jonathanc-n commented on code in PR #16450:
URL: https://github.com/apache/datafusion/pull/16450#discussion_r2155615159
##########
datafusion/core/src/physical_planner.rs:
##########
@@ -1009,95 +1012,99 @@ impl DefaultPhysicalPlanner {
let left_df_schema = left.schema();
let right_df_schema = right.schema();
let execution_props = session_state.execution_props();
- let join_on = keys
- .iter()
- .map(|(l, r)| {
- let l = create_physical_expr(l, left_df_schema,
execution_props)?;
- let r =
- create_physical_expr(r, right_df_schema,
execution_props)?;
- Ok((l, r))
- })
- .collect::<Result<join_utils::JoinOn>>()?;
-
- let join_filter = match filter {
- Some(expr) => {
- // Extract columns from filter expression and saved in
a HashSet
- let cols = expr.column_refs();
- // Collect left & right field indices, the field
indices are sorted in ascending order
- let left_field_indices = cols
- .iter()
- .filter_map(|c|
left_df_schema.index_of_column(c).ok())
- .sorted()
- .collect::<Vec<_>>();
- let right_field_indices = cols
- .iter()
- .filter_map(|c|
right_df_schema.index_of_column(c).ok())
- .sorted()
- .collect::<Vec<_>>();
+ // We declare a threshold here of 5 rows as NestedLoopJoins
tend to better when one
+ // of the tables are small.
+ let threshold = session_state
+ .config_options()
+ .optimizer
+ .nested_loop_equijoin_threshold;
+ let left_rows = *physical_left
+ // We set the partition to None here to draw the num_rows
from the plan
+ .partition_statistics(None)?
+ .num_rows
+ .get_value()
+ .unwrap()
+ <= threshold;
+ let right_rows = *physical_right
+ .partition_statistics(None)?
+ .num_rows
+ .get_value()
+ .unwrap()
+ <= threshold;
+ let use_nested_loop_join_equijoin = left_rows || right_rows;
+
+ // If we can use a nested loop join then `join_on` will be
empty because
+ // the expressions are moved into the join filter.
+ let join_on: JoinOn = if use_nested_loop_join_equijoin {
+ Vec::new()
+ } else {
+ keys.iter()
+ .map(|(l, r)| {
+ let l =
+ create_physical_expr(l, left_df_schema,
execution_props)?;
+ let r = create_physical_expr(
+ r,
+ right_df_schema,
+ execution_props,
+ )?;
+ Ok((l, r))
+ })
+ .collect::<Result<_>>()?
+ };
- // Collect DFFields and Fields required for
intermediate schemas
- let (filter_df_fields, filter_fields): (Vec<_>,
Vec<_>) =
- left_field_indices
- .clone()
- .into_iter()
- .map(|i| {
- (
- left_df_schema.qualified_field(i),
-
physical_left.schema().field(i).clone(),
- )
+ // If we can use nested loop join then we will combine the
expressions in `join_on`
+ // and pass it into the join filter; create your join filters
normally otherwise.
+ let join_filter: Option<JoinFilter> = if
use_nested_loop_join_equijoin {
+ let combined_join_on_expression: Expr = filter
Review Comment:
It seems that when we combine the `JoinOn` expressions here it will cause an
error when both sides in the expression have the same unqualified name leading
to duplicate unqualified fields. Is there a function that is able to qualify it
with the schema, I can't seem to find one.
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