b-tree

构建哈希的过程 select过程 长字符串下,构建索引可通过自定义哈希作为索引,本人通过实验,在3百多个数据记录的下,性能效果很明显,完全不是一个等级.以下为索引前后几种情况对比 无索引的url:直接通过无索引url 通过构建url的哈希索引:用bigint类型存储索引字段crc_url 在哈希索引下,几乎都是0秒完成. 当然,如果直接使用url作为索引,即用B-Tree存储url存储的内容会很大. 题外话: 在where字句中,优化器会根据查询条件是否存在索引,优先进行索引查询. 如下为例子: 将无索引的url放在前面,跟放在后面,效果是一样的 来源： https://www.cnblogs.com/listened/p/11978829.html

As of now,this program traverses in level order but just prints out the numbers.I want to know how to print it so it could look something like the picture below or just a fancy way to show the different levels of the tree and its numbers. num1 / \ num2,num3 num4,num5 The thing I dont understand is how to tell which numbers are suppose to go into there respective level.Here is the code: // C++ program for B-Tree insertion #include<iostream> #include <queue> using namespace std; int ComparisonCount = 0; // A BTree node class BTreeNode { int *keys; // An array of keys int t; // Minimum degree

So, here is my little problem. Let's say I have a list of buckets a 0 ... a n which respectively contain L <= c 0 ... c n < H items. I can decide of the L and H limits. I could even update them dynamically, though I don't think it would help much. The order of the buckets matter. I can't go and swap them around. Now, I'd like to index these buckets so that: I know the total count of items I can look-up the ith element I can add/remove items from any bucket and update the index efficiently Seems easy right ? Seeing these criteria I immediately thought about a Fenwick Tree. That's what they are

Has anyone seen an implementation of the STL where stl::set is not implemented as a red-black tree? The reason I ask is that, in my experiments, B-2B trees outperform stl::set (and other red-black tree implementations) by a factor of 2 to 4 depending on the value of B. I'm curious if there is a compelling reason to use red-black trees when there appear to be faster data structures available. Some folks over at Google actually built a B-tree based implementation of the C++ standard library containers. They seem to have much better performance than standard binary tree implementations. There is

Since MySQL uses BTREE by default when creating an index, is there some instance when I can use HASH ? For example, if my table only consists of Foreign Keys which are just INT UNSIGNED values. Is it a good improvement to override BTREE with HASH in this case? Not sure if it matters, but I'm using InnoDB . The HASH index type is only supported for MEMORY (aka HEAP ) storage engine. 来源： https://stackoverflow.com/questions/14418260/mysql-when-can-i-use-hash-instead-of-btree

Since database data is organized in 8k pages in a B-tree, and likewise for PK information information, it should be possible for each table in the database to calculate the height of the B-Tree. Thus revealing how many jumps it takes to reach certain data. Since both row size and PK size is of great importance, it is difficult to calculate since eg varchar(250) need not take up 250 bytes. 1) Is there a way to get the info out of SQL Server? 2) if not, is it possible to give a rough estimate using some code analyzing the tables of the db? YES! Of course! Check out the DMV = dynamic management