Access plan for query without hash index.
不使用hash索引的查询的访问计划。
Access plan for query accessing a hash index.
访问hash索引的查询的访问计划。
This article USES a rather basic hash index as an example.
本文使用一个非常基本的hash索引作为例子。
Listing 1. Implementation of the hash index key generator.
清单1.hash索引键生成器的实现。
Hash index model is to check the index data and create the bitmap index of the index data.
哈希索引亡储模式从要非判续索引数据非否亡反在，树立索引数据的位图索引。
Thus, the search in a large file using a hash index is faster than scanning the entire file.
因此，对于较大的文件，使用hash索引进行搜索比扫描整个文件更快。
Fortunately, the whole discussion of overflows does not apply to the hash index that I implemented.
幸运的是，针对溢出的全部讨论不会出现在我所实现的hash索引。
The index storage models of the hybrid index mechanism have three models: tree model, hash index model and inverted file model.
混纯索引机造采取的索引亡储模型从要无树、哈希和反排文件三类方式。
The various hash index methods are intended to address different aspects of the hashing itself, like the handling of collisions or overflows.
不同的hash索引方法用于解决hash本身的不同方面，例如处理冲突或溢出。
A hash index is often implemented using an array where each element in the array is a hash bucket with a fixed size, as is depicted in Figure 1.
hash索引常常使用一个数组来实现，数组中的每个元素是一个有固定大小的hashbucket，如图1所示。
The first example in this article describes the basic concepts of hash indexes and illustrates how a hash index can easily be integrated in your DB2 database.
本文中的第一个例子描述hash索引的基本概念，并演示如何将hash索引轻松地集成到DB 2数据库中。
As before with the hash index, you need to define a key generator UDF, a range producer UDF, the index extension, and finally a UDF that exploits the index extension if possible.
与hash索引一样，这里需要定义一个键生成器udf、一个范围生成器udf、一个索引扩展以及一个利用索引扩展的UDF。
With a hash index you can find a record in a given file in constant time by generating a hash value for the record and then looking up the record in the file based on that hash value.
借助hash索引，可以通过为记录生成一个hash值，然后根据这个hash值在文件中查找该记录，从而在固定的时间内在给定的文件中找到一条记录。
Those details are not discussed here; instead this article describes the general idea and its adaptation to integrate a hash index into a database system using the DB2 index extension mechanism.
本文并不讨论这些细节，而是描述一般的思想，以及使用DB 2索引扩展机制将hash索引集成到数据库系统中。
The optimizer will choose dynamic hash join if none of the joined columns has index.
如果连接列上不存在索引，那么优化器将选择动态哈希连接。
This index contains hash values of the actual strings and can be used for equality predicates only, not for range predicates.
该索引包含实际字符串的散列值，只能用于等式谓词，不能用于范围谓词。
When parsing the input string into its numerical constituents, we use the hash % idx_for_mon, which holds the numerical index of each month (1.. 12) given its acronym.
当将输入的字符串解析为数字时，我们使用的是哈希表(hash) % idx_for_mon，它保持了每个月份(1. .12)给定缩写的数字索引。
A Ruby hash (or associative array) is a data structure that allows you to define the key for storage (instead of a simple numerical index).
Ruby散列表(或关联数组)是一种数据结构，允许您定义用于存储的键(而不是简单的数值索引)。
All numeric hash values are stored using the index extension in the DB2-internal B-Tree index.
所有数值型hash值都使用DB 2内部的B -树索引中的索引扩展来存储。
Thus, all indexed strings with the same hash code are returned from the index scan as possible candidates.
因此，索引扫描返回所有具有相同hash码的已索引的字符串，作为可能的候选者。
The generated hash value is numeric, and I use this value as single index key for the string being indexed.
生成的hash值是数值型的，我使用这个值作为被索引的字符串的索引键。
With a Forest of Trees index, the database architect can specify one or more of the leading columns to be used to create a hash key.
有了ForestofTrees索引，数据库架构师可以指定一个和多个引导列来创建一个hash键。
To retrieve an item from a subtree, you compute a hash value from the column you chose when creating the index.
从子树中检索一个项目，当您创建索引时，可以从所选的列中计算出一个hash值。
If the data is stored sequentially, the time to find the item is proportional to the size of the list. For each element, a hash function calculates a number, which is used as an index into the table.
如果数据是序列存储的，从中查询一个项的时间取决于数据列的大小。
In most cases neither of these situations exist, and the hash function will need to compress a larger range of keys into a smaller range of index Numbers.
大多数情况下，这种情况不会发生，哈希函数需要把较大的关键字值范围压缩成较小的数组下标的范围。
After introducing the organization form of the memory database, it focuses on the HASH arithmetic of index and protection of integrity and consistence of the data.
在介绍了内存数据库的组织形式之后，本文重点分析了索引HASH算法以及数据完整性和一致性保护。
When a hash collision occurs, the system won't store the new data because it sees that its hash number already exists in the index... This is called a false positive, and can result in data loss.
当哈希冲突发生时，系统不会储存新数据，因为它看到它的散列人数已经存在于索引…这就是所谓的假阳性，并可能造成数据丢失。
When a piece of data receives a hash number, that number is then compared with the index of other existing hash Numbers.
当一块数据接收一个哈希数字，这个数字则比其他现有的哈希号码索引。
Memory data are stored in table set, and data index is set up by hash table.
采用表集的方式存储各类内存数据，通过哈希表建立数据索引。
It has also brought forward an integrated file management system and the data management ways of multiple index and hash link-table.
提出了集成文件管理体系以及多级索引和哈希链表的数据管理方式；
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