Optimizing C++/General optimization techniques/Other techniques

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Query cursor[edit | edit source]

Instead of defining a function that returns a collection (aka snapshot), define a function that returns an iterator (aka cursor or dynaset), with which you can generate or possibly change the required data.

This technique is particularly useful for database queries, but is applicable also to internal data structures.

Let's assume you have a collection (or a set of collections) encapsulated in a class. Such class exposes one or more member functions to extract (or filter) a subset from such collection.

A way to get it is to construct a new collection, to copy the extracted data into it, and to return such collection. In the database jargon, such collection is called snapshot.

This technique is effective but inefficient, as the allocation and copy of the snapshot takes a lot of time and a lot of storage space. In addition, it has the shortcoming that, until all the data has been extracted, you cannot proceed to process the already extracted data.

Here is an equivalent but more efficient technique.

The query function returns an iterator. In database jargon, such iterator is called cursor or dynaset. The caller uses such iterator to extract, one at a time, the data filtered, and possibly to change them.

Notice that this solution is not exactly equivalent, as if during the iterator use the collection is changed by another function call, possibly coming from another thread, it may happen that the iterator is invalidated, or just that the filtered collection do not corresponds to the specified criteria. Therefore, you can apply this technique only when you are sure that the underlying collection is not changed in any way, except by the iterator itself, during the whole life of the iterator.

This technique is independent of the programming language, as the iterator concept is an abstract design pattern.

Binary search[edit | edit source]

If you have to do many searches in a rarely changed collection, instead of using a search tree or a hash table, you can get a speed up if you put the data in an array, sort the array, and do binary searches on it.

A binary search on an array has logarithmic complexity, like search trees, but has the advantage of compactness and locality of reference typical of arrays.

If the array is changed, this algorithm may still be competitive, as long as the changes are much less frequent than searches.

If every collection change consists in very few insertions or changes or deletions of elements, it is better to shift the array at every change operation. Instead, if a collection change is more bulky, it is better to recreate and sort the whole array.

In C++, if the array length is not a compile-time constant, use a vector.

Singly-linked lists[edit | edit source]

If for a list you don't need bidirectional iterators, you don't need to insert elements at the end or before the current element, and you don't need to know how many elements there are in the list, use a singly-linked list, instead of a doubly-linked list.

Such container, although it has many shortcomings, occupies less space and it is faster.

Typically, the heading of a doubly-linked list contains a pointer to the head of the list, a pointer to the back, and the counter of elements, while the heading of a singly-linked list contains only a pointer to the head of the list. In addition, typically, every node of a doubly-linked list contains a pointer to the previous node and a pointer to the next node, while every node of a singly-linked list contains only a pointer to the next node. At last, every element insertion into a doubly-linked list must update four pointers and increment a counter, while every element insertion into a singly-linked list must only update two pointers.

In the C++ standard library, the std::list container is implemented by a doubly-linked list. The slist container, non-standard but available in various libraries, and the forward_list container, that will be in C++11 standard library, are implemented by singly-linked lists.