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perf: Prevent deferring filters with remote functions #1220

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perf: Prevent deferring filters with remote functions #1220

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perf: Prevent deferring filters with remote functions
TrevorBergeron Dec 14, 2024
d780dde
Merge branch 'main' into remote_op_reproject
TrevorBergeron Dec 16, 2024
477e50f
Merge branch 'main' into remote_op_reproject
tswast Dec 27, 2024
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11 changes: 11 additions & 0 deletions bigframes/core/compile/compiled.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,7 @@
import bigframes.core.sql
from bigframes.core.window_spec import RangeWindowBounds, RowsWindowBounds, WindowSpec
import bigframes.dtypes
import bigframes.operations
import bigframes.operations.aggregations as agg_ops

ORDER_ID_COLUMN = "bigframes_ordering_id"
Expand Down Expand Up @@ -132,12 +133,22 @@ def projection(
expression_id_pairs: typing.Tuple[typing.Tuple[ex.Expression, str], ...],
) -> T:
"""Apply an expression to the ArrayValue and assign the output to a column."""

# Remote ops are expensive so force reprojection before to ensure filters get applied first
any_remote_op = any(
ex.contains_op(expr, bigframes.operations.RemoteFunctionOp)
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@tswast tswast Dec 27, 2024 •
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I'm a little bit concerned about hard coding remote function op here. Would it be better to flag each scalar operator as "expensive" / "cheap"?

Certainly there's a spectrum of costs possible, but a distinction could be made if there's an associated cost (e.g. cloud run CPU in this case) per call.

For example, LLM-style ops, BQML prediction (though that's not a scalar in BigQuery terms, though maybe could be in Bigframes eventually), could similarly have an "there's an extra cost associated here" flag.

for expr, _ in expression_id_pairs
)
if any_remote_op and len(self._predicates) > 0:
return self._reproject_to_table().projection(expression_id_pairs)

bindings = {col: self._get_ibis_column(col) for col in self.column_ids}
new_values = [
op_compiler.compile_expression(expression, bindings).name(id)
for expression, id in expression_id_pairs
]
result = self._select(tuple([*self._columns, *new_values])) # type: ignore

return result

def selection(
Expand Down
9 changes: 9 additions & 0 deletions bigframes/core/expression.py
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Original file line number Diff line number Diff line change
Expand Up @@ -40,6 +40,15 @@ def free_var(id: str) -> UnboundVariableExpression:
return UnboundVariableExpression(id)


def contains_op(expr: Expression, op: type[bigframes.operations.ScalarOp]):
if isinstance(expr, OpExpression):
if isinstance(expr.op, op):
return True
else:
return any(contains_op(subexpr, op) for subexpr in expr.inputs)
return False


@dataclasses.dataclass(frozen=True)
class Aggregation(abc.ABC):
"""Represents windowing or aggregation over a column."""
Expand Down
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