /></head><body><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="3" align="center">Design</th></tr><tr><td width="20%" align="left"><a accesskey="p" href="policy_data_structures_using.html">Prev</a> </td><th width="60%" align="center">Chapter 21. Policy-Based Data Structures</th><td width="20%" align="right"> <a accesskey="n" href="policy_based_data_structures_test.html">Next</a></td></tr></table><hr /></div><div class="section"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a id="containers.pbds.design"></a>Design</h2></div></div></div><p></p><div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="pbds.design.concepts"></a>Concepts</h3></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.concepts.null_type"></a>Null Policy Classes</h4></div></div></div><p>
Associative containers are typically parametrized by various
policies. For example, a hash-based associative container is
parametrized by a hash-functor, transforming each key into an
non-negative numerical type. Each such value is then further mapped
into a position within the table. The mapping of a key into a
position within the table is therefore a two-step process.
</p><p>
In some cases, instantiations are redundant. For example, when the
keys are integers, it is possible to use a redundant hash policy,
which transforms each key into its value.
</p><p>
In some other cases, these policies are irrelevant. For example, a
hash-based associative container might transform keys into positions
within a table by a different method than the two-step method
described above. In such a case, the hash functor is simply
irrelevant.
</p><p>
When a policy is either redundant or irrelevant, it can be replaced
by <code class="classname">null_type</code>.
</p><p>
For example, a <span class="emphasis"><em>set</em></span> is an associative
container with one of its template parameters (the one for the
mapped type) replaced with <code class="classname">null_type</code>. Other
places simplifications are made possible with this technique
include node updates in tree and trie data structures, and hash
and probe functions for hash data structures.
</p></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.concepts.associative_semantics"></a>Map and Set Semantics</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.associative_semantics.set_vs_map"></a>
Distinguishing Between Maps and Sets
</h5></div></div></div><p>
Anyone familiar with the standard knows that there are four kinds
of associative containers: maps, sets, multimaps, and
multisets. The map datatype associates each key to
some data.
</p><p>
Sets are associative containers that simply store keys -
they do not map them to anything. In the standard, each map class
has a corresponding set class. E.g.,
<code class="classname">std::map&lt;int, char&gt;</code> maps each
<code class="classname">int</code> to a <code class="classname">char</code>, but
<code class="classname">std::set&lt;int, char&gt;</code> simply stores
<code class="classname">int</code>s. In this library, however, there are no
distinct classes for maps and sets. Instead, an associative
container's <code class="classname">Mapped</code> template parameter is a policy: if
it is instantiated by <code class="classname">null_type</code>, then it
is a "set"; otherwise, it is a "map". E.g.,
</p><pre class="programlisting">
cc_hash_table&lt;int, char&gt;
</pre><p>
is a "map" mapping each <span class="type">int</span> value to a <span class="type">
char</span>, but
</p><pre class="programlisting">
cc_hash_table&lt;int, null_type&gt;
</pre><p>
is a type that uniquely stores <span class="type">int</span> values.
</p><p>Once the <code class="classname">Mapped</code> template parameter is instantiated
by <code class="classname">null_type</code>, then
the "set" acts very similarly to the standard's sets - it does not
map each key to a distinct <code class="classname">null_type</code> object. Also,
, the container's <span class="type">value_type</span> is essentially
its <span class="type">key_type</span> - just as with the standard's sets
.</p><p>
The standard's multimaps and multisets allow, respectively,
non-uniquely mapping keys and non-uniquely storing keys. As
discussed, the
reasons why this might be necessary are 1) that a key might be
decomposed into a primary key and a secondary key, 2) that a
key might appear more than once, or 3) any arbitrary
combination of 1)s and 2)s. Correspondingly,
one should use 1) "maps" mapping primary keys to secondary
keys, 2) "maps" mapping keys to size types, or 3) any arbitrary
combination of 1)s and 2)s. Thus, for example, an
<code class="classname">std::multiset&lt;int&gt;</code> might be used to store
multiple instances of integers, but using this library's
containers, one might use
</p><pre class="programlisting">
tree&lt;int, size_t&gt;
</pre><p>
i.e., a <code class="classname">map</code> of <span class="type">int</span>s to
<span class="type">size_t</span>s.
</p><p>
These "multimaps" and "multisets" might be confusing to
anyone familiar with the standard's <code class="classname">std::multimap</code> and
<code class="classname">std::multiset</code>, because there is no clear
correspondence between the two. For example, in some cases
where one uses <code class="classname">std::multiset</code> in the standard, one might use
in this library a "multimap" of "multisets" - i.e., a
container that maps primary keys each to an associative
container that maps each secondary key to the number of times
it occurs.
</p><p>
When one uses a "multimap," one should choose with care the
type of container used for secondary keys.
</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.associative_semantics.multi"></a>Alternatives to <code class="classname">std::multiset</code> and <code class="classname">std::multimap</code></h5></div></div></div><p>
Brace onself: this library does not contain containers like
<code class="classname">std::multimap</code> or
<code class="classname">std::multiset</code>. Instead, these data
structures can be synthesized via manipulation of the
<code class="classname">Mapped</code> template parameter.
</p><p>
One maps the unique part of a key - the primary key, into an
associative-container of the (originally) non-unique parts of
the key - the secondary key. A primary associative-container
is an associative container of primary keys; a secondary
associative-container is an associative container of
secondary keys.
</p><p>
Stepping back a bit, and starting in from the beginning.
</p><p>
Maps (or sets) allow mapping (or storing) unique-key values.
The standard library also supplies associative containers which
map (or store) multiple values with equivalent keys:
<code class="classname">std::multimap</code>, <code class="classname">std::multiset</code>,
<code class="classname">std::tr1::unordered_multimap</code>, and
<code class="classname">unordered_multiset</code>. We first discuss how these might
be used, then why we think it is best to avoid them.
</p><p>
Suppose one builds a simple bank-account application that
records for each client (identified by an <code class="classname">std::string</code>)
and account-id (marked by an <span class="type">unsigned long</span>) -
the balance in the account (described by a
<span class="type">float</span>). Suppose further that ordering this
information is not useful, so a hash-based container is
preferable to a tree based container. Then one can use
</p><pre class="programlisting">
std::tr1::unordered_map&lt;std::pair&lt;std::string, unsigned long&gt;, float, ...&gt;
</pre><p>
which hashes every combination of client and account-id. This
might work well, except for the fact that it is now impossible
to efficiently list all of the accounts of a specific client
(this would practically require iterating over all
entries). Instead, one can use
</p><pre class="programlisting">
std::tr1::unordered_multimap&lt;std::pair&lt;std::string, unsigned long&gt;, float, ...&gt;
</pre><p>
which hashes every client, and decides equivalence based on
client only. This will ensure that all accounts belonging to a
specific user are stored consecutively.
</p><p>
Also, suppose one wants an integers' priority queue
(a container that supports <code class="function">push</code>,
<code class="function">pop</code>, and <code class="function">top</code> operations, the last of which
returns the largest <span class="type">int</span>) that also supports
operations such as <code class="function">find</code> and <code class="function">lower_bound</code>. A
reasonable solution is to build an adapter over
<code class="classname">std::set&lt;int&gt;</code>. In this adapter,
<code class="function">push</code> will just call the tree-based
associative container's <code class="function">insert</code> method; <code class="function">pop</code>
will call its <code class="function">end</code> method, and use it to return the
preceding element (which must be the largest). Then this might
work well, except that the container object cannot hold
multiple instances of the same integer (<code class="function">push(4)</code>,
will be a no-op if <code class="constant">4</code> is already in the
container object). If multiple keys are necessary, then one
might build the adapter over an
<code class="classname">std::multiset&lt;int&gt;</code>.
</p><p>
The standard library's non-unique-mapping containers are useful
when (1) a key can be decomposed in to a primary key and a
secondary key, (2) a key is needed multiple times, or (3) any
combination of (1) and (2).
</p><p>
The graphic below shows how the standard library's container
design works internally; in this figure nodes shaded equally
represent equivalent-key values. Equivalent keys are stored
consecutively using the properties of the underlying data
structure: binary search trees (label A) store equivalent-key
values consecutively (in the sense of an in-order walk)
naturally; collision-chaining hash tables (label B) store
equivalent-key values in the same bucket, the bucket can be
arranged so that equivalent-key values are consecutive.
</p><div class="figure"><a id="id-1.3.5.8.4.3.3.3.14"></a><p class="title"><strong>Figure 21.8. Non-unique Mapping Standard Containers</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_embedded_lists_1.png" align="middle" alt="Non-unique Mapping Standard Containers" /></div></div></div><br class="figure-break" /><p>
Put differently, the standards' non-unique mapping
associative-containers are associative containers that map
primary keys to linked lists that are embedded into the
container. The graphic below shows again the two
containers from the first graphic above, this time with
the embedded linked lists of the grayed nodes marked
explicitly.
</p><div class="figure"><a id="fig.pbds_embedded_lists_2"></a><p class="title"><strong>Figure 21.9. 
Effect of embedded lists in
<code class="classname">std::multimap</code>
</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_embedded_lists_2.png" align="middle" alt="Effect of embedded lists in std::multimap" /></div></div></div><br class="figure-break" /><p>
These embedded linked lists have several disadvantages.
</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
The underlying data structure embeds the linked lists
according to its own consideration, which means that the
search path for a value might include several different
equivalent-key values. For example, the search path for the
the black node in either of the first graphic, labels A or B,
includes more than a single gray node.
</p></li><li class="listitem"><p>
The links of the linked lists are the underlying data
structures' nodes, which typically are quite structured. In
the case of tree-based containers (the grapic above, label
B), each "link" is actually a node with three pointers (one
to a parent and two to children), and a
relatively-complicated iteration algorithm. The linked
lists, therefore, can take up quite a lot of memory, and
iterating over all values equal to a given key (through the
return value of the standard
library's <code class="function">equal_range</code>) can be
expensive.
</p></li><li class="listitem"><p>
The primary key is stored multiply; this uses more memory.
</p></li><li class="listitem"><p>
Finally, the interface of this design excludes several
useful underlying data structures. Of all the unordered
self-organizing data structures, practically only
collision-chaining hash tables can (efficiently) guarantee
that equivalent-key values are stored consecutively.
</p></li></ol></div><p>
The above reasons hold even when the ratio of secondary keys to
primary keys (or average number of identical keys) is small, but
when it is large, there are more severe problems:
</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
The underlying data structures order the links inside each
embedded linked-lists according to their internal
considerations, which effectively means that each of the
links is unordered. Irrespective of the underlying data
structure, searching for a specific value can degrade to
linear complexity.
</p></li><li class="listitem"><p>
Similarly to the above point, it is impossible to apply
to the secondary keys considerations that apply to primary
keys. For example, it is not possible to maintain secondary
keys by sorted order.
</p></li><li class="listitem"><p>
While the interface "understands" that all equivalent-key
values constitute a distinct list (through
<code class="function">equal_range</code>), the underlying data
structure typically does not. This means that operations such
as erasing from a tree-based container all values whose keys
are equivalent to a a given key can be super-linear in the
size of the tree; this is also true also for several other
operations that target a specific list.
</p></li></ol></div><p>
In this library, all associative containers map
(or store) unique-key values. One can (1) map primary keys to
secondary associative-containers (containers of
secondary keys) or non-associative containers (2) map identical
keys to a size-type representing the number of times they
occur, or (3) any combination of (1) and (2). Instead of
allowing multiple equivalent-key values, this library
supplies associative containers based on underlying
data structures that are suitable as secondary
associative-containers.
</p><p>
In the figure below, labels A and B show the equivalent
underlying data structures in this library, as mapped to the
first graphic above. Labels A and B, respectively. Each shaded
box represents some size-type or secondary
associative-container.
</p><div class="figure"><a id="id-1.3.5.8.4.3.3.3.23"></a><p class="title"><strong>Figure 21.10. Non-unique Mapping Containers</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_embedded_lists_3.png" align="middle" alt="Non-unique Mapping Containers" /></div></div></div><br class="figure-break" /><p>
In the first example above, then, one would use an associative
container mapping each user to an associative container which
maps each application id to a start time (see
<code class="filename">example/basic_multimap.cc</code>); in the second
example, one would use an associative container mapping
each <code class="classname">int</code> to some size-type indicating the
number of times it logically occurs
(see <code class="filename">example/basic_multiset.cc</code>.
</p><p>
See the discussion in list-based container types for containers
especially suited as secondary associative-containers.
</p></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.concepts.iterator_semantics"></a>Iterator Semantics</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.iterator_semantics.point_and_range"></a>Point and Range Iterators</h5></div></div></div><p>
Iterator concepts are bifurcated in this design, and are
comprised of point-type and range-type iteration.
</p><p>
A point-type iterator is an iterator that refers to a specific
element as returned through an
associative-container's <code class="function">find</code> method.
</p><p>
A range-type iterator is an iterator that is used to go over a
sequence of elements, as returned by a container's
<code class="function">find</code> method.
</p><p>
A point-type method is a method that
returns a point-type iterator; a range-type method is a method
that returns a range-type iterator.
</p><p>For most containers, these types are synonymous; for
self-organizing containers, such as hash-based containers or
priority queues, these are inherently different (in any
implementation, including that of C++ standard library
components), but in this design, it is made explicit. They are
distinct types.
</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.iterator_semantics.both"></a>Distinguishing Point and Range Iterators</h5></div></div></div><p>When using this library, is necessary to differentiate
between two types of methods and iterators: point-type methods and
iterators, and range-type methods and iterators. Each associative
container's interface includes the methods:</p><pre class="programlisting">
point_const_iterator
find(const_key_reference r_key) const;
point_iterator
find(const_key_reference r_key);
std::pair&lt;point_iterator,bool&gt;
insert(const_reference r_val);
</pre><p>The relationship between these iterator types varies between
container types. The figure below
shows the most general invariant between point-type and
range-type iterators: In <span class="emphasis"><em>A</em></span> <code class="literal">iterator</code>, can
always be converted to <code class="literal">point_iterator</code>. In <span class="emphasis"><em>B</em></span>
shows invariants for order-preserving containers: point-type
iterators are synonymous with range-type iterators.
Orthogonally, <span class="emphasis"><em>C</em></span>shows invariants for "set"
containers: iterators are synonymous with const iterators.</p><div class="figure"><a id="id-1.3.5.8.4.3.4.3.5"></a><p class="title"><strong>Figure 21.11. Point Iterator Hierarchy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_point_iterator_hierarchy.png" align="middle" alt="Point Iterator Hierarchy" /></div></div></div><br class="figure-break" /><p>Note that point-type iterators in self-organizing containers
(hash-based associative containers) lack movement
operators, such as <code class="literal">operator++</code> - in fact, this
is the reason why this library differentiates from the standard C++ librarys
design on this point.</p><p>Typically, one can determine an iterator's movement
capabilities using
<code class="literal">std::iterator_traits&lt;It&gt;iterator_category</code>,
which is a <code class="literal">struct</code> indicating the iterator's
movement capabilities. Unfortunately, none of the standard predefined
categories reflect a pointer's <span class="emphasis"><em>not</em></span> having any
movement capabilities whatsoever. Consequently,
<code class="literal">pb_ds</code> adds a type
<code class="literal">trivial_iterator_tag</code> (whose name is taken from
a concept in C++ standardese, which is the category of iterators
with no movement capabilities.) All other standard C++ library
tags, such as <code class="literal">forward_iterator_tag</code> retain their
common use.</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="pbds.design.concepts.invalidation"></a>Invalidation Guarantees</h5></div></div></div><p>
If one manipulates a container object, then iterators previously
obtained from it can be invalidated. In some cases a
previously-obtained iterator cannot be de-referenced; in other cases,
the iterator's next or previous element might have changed
unpredictably. This corresponds exactly to the question whether a
point-type or range-type iterator (see previous concept) is valid or
not. In this design, one can query a container (in compile time) about
its invalidation guarantees.
</p><p>
Given three different types of associative containers, a modifying
operation (in that example, <code class="function">erase</code>) invalidated
iterators in three different ways: the iterator of one container
remained completely valid - it could be de-referenced and
incremented; the iterator of a different container could not even be
de-referenced; the iterator of the third container could be
de-referenced, but its "next" iterator changed unpredictably.
</p><p>
Distinguishing between find and range types allows fine-grained
invalidation guarantees, because these questions correspond exactly
to the question of whether point-type iterators and range-type
iterators are valid. The graphic below shows tags corresponding to
different types of invalidation guarantees.
</p><div class="figure"><a id="id-1.3.5.8.4.3.4.4.5"></a><p class="title"><strong>Figure 21.12. Invalidation Guarantee Tags Hierarchy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_invalidation_tag_hierarchy.png" align="middle" alt="Invalidation Guarantee Tags Hierarchy" /></div></div></div><br class="figure-break" /><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
<code class="classname">basic_invalidation_guarantee</code>
corresponds to a basic guarantee that a point-type iterator,
a found pointer, or a found reference, remains valid as long
as the container object is not modified.
</p></li><li class="listitem"><p>
<code class="classname">point_invalidation_guarantee</code>
corresponds to a guarantee that a point-type iterator, a
found pointer, or a found reference, remains valid even if
the container object is modified.
</p></li><li class="listitem"><p>
<code class="classname">range_invalidation_guarantee</code>
corresponds to a guarantee that a range-type iterator remains
valid even if the container object is modified.
</p></li></ul></div><p>To find the invalidation guarantee of a
container, one can use</p><pre class="programlisting">
typename container_traits&lt;Cntnr&gt;::invalidation_guarantee
</pre><p>Note that this hierarchy corresponds to the logic it
represents: if a container has range-invalidation guarantees,
then it must also have find invalidation guarantees;
correspondingly, its invalidation guarantee (in this case
<code class="classname">range_invalidation_guarantee</code>)
can be cast to its base class (in this case <code class="classname">point_invalidation_guarantee</code>).
This means that this this hierarchy can be used easily using
standard metaprogramming techniques, by specializing on the
type of <code class="literal">invalidation_guarantee</code>.</p><p>
These types of problems were addressed, in a more general
setting, in <a class="xref" href="policy_data_structures.html#biblio.meyers96more" title="More Effective C++: 35 New Ways to Improve Your Programs and Designs">[biblio.meyers96more]</a> - Item 2. In
our opinion, an invalidation-guarantee hierarchy would solve
these problems in all container types - not just associative
containers.
</p></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.concepts.genericity"></a>Genericity</h4></div></div></div><p>
The design attempts to address the following problem of
data-structure genericity. When writing a function manipulating
a generic container object, what is the behavior of the object?
Suppose one writes
</p><pre class="programlisting">
template&lt;typename Cntnr&gt;
void
some_op_sequence(Cntnr &amp;r_container)
{
...
}
</pre><p>
then one needs to address the following questions in the body
of <code class="function">some_op_sequence</code>:
</p><div class="itemizedlist"><ul class="itemizedlist" style="list-style-type: disc; "><li class="listitem"><p>
Which types and methods does <code class="literal">Cntnr</code> support?
Containers based on hash tables can be queries for the
hash-functor type and object; this is meaningless for tree-based
containers. Containers based on trees can be split, joined, or
can erase iterators and return the following iterator; this
cannot be done by hash-based containers.
</p></li><li class="listitem"><p>
What are the exception and invalidation guarantees
of <code class="literal">Cntnr</code>? A container based on a probing
hash-table invalidates all iterators when it is modified; this
is not the case for containers based on node-based
trees. Containers based on a node-based tree can be split or
joined without exceptions; this is not the case for containers
based on vector-based trees.
</p></li><li class="listitem"><p>
How does the container maintain its elements? Tree-based and
Trie-based containers store elements by key order; others,
typically, do not. A container based on a splay trees or lists
with update policies "cache" "frequently accessed" elements;
containers based on most other underlying data structures do
not.
</p></li><li class="listitem"><p>
How does one query a container about characteristics and
capabilities? What is the relationship between two different
data structures, if anything?
</p></li></ul></div><p>The remainder of this section explains these issues in
detail.</p><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.genericity.tag"></a>Tag</h5></div></div></div><p>
Tags are very useful for manipulating generic types. For example, if
<code class="literal">It</code> is an iterator class, then <code class="literal">typename
It::iterator_category</code> or <code class="literal">typename
std::iterator_traits&lt;It&gt;::iterator_category</code> will
yield its category, and <code class="literal">typename
std::iterator_traits&lt;It&gt;::value_type</code> will yield its
value type.
</p><p>
This library contains a container tag hierarchy corresponding to the
diagram below.
</p><div class="figure"><a id="id-1.3.5.8.4.3.5.7.4"></a><p class="title"><strong>Figure 21.13. Container Tag Hierarchy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_container_tag_hierarchy.png" align="middle" alt="Container Tag Hierarchy" /></div></div></div><br class="figure-break" /><p>
Given any container <span class="type">Cntnr</span>, the tag of
the underlying data structure can be found via <code class="literal">typename
Cntnr::container_category</code>.
</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="concepts.genericity.traits"></a>Traits</h5></div></div></div><p></p><p>Additionally, a traits mechanism can be used to query a
container type for its attributes. Given any container
<code class="literal">Cntnr</code>, then <code class="literal">&lt;Cntnr&gt;</code>
is a traits class identifying the properties of the
container.</p><p>To find if a container can throw when a key is erased (which
is true for vector-based trees, for example), one can
use
</p><pre class="programlisting">container_traits&lt;Cntnr&gt;::erase_can_throw</pre><p>
Some of the definitions in <code class="classname">container_traits</code>
are dependent on other
definitions. If <code class="classname">container_traits&lt;Cntnr&gt;::order_preserving</code>
is <code class="constant">true</code> (which is the case for containers
based on trees and tries), then the container can be split or
joined; in this
case, <code class="classname">container_traits&lt;Cntnr&gt;::split_join_can_throw</code>
indicates whether splits or joins can throw exceptions (which is
true for vector-based trees);
otherwise <code class="classname">container_traits&lt;Cntnr&gt;::split_join_can_throw</code>
will yield a compilation error. (This is somewhat similar to a
compile-time version of the COM model).
</p></div></div></div><div class="section"><div class="titlepage"><div><div><h3 class="title"><a id="pbds.design.container"></a>By Container</h3></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.container.hash"></a>hash</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.hash.interface"></a>Interface</h5></div></div></div><p>
The collision-chaining hash-based container has the
following declaration.</p><pre class="programlisting">
template&lt;
typename Key,
typename Mapped,
typename Hash_Fn = std::hash&lt;Key&gt;,
typename Eq_Fn = std::equal_to&lt;Key&gt;,
typename Comb_Hash_Fn = direct_mask_range_hashing&lt;&gt;
typename Resize_Policy = default explained below.
bool Store_Hash = false,
typename Allocator = std::allocator&lt;char&gt; &gt;
class cc_hash_table;
</pre><p>The parameters have the following meaning:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">Key</code> is the key type.</p></li><li class="listitem"><p><code class="classname">Mapped</code> is the mapped-policy.</p></li><li class="listitem"><p><code class="classname">Hash_Fn</code> is a key hashing functor.</p></li><li class="listitem"><p><code class="classname">Eq_Fn</code> is a key equivalence functor.</p></li><li class="listitem"><p><code class="classname">Comb_Hash_Fn</code> is a range-hashing_functor;
it describes how to translate hash values into positions
within the table. </p></li><li class="listitem"><p><code class="classname">Resize_Policy</code> describes how a container object
should change its internal size. </p></li><li class="listitem"><p><code class="classname">Store_Hash</code> indicates whether the hash value
should be stored with each entry. </p></li><li class="listitem"><p><code class="classname">Allocator</code> is an allocator
type.</p></li></ol></div><p>The probing hash-based container has the following
declaration.</p><pre class="programlisting">
template&lt;
typename Key,
typename Mapped,
typename Hash_Fn = std::hash&lt;Key&gt;,
typename Eq_Fn = std::equal_to&lt;Key&gt;,
typename Comb_Probe_Fn = direct_mask_range_hashing&lt;&gt;
typename Probe_Fn = default explained below.
typename Resize_Policy = default explained below.
bool Store_Hash = false,
typename Allocator = std::allocator&lt;char&gt; &gt;
class gp_hash_table;
</pre><p>The parameters are identical to those of the
collision-chaining container, except for the following.</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">Comb_Probe_Fn</code> describes how to transform a probe
sequence into a sequence of positions within the table.</p></li><li class="listitem"><p><code class="classname">Probe_Fn</code> describes a probe sequence policy.</p></li></ol></div><p>Some of the default template values depend on the values of
other parameters, and are explained below.</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.hash.details"></a>Details</h5></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.hash.details.hash_policies"></a>Hash Policies</h6></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="details.hash_policies.general"></a>General</h6></div></div></div><p>Following is an explanation of some functions which hashing
involves. The graphic below illustrates the discussion.</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.2.2.3"></a><p class="title"><strong>Figure 21.14. Hash functions, ranged-hash functions, and
range-hashing functions</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_hash_ranged_hash_range_hashing_fns.png" align="middle" alt="Hash functions, ranged-hash functions, and range-hashing functions" /></div></div></div><br class="figure-break" /><p>Let U be a domain (e.g., the integers, or the
strings of 3 characters). A hash-table algorithm needs to map
elements of U "uniformly" into the range [0,..., m -
1] (where m is a non-negative integral value, and
is, in general, time varying). I.e., the algorithm needs
a ranged-hash function</p><p>
f : U × Z<sub>+</sub> → Z<sub>+</sub>
</p><p>such that for any u in U ,</p><p>0 ≤ f(u, m) ≤ m - 1</p><p>and which has "good uniformity" properties (say
<a class="xref" href="policy_data_structures.html#biblio.knuth98sorting" title="The Art of Computer Programming - Sorting and Searching">[biblio.knuth98sorting]</a>.)
One
common solution is to use the composition of the hash
function</p><p>h : U → Z<sub>+</sub> ,</p><p>which maps elements of U into the non-negative
integrals, and</p><p>g : Z<sub>+</sub> × Z<sub>+</sub> →
Z<sub>+</sub>,</p><p>which maps a non-negative hash value, and a non-negative
range upper-bound into a non-negative integral in the range
between 0 (inclusive) and the range upper bound (exclusive),
i.e., for any r in Z<sub>+</sub>,</p><p>0 ≤ g(r, m) ≤ m - 1</p><p>The resulting ranged-hash function, is</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.2.15"></a><p class="title"><strong>Equation 21.1. Ranged Hash Function</strong></p><div class="equation-contents"><span class="mathphrase">
f(u , m) = g(h(u), m)
</span></div></div><br class="equation-break" /><p>From the above, it is obvious that given g and
h, f can always be composed (however the converse
is not true). The standard's hash-based containers allow specifying
a hash function, and use a hard-wired range-hashing function;
the ranged-hash function is implicitly composed.</p><p>The above describes the case where a key is to be mapped
into a single position within a hash table, e.g.,
in a collision-chaining table. In other cases, a key is to be
mapped into a sequence of positions within a table,
e.g., in a probing table. Similar terms apply in this
case: the table requires a ranged probe function,
mapping a key into a sequence of positions withing the table.
This is typically achieved by composing a hash function
mapping the key into a non-negative integral type, a
probe function transforming the hash value into a
sequence of hash values, and a range-hashing function
transforming the sequence of hash values into a sequence of
positions.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="details.hash_policies.range"></a>Range Hashing</h6></div></div></div><p>Some common choices for range-hashing functions are the
division, multiplication, and middle-square methods (<a class="xref" href="policy_data_structures.html#biblio.knuth98sorting" title="The Art of Computer Programming - Sorting and Searching">[biblio.knuth98sorting]</a>), defined
as</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.3.3"></a><p class="title"><strong>Equation 21.2. Range-Hashing, Division Method</strong></p><div class="equation-contents"><span class="mathphrase">
g(r, m) = r mod m
</span></div></div><br class="equation-break" /><p>g(r, m) = ⌈ u/v ( a r mod v ) ⌉</p><p>and</p><p>g(r, m) = ⌈ u/v ( r<sup>2</sup> mod v ) ⌉</p><p>respectively, for some positive integrals u and
v (typically powers of 2), and some a. Each of
these range-hashing functions works best for some different
setting.</p><p>The division method (see above) is a
very common choice. However, even this single method can be
implemented in two very different ways. It is possible to
implement using the low
level % (modulo) operation (for any m), or the
low level &amp; (bit-mask) operation (for the case where
m is a power of 2), i.e.,</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.3.9"></a><p class="title"><strong>Equation 21.3. Division via Prime Modulo</strong></p><div class="equation-contents"><span class="mathphrase">
g(r, m) = r % m
</span></div></div><br class="equation-break" /><p>and</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.3.11"></a><p class="title"><strong>Equation 21.4. Division via Bit Mask</strong></p><div class="equation-contents"><span class="mathphrase">
g(r, m) = r &amp; m - 1, (with m =
2<sup>k</sup> for some k)
</span></div></div><br class="equation-break" /><p>respectively.</p><p>The % (modulo) implementation has the advantage that for
m a prime far from a power of 2, g(r, m) is
affected by all the bits of r (minimizing the chance of
collision). It has the disadvantage of using the costly modulo
operation. This method is hard-wired into SGI's implementation
.</p><p>The &amp; (bit-mask) implementation has the advantage of
relying on the fast bit-wise and operation. It has the
disadvantage that for g(r, m) is affected only by the
low order bits of r. This method is hard-wired into
Dinkumware's implementation.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="details.hash_policies.ranged"></a>Ranged Hash</h6></div></div></div><p>In cases it is beneficial to allow the
client to directly specify a ranged-hash hash function. It is
true, that the writer of the ranged-hash function cannot rely
on the values of m having specific numerical properties
suitable for hashing (in the sense used in <a class="xref" href="policy_data_structures.html#biblio.knuth98sorting" title="The Art of Computer Programming - Sorting and Searching">[biblio.knuth98sorting]</a>), since
the values of m are determined by a resize policy with
possibly orthogonal considerations.</p><p>There are two cases where a ranged-hash function can be
superior. The firs is when using perfect hashing: the
second is when the values of m can be used to estimate
the "general" number of distinct values required. This is
described in the following.</p><p>Let</p><p>
s = [ s<sub>0</sub>,..., s<sub>t - 1</sub>]
</p><p>be a string of t characters, each of which is from
domain S. Consider the following ranged-hash
function:</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.4.7"></a><p class="title"><strong>Equation 21.5. 
A Standard String Hash Function
</strong></p><div class="equation-contents"><span class="mathphrase">
f<sub>1</sub>(s, m) = ∑ <sub>i =
0</sub><sup>t - 1</sup> s<sub>i</sub> a<sup>i</sup> mod m
</span></div></div><br class="equation-break" /><p>where a is some non-negative integral value. This is
the standard string-hashing function used in SGI's
implementation (with a = 5). Its advantage is that
it takes into account all of the characters of the string.</p><p>Now assume that s is the string representation of a
of a long DNA sequence (and so S = {'A', 'C', 'G',
'T'}). In this case, scanning the entire string might be
prohibitively expensive. A possible alternative might be to use
only the first k characters of the string, where</p><p>|S|<sup>k</sup> ≥ m ,</p><p>i.e., using the hash function</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.2.4.12"></a><p class="title"><strong>Equation 21.6. 
Only k String DNA Hash
</strong></p><div class="equation-contents"><span class="mathphrase">
f<sub>2</sub>(s, m) = ∑ <sub>i
= 0</sub><sup>k - 1</sup> s<sub>i</sub> a<sup>i</sup> mod m
</span></div></div><br class="equation-break" /><p>requiring scanning over only</p><p>k = log<sub>4</sub>( m )</p><p>characters.</p><p>Other more elaborate hash-functions might scan k
characters starting at a random position (determined at each
resize), or scanning k random positions (determined at
each resize), i.e., using</p><p>f<sub>3</sub>(s, m) = ∑ <sub>i =
r</sub>0<sup>r<sub>0</sub> + k - 1</sup> s<sub>i</sub>
a<sup>i</sup> mod m ,</p><p>or</p><p>f<sub>4</sub>(s, m) = ∑ <sub>i = 0</sub><sup>k -
1</sup> s<sub>r</sub>i a<sup>r<sub>i</sub></sup> mod
m ,</p><p>respectively, for r<sub>0</sub>,..., r<sub>k-1</sub>
each in the (inclusive) range [0,...,t-1].</p><p>It should be noted that the above functions cannot be
decomposed as per a ranged hash composed of hash and range hashing.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="details.hash_policies.implementation"></a>Implementation</h6></div></div></div><p>This sub-subsection describes the implementation of
the above in this library. It first explains range-hashing
functions in collision-chaining tables, then ranged-hash
functions in collision-chaining tables, then probing-based
tables, and finally lists the relevant classes in this
library.</p><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="hash_policies.implementation.collision-chaining"></a>
Range-Hashing and Ranged-Hashes in Collision-Chaining Tables
</h6></div></div></div><p><code class="classname">cc_hash_table</code> is
parametrized by <code class="classname">Hash_Fn</code> and <code class="classname">Comb_Hash_Fn</code>, a
hash functor and a combining hash functor, respectively.</p><p>In general, <code class="classname">Comb_Hash_Fn</code> is considered a
range-hashing functor. <code class="classname">cc_hash_table</code>
synthesizes a ranged-hash function from <code class="classname">Hash_Fn</code> and
<code class="classname">Comb_Hash_Fn</code>. The figure below shows an <code class="classname">insert</code> sequence
diagram for this case. The user inserts an element (point A),
the container transforms the key into a non-negative integral
using the hash functor (points B and C), and transforms the
result into a position using the combining functor (points D
and E).</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.2.5.3.4"></a><p class="title"><strong>Figure 21.15. Insert hash sequence diagram</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_hash_range_hashing_seq_diagram.png" align="middle" alt="Insert hash sequence diagram" /></div></div></div><br class="figure-break" /><p>If <code class="classname">cc_hash_table</code>'s
hash-functor, <code class="classname">Hash_Fn</code> is instantiated by <code class="classname">null_type</code> , then <code class="classname">Comb_Hash_Fn</code> is taken to be
a ranged-hash function. The graphic below shows an <code class="function">insert</code> sequence
diagram. The user inserts an element (point A), the container
transforms the key into a position using the combining functor
(points B and C).</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.2.5.3.6"></a><p class="title"><strong>Figure 21.16. Insert hash sequence diagram with a null policy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_hash_range_hashing_seq_diagram2.png" align="middle" alt="Insert hash sequence diagram with a null policy" /></div></div></div><br class="figure-break" /></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="hash_policies.implementation.probe"></a>
Probing tables
</h6></div></div></div><p><code class="classname">gp_hash_table</code> is parametrized by
<code class="classname">Hash_Fn</code>, <code class="classname">Probe_Fn</code>,
and <code class="classname">Comb_Probe_Fn</code>. As before, if
<code class="classname">Hash_Fn</code> and <code class="classname">Probe_Fn</code>
are both <code class="classname">null_type</code>, then
<code class="classname">Comb_Probe_Fn</code> is a ranged-probe
functor. Otherwise, <code class="classname">Hash_Fn</code> is a hash
functor, <code class="classname">Probe_Fn</code> is a functor for offsets
from a hash value, and <code class="classname">Comb_Probe_Fn</code>
transforms a probe sequence into a sequence of positions within
the table.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="hash_policies.implementation.predefined"></a>
Pre-Defined Policies
</h6></div></div></div><p>This library contains some pre-defined classes
implementing range-hashing and probing functions:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">direct_mask_range_hashing</code>
and <code class="classname">direct_mod_range_hashing</code>
are range-hashing functions based on a bit-mask and a modulo
operation, respectively.</p></li><li class="listitem"><p><code class="classname">linear_probe_fn</code>, and
<code class="classname">quadratic_probe_fn</code> are
a linear probe and a quadratic probe function,
respectively.</p></li></ol></div><p>
The graphic below shows the relationships.
</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.2.5.5.5"></a><p class="title"><strong>Figure 21.17. Hash policy class diagram</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_hash_policy_cd.png" align="middle" alt="Hash policy class diagram" /></div></div></div><br class="figure-break" /></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.hash.details.resize_policies"></a>Resize Policies</h6></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.general"></a>General</h6></div></div></div><p>Hash-tables, as opposed to trees, do not naturally grow or
shrink. It is necessary to specify policies to determine how
and when a hash table should change its size. Usually, resize
policies can be decomposed into orthogonal policies:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>A size policy indicating how a hash table
should grow (e.g., it should multiply by powers of
2).</p></li><li class="listitem"><p>A trigger policy indicating when a hash
table should grow (e.g., a load factor is
exceeded).</p></li></ol></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.size"></a>Size Policies</h6></div></div></div><p>Size policies determine how a hash table changes size. These
policies are simple, and there are relatively few sensible
options. An exponential-size policy (with the initial size and
growth factors both powers of 2) works well with a mask-based
range-hashing function, and is the
hard-wired policy used by Dinkumware. A
prime-list based policy works well with a modulo-prime range
hashing function and is the hard-wired policy used by SGI's
implementation.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.trigger"></a>Trigger Policies</h6></div></div></div><p>Trigger policies determine when a hash table changes size.
Following is a description of two policies: load-check
policies, and collision-check policies.</p><p>Load-check policies are straightforward. The user specifies
two factors, Α<sub>min</sub> and
Α<sub>max</sub>, and the hash table maintains the
invariant that</p><p>Α<sub>min</sub> ≤ (number of
stored elements) / (hash-table size) ≤
Α<sub>max</sub>
</p><p>Collision-check policies work in the opposite direction of
load-check policies. They focus on keeping the number of
collisions moderate and hoping that the size of the table will
not grow very large, instead of keeping a moderate load-factor
and hoping that the number of collisions will be small. A
maximal collision-check policy resizes when the longest
probe-sequence grows too large.</p><p>Consider the graphic below. Let the size of the hash table
be denoted by m, the length of a probe sequence be denoted by k,
and some load factor be denoted by Α. We would like to
calculate the minimal length of k, such that if there were Α
m elements in the hash table, a probe sequence of length k would
be found with probability at most 1/m.</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.3.4.7"></a><p class="title"><strong>Figure 21.18. Balls and bins</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_balls_and_bins.png" align="middle" alt="Balls and bins" /></div></div></div><br class="figure-break" /><p>Denote the probability that a probe sequence of length
k appears in bin i by p<sub>i</sub>, the
length of the probe sequence of bin i by
l<sub>i</sub>, and assume uniform distribution. Then</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.3.4.9"></a><p class="title"><strong>Equation 21.7. 
Probability of Probe Sequence of Length k
</strong></p><div class="equation-contents"><span class="mathphrase">
p<sub>1</sub> =
</span></div></div><br class="equation-break" /><p>P(l<sub>1</sub> ≥ k) =</p><p>
P(l<sub>1</sub> ≥ α ( 1 + k / α - 1) ≤ (a)
</p><p>
e ^ ( - ( α ( k / α - 1 )<sup>2</sup> ) /2)
</p><p>where (a) follows from the Chernoff bound (<a class="xref" href="policy_data_structures.html#biblio.motwani95random" title="Randomized Algorithms">[biblio.motwani95random]</a>). To
calculate the probability that some bin contains a probe
sequence greater than k, we note that the
l<sub>i</sub> are negatively-dependent
(<a class="xref" href="policy_data_structures.html#biblio.dubhashi98neg" title="Balls and bins: A study in negative dependence">[biblio.dubhashi98neg]</a>)
. Let
I(.) denote the indicator function. Then</p><div class="equation"><a id="id-1.3.5.8.4.4.2.3.3.4.14"></a><p class="title"><strong>Equation 21.8. 
Probability Probe Sequence in Some Bin
</strong></p><div class="equation-contents"><span class="mathphrase">
P( exists<sub>i</sub> l<sub>i</sub> ≥ k ) =
</span></div></div><br class="equation-break" /><p>P ( ∑ <sub>i = 1</sub><sup>m</sup>
I(l<sub>i</sub> ≥ k) ≥ 1 ) =</p><p>P ( ∑ <sub>i = 1</sub><sup>m</sup> I (
l<sub>i</sub> ≥ k ) ≥ m p<sub>1</sub> ( 1 + 1 / (m
p<sub>1</sub>) - 1 ) ) ≤ (a)</p><p>e ^ ( ( - m p<sub>1</sub> ( 1 / (m p<sub>1</sub>)
- 1 ) <sup>2</sup> ) / 2 ) ,</p><p>where (a) follows from the fact that the Chernoff bound can
be applied to negatively-dependent variables (<a class="xref" href="policy_data_structures.html#biblio.dubhashi98neg" title="Balls and bins: A study in negative dependence">[biblio.dubhashi98neg]</a>). Inserting the first probability
equation into the second one, and equating with 1/m, we
obtain</p><p>k ~ √ ( 2 α ln 2 m ln(m) )
) .</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.impl"></a>Implementation</h6></div></div></div><p>This sub-subsection describes the implementation of the
above in this library. It first describes resize policies and
their decomposition into trigger and size policies, then
describes pre-defined classes, and finally discusses controlled
access the policies' internals.</p><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.impl.decomposition"></a>Decomposition</h6></div></div></div><p>Each hash-based container is parametrized by a
<code class="classname">Resize_Policy</code> parameter; the container derives
<code class="classname">public</code>ly from <code class="classname">Resize_Policy</code>. For
example:</p><pre class="programlisting">
cc_hash_table&lt;typename Key,
typename Mapped,
...
typename Resize_Policy
...&gt; : public Resize_Policy
</pre><p>As a container object is modified, it continuously notifies
its <code class="classname">Resize_Policy</code> base of internal changes
(e.g., collisions encountered and elements being
inserted). It queries its <code class="classname">Resize_Policy</code> base whether
it needs to be resized, and if so, to what size.</p><p>The graphic below shows a (possible) sequence diagram
of an insert operation. The user inserts an element; the hash
table notifies its resize policy that a search has started
(point A); in this case, a single collision is encountered -
the table notifies its resize policy of this (point B); the
container finally notifies its resize policy that the search
has ended (point C); it then queries its resize policy whether
a resize is needed, and if so, what is the new size (points D
to G); following the resize, it notifies the policy that a
resize has completed (point H); finally, the element is
inserted, and the policy notified (point I).</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.3.5.3.6"></a><p class="title"><strong>Figure 21.19. Insert resize sequence diagram</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_insert_resize_sequence_diagram1.png" align="middle" alt="Insert resize sequence diagram" /></div></div></div><br class="figure-break" /><p>In practice, a resize policy can be usually orthogonally
decomposed to a size policy and a trigger policy. Consequently,
the library contains a single class for instantiating a resize
policy: <code class="classname">hash_standard_resize_policy</code>
is parametrized by <code class="classname">Size_Policy</code> and
<code class="classname">Trigger_Policy</code>, derives <code class="classname">public</code>ly from
both, and acts as a standard delegate (<a class="xref" href="policy_data_structures.html#biblio.gof" title="Design Patterns - Elements of Reusable Object-Oriented Software">[biblio.gof]</a>)
to these policies.</p><p>The two graphics immediately below show sequence diagrams
illustrating the interaction between the standard resize policy
and its trigger and size policies, respectively.</p><div class="figure"><a id="id-1.3.5.8.4.4.2.3.3.5.3.9"></a><p class="title"><strong>Figure 21.20. Standard resize policy trigger sequence
diagram</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_insert_resize_sequence_diagram2.png" align="middle" alt="Standard resize policy trigger sequence diagram" /></div></div></div><br class="figure-break" /><div class="figure"><a id="id-1.3.5.8.4.4.2.3.3.5.3.10"></a><p class="title"><strong>Figure 21.21. Standard resize policy size sequence
diagram</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_insert_resize_sequence_diagram3.png" align="middle" alt="Standard resize policy size sequence diagram" /></div></div></div><br class="figure-break" /></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.impl.predefined"></a>Predefined Policies</h6></div></div></div><p>The library includes the following
instantiations of size and trigger policies:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">hash_load_check_resize_trigger</code>
implements a load check trigger policy.</p></li><li class="listitem"><p><code class="classname">cc_hash_max_collision_check_resize_trigger</code>
implements a collision check trigger policy.</p></li><li class="listitem"><p><code class="classname">hash_exponential_size_policy</code>
implements an exponential-size policy (which should be used
with mask range hashing).</p></li><li class="listitem"><p><code class="classname">hash_prime_size_policy</code>
implementing a size policy based on a sequence of primes
(which should
be used with mod range hashing</p></li></ol></div><p>The graphic below gives an overall picture of the resize-related
classes. <code class="classname">basic_hash_table</code>
is parametrized by <code class="classname">Resize_Policy</code>, which it subclasses
publicly. This class is currently instantiated only by <code class="classname">hash_standard_resize_policy</code>.
<code class="classname">hash_standard_resize_policy</code>
itself is parametrized by <code class="classname">Trigger_Policy</code> and
<code class="classname">Size_Policy</code>. Currently, <code class="classname">Trigger_Policy</code> is
instantiated by <code class="classname">hash_load_check_resize_trigger</code>,
or <code class="classname">cc_hash_max_collision_check_resize_trigger</code>;
<code class="classname">Size_Policy</code> is instantiated by <code class="classname">hash_exponential_size_policy</code>,
or <code class="classname">hash_prime_size_policy</code>.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="resize_policies.impl.internals"></a>Controling Access to Internals</h6></div></div></div><p>There are cases where (controlled) access to resize
policies' internals is beneficial. E.g., it is sometimes
useful to query a hash-table for the table's actual size (as
opposed to its <code class="function">size()</code> - the number of values it
currently holds); it is sometimes useful to set a table's
initial size, externally resize it, or change load factors.</p><p>Clearly, supporting such methods both decreases the
encapsulation of hash-based containers, and increases the
diversity between different associative-containers' interfaces.
Conversely, omitting such methods can decrease containers'
flexibility.</p><p>In order to avoid, to the extent possible, the above
conflict, the hash-based containers themselves do not address
any of these questions; this is deferred to the resize policies,
which are easier to change or replace. Thus, for example,
neither <code class="classname">cc_hash_table</code> nor
<code class="classname">gp_hash_table</code>
contain methods for querying the actual size of the table; this
is deferred to <code class="classname">hash_standard_resize_policy</code>.</p><p>Furthermore, the policies themselves are parametrized by
template arguments that determine the methods they support
(
<a class="xref" href="policy_data_structures.html#biblio.alexandrescu01modern" title="Modern C++ Design: Generic Programming and Design Patterns Applied">[biblio.alexandrescu01modern]</a>
shows techniques for doing so). <code class="classname">hash_standard_resize_policy</code>
is parametrized by <code class="classname">External_Size_Access</code> that
determines whether it supports methods for querying the actual
size of the table or resizing it. <code class="classname">hash_load_check_resize_trigger</code>
is parametrized by <code class="classname">External_Load_Access</code> that
determines whether it supports methods for querying or
modifying the loads. <code class="classname">cc_hash_max_collision_check_resize_trigger</code>
is parametrized by <code class="classname">External_Load_Access</code> that
determines whether it supports methods for querying the
load.</p><p>Some operations, for example, resizing a container at
run time, or changing the load factors of a load-check trigger
policy, require the container itself to resize. As mentioned
above, the hash-based containers themselves do not contain
these types of methods, only their resize policies.
Consequently, there must be some mechanism for a resize policy
to manipulate the hash-based container. As the hash-based
container is a subclass of the resize policy, this is done
through virtual methods. Each hash-based container has a
<code class="classname">private</code> <code class="classname">virtual</code> method:</p><pre class="programlisting">
virtual void
do_resize
(size_type new_size);
</pre><p>which resizes the container. Implementations of
<code class="classname">Resize_Policy</code> can export public methods for resizing
the container externally; these methods internally call
<code class="classname">do_resize</code> to resize the table.</p></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.hash.details.policy_interaction"></a>Policy Interactions</h6></div></div></div><p>
</p><p>Hash-tables are unfortunately especially susceptible to
choice of policies. One of the more complicated aspects of this
is that poor combinations of good policies can form a poor
container. Following are some considerations.</p><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="policy_interaction.probesizetrigger"></a>probe/size/trigger</h6></div></div></div><p>Some combinations do not work well for probing containers.
For example, combining a quadratic probe policy with an
exponential size policy can yield a poor container: when an
element is inserted, a trigger policy might decide that there
is no need to resize, as the table still contains unused
entries; the probe sequence, however, might never reach any of
the unused entries.</p><p>Unfortunately, this library cannot detect such problems at
compilation (they are halting reducible). It therefore defines
an exception class <code class="classname">insert_error</code> to throw an
exception in this case.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="policy_interaction.hashtrigger"></a>hash/trigger</h6></div></div></div><p>Some trigger policies are especially susceptible to poor
hash functions. Suppose, as an extreme case, that the hash
function transforms each key to the same hash value. After some
inserts, a collision detecting policy will always indicate that
the container needs to grow.</p><p>The library, therefore, by design, limits each operation to
one resize. For each <code class="classname">insert</code>, for example, it queries
only once whether a resize is needed.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="policy_interaction.eqstorehash"></a>equivalence functors/storing hash values/hash</h6></div></div></div><p><code class="classname">cc_hash_table</code> and
<code class="classname">gp_hash_table</code> are
parametrized by an equivalence functor and by a
<code class="classname">Store_Hash</code> parameter. If the latter parameter is
<code class="classname">true</code>, then the container stores with each entry
a hash value, and uses this value in case of collisions to
determine whether to apply a hash value. This can lower the
cost of collision for some types, but increase the cost of
collisions for other types.</p><p>If a ranged-hash function or ranged probe function is
directly supplied, however, then it makes no sense to store the
hash value with each entry. This library's container will
fail at compilation, by design, if this is attempted.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="policy_interaction.sizeloadtrigger"></a>size/load-check trigger</h6></div></div></div><p>Assume a size policy issues an increasing sequence of sizes
a, a q, a q<sup>1</sup>, a q<sup>2</sup>, ... For
example, an exponential size policy might issue the sequence of
sizes 8, 16, 32, 64, ...</p><p>If a load-check trigger policy is used, with loads
α<sub>min</sub> and α<sub>max</sub>,
respectively, then it is a good idea to have:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>α<sub>max</sub> ~ 1 / q</p></li><li class="listitem"><p>α<sub>min</sub> &lt; 1 / (2 q)</p></li></ol></div><p>This will ensure that the amortized hash cost of each
modifying operation is at most approximately 3.</p><p>α<sub>min</sub> ~ α<sub>max</sub> is, in
any case, a bad choice, and α<sub>min</sub> &gt;
α <sub>max</sub> is horrendous.</p></div></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.container.tree"></a>tree</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.tree.interface"></a>Interface</h5></div></div></div><p>The tree-based container has the following declaration:</p><pre class="programlisting">
template&lt;
typename Key,
typename Mapped,
typename Cmp_Fn = std::less&lt;Key&gt;,
typename Tag = rb_tree_tag,
template&lt;
typename Const_Node_Iterator,
typename Node_Iterator,
typename Cmp_Fn_,
typename Allocator_&gt;
class Node_Update = null_node_update,
typename Allocator = std::allocator&lt;char&gt; &gt;
class tree;
</pre><p>The parameters have the following meaning:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">Key</code> is the key type.</p></li><li class="listitem"><p><code class="classname">Mapped</code> is the mapped-policy.</p></li><li class="listitem"><p><code class="classname">Cmp_Fn</code> is a key comparison functor</p></li><li class="listitem"><p><code class="classname">Tag</code> specifies which underlying data structure
to use.</p></li><li class="listitem"><p><code class="classname">Node_Update</code> is a policy for updating node
invariants.</p></li><li class="listitem"><p><code class="classname">Allocator</code> is an allocator
type.</p></li></ol></div><p>The <code class="classname">Tag</code> parameter specifies which underlying
data structure to use. Instantiating it by <code class="classname">rb_tree_tag</code>, <code class="classname">splay_tree_tag</code>, or
<code class="classname">ov_tree_tag</code>,
specifies an underlying red-black tree, splay tree, or
ordered-vector tree, respectively; any other tag is illegal.
Note that containers based on the former two contain more types
and methods than the latter (e.g.,
<code class="classname">reverse_iterator</code> and <code class="classname">rbegin</code>), and different
exception and invalidation guarantees.</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.tree.details"></a>Details</h5></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.tree.node"></a>Node Invariants</h6></div></div></div><p>Consider the two trees in the graphic below, labels A and B. The first
is a tree of floats; the second is a tree of pairs, each
signifying a geometric line interval. Each element in a tree is referred to as a node of the tree. Of course, each of
these trees can support the usual queries: the first can easily
search for <code class="classname">0.4</code>; the second can easily search for
<code class="classname">std::make_pair(10, 41)</code>.</p><p>Each of these trees can efficiently support other queries.
The first can efficiently determine that the 2rd key in the
tree is <code class="constant">0.3</code>; the second can efficiently determine
whether any of its intervals overlaps
</p><pre class="programlisting">std::make_pair(29,42)</pre><p> (useful in geometric
applications or distributed file systems with leases, for
example). It should be noted that an <code class="classname">std::set</code> can
only solve these types of problems with linear complexity.</p><p>In order to do so, each tree stores some metadata in
each node, and maintains node invariants (see <a class="xref" href="policy_data_structures.html#biblio.clrs2001" title="Introduction to Algorithms, 2nd edition">[biblio.clrs2001]</a>.) The first stores in
each node the size of the sub-tree rooted at the node; the
second stores at each node the maximal endpoint of the
intervals at the sub-tree rooted at the node.</p><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.5"></a><p class="title"><strong>Figure 21.22. Tree node invariants</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_tree_node_invariants.png" align="middle" alt="Tree node invariants" /></div></div></div><br class="figure-break" /><p>Supporting such trees is difficult for a number of
reasons:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>There must be a way to specify what a node's metadata
should be (if any).</p></li><li class="listitem"><p>Various operations can invalidate node
invariants. The graphic below shows how a right rotation,
performed on A, results in B, with nodes x and y having
corrupted invariants (the grayed nodes in C). The graphic shows
how an insert, performed on D, results in E, with nodes x and y
having corrupted invariants (the grayed nodes in F). It is not
feasible to know outside the tree the effect of an operation on
the nodes of the tree.</p></li><li class="listitem"><p>The search paths of standard associative containers are
defined by comparisons between keys, and not through
metadata.</p></li><li class="listitem"><p>It is not feasible to know in advance which methods trees
can support. Besides the usual <code class="classname">find</code> method, the
first tree can support a <code class="classname">find_by_order</code> method, while
the second can support an <code class="classname">overlaps</code> method.</p></li></ol></div><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.8"></a><p class="title"><strong>Figure 21.23. Tree node invalidation</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_tree_node_invalidations.png" align="middle" alt="Tree node invalidation" /></div></div></div><br class="figure-break" /><p>These problems are solved by a combination of two means:
node iterators, and template-template node updater
parameters.</p><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.tree.node.iterators"></a>Node Iterators</h6></div></div></div><p>Each tree-based container defines two additional iterator
types, <code class="classname">const_node_iterator</code>
and <code class="classname">node_iterator</code>.
These iterators allow descending from a node to one of its
children. Node iterator allow search paths different than those
determined by the comparison functor. The <code class="classname">tree</code>
supports the methods:</p><pre class="programlisting">
const_node_iterator
node_begin() const;
node_iterator
node_begin();
const_node_iterator
node_end() const;
node_iterator
node_end();
</pre><p>The first pairs return node iterators corresponding to the
root node of the tree; the latter pair returns node iterators
corresponding to a just-after-leaf node.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.tree.node.updator"></a>Node Updator</h6></div></div></div><p>The tree-based containers are parametrized by a
<code class="classname">Node_Update</code> template-template parameter. A
tree-based container instantiates
<code class="classname">Node_Update</code> to some
<code class="classname">node_update</code> class, and publicly subclasses
<code class="classname">node_update</code>. The graphic below shows this
scheme, as well as some predefined policies (which are explained
below).</p><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.11.3"></a><p class="title"><strong>Figure 21.24. A tree and its update policy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_tree_node_updator_policy_cd.png" align="middle" alt="A tree and its update policy" /></div></div></div><br class="figure-break" /><p><code class="classname">node_update</code> (an instantiation of
<code class="classname">Node_Update</code>) must define <code class="classname">metadata_type</code> as
the type of metadata it requires. For order statistics,
e.g., <code class="classname">metadata_type</code> might be <code class="classname">size_t</code>.
The tree defines within each node a <code class="classname">metadata_type</code>
object.</p><p><code class="classname">node_update</code> must also define the following method
for restoring node invariants:</p><pre class="programlisting">
void
operator()(node_iterator nd_it, const_node_iterator end_nd_it)
</pre><p>In this method, <code class="varname">nd_it</code> is a
<code class="classname">node_iterator</code> corresponding to a node whose
A) all descendants have valid invariants, and B) its own
invariants might be violated; <code class="classname">end_nd_it</code> is
a <code class="classname">const_node_iterator</code> corresponding to a
just-after-leaf node. This method should correct the node
invariants of the node pointed to by
<code class="classname">nd_it</code>. For example, say node x in the
graphic below label A has an invalid invariant, but its' children,
y and z have valid invariants. After the invocation, all three
nodes should have valid invariants, as in label B.</p><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.11.8"></a><p class="title"><strong>Figure 21.25. Restoring node invariants</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_restoring_node_invariants.png" align="middle" alt="Restoring node invariants" /></div></div></div><br class="figure-break" /><p>When a tree operation might invalidate some node invariant,
it invokes this method in its <code class="classname">node_update</code> base to
restore the invariant. For example, the graphic below shows
an <code class="function">insert</code> operation (point A); the tree performs some
operations, and calls the update functor three times (points B,
C, and D). (It is well known that any <code class="function">insert</code>,
<code class="function">erase</code>, <code class="function">split</code> or <code class="function">join</code>, can restore
all node invariants by a small number of node invariant updates (<a class="xref" href="policy_data_structures.html#biblio.clrs2001" title="Introduction to Algorithms, 2nd edition">[biblio.clrs2001]</a>)
.</p><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.11.10"></a><p class="title"><strong>Figure 21.26. Insert update sequence</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_update_seq_diagram.png" align="middle" alt="Insert update sequence" /></div></div></div><br class="figure-break" /><p>To complete the description of the scheme, three questions
need to be answered:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>How can a tree which supports order statistics define a
method such as <code class="classname">find_by_order</code>?</p></li><li class="listitem"><p>How can the node updater base access methods of the
tree?</p></li><li class="listitem"><p>How can the following cyclic dependency be resolved?
<code class="classname">node_update</code> is a base class of the tree, yet it
uses node iterators defined in the tree (its child).</p></li></ol></div><p>The first two questions are answered by the fact that
<code class="classname">node_update</code> (an instantiation of
<code class="classname">Node_Update</code>) is a <span class="emphasis"><em>public</em></span> base class
of the tree. Consequently:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>Any public methods of
<code class="classname">node_update</code> are automatically methods of
the tree (<a class="xref" href="policy_data_structures.html#biblio.alexandrescu01modern" title="Modern C++ Design: Generic Programming and Design Patterns Applied">[biblio.alexandrescu01modern]</a>).
Thus an order-statistics node updater,
<code class="classname">tree_order_statistics_node_update</code> defines
the <code class="function">find_by_order</code> method; any tree
instantiated by this policy consequently supports this method as
well.</p></li><li class="listitem"><p>In C++, if a base class declares a method as
<code class="literal">virtual</code>, it is
<code class="literal">virtual</code> in its subclasses. If
<code class="classname">node_update</code> needs to access one of the
tree's methods, say the member function
<code class="function">end</code>, it simply declares that method as
<code class="literal">virtual</code> abstract.</p></li></ol></div><p>The cyclic dependency is solved through template-template
parameters. <code class="classname">Node_Update</code> is parametrized by
the tree's node iterators, its comparison functor, and its
allocator type. Thus, instantiations of
<code class="classname">Node_Update</code> have all information
required.</p><p>This library assumes that constructing a metadata object and
modifying it are exception free. Suppose that during some method,
say <code class="classname">insert</code>, a metadata-related operation
(e.g., changing the value of a metadata) throws an exception. Ack!
Rolling back the method is unusually complex.</p><p>Previously, a distinction was made between redundant
policies and null policies. Node invariants show a
case where null policies are required.</p><p>Assume a regular tree is required, one which need not
support order statistics or interval overlap queries.
Seemingly, in this case a redundant policy - a policy which
doesn't affect nodes' contents would suffice. This, would lead
to the following drawbacks:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>Each node would carry a useless metadata object, wasting
space.</p></li><li class="listitem"><p>The tree cannot know if its
<code class="classname">Node_Update</code> policy actually modifies a
node's metadata (this is halting reducible). In the graphic
below, assume the shaded node is inserted. The tree would have
to traverse the useless path shown to the root, applying
redundant updates all the way.</p></li></ol></div><div class="figure"><a id="id-1.3.5.8.4.4.3.3.2.11.20"></a><p class="title"><strong>Figure 21.27. Useless update path</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_rationale_null_node_updator.png" align="middle" alt="Useless update path" /></div></div></div><br class="figure-break" /><p>A null policy class, <code class="classname">null_node_update</code>
solves both these problems. The tree detects that node
invariants are irrelevant, and defines all accordingly.</p></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.tree.details.split"></a>Split and Join</h6></div></div></div><p>Tree-based containers support split and join methods.
It is possible to split a tree so that it passes
all nodes with keys larger than a given key to a different
tree. These methods have the following advantages over the
alternative of externally inserting to the destination
tree and erasing from the source tree:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>These methods are efficient - red-black trees are split
and joined in poly-logarithmic complexity; ordered-vector
trees are split and joined at linear complexity. The
alternatives have super-linear complexity.</p></li><li class="listitem"><p>Aside from orders of growth, these operations perform
few allocations and de-allocations. For red-black trees, allocations are not performed,
and the methods are exception-free. </p></li></ol></div></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.container.trie"></a>Trie</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.trie.interface"></a>Interface</h5></div></div></div><p>The trie-based container has the following declaration:</p><pre class="programlisting">
template&lt;typename Key,
typename Mapped,
typename Cmp_Fn = std::less&lt;Key&gt;,
typename Tag = pat_trie_tag,
template&lt;typename Const_Node_Iterator,
typename Node_Iterator,
typename E_Access_Traits_,
typename Allocator_&gt;
class Node_Update = null_node_update,
typename Allocator = std::allocator&lt;char&gt; &gt;
class trie;
</pre><p>The parameters have the following meaning:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">Key</code> is the key type.</p></li><li class="listitem"><p><code class="classname">Mapped</code> is the mapped-policy.</p></li><li class="listitem"><p><code class="classname">E_Access_Traits</code> is described in below.</p></li><li class="listitem"><p><code class="classname">Tag</code> specifies which underlying data structure
to use, and is described shortly.</p></li><li class="listitem"><p><code class="classname">Node_Update</code> is a policy for updating node
invariants. This is described below.</p></li><li class="listitem"><p><code class="classname">Allocator</code> is an allocator
type.</p></li></ol></div><p>The <code class="classname">Tag</code> parameter specifies which underlying
data structure to use. Instantiating it by <code class="classname">pat_trie_tag</code>, specifies an
underlying PATRICIA trie (explained shortly); any other tag is
currently illegal.</p><p>Following is a description of a (PATRICIA) trie
(this implementation follows <a class="xref" href="policy_data_structures.html#biblio.okasaki98mereable" title="Fast mergeable integer maps">[biblio.okasaki98mereable]</a> and
<a class="xref" href="policy_data_structures.html#biblio.filliatre2000ptset" title="Ptset: Sets of integers implemented as Patricia trees">[biblio.filliatre2000ptset]</a>).
</p><p>A (PATRICIA) trie is similar to a tree, but with the
following differences:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>It explicitly views keys as a sequence of elements.
E.g., a trie can view a string as a sequence of
characters; a trie can view a number as a sequence of
bits.</p></li><li class="listitem"><p>It is not (necessarily) binary. Each node has fan-out n
+ 1, where n is the number of distinct
elements.</p></li><li class="listitem"><p>It stores values only at leaf nodes.</p></li><li class="listitem"><p>Internal nodes have the properties that A) each has at
least two children, and B) each shares the same prefix with
any of its descendant.</p></li></ol></div><p>A (PATRICIA) trie has some useful properties:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>It can be configured to use large node fan-out, giving it
very efficient find performance (albeit at insertion
complexity and size).</p></li><li class="listitem"><p>It works well for common-prefix keys.</p></li><li class="listitem"><p>It can support efficiently queries such as which
keys match a certain prefix. This is sometimes useful in file
systems and routers, and for "type-ahead" aka predictive text matching
on mobile devices.</p></li></ol></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.trie.details"></a>Details</h5></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.trie.details.etraits"></a>Element Access Traits</h6></div></div></div><p>A trie inherently views its keys as sequences of elements.
For example, a trie can view a string as a sequence of
characters. A trie needs to map each of n elements to a
number in {0, n - 1}. For example, a trie can map a
character <code class="varname">c</code> to
</p><pre class="programlisting">static_cast&lt;size_t&gt;(c)</pre><p>.</p><p>Seemingly, then, a trie can assume that its keys support
(const) iterators, and that the <code class="classname">value_type</code> of this
iterator can be cast to a <code class="classname">size_t</code>. There are several
reasons, though, to decouple the mechanism by which the trie
accesses its keys' elements from the trie:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>In some cases, the numerical value of an element is
inappropriate. Consider a trie storing DNA strings. It is
logical to use a trie with a fan-out of 5 = 1 + |{'A', 'C',
'G', 'T'}|. This requires mapping 'T' to 3, though.</p></li><li class="listitem"><p>In some cases the keys' iterators are different than what
is needed. For example, a trie can be used to search for
common suffixes, by using strings'
<code class="classname">reverse_iterator</code>. As another example, a trie mapping
UNICODE strings would have a huge fan-out if each node would
branch on a UNICODE character; instead, one can define an
iterator iterating over 8-bit (or less) groups.</p></li></ol></div><p>trie is,
consequently, parametrized by <code class="classname">E_Access_Traits</code> -
traits which instruct how to access sequences' elements.
<code class="classname">string_trie_e_access_traits</code>
is a traits class for strings. Each such traits define some
types, like:</p><pre class="programlisting">
typename E_Access_Traits::const_iterator
</pre><p>is a const iterator iterating over a key's elements. The
traits class must also define methods for obtaining an iterator
to the first and last element of a key.</p><p>The graphic below shows a
(PATRICIA) trie resulting from inserting the words: "I wish
that I could ever see a poem lovely as a trie" (which,
unfortunately, does not rhyme).</p><p>The leaf nodes contain values; each internal node contains
two <code class="classname">typename E_Access_Traits::const_iterator</code>
objects, indicating the maximal common prefix of all keys in
the sub-tree. For example, the shaded internal node roots a
sub-tree with leafs "a" and "as". The maximal common prefix is
"a". The internal node contains, consequently, to const
iterators, one pointing to <code class="varname">'a'</code>, and the other to
<code class="varname">'s'</code>.</p><div class="figure"><a id="id-1.3.5.8.4.4.4.3.2.10"></a><p class="title"><strong>Figure 21.28. A PATRICIA trie</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_pat_trie.png" align="middle" alt="A PATRICIA trie" /></div></div></div><br class="figure-break" /></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.trie.details.node"></a>Node Invariants</h6></div></div></div><p>Trie-based containers support node invariants, as do
tree-based containers. There are two minor
differences, though, which, unfortunately, thwart sharing them
sharing the same node-updating policies:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>A trie's <code class="classname">Node_Update</code> template-template
parameter is parametrized by <code class="classname">E_Access_Traits</code>, while
a tree's <code class="classname">Node_Update</code> template-template parameter is
parametrized by <code class="classname">Cmp_Fn</code>.</p></li><li class="listitem"><p>Tree-based containers store values in all nodes, while
trie-based containers (at least in this implementation) store
values in leafs.</p></li></ol></div><p>The graphic below shows the scheme, as well as some predefined
policies (which are explained below).</p><div class="figure"><a id="id-1.3.5.8.4.4.4.3.3.5"></a><p class="title"><strong>Figure 21.29. A trie and its update policy</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_trie_node_updator_policy_cd.png" align="middle" alt="A trie and its update policy" /></div></div></div><br class="figure-break" /><p>This library offers the following pre-defined trie node
updating policies:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
<code class="classname">trie_order_statistics_node_update</code>
supports order statistics.
</p></li><li class="listitem"><p><code class="classname">trie_prefix_search_node_update</code>
supports searching for ranges that match a given prefix.</p></li><li class="listitem"><p><code class="classname">null_node_update</code>
is the null node updater.</p></li></ol></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.trie.details.split"></a>Split and Join</h6></div></div></div><p>Trie-based containers support split and join methods; the
rationale is equal to that of tree-based containers supporting
these methods.</p></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.container.list"></a>List</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.list.interface"></a>Interface</h5></div></div></div><p>The list-based container has the following declaration:</p><pre class="programlisting">
template&lt;typename Key,
typename Mapped,
typename Eq_Fn = std::equal_to&lt;Key&gt;,
typename Update_Policy = move_to_front_lu_policy&lt;&gt;,
typename Allocator = std::allocator&lt;char&gt; &gt;
class list_update;
</pre><p>The parameters have the following meaning:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
<code class="classname">Key</code> is the key type.
</p></li><li class="listitem"><p>
<code class="classname">Mapped</code> is the mapped-policy.
</p></li><li class="listitem"><p>
<code class="classname">Eq_Fn</code> is a key equivalence functor.
</p></li><li class="listitem"><p>
<code class="classname">Update_Policy</code> is a policy updating positions in
the list based on access patterns. It is described in the
following subsection.
</p></li><li class="listitem"><p>
<code class="classname">Allocator</code> is an allocator type.
</p></li></ol></div><p>A list-based associative container is a container that
stores elements in a linked-list. It does not order the elements
by any particular order related to the keys. List-based
containers are primarily useful for creating "multimaps". In fact,
list-based containers are designed in this library expressly for
this purpose.</p><p>List-based containers might also be useful for some rare
cases, where a key is encapsulated to the extent that only
key-equivalence can be tested. Hash-based containers need to know
how to transform a key into a size type, and tree-based containers
need to know if some key is larger than another. List-based
associative containers, conversely, only need to know if two keys
are equivalent.</p><p>Since a list-based associative container does not order
elements by keys, is it possible to order the list in some
useful manner? Remarkably, many on-line competitive
algorithms exist for reordering lists to reflect access
prediction. (See <a class="xref" href="policy_data_structures.html#biblio.motwani95random" title="Randomized Algorithms">[biblio.motwani95random]</a> and <a class="xref" href="policy_data_structures.html#biblio.andrew04mtf" title="MTF, Bit, and COMB: A Guide to Deterministic and Randomized Algorithms for the List Update Problem">[biblio.andrew04mtf]</a>).
</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.list.details"></a>Details</h5></div></div></div><p>
</p><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.list.details.ds"></a>Underlying Data Structure</h6></div></div></div><p>The graphic below shows a
simple list of integer keys. If we search for the integer 6, we
are paying an overhead: the link with key 6 is only the fifth
link; if it were the first link, it could be accessed
faster.</p><div class="figure"><a id="id-1.3.5.8.4.4.5.3.3.3"></a><p class="title"><strong>Figure 21.30. A simple list</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_simple_list.png" align="middle" alt="A simple list" /></div></div></div><br class="figure-break" /><p>List-update algorithms reorder lists as elements are
accessed. They try to determine, by the access history, which
keys to move to the front of the list. Some of these algorithms
require adding some metadata alongside each entry.</p><p>For example, in the graphic below label A shows the counter
algorithm. Each node contains both a key and a count metadata
(shown in bold). When an element is accessed (e.g. 6) its count is
incremented, as shown in label B. If the count reaches some
predetermined value, say 10, as shown in label C, the count is set
to 0 and the node is moved to the front of the list, as in label
D.
</p><div class="figure"><a id="id-1.3.5.8.4.4.5.3.3.6"></a><p class="title"><strong>Figure 21.31. The counter algorithm</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_list_update.png" align="middle" alt="The counter algorithm" /></div></div></div><br class="figure-break" /></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.list.details.policies"></a>Policies</h6></div></div></div><p>this library allows instantiating lists with policies
implementing any algorithm moving nodes to the front of the
list (policies implementing algorithms interchanging nodes are
unsupported).</p><p>Associative containers based on lists are parametrized by a
<code class="classname">Update_Policy</code> parameter. This parameter defines the
type of metadata each node contains, how to create the
metadata, and how to decide, using this metadata, whether to
move a node to the front of the list. A list-based associative
container object derives (publicly) from its update policy.
</p><p>An instantiation of <code class="classname">Update_Policy</code> must define
internally <code class="classname">update_metadata</code> as the metadata it
requires. Internally, each node of the list contains, besides
the usual key and data, an instance of <code class="classname">typename
Update_Policy::update_metadata</code>.</p><p>An instantiation of <code class="classname">Update_Policy</code> must define
internally two operators:</p><pre class="programlisting">
update_metadata
operator()();
bool
operator()(update_metadata &amp;);
</pre><p>The first is called by the container object, when creating a
new node, to create the node's metadata. The second is called
by the container object, when a node is accessed (
when a find operation's key is equivalent to the key of the
node), to determine whether to move the node to the front of
the list.
</p><p>The library contains two predefined implementations of
list-update policies. The first
is <code class="classname">lu_counter_policy</code>, which implements the
counter algorithm described above. The second is
<code class="classname">lu_move_to_front_policy</code>,
which unconditionally move an accessed element to the front of
the list. The latter type is very useful in this library,
since there is no need to associate metadata with each element.
(See <a class="xref" href="policy_data_structures.html#biblio.andrew04mtf" title="MTF, Bit, and COMB: A Guide to Deterministic and Randomized Algorithms for the List Update Problem">[biblio.andrew04mtf]</a>
</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.list.details.mapped"></a>Use in Multimaps</h6></div></div></div><p>In this library, there are no equivalents for the standard's
multimaps and multisets; instead one uses an associative
container mapping primary keys to secondary keys.</p><p>List-based containers are especially useful as associative
containers for secondary keys. In fact, they are implemented
here expressly for this purpose.</p><p>To begin with, these containers use very little per-entry
structure memory overhead, since they can be implemented as
singly-linked lists. (Arrays use even lower per-entry memory
overhead, but they are less flexible in moving around entries,
and have weaker invalidation guarantees).</p><p>More importantly, though, list-based containers use very
little per-container memory overhead. The memory overhead of an
empty list-based container is practically that of a pointer.
This is important for when they are used as secondary
associative-containers in situations where the average ratio of
secondary keys to primary keys is low (or even 1).</p><p>In order to reduce the per-container memory overhead as much
as possible, they are implemented as closely as possible to
singly-linked lists.</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
List-based containers do not store internally the number
of values that they hold. This means that their <code class="function">size</code>
method has linear complexity (just like <code class="classname">std::list</code>).
Note that finding the number of equivalent-key values in a
standard multimap also has linear complexity (because it must be
done, via <code class="function">std::distance</code> of the
multimap's <code class="function">equal_range</code> method), but usually with
higher constants.
</p></li><li class="listitem"><p>
Most associative-container objects each hold a policy
object (a hash-based container object holds a
hash functor). List-based containers, conversely, only have
class-wide policy objects.
</p></li></ol></div></div></div></div><div class="section"><div class="titlepage"><div><div><h4 class="title"><a id="pbds.design.container.priority_queue"></a>Priority Queue</h4></div></div></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.priority_queue.interface"></a>Interface</h5></div></div></div><p>The priority queue container has the following
declaration:
</p><pre class="programlisting">
template&lt;typename Value_Type,
typename Cmp_Fn = std::less&lt;Value_Type&gt;,
typename Tag = pairing_heap_tag,
typename Allocator = std::allocator&lt;char &gt; &gt;
class priority_queue;
</pre><p>The parameters have the following meaning:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p><code class="classname">Value_Type</code> is the value type.</p></li><li class="listitem"><p><code class="classname">Cmp_Fn</code> is a value comparison functor</p></li><li class="listitem"><p><code class="classname">Tag</code> specifies which underlying data structure
to use.</p></li><li class="listitem"><p><code class="classname">Allocator</code> is an allocator
type.</p></li></ol></div><p>The <code class="classname">Tag</code> parameter specifies which underlying
data structure to use. Instantiating it by<code class="classname">pairing_heap_tag</code>,<code class="classname">binary_heap_tag</code>,
<code class="classname">binomial_heap_tag</code>,
<code class="classname">rc_binomial_heap_tag</code>,
or <code class="classname">thin_heap_tag</code>,
specifies, respectively,
an underlying pairing heap (<a class="xref" href="policy_data_structures.html#biblio.fredman86pairing" title="The pairing heap: a new form of self-adjusting heap">[biblio.fredman86pairing]</a>),
binary heap (<a class="xref" href="policy_data_structures.html#biblio.clrs2001" title="Introduction to Algorithms, 2nd edition">[biblio.clrs2001]</a>),
binomial heap (<a class="xref" href="policy_data_structures.html#biblio.clrs2001" title="Introduction to Algorithms, 2nd edition">[biblio.clrs2001]</a>),
a binomial heap with a redundant binary counter (<a class="xref" href="policy_data_structures.html#biblio.maverick_lowerbounds" title="Deamortization - Part 2: Binomial Heaps">[biblio.maverick_lowerbounds]</a>),
or a thin heap (<a class="xref" href="policy_data_structures.html#biblio.kt99fat_heaps" title="New Heap Data Structures">[biblio.kt99fat_heaps]</a>).
</p><p>
As mentioned in the tutorial,
<code class="classname">__gnu_pbds::priority_queue</code> shares most of the
same interface with <code class="classname">std::priority_queue</code>.
E.g. if <code class="varname">q</code> is a priority queue of type
<code class="classname">Q</code>, then <code class="function">q.top()</code> will
return the "largest" value in the container (according to
<code class="classname">typename
Q::cmp_fn</code>). <code class="classname">__gnu_pbds::priority_queue</code>
has a larger (and very slightly different) interface than
<code class="classname">std::priority_queue</code>, however, since typically
<code class="classname">push</code> and <code class="classname">pop</code> are deemed
insufficient for manipulating priority-queues. </p><p>Different settings require different priority-queue
implementations which are described in later; see traits
discusses ways to differentiate between the different traits of
different implementations.</p></div><div class="section"><div class="titlepage"><div><div><h5 class="title"><a id="container.priority_queue.details"></a>Details</h5></div></div></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.priority_queue.details.iterators"></a>Iterators</h6></div></div></div><p>There are many different underlying-data structures for
implementing priority queues. Unfortunately, most such
structures are oriented towards making <code class="function">push</code> and
<code class="function">top</code> efficient, and consequently don't allow efficient
access of other elements: for instance, they cannot support an efficient
<code class="function">find</code> method. In the use case where it
is important to both access and "do something with" an
arbitrary value, one would be out of luck. For example, many graph algorithms require
modifying a value (typically increasing it in the sense of the
priority queue's comparison functor).</p><p>In order to access and manipulate an arbitrary value in a
priority queue, one needs to reference the internals of the
priority queue from some form of an associative container -
this is unavoidable. Of course, in order to maintain the
encapsulation of the priority queue, this needs to be done in a
way that minimizes exposure to implementation internals.</p><p>In this library the priority queue's <code class="function">insert</code>
method returns an iterator, which if valid can be used for subsequent <code class="function">modify</code> and
<code class="function">erase</code> operations. This both preserves the priority
queue's encapsulation, and allows accessing arbitrary values (since the
returned iterators from the <code class="function">push</code> operation can be
stored in some form of associative container).</p><p>Priority queues' iterators present a problem regarding their
invalidation guarantees. One assumes that calling
<code class="function">operator++</code> on an iterator will associate it
with the "next" value. Priority-queues are
self-organizing: each operation changes what the "next" value
means. Consequently, it does not make sense that <code class="function">push</code>
will return an iterator that can be incremented - this can have
no possible use. Also, as in the case of hash-based containers,
it is awkward to define if a subsequent <code class="function">push</code> operation
invalidates a prior returned iterator: it invalidates it in the
sense that its "next" value is not related to what it
previously considered to be its "next" value. However, it might not
invalidate it, in the sense that it can be
de-referenced and used for <code class="function">modify</code> and <code class="function">erase</code>
operations.</p><p>Similarly to the case of the other unordered associative
containers, this library uses a distinction between
point-type and range type iterators. A priority queue's <code class="classname">iterator</code> can always be
converted to a <code class="classname">point_iterator</code>, and a
<code class="classname">const_iterator</code> can always be converted to a
<code class="classname">point_const_iterator</code>.</p><p>The following snippet demonstrates manipulating an arbitrary
value:</p><pre class="programlisting">
// A priority queue of integers.
priority_queue&lt;int &gt; p;
// Insert some values into the priority queue.
priority_queue&lt;int &gt;::point_iterator it = p.push(0);
p.push(1);
p.push(2);
// Now modify a value.
p.modify(it, 3);
assert(p.top() == 3);
</pre><p>It should be noted that an alternative design could embed an
associative container in a priority queue. Could, but most
probably should not. To begin with, it should be noted that one
could always encapsulate a priority queue and an associative
container mapping values to priority queue iterators with no
performance loss. One cannot, however, "un-encapsulate" a priority
queue embedding an associative container, which might lead to
performance loss. Assume, that one needs to associate each value
with some data unrelated to priority queues. Then using
this library's design, one could use an
associative container mapping each value to a pair consisting of
this data and a priority queue's iterator. Using the embedded
method would need to use two associative containers. Similar
problems might arise in cases where a value can reside
simultaneously in many priority queues.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.priority_queue.details.d"></a>Underlying Data Structure</h6></div></div></div><p>There are three main implementations of priority queues: the
first employs a binary heap, typically one which uses a
sequence; the second uses a tree (or forest of trees), which is
typically less structured than an associative container's tree;
the third simply uses an associative container. These are
shown in the graphic below, in labels A1 and A2, label B, and label C.</p><div class="figure"><a id="id-1.3.5.8.4.4.6.3.3.3"></a><p class="title"><strong>Figure 21.32. Underlying Priority-Queue Data-Structures.</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_priority_queue_different_underlying_dss.png" align="middle" alt="Underlying Priority-Queue Data-Structures." /></div></div></div><br class="figure-break" /><p>Roughly speaking, any value that is both pushed and popped
from a priority queue must incur a logarithmic expense (in the
amortized sense). Any priority queue implementation that would
avoid this, would violate known bounds on comparison-based
sorting (see <a class="xref" href="policy_data_structures.html#biblio.clrs2001" title="Introduction to Algorithms, 2nd edition">[biblio.clrs2001]</a> and <a class="xref" href="policy_data_structures.html#biblio.brodal96priority" title="Worst-case efficient priority queues">[biblio.brodal96priority]</a>).
</p><p>Most implementations do
not differ in the asymptotic amortized complexity of
<code class="function">push</code> and <code class="function">pop</code> operations, but they differ in
the constants involved, in the complexity of other operations
(e.g., <code class="function">modify</code>), and in the worst-case
complexity of single operations. In general, the more
"structured" an implementation (i.e., the more internal
invariants it possesses) - the higher its amortized complexity
of <code class="function">push</code> and <code class="function">pop</code> operations.</p><p>This library implements different algorithms using a
single class: <code class="classname">priority_queue</code>.
Instantiating the <code class="classname">Tag</code> template parameter, "selects"
the implementation:</p><div class="orderedlist"><ol class="orderedlist" type="1"><li class="listitem"><p>
Instantiating <code class="classname">Tag = binary_heap_tag</code> creates
a binary heap of the form in represented in the graphic with labels A1 or A2. The former is internally
selected by priority_queue
if <code class="classname">Value_Type</code> is instantiated by a primitive type
(e.g., an <span class="type">int</span>); the latter is
internally selected for all other types (e.g.,
<code class="classname">std::string</code>). This implementations is relatively
unstructured, and so has good <code class="classname">push</code> and <code class="classname">pop</code>
performance; it is the "best-in-kind" for primitive
types, e.g., <span class="type">int</span>s. Conversely, it has
high worst-case performance, and can support only linear-time
<code class="function">modify</code> and <code class="function">erase</code> operations.</p></li><li class="listitem"><p>Instantiating <code class="classname">Tag =
pairing_heap_tag</code> creates a pairing heap of the form
in represented by label B in the graphic above. This
implementations too is relatively unstructured, and so has good
<code class="function">push</code> and <code class="function">pop</code>
performance; it is the "best-in-kind" for non-primitive types,
e.g., <code class="classname">std:string</code>s. It also has very good
worst-case <code class="function">push</code> and
<code class="function">join</code> performance (O(1)), but has high
worst-case <code class="function">pop</code>
complexity.</p></li><li class="listitem"><p>Instantiating <code class="classname">Tag =
binomial_heap_tag</code> creates a binomial heap of the
form repsented by label B in the graphic above. This
implementations is more structured than a pairing heap, and so
has worse <code class="function">push</code> and <code class="function">pop</code>
performance. Conversely, it has sub-linear worst-case bounds for
<code class="function">pop</code>, e.g., and so it might be preferred in
cases where responsiveness is important.</p></li><li class="listitem"><p>Instantiating <code class="classname">Tag =
rc_binomial_heap_tag</code> creates a binomial heap of the
form represented in label B above, accompanied by a redundant
counter which governs the trees. This implementations is
therefore more structured than a binomial heap, and so has worse
<code class="function">push</code> and <code class="function">pop</code>
performance. Conversely, it guarantees O(1)
<code class="function">push</code> complexity, and so it might be
preferred in cases where the responsiveness of a binomial heap
is insufficient.</p></li><li class="listitem"><p>Instantiating <code class="classname">Tag =
thin_heap_tag</code> creates a thin heap of the form
represented by the label B in the graphic above. This
implementations too is more structured than a pairing heap, and
so has worse <code class="function">push</code> and
<code class="function">pop</code> performance. Conversely, it has better
worst-case and identical amortized complexities than a Fibonacci
heap, and so might be more appropriate for some graph
algorithms.</p></li></ol></div><p>Of course, one can use any order-preserving associative
container as a priority queue, as in the graphic above label C, possibly by creating an adapter class
over the associative container (much as
<code class="classname">std::priority_queue</code> can adapt <code class="classname">std::vector</code>).
This has the advantage that no cross-referencing is necessary
at all; the priority queue itself is an associative container.
Most associative containers are too structured to compete with
priority queues in terms of <code class="function">push</code> and <code class="function">pop</code>
performance.</p></div><div class="section"><div class="titlepage"><div><div><h6 class="title"><a id="container.priority_queue.details.traits"></a>Traits</h6></div></div></div><p>It would be nice if all priority queues could
share exactly the same behavior regardless of implementation. Sadly, this is not possible. Just one for instance is in join operations: joining
two binary heaps might throw an exception (not corrupt
any of the heaps on which it operates), but joining two pairing
heaps is exception free.</p><p>Tags and traits are very useful for manipulating generic
types. <code class="classname">__gnu_pbds::priority_queue</code>
publicly defines <code class="classname">container_category</code> as one of the tags. Given any
container <code class="classname">Cntnr</code>, the tag of the underlying
data structure can be found via <code class="classname">typename
Cntnr::container_category</code>; this is one of the possible tags shown in the graphic below.
</p><div class="figure"><a id="id-1.3.5.8.4.4.6.3.4.4"></a><p class="title"><strong>Figure 21.33. Priority-Queue Data-Structure Tags.</strong></p><div class="figure-contents"><div class="mediaobject" align="center"><img src="../images/pbds_priority_queue_tag_hierarchy.png" align="middle" alt="Priority-Queue Data-Structure Tags." /></div></div></div><br class="figure-break" /><p>Additionally, a traits mechanism can be used to query a
container type for its attributes. Given any container
<code class="classname">Cntnr</code>, then </p><pre class="programlisting">__gnu_pbds::container_traits&lt;Cntnr&gt;</pre><p>
is a traits class identifying the properties of the
container.</p><p>To find if a container might throw if two of its objects are
joined, one can use
</p><pre class="programlisting">
container_traits&lt;Cntnr&gt;::split_join_can_throw
</pre><p>
</p><p>
Different priority-queue implementations have different invalidation guarantees. This is
especially important, since there is no way to access an arbitrary
value of priority queues except for iterators. Similarly to
associative containers, one can use
</p><pre class="programlisting">
container_traits&lt;Cntnr&gt;::invalidation_guarantee
</pre><p>
to get the invalidation guarantee type of a priority queue.</p><p>It is easy to understand from the graphic above, what <code class="classname">container_traits&lt;Cntnr&gt;::invalidation_guarantee</code>
will be for different implementations. All implementations of
type represented by label B have <code class="classname">point_invalidation_guarantee</code>:
the container can freely internally reorganize the nodes -
range-type iterators are invalidated, but point-type iterators
are always valid. Implementations of type represented by labels A1 and A2 have <code class="classname">basic_invalidation_guarantee</code>:
the container can freely internally reallocate the array - both
point-type and range-type iterators might be invalidated.</p><p>
This has major implications, and constitutes a good reason to avoid
using binary heaps. A binary heap can perform <code class="function">modify</code>
or <code class="function">erase</code> efficiently given a valid point-type
iterator. However, in order to supply it with a valid point-type
iterator, one needs to iterate (linearly) over all
values, then supply the relevant iterator (recall that a
range-type iterator can always be converted to a point-type
iterator). This means that if the number of <code class="function">modify</code> or
<code class="function">erase</code> operations is non-negligible (say
super-logarithmic in the total sequence of operations) - binary
heaps will perform badly.
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