# btree_learning

**Repository Path**: saltedf/btree_learning

## Basic Information

- **Project Name**: btree_learning
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-01
- **Last Updated**: 2022-02-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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<int32_t>` has an overhead
of 16 bytes for every 4 byte set element (on a 32-bit operating system); the
corresponding `btree::set<int32_t>` 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.