b-tree

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: I'm trying to memory map a huge file (approx. 100GB) in order to store a B-Tree with billions of key-value pairs. The memory is to small to keep all data in memory therefore I'm trying to map a file from disk and instead of using malloc I return and increment a pointer to the mapped region. #define MEMORY_SIZE 300000000 unsigned char *mem_buffer; void *start_ptr; void *my_malloc(int size) { unsigned char *ptr = mem_buffer; mem_buffer += size; return ptr; } void *my_calloc(int size, int object_size) { unsigned char *ptr = mem_buffer; mem

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 问题: As a programmer when should I consider using a RB tree, B- tree or an AVL tree? What are the key points that needs to be considered before deciding on the choice? Can someone please explain with a scenario for each tree structure why it is chosen over others with reference to the key points? 回答1: Take this with a pinch of salt: B-tree when you're managing more than thousands of items and you're paging them from a disk or some slow storage medium. RB tree when you're doing fairly frequent inserts, deletes and retrievals on the tree. AVL tree

什么是B-Tree 　　B-Tree就是我们常说的B树，一定不要读成B减树，否则就很丢人了。B树这种数据结构常常用于实现数据库索引，因为它的查找效率比较高。 磁盘IO与预读 磁盘读取依靠的是机械运动，分为寻道时间、旋转延迟、传输时间三个部分，这三个部分耗时相加就是一次磁盘IO的时间，大概9ms左右。这个成本是访问内存的十万倍左右；正是由于磁盘IO是非常昂贵的操作，所以计算机操作系统对此做了优化：预读；每一次IO时，不仅仅把当前磁盘地址的数据加载到内存，同时也把相邻数据也加载到内存缓冲区中。因为局部预读原理说明：当访问一个地址数据的时候，与其相邻的数据很快也会被访问到。每次磁盘IO读取的数据我们称之为一页（page）。一页的大小与操作系统有关，一般为4k或者8k。这也就意味着读取一页内数据的时候，实际上发生了一次磁盘IO。 B-Tree与二叉查找树的对比 　　我们知道二叉查找树查询的时间复杂度是O（logN），查找速度最快和比较次数最少，既然性能已经如此优秀，但为什么实现索引是使用B-Tree而不是二叉查找树， 关键因素是 磁盘IO的次数 。 数据库索引是存储在磁盘上，当表中的数据量比较大时，索引的大小也跟着增长，达到几个G甚至更多。当我们利用索引进行查询的时候，不可能把索引全部加载到内存中，只能逐一加载每个磁盘页，这里的磁盘页就对应索引树的节点。 一、 二叉树

版权声明：转载请标明出处~~ https://blog.csdn.net/zzzgd_666/article/details/90722011 一. mysql的索引 mysql常用的索引有以下几种: hash索引 hash索引通过hash值来匹配对应的数据,类似键值对的形式,查找的时候可以精准一次定位,但是对于范围查询,排序效率不高,并且hash索引不能避免全表扫描,因为hash值并不能完全保证一个hash值匹配一个数据(hash冲突),还是需要比对实际数据 btree索引 MySQL里默认和最常用的索引类型,利用二分查找的思想构建的数据结构 全文索引 myisam引擎支持全文索引,innodb在mysql5.6以后也支持全文索引,不过基于mysql很少存储text大文本数据,全文检索也被es替代 二. 二叉树到B-Tree和B+Tree 2.1 二叉树到平衡二叉树 二叉树是一种基础的树结构,它通常由一个根节点和衍生的分支组成.它的特点是: 每个节点都最多只有两个子节点(分支) 二叉树只是一个树结构,在实际应用中还有一种特殊的二叉树,叫二叉查找树. 它在二叉树的基础上,多了一个特点,左边的节点都比右边的节点小 但是可能会出现特殊情况,就会从树状结构变成链表结构,查询效率大打折扣.结构如下 为了解决这个问题,又出现了平衡二叉树. 平衡二叉树的特点就是

Is it 2n? Just checking. Terminology The Order of a B-Tree is inconstantly defined in the literature. (see for example the terminology section of Wikipedia's article on B-Trees ) Some authors consider it to be the minimum number of keys a non-leaf node may hold, while others consider it to be the maximum number of children nodes a non-leaf node may hold (which is one more than the maximum number of keys such a node could hold). Yet many others skirt around the ambiguity by assuming a fixed length key (and fixed sized nodes), which makes the minimum and maximum the same, hence the two

Anyone know of any good examples of a simple BTree implementation in Javascript? I have a bunch of "things" arriving randomly, and want to insert each one efficiently. Ultimately, each new one will get inserted into the DOM based on where it ends up in the tree. I can code this from scratch but would rather not reinvent any wheels. thanks Does this help? - Computer science in JavaScript: Binary search tree, Part 1 If it matters, I found that storing this kind of data as a literal tree was less efficient than storing it as an already-sorted array and doing binary search on the array to splice

If I am implementing in-memory(RAM) search operation with b trees, then would it be better in terms of caching or some other effects when compared with binary trees? What I know is binary search tress---O(log n) btrees ---------------O(c log n) there was a lot of discussion regarding that on various blogs. Algorithmic complexity is the same, since O(log b n) = O(c log n) = O(log n) but the constant factors, which are hidden in big-O notation, could vary noticeably, depending on implementation and hardware. B-trees were designed for platter hard disks, which have a large access time (moving the