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From orhankislal <...@git.apache.org>
Subject [GitHub] incubator-madlib pull request #54: Pivoting: Phase 2
Date Fri, 08 Jul 2016 18:57:35 GMT
Github user orhankislal commented on a diff in the pull request:

    https://github.com/apache/incubator-madlib/pull/54#discussion_r70123955
  
    --- Diff: src/ports/postgres/modules/utilities/pivot.py_in ---
    @@ -58,66 +61,164 @@ def pivot(schema_madlib, source_table, out_table,
                                     pivoted table
             @param aggregate_func   The aggregate function to be applied to the
                                     values
    +        @param fill_value       If specified, determines how to fill NULL
    +                                values resulting from pivot operation
    +        @param keep_null        The flag for determining how to handle NULL
    +                                values in pivot columns
         """
    -
         """
         Assume we have the following table
             pivset( id INTEGER, piv FLOAT8, val FLOAT8 )
    -    where the piv column has 3 distinct values (10.0, 20.0 and 30.0).
    +    where the piv column has 3 distinct values (10, 20 and 30).
         If the pivot function call is :
             SELECT madlib.pivot('pivset', 'pivout', 'id', 'piv', 'val');
         We want to construct the following sql code to pivot the table.
             CREATE TABLE pivout AS (SELECT id,
    -        sum(CASE WHEN "piv" = '10.0' THEN val ELSE NULL END ) as "piv_10.0",
    -        sum(CASE WHEN "piv" = '20.0' THEN val ELSE NULL END ) as "piv_20.0",
    -        sum(CASE WHEN "piv" = '30.0' THEN val ELSE NULL END ) as "piv_30.0"
    +        avg(CASE WHEN "piv" = '10' THEN val ELSE NULL END ) as "val_avg_piv_10",
    +        avg(CASE WHEN "piv" = '20' THEN val ELSE NULL END ) as "val_avg_piv_20",
    +        avg(CASE WHEN "piv" = '30' THEN val ELSE NULL END ) as "val_avg_piv_30"
             FROM pivset GROUP BY id ORDER BY id)
     
         """
         indices = split_quoted_delimited_str(index)
    -    pcol = split_quoted_delimited_str(pivot_cols)
    -    pval = split_quoted_delimited_str(pivot_values)
    -    validate_pivot_coding(source_table, out_table, indices, pcol, pval)
    +    pcols = split_quoted_delimited_str(pivot_cols)
    +    pvals = split_quoted_delimited_str(pivot_values)
    +    validate_pivot_coding(source_table, out_table, indices, pcols, pvals)
    +
    +    # Strip end quotes from pivot columns
    +    pcols = [strip_end_quotes(pcol.strip()) for pcol in pcols]
    +
    +    # Parse the aggregate_func as a dictionary
    +    try:
    +        param_types = dict.fromkeys(pvals, list)
    +        param_types['__madlib_def_pval__'] = list
    +        agg_dict = extract_keyvalue_params(aggregate_func,param_types)
    +    except KeyError, e:
    +        with MinWarning("warning"):
    +            plpy.warning("Pivot: Not all columns from '{aggregate_func}' present"
    +            " in '{pivot_values}'".format(**locals()))
    +            raise
    +
    +    # If the dictionary is empty, parse it as a list
    +    agg_set = split_quoted_delimited_str(aggregate_func) if len(agg_dict) < 1 \
    +         else []
    +    # If the list is empty set 'avg' as default
    +    agg_dict['__madlib_def_agg__'] = ['avg'] if len(agg_set) < 1 else agg_set
    +
    +    # Find the distinct values of pivot_cols
    +    # Note that the distinct values are appended in order of users list
    +    # This ordering is important when we access pivot_comb entries
    +    distinct_str = ["SELECT "]
    +    distinct_str.append(
    +        ', '.join("array_agg(DISTINCT {pcol}) AS {pcol}_values".
    +            format(pcol=pcol) for pcol in pcols))
    +    distinct_str.append(" FROM " + source_table)
    +    distinct_values = plpy.execute(''.join(distinct_str))
    +
    +    # Collect the distinc values for every pivot column
    +    pcol_distinct_values = []
    +    for pcol in pcols:
    +        # Read the distinct values for this pcol
    +        pcol_tmp = [item for item in
    +            distinct_values[0][pcol+"_values"]]
    +        # Remove null values if keep null is not true
    +        if not keep_null:
    +            pcol_tmp = filter(None,pcol_tmp)
    --- End diff --
    
    This removes 0 values in addition to NULLs. I replaced it with `pcol_tmp = [x for x in
pcol_tmp if x is not None]`


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