b-tree | 易学教程

B+ tree or B-tree

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问题 I am learning about postgresql internals and I am wondering or postgresql B-tree index is actually classic B-tree or B+tree? To spell it out, that means the nodes contain only keys or key-value pairs? 回答1: B-trees. Only keys. The point of indexes is to store keys to begin with. Data lies in tables, which are logical heaps. Here is a related chapter on Wikipedia. The physical storage of B-tree indexes and tables is otherwise very similar. They use the same data pages with mostly the same page

how B-tree indexing works in mysql

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问题 When I create an index for a table in mysql, I see that the index_type is type BTREE . Now although I understand about btree(s), I do not quiet understand how it stores the index and how the database searches the records based on this. I mean, btree is excellent for databases to perform read and writes large blocks of data, when we create an index for column type of Primary key , what I understand is, it creates a tree and splitting the values for the root based on the value type of the root.

索引的分类--B-Tree索引和Hash索引

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索引是存储引擎用来快速查找记录的一种数据结构，按照实现的方式有不同的种类，想B-Tree索引，hash索引，空间数据索引和全文索引等。下面主要说一下B-Tree索引和Hash索引。人们在谈论索引的时候如果没有特别说明，一般指的是B-Tree索引。B-Tree索引是使用B-Tree数据结构来存储索引的。B-Tree通常意味着所有的值是按照顺序存储的。B-Tree树有如下几个特征： ⑴树中每个结点至多有m 棵子树； ⑵若根结点不是叶子结点，则至少有两棵子树； ⑶除根结点之外的所有非终端结点至少有[m/2] 棵子树； ⑷所有的非终端结点中包含以下信息数据：（n，A0，K1，A1，K2，…，Kn，An）其中：Ki（i=1,2,…,n）为关键码，且Ki<Ki+1， Ai 为指向子树根结点的指针(i=0,1,…,n)，且指针Ai-1 所指子树中所有结点的关键码均小于Ki (i=1,2,…,n)，An 所指子树中所有结点的关键码均大于Kn. n 为关键码的个数。 ⑸所有的叶子结点都出现在同一层次上，并且不带信息（可以看作是外部结点或查找失败的结点，实际上这些结点不存在，指向这些结点的指针为空）。即所有叶节点具有相同的深度，等于树高度。对于B-Tree索引，存储引擎在查找记录的时候不再是通过扫描全表来获取需要的数据么人是从索引的根节点进行搜索，根节点的槽中存放了指向子节点的指针

saving Btrees to a disk file and read it

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I want to save a Btree(not sure a binary one) in a disk file. and then read it to the memory. some Level-order traversal may be a good way for a binary Btree. but if it is not a binary one. I build up the Btree from the leafnode to the rootnode in memory. I believe that I have to define some structures in the disk file and output the tree nodes. using some extra tag to identify a node in the file? how to traversal may be the key problem here. I coudn't figure out a good way to save the nodes and the pointers. and then read it. rconstruct the tree in memory. any good ideas?. thanks a lot. If

Looking for a disk-based B+ tree implementation in C++ or C [closed]

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问题 I am looking for a lightweight open source paging B+ tree implementation that uses a disk file for storing the tree. So far I have found only memory-based implementations, or something that has dependency on QT (?!) and does not even compile. Modern C++ is preferred, but C will do too. I prefer to avoid full embeddable DBMS solution, because: 1) for my needs bare bone index that can use the simplest possible disk file organization is enough, no need for concurrency, atomicity and everything

Discovering the Computer Science Behind Postgres Indexes

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This is the last in a series of Postgres posts that Pat Shaughnessy wrote based on his presentation at the Barcelona Ruby Conference . You can also watch the video recording of the presentation . The series was originally published on his personal blog , and we are republishing it on Codeship with his kind permission. You can also read posts one , two , and three in the series. We all know indexes are one of the most powerful and important features of relational database servers. How do you search for a value quickly? Create an index. What do you have to remember to do when joining two tables

Java On-Memory Efficient Key-Value Store

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问题 I have store 111 million key-value pairs (one key can have multiple values - maximum 2/3) whose key are 50 bit Integers and values are 32 bit (maximum) Integers. Now, my requirements are: Fast Insertion of (Key, Value) pair [allowing duplicates] Fast retrieving of value/values based on key. A nice solution of it is given here based on MultiMap. However, I want to store more key-values pairs in main memory with no/little bit performance penalty. I studied from web articles that B+ Tree, R+

mysql索引原理及优化（三）

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B+Tree原理详解 MyISAM中的B+树 MYISAM 中叶子节点的数据区域存储的是数据记录的地址主键索引辅助索引 MyISAM存储引擎在使用索引查询数据时，会先根据索引查找到数据地址，再根据地址查询到具体的数据。并且主键索引和辅助索引没有太多区别。 InnoDB中的B+树 InnoDB 中主键索引的叶子节点的数据区域存储的是数据记录，辅助索引存储的是主键值主键索引辅助索引 Innodb中的主键索引和实际数据时绑定在一起的，也就是说Innodb的一个表一定要有主键索引，如果一个表没有手动建立主键索引，Innodb会查看有没有唯一索引，如果有则选用唯一索引作为主键索引，如果连唯一索引也没有，则会默认建立一个隐藏的主键索引（用户不可见）。所以，我们在使用Innodb作为存储引擎时，我们最好：手动建立主键索引尽量利用主键索引查询 MyISAM 存储引擎在使用索引查询数据时，会先根据索引查找到数据地址，再根据地址查询到具体的数据。并且主键索引和辅助索引没有太多区别。 InnoDB 中主键索引的叶子节点的数据区域存储的是数据记录，辅助索引存储的是主键值所以：Innodb的主键索引要比MyISAM的主键索引查询效率要高（少一次磁盘IO ），并且比辅助索引也要高很多。 =========================================

Best data structure for crossword puzzle search

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问题 I have a large database for solving crossword puzzles, consisting of a word and a description. My application allows searching for words of a specific length and characters on specific positions (this is done the hard way ... go through all words and check each). Plus a search by description (if necessary) For instance find word _ _ A _ _ B (6 letter word, third character A and last B) I would like to index the words in such way that the searching would be really fast. My first idea was to

用示例说明B-Tree索引性能优于BitMap索引

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一、实验说明：操作系统：rhel 5.4 x86 数据库：Oracle 11g R2 实验说明：该实验是为了说明B-Tree索引性能优于BitMap索引的情况。二、实验操作：首先创建一张t_btree表，并建立B-Tree索引，索引键是object_id： 1 SQL > create table t_btree as select * from dba_objects; 2 3 Table created. 4 5 SQL > create index ind_tree on t_btree( object_id ); 6 7 Index created. 执行两次下面的查询语句，并显示执行计划： 1 SQL > set autotrace traceonly; 2 SQL > select * from t_btree where object_id = 9899 ; 3 4 5 Execution Plan 6 -- -------------------------------------------------------- 7 Plan hash value: 447474086 8 9 -- --------------------------------------------------------------------------------------

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