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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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Frank McQuillan updated MADLIB-1265:
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Fix Version/s: (was: v2.0)
v2.1
> 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
> Assignee: Nikhil
> Priority: Major
> Fix For: v2.1
>
>
> Story
> We need to productize the read data code so that the madlib.fit function can
> call it along with the user defined python script.
> Details
> * Figure out how to copy read_data.py and user code to all hosts?
> * The read_data script will call the user data script and then write the
> model. This way the user can easily iterate on their python file.
> * Think about error handling in read_data.py. If we write all errors to a log
> file, do we delete the log file every time the madlib.fit udf is called? Do
> we need to rotate the log files?
> * We need to make sure that we take a lock on model file while writing. An
> alternative to avoid the need for locking is to create one file per segment.
> We can append the segment id in the file name and use that name to create the
> external readable table.
> * read_data.py can be copied to all the segments during madlib install. This
> file can take the user_defined_module as an argument which will then be
> dynamically imported.
> * How will the python memory be managed ? The postgres process for each
> spawns a connection process because of the INSERT which in turn spawns
> another process run our executable command from the CREATE WRITABLE EXTERNAL
> WEB TABLE definition. This means that the memory is prob not restricted by
> greenplum.
> * Should read_data.py also get the column names and the grouping col
> information? Can we pass the metadata without duplication ? madlib.fit which
> will be a plpython function can take care of this:
> a. Get the absolute path of the user defined python file and copy it to
> all the segments ( this is up for discussion, maybe there is a better way to
> copy the user defined code to all the segments)
> b. Parse the columns to include and get the types of all the columns
> using plpy.execute(). Write this metadata information along with any other
> relevant information to a yml file and copy this to all the segments.
> * The grp col value should be written to the model file.
> * Since the data is read through a pipe, read_data.py can also provide an api
> to stream rows to the user defined python file.
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