Imitate data skew using an auxiliary table.
使用辅助表模仿数据倾斜。
Routines that can help you estimate the data skew for new and existing partitioning keys.
可帮助您评估新的和已有分区键的数据倾斜的例程。
This input parameter specifies the percentage of data to be used in the data skew estimation.
这个输入参数指定用在数据倾斜估算中的数据的百分比。
This example tests the data skew in a scenario in which the partitioning key was changed to S_NATIONKEY.
这个示例测试的是分区键被更改为S_NATIONKEY的场景中的数据倾斜。
If some values for a column occur much more frequently than others, the data has data skew. For example.
如果一列中的某些值所出现的频率比其他的要大很多，则该数据存在数据倾斜(data skew)。
This example tests the data skew in a scenario in which the partitioning key was changed from S_NATIONKEY to S_ID.
这个示例测试的是分区键从 S_NATIONKEY 更改为 S_ID 的场景中的数据倾斜。
From the best practices perspective, the table data skew on every individual database partition should be no more than 10%.
从最佳实践的角度来看，各个数据库分区上的表数据倾斜应该不多于10%。
In summary, the accuracy of statistics depends on the sampling rate, the data skew, and data clustering for data sampling.
总之，统计信息的准确性取决于抽样率、数据倾斜(data skew)以及用于数据抽样的数据群集。
The purpose of this parameter is to improve performance and minimize the space usage when estimating data skew with new partitioning keys.
此参数的目的是改善性能并在用新分区键估算数据倾斜时最小化空间使用。
You are either designing a new table that will be hash-partitioned, or you have an existing hash-partitioned table that might have a data skew problem.
您正在设计一个将被哈希分区的新表，或者您已经有了一个哈希分区的表，并且此表有可能会存在数据倾斜问题。
As this example demonstrates, the new partitioning key causes minimal data skew and is a much better choice than the original S_NATIONAL key from example 1.
正如这个示例所展示的，新的分区键带来了极少的数据倾斜，因此比示例1中的原始s_national键好很多。
Whenever you have a reason to expect data skew in production data, you might want to reproduce data skew in your test data. First, store the estimated frequencies in a table.
每当您有理由期望在生产数据中出现数据倾斜时，您就可能需要在测试数据中再现数据倾斜，首先，在一个表中存储预计频率。
The analysis and experiment results show that this algorithm effectively resolves the data skew problem in Computer Cluster. It can be fit for searching in the massive data.
分析和实验表明，该算法适合于海量数据查询并能有效地解决机群并行环境下数据偏斜所造成的查询性能低下的问题。
Table data skew refers to a difference between the number of records in a table on particular database partitions and the average number of records across all database partitions for this table.
表数据倾斜指的是特定的一些数据库分区上的某个表内的记录数与这个表所跨的所有数据库分区的平均记录数之间的差额。
Data skew has great effects on the performance of parallel database systems.
数据倾斜对并行数据库系统性能的影响极大。
Data skew has great effects on the performance of parallel database systems.
数据倾斜对并行数据库系统性能的影响极大。
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