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https://issues.apache.org/jira/browse/MADLIB-1265?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Nikhil updated MADLIB-1265:
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Summary: Formalize the read data code for parallel segment data loading
(was: Load data from database into PL/Python)
> Formalize the read data code for parallel segment data loading
> --------------------------------------------------------------
>
> Key: MADLIB-1265
> URL: https://issues.apache.org/jira/browse/MADLIB-1265
> Project: Apache MADlib
> Issue Type: New Feature
> Components: Module: Utilities
> Reporter: Frank McQuillan
> Priority: Major
> Fix For: v2.0
>
>
> Story
> `As a data scientist`
> I want to easily and efficiently load data from the database into PL/Python
> memory
> `so that`
> I can use the loaded data in my PL/Python code.
> Interface
> {code}
> load_to_plpythonu (
> source_table, -- source table
> list_of_columns, -- columns you
> want in GD, could be '*'
> list_of_columns_to_exclude -- columns explicitly
> not to load
> );
> {code}
> Arguments
> {code}
> source_table
> TEXT. Name of the table containing the data to load.
> list_of_columns
> TEXT. Comma-separated string of column names or expressions to load.
> Can also be '*' implying all columns are to be loaded (except for the ones
> included
> in the next argument that lists exclusions). The types of the columns can be
> mixed.
> Array columns can also be included in the list and will be loaded as is
> (i.e., not be flattened). (???)
> list_of_columns_to_exclude
> TEXT. Comma-separated string of column names to exclude from load. Typically
> used when 'list_of_columns' is set to '*'.
> {code}
> Details
> 1) This function will user facing and also will be called internally by other
> MADlib functions in the area of data parallel models.
> 2) The interface above is modeled on DT/RF. I think it should be the same
> general idea.
> Open questions
> 1) Is the interface above the correct one? Are there any parameters missing?
> 2) Can we support array columns, and is it necessary to flatten them? i.e.,
> can we leave them unflattened, since that is preferable?
> Acceptance
> 1) Load MNIST data set from PG or GP into PL/Python and print out the a few
> rows of the data.
> 2) Load array columns and mixed type data into PL/Python and confirm that
> types and formats are preserved.
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