https://github.com/kronuz/cpp-btree
Modern C++ B-tree containers
https://github.com/kronuz/cpp-btree
btree c-plus-plus map modern-cpp multimap multiset set
Last synced: about 11 hours ago
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Modern C++ B-tree containers
- Host: GitHub
- URL: https://github.com/kronuz/cpp-btree
- Owner: Kronuz
- License: apache-2.0
- Created: 2019-08-02T18:19:08.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-08-11T19:54:03.000Z (almost 2 years ago)
- Last Synced: 2025-05-19T18:09:19.889Z (about 2 months ago)
- Topics: btree, c-plus-plus, map, modern-cpp, multimap, multiset, set
- Language: C++
- Size: 34.2 KB
- Stars: 272
- Watchers: 8
- Forks: 53
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# C++ B-tree
Code in this repository is based on
[Google's B-tree implementation](https://code.google.com/archive/p/cpp-btree/).
C++ B-tree is a template library that implements ordered in-memory containers
based on a B-tree data structure. Similar to the STL `std::map`, `std::set`,
`std::multimap`, and `std::multiset` templates, this library provides
`btree::map`, `btree::set`, `btree::multimap` and `btree::multiset`.
This difers from the original project by Google in that containers behave more
like modern STL (C++17) and are an almost drop-in replacements (except for the
iterator invalidation, see below); including support for `emplace` and
`try_emplace` as well as values in the map not needing to have a default
constructor.
C++ B-tree containers have a few advantages compared with the standard
containers, which are typically implemented using Red-Black trees. Nodes in a
Red-Black tree require three pointers per entry (plus 1 bit), whereas B-trees
on average make use of fewer than one pointer per entry, leading to
**significant memory savings**. For example, a `set` has an overhead
of 16 bytes for every 4 byte set element (on a 32-bit operating system); the
corresponding `btree::set` has an overhead of around 1 byte per set
element.
B-trees are widely known as data structures for secondary storage, because they
keep disk seeks to a minimum. For an in-memory data structure, the same property
yields a performance boost by keeping cache-line misses to a minimum. C++ B-tree
containers make better use of the cache by performing multiple key-comparisons
per node when searching the tree. Although B-tree algorithms are more complex,
compared with the Red-Black tree algorithms, the improvement in cache behavior
may account for a **significant speedup** in accessing large containers.
The C++ B-tree containers are not without drawbacks, however. Unlike the
standard STL containers, modifying a C++ B-tree container
**invalidates all outstanding iterators** on that container.