这已经是一个较好的值，因为它可以保证优化器在使用分位数统计信息的情况下对确定过滤因子的估计误差最大只有5%。
This is already a good value because it guarantees that the optimizer, by using the quantile statistics, only has an estimated maximum error of 5% for the determined filter factors.
这些视图包括一些包含对查询优化器有用的统计信息的列。
These views include columns containing statistical information that is useful to the query optimizer.
DB 2优化器然后查询存储在DB 2系统目录中的系统信息和统计信息，以确定完成满足SQL请求所必需的任务的最佳方法。
The DB2 optimizer then queries system information and statistics stored in the DB2 system catalog to determine the best method of accomplishing the tasks necessary to satisfy the SQL request.
如果IN列表中包含参数标记符或主机变量，使优化器不能使用编目统计信息来确定选择性，就会出现这种情况。
This can occur if the IN list contains parameter markers or host variables which prevent the optimizer from using catalog statistics to determine the selectivity.
这使得查询优化器可以更有效地分析复杂的查询，因为现在统计信息近似地反映实际成本。
This allows the query optimizer to do a better job of analyzing the complex query, as the statistics can now closely reflect the actual cost.
统计信息还可帮助优化器确定表中有多少行正被查询，以及预测有多少行符合给定的条件。
Statistics also help the optimizer ascertain how many rows exist in tables being queried and predict how many rows will qualify for given conditions.
您还研究了参数标记/主变量的使用会为DB2优化器对分布统计信息的考虑带来怎样的限制。
You have also examined how the use of parameter markers/host variables may limit the consideration of distribution statistics by the DB2 optimizer.
为使优化器能够使用可用的分布统计信息，带有具体值的谓词极为重要。
Predicates with concrete values are important for the optimizer to be able to use available distribution statistics.
目录统计信息的更新将为优化器提供最新的地图，以便在整个地形中快速定位。
Updating the catalog statistics would provide the optimizer with the most current map to navigate quickly through the terrain.
这个示例解释说明了部分统计信息对于优化器估计基数的能力的影响。
This example illustrates the effect that partial statistics have on the ability of the optimizer to estimate the cardinality.
但是在某些情况下，在创建昵称之后，可能需要添加索引信息和统计信息，以便让优化器下推与昵称一起使用的谓词。
But in some cases, it may be necessary to add index information and statistics to the nickname after it is created in order to get the optimizer to push down predicates used with the nickname.
准确的索引信息和统计信息是查询优化器中基于成本的决策的基础。
Accurate index information and statistics are fundamental to cost-based decisions in the query optimizer.
您可以从以上步骤看到，这个优化器基于可用的统计信息选择了最优的存取计划。
As you've seen in the steps above, the optimizer selects the optimal access plan based on available statistics.
在很多情况下，这样做可以为优化器提供接近于第一个命令的精确性的统计信息，但是可以更快地返回结果。
In many cases, this will provide the optimizer with nearly as accurate statistics as the first command, but will return results much faster.
优化器根据可用的统计信息计算基数的估计值，即预计查询返回的行数。
The optimizer calculates its cardinality estimate — that is, the estimated number of rows returned by the query — based on the statistics available.
优化器是依靠统计信息来计算可选查询执行计划（QEP）的开销，同时选择出最优的计划。
The optimizer relies on statistics to properly cost alternative query execution plans (QEPs) and choose the most optimal plan.
在使用RUNSTATS之后需要重新绑定使用静态SQL的应用程序，这样查询优化器就可以选择新统计信息所给出的最佳存取方案。
Rebind applications that use static SQL after using RUNSTATS so that the query optimizer can choose the best access plan given the new statistics.
实际上，通过基本统计信息，DB 2优化器只能估计' VALUE_Z '在COLUMN_Y中出现的频率。
Indeed, from the basic statistics, the DB2 optimizer can only estimate the frequency of 'VALUE_Z' in COLUMN_Y.
如果有了准确的统计信息，那么查询优化器一般能够得出正确的选择性估计。
Given accurate statistics, the query optimizer generally comes up with correct selectivity estimates.
本文解释什么是分布统计信息、分布统计信息在哪些情况下尤为重要，以及应用程序开发人员应该考虑些什么，才能使DB 2优化器创建有效的访问计划。
This article explains distribution statistics, when they are important, and what application developers should consider so that the DB2 optimizer can create efficient access plans.
您还应该考虑在MQT 上执行runstats，以便查询优化器拥有关于 MQT 中的行数以及其他统计信息的准确信息。
You should also consider executing runstats on the MQT, so that the query optimizer has accurate information about the number of rows in the MQT and other statistics.
DB 2优化器对分布统计信息的使用——示例。
Usage of distribution statistics by the DB2 optimizer - a sample.
不管哪种模式(3nf或Star)，Teradata查询优化器依赖于统计信息帮助确定最佳数据访问路径。
Regardless of schema (3nf or Star), the Teradata query optimizer relies on statistics to help it determine the best way to access data.
查询优化器很大程度上依赖于这些统计信息——尤其是card、colcard、high2key和low2key。
The query optimizer relies heavily on these statistics — especially card, colcard, high2key and low2key.
您可以利用DB2 9.5 中的多列统计信息的扩展用途来为优化器提供更多的信息，从而使优化器更好地估计基数，选择最佳的查询访问计划。
You can leverage the extended use of multi-column statistics in DB2 9.5 to provide the optimizer more information to better estimate the cardinality in order to choose an optimal query access plan.
优化器的操作基于数据表中的行数、数据表或索引使用的空间以及其他信息的相关统计数据。
The optimizer ACTS based on statistical data about the number of rows in a table, the use of space by a table or index, and other information.
首先来分析一下，在没有分布统计信息，而只有cars表的基本统计信息及其索引的情况下，优化器选择的访问计划是怎样的。
First analyze the access plan that the optimizer chooses when no distribution statistics are available, which is only basic statistics for the table CARS, and that its indexes exist.
而一旦生成了关于“TYPE”列的分布统计信息，优化器即可了解每种型号真正的出现频率。
When distribution statistics are generated for the column "TYPE," the optimizer knows the real frequency of each model.
使DB 2统计信息保持最新：如果没有存储在DB 2系统目录中的统计信息，优化器在优化任何事物时都会遇到困难。
Keep DB2 statistics up-to-date: Without the statistics stored in the DB2 'system catalog, the optimizer will have a difficult time optimizing anything.
下面列举了一些统计信息，这些统计信息可以帮助给优化器定义数据模型。
Examples of the statistics available which help define the data model to the optimizer include.
应用推荐