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Radoop Documentation, Release 2.1
13.16.5 Generate Rank
Synopsis
This operator generates the (dense) rank of each row within the given partition.
Description
The rank of a row is one plus the count of ranks before the given row. The dense rank of a row is one plus the count of
distinct ranks before the given row.
The operator adds a design-time warning, if the partition_by parameter list is empty. The reason is that if no grouping
(partitioning) is defined with this parameter, the operator will generate a global rank attribute after sorting the whole
data set. This can be a very slow operation for a large data set and is probably not what you want to do. If you wan to
add a unique ID variable to the data set, use the Generate ID operator.
Please note that this operator is only supported starting with Hive 0.11. If you use an older server release, please
update, if you want to use this operator.
Input:
• example set input:
– expects: HadoopExampleSet, specified attribute
Output:
• example set output
• original
Parameters:
• attribute name: Attribute name
• partition by: Ordered list of the partitioning attributes.
• order by: The attributes and sorting directions which should be used to determine the order of the data before
the ranking is applied.
• dense rank: Dense Rank returns the rank of rows within the partition of a result set, without any gaps in the
ranking.
– Default value: false
13.16.6 Principal Component Analysis
Synopsis
This operator performs a Principal Component Analysis (PCA) using the covariance matrix. The user can specify the
amount of variance to cover in the original data while retaining the best number of principal components. The user
can also specify manually the number of principal components.
Description
Principal component analysis (PCA) is an attribute reduction procedure. It is useful when you have obtained data
on a number of attributes (possibly a large number of attributes), and believe that there is some redundancy in those
attributes. In this case, redundancy means that some of the attributes are correlated with one another, possibly because
13.16. Transformation - Attribute Set Reduction and Transformation
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