upsert_util.py

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from pyarrow import Table as pyarrow_table
import pyarrow as pa
from pyarrow import compute as pc
from pyiceberg import table as pyiceberg_table

from pyiceberg.expressions import (
    BooleanExpression,
    And,
    EqualTo,
    Or,
    In,
)

def get_filter_list(df: pyarrow_table, join_cols: list) -> BooleanExpression:

    unique_keys = df.select(join_cols).group_by(join_cols).aggregate([])

    pred = None

    if len(join_cols) == 1:
        pred = In(join_cols[0], unique_keys[0].to_pylist())
    else:
        pred = Or(*[
            And(*[
                EqualTo(col, row[col])
                for col in join_cols
            ])
            for row in unique_keys.to_pylist()
        ])

    return pred

def dups_check_in_source(df: pyarrow_table, join_cols: list) -> bool:
    """
    This function checks if there are duplicate rows in the source table based on the join columns.
    It returns True if there are duplicate rows in the source table, otherwise it returns False.
    """
    # Check for duplicates in the source table
    source_dup_count = len(
        df.select(join_cols)
            .group_by(join_cols)
            .aggregate([([], "count_all")])
            .filter(pc.field("count_all") > 1)
    )
    
    return source_dup_count > 0

def get_rows_to_update(source_table: pa.Table, target_table: pa.Table, join_cols: list) -> pa.Table:
    
    """
        This function takes the source_table, trims it down to rows that match in both source and target.
        It then does a scan for the non-key columns to see if any are mis-aligned before returning the final row set to update
    """
    
    all_columns = set(source_table.column_names)
    join_cols_set = set(join_cols)

    non_key_cols = list(all_columns - join_cols_set)

    
    match_expr = None

    for col in join_cols:
        target_values = target_table.column(col).to_pylist()
        expr = pc.field(col).isin(target_values)

        if match_expr is None:
            match_expr = expr
        else:
            match_expr = match_expr & expr

    
    matching_source_rows = source_table.filter(match_expr)

    rows_to_update = []

    for index in range(matching_source_rows.num_rows):
        
        source_row = matching_source_rows.slice(index, 1)

        
        target_filter = None

        for col in join_cols:
            target_value = source_row.column(col)[0].as_py()  
            if target_filter is None:
                target_filter = pc.field(col) == target_value
            else:
                target_filter = target_filter & (pc.field(col) == target_value)

        matching_target_row = target_table.filter(target_filter)

        if matching_target_row.num_rows > 0:
            needs_update = False

            for non_key_col in non_key_cols:
                source_value = source_row.column(non_key_col)[0].as_py()
                target_value = matching_target_row.column(non_key_col)[0].as_py()

                if source_value != target_value:
                    needs_update = True
                    break 

            if needs_update:
                rows_to_update.append(source_row)

    if rows_to_update:
        rows_to_update_table = pa.concat_tables(rows_to_update)
    else:
        rows_to_update_table = pa.Table.from_arrays([], names=source_table.column_names)

    common_columns = set(source_table.column_names).intersection(set(target_table.column_names))
    rows_to_update_table = rows_to_update_table.select(list(common_columns))

    return rows_to_update_table

def get_rows_to_insert(source_table: pa.Table, target_table: pa.Table, join_cols: list) -> pa.Table:
  
    source_filter_expr = None

    for col in join_cols:

        target_values = target_table.column(col).to_pylist()
        expr = pc.field(col).isin(target_values)

        if source_filter_expr is None:
            source_filter_expr = expr
        else:
            source_filter_expr = source_filter_expr & expr

    non_matching_expr = ~source_filter_expr

    source_columns = set(source_table.column_names)
    target_columns = set(target_table.column_names)
  
    common_columns = source_columns.intersection(target_columns)

    non_matching_rows = source_table.filter(non_matching_expr).select(common_columns)

    return non_matching_rows