pub fn partition(self, available: HashMap<usize, usize>, input_arity: usize) -> (Self, Self) {
        // Map expressions, filter predicates, and projections for `before` and `after`.
        let mut before_expr = Vec::new();
        let mut before_pred = Vec::new();
        let mut before_proj = Vec::new();
        let mut after_expr = Vec::new();
        let mut after_pred = Vec::new();
        let mut after_proj = Vec::new();

        // Track which output columns must be preserved in the output of `before`.
        let mut demanded = HashSet::new();
        demanded.extend(0..self.input_arity);
        demanded.extend(self.projection.iter());

        // Determine which map expressions can be computed from the available subset.
        // Some expressions may depend on other expressions, but by evaluating them
        // in forward order we should accurately determine the available expressions.
        let mut available_expr = vec![false; self.input_arity];
        // Initialize available columns from `available`, which is then not used again.
        for index in available.keys() {
            available_expr[*index] = true;
        }
        for expr in self.expressions.into_iter() {
            // We treat an expression as available if its supporting columns are available,
            // and if it is not a literal (we want to avoid pushing down literals). This
            // choice is ad-hoc, but the intent is that we partition the operators so
            // that we can reduce the row representation size and total computation.
            // Pushing down literals harms the former and does nothing for the latter.
            // In the future, we'll want to have a harder think about this trade-off, as
            // we are certainly making sub-optimal decisions by pushing down all available
            // work.
            // TODO(mcsherry): establish better principles about what work to push down.
            let is_available =
                expr.support().into_iter().all(|i| available_expr[i]) && !expr.is_literal();
            if is_available {
                before_expr.push(expr);
            } else {
                demanded.extend(expr.support());
                after_expr.push(expr);
            }
            available_expr.push(is_available);
        }

        // Determine which predicates can be computed from the available subset.
        for (_when, pred) in self.predicates.into_iter() {
            let is_available = pred.support().into_iter().all(|i| available_expr[i]);
            if is_available {
                before_pred.push(pred);
            } else {
                demanded.extend(pred.support());
                after_pred.push(pred);
            }
        }

        // Map from prior output location to location in un-projected `before`.
        // This map is used to correct references in `before` but it should be
        // adjusted to reflect `before`s projection prior to use in `after`.
        let mut before_map = available;
        // Input columns include any additional undescribed columns that may
        // not be captured by the `available` argument, so we must independently
        // track the current number of columns (vs relying on `before_map.len()`).
        let mut input_columns = input_arity;
        for index in self.input_arity..available_expr.len() {
            if available_expr[index] {
                before_map.insert(index, input_columns);
                input_columns += 1;
            }
        }

        // Permute the column references in `before` expressions and predicates.
        for expr in before_expr.iter_mut() {
            expr.permute_map(&before_map);
        }
        for pred in before_pred.iter_mut() {
            pred.permute_map(&before_map);
        }

        // Demand information determines `before`s output projection.
        // Specifically, we produce all input columns in the output, as well as
        // any columns that are available and demanded.
        before_proj.extend(0..input_arity);
        for index in self.input_arity..available_expr.len() {
            // If an intermediate result is both available and demanded,
            // we should produce it as output.
            if available_expr[index] && demanded.contains(&index) {
                // Use the new location of `index`.
                before_proj.push(before_map[&index]);
            }
        }

        // Map from prior output locations to location in post-`before` columns.
        // This map is used to correct references in `after`.
        // The presumption is that `after` will be presented with all input columns,
        // followed by the output columns introduced by `before` in order.
        let mut after_map = HashMap::new();
        for index in 0..self.input_arity {
            after_map.insert(index, index);
        }
        for index in self.input_arity..available_expr.len() {
            // If an intermediate result is both available and demanded,
            // it was produced as output.
            if available_expr[index] && demanded.contains(&index) {
                // We expect to find the output as far after `self.input_arity` as
                // it was produced after `input_arity` in the output of `before`.
                let location = self.input_arity
                    + (before_proj
                        .iter()
                        .position(|x| x == &before_map[&index])
                        .unwrap()
                        - input_arity);
                after_map.insert(index, location);
            }
        }
        // We must now re-map the remaining non-demanded expressions, which are
        // contiguous rather than potentially interspersed.
        for index in self.input_arity..available_expr.len() {
            if !available_expr[index] {
                after_map.insert(index, after_map.len());
            }
        }

        // Permute the column references in `after` expressions and predicates.
        for expr in after_expr.iter_mut() {
            expr.permute_map(&after_map);
        }
        for pred in after_pred.iter_mut() {
            pred.permute_map(&after_map);
        }
        // Populate `after` projection with the new locations of `self.projection`.
        for index in self.projection {
            after_proj.push(after_map[&index]);
        }

        // Form and return the before and after MapFilterProject instances.
        let before = Self::new(input_arity)
            .map(before_expr)
            .filter(before_pred)
            .project(before_proj.clone());
        let after = Self::new(self.input_arity + (before_proj.len() - input_arity))
            .map(after_expr)
            .filter(after_pred)
            .project(after_proj);
        (before, after)
    }
/// Partitions `self` into two instances, one of which can be eagerly applied.
///
/// The `available` argument indicates which input columns are available (keys)
/// and in which positions (values). This information may allow some maps and
/// filters to execute. The `input_arity` argument reports the total number of
/// input columns (which may include some not present in `available`)
///
/// This method partitions `self` in two parts, `(before, after)`, where `before`
/// can be applied on columns present as keys in `available`, and `after` must
/// await the introduction of the other input columns.
///
/// The `before` instance will *append* any columns that can be determined from
/// `available` but will project away any of these columns that are not needed by
/// `after`. Importantly, this means that `before` will leave intact *all* input
/// columns including those not referenced in `available`.
///
/// The `after` instance will presume all input columns are available, followed
/// by the appended columns of the `before` instance. It may be that some input
/// columns can be projected away in `before` if `after` does not need them, but
/// we leave that as something the caller can apply if needed (it is otherwise
/// complicated to negotiate which input columns `before` should retain).
///
/// To correctly reconstruct `self` from `before` and `after`, one must introduce
/// additional input columns, permute all input columns to their locations as
/// expected by `self`, follow this by new columns appended by `before`, and
/// remove all other columns that may be present.
///
/// # Example
///
/// ```rust
/// use expr::{BinaryFunc, MapFilterProject, MirScalarExpr};
///
/// // imagine an action on columns (a, b, c, d).
/// let original = MapFilterProject::new(4).map(vec![
///    MirScalarExpr::column(0).call_binary(MirScalarExpr::column(1), BinaryFunc::AddInt64),
///    MirScalarExpr::column(2).call_binary(MirScalarExpr::column(4), BinaryFunc::AddInt64),
///    MirScalarExpr::column(3).call_binary(MirScalarExpr::column(5), BinaryFunc::AddInt64),
/// ]).project(vec![6]);
///
/// // Imagine we start with columns (b, x, a, y, c).
/// //
/// // The `partition` method requires a map from *expected* input columns to *actual*
/// // input columns. In the example above, the columns a, b, and c exist, and are at
/// // locations 2, 0, and 4 respectively. We must construct a map to this effect.
/// let mut available_columns = std::collections::HashMap::new();
/// available_columns.insert(0, 2);
/// available_columns.insert(1, 0);
/// available_columns.insert(2, 4);
/// // Partition `original` using the available columns and current input arity.
/// // This informs `partition` which columns are available, where they can be found,
/// // and how many columns are not relevant but should be preserved.
/// let (before, after) = original.partition(available_columns, 5);
///
/// // `before` sees all five input columns, and should append `a + b + c`.
/// assert_eq!(before, MapFilterProject::new(5).map(vec![
///    MirScalarExpr::column(2).call_binary(MirScalarExpr::column(0), BinaryFunc::AddInt64),
///    MirScalarExpr::column(4).call_binary(MirScalarExpr::column(5), BinaryFunc::AddInt64),
/// ]).project(vec![0, 1, 2, 3, 4, 6]));
///
/// // `after` expects to see `(a, b, c, d, a + b + c)`.
/// assert_eq!(after, MapFilterProject::new(5).map(vec![
///    MirScalarExpr::column(3).call_binary(MirScalarExpr::column(4), BinaryFunc::AddInt64)
/// ]).project(vec![5]));
///
/// // To reconstruct `self`, we must introduce the columns that are not present,
/// // and present them in the order intended by `self`. In this example, we must
/// // introduce colunm d and permute the columns so that they begin (a, b, c, d).
/// // The columns x and y must be projected away, and any columns introduced by
/// // `begin` must be retained in their current order.
///
/// // The `after` instance expects to be provided with all inputs, but it
/// // may not need all inputs. The `demand()` and `permute()` methods can
/// // optimize the representation.
/// ```
Partitions `self` into two instances, one of which can be eagerly applied.
The `available` argument indicates which input columns are available (keys)
and in which positions (values). This information may allow some maps and
filters to execute. The `input_arity` argument reports the total number of
input columns (which may include some not present in `available`)





The `after` instance will presume all input columns are available, followed
by the appended columns of the `before` instance.


Partitions ` self ` into two instances one of which can be eagerly applied . The ` available ` argument indicates which input columns are available ( keys ) and in which positions ( values ) . This information may allow some maps and filters to execute . The ` input_arity ` argument reports the total number of input columns ( which may include some not present in ` available ` ) The ` after ` instance will presume all input columns are available followed by the appended columns of the ` before ` instance .
pub fn partition(self, available: HashMap<usize, usize>, input_arity: usize) -> (Self, Self) {
        // Map expressions, filter predicates, and projections for `before` and `after`.
        let mut before_expr = Vec::new();
        let mut before_pred = Vec::new();
        let mut before_proj = Vec::new();
        let mut after_expr = Vec::new();
        let mut after_pred = Vec::new();
        let mut after_proj = Vec::new();
        // Track which output columns must be preserved in the output of `before`.
        let mut demanded = HashSet::new();
        demanded.extend(0..self.input_arity);
        demanded.extend(self.projection.iter());
        // Determine which map expressions can be computed from the available subset.
        // Some expressions may depend on other expressions, but by evaluating them
        // in forward order we should accurately determine the available expressions.
        let mut available_expr = vec![false; self.input_arity];
        // Initialize available columns from `available`, which is then not used again.
        for index in available.keys() {
            available_expr[*index] = true;
        }
        for expr in self.expressions.into_iter() {
            // We treat an expression as available if its supporting columns are available,
            // and if it is not a literal (we want to avoid pushing down literals). This
            // choice is ad-hoc, but the intent is that we partition the operators so
            // that we can reduce the row representation size and total computation.
            // Pushing down literals harms the former and does nothing for the latter.
            // In the future, we'll want to have a harder think about this trade-off, as
            // we are certainly making sub-optimal decisions by pushing down all available
            // work.
            // TODO(mcsherry): establish better principles about what work to push down.
            let is_available =
                expr.support().into_iter().all(|i| available_expr[i]) && !expr.is_literal();
            if is_available {
                before_expr.push(expr);
            } else {
                demanded.extend(expr.support());
                after_expr.push(expr);
            }
            available_expr.push(is_available);
        }
        // Determine which predicates can be computed from the available subset.
        for (_when, pred) in self.predicates.into_iter() {
            let is_available = pred.support().into_iter().all(|i| available_expr[i]);
            if is_available {
                before_pred.push(pred);
            } else {
                demanded.extend(pred.support());
                after_pred.push(pred);
            }
        }
        // Map from prior output location to location in un-projected `before`.
        // This map is used to correct references in `before` but it should be
        // adjusted to reflect `before`s projection prior to use in `after`.
        let mut before_map = available;
        // Input columns include any additional undescribed columns that may
        // not be captured by the `available` argument, so we must independently
        // track the current number of columns (vs relying on `before_map.len()`).
        let mut input_columns = input_arity;
        for index in self.input_arity..available_expr.len() {
            if available_expr[index] {
                before_map.insert(index, input_columns);
                input_columns += 1;
            }
        }
        // Permute the column references in `before` expressions and predicates.
        for expr in before_expr.iter_mut() {
            expr.permute_map(&before_map);
        }
        for pred in before_pred.iter_mut() {
            pred.permute_map(&before_map);
        }
        // Demand information determines `before`s output projection.
        // Specifically, we produce all input columns in the output, as well as
        // any columns that are available and demanded.
        before_proj.extend(0..input_arity);
        for index in self.input_arity..available_expr.len() {
            // If an intermediate result is both available and demanded,
            // we should produce it as output.
            if available_expr[index] && demanded.contains(&index) {
                // Use the new location of `index`.
                before_proj.push(before_map[&index]);
            }
        }
        // Map from prior output locations to location in post-`before` columns.
        // This map is used to correct references in `after`.
        // The presumption is that `after` will be presented with all input columns,
        // followed by the output columns introduced by `before` in order.
        let mut after_map = HashMap::new();
        for index in 0..self.input_arity {
            after_map.insert(index, index);
        }
        for index in self.input_arity..available_expr.len() {
            // If an intermediate result is both available and demanded,
            // it was produced as output.
            if available_expr[index] && demanded.contains(&index) {
                // We expect to find the output as far after `self.input_arity` as
                // it was produced after `input_arity` in the output of `before`.
                let location = self.input_arity
                    + (before_proj
                        .iter()
                        .position(|x| x == &before_map[&index])
                        .unwrap()
                        - input_arity);
                after_map.insert(index, location);
            }
        }
        // We must now re-map the remaining non-demanded expressions, which are
        // contiguous rather than potentially interspersed.
        for index in self.input_arity..available_expr.len() {
            if !available_expr[index] {
                after_map.insert(index, after_map.len());
            }
        }
        // Permute the column references in `after` expressions and predicates.
        for expr in after_expr.iter_mut() {
            expr.permute_map(&after_map);
        }
        for pred in after_pred.iter_mut() {
            pred.permute_map(&after_map);
        }
        // Populate `after` projection with the new locations of `self.projection`.
        for index in self.projection {
            after_proj.push(after_map[&index]);
        }
        // Form and return the before and after MapFilterProject instances.
        let before = Self::new(input_arity)
            .map(before_expr)
            .filter(before_pred)
            .project(before_proj.clone());
        let after = Self::new(self.input_arity + (before_proj.len() - input_arity))
            .map(after_expr)
            .filter(after_pred)
            .project(after_proj);
        (before, after)
    }
pub fn partition ( self , available : HashMap < usize , usize > , input_arity : usize ) -> ( Self , Self ) { // Map expressions, filter predicates, and projections for `before` and `after`. let mut before_expr = Vec :: new ( ) ; let mut before_pred = Vec :: new ( ) ; let mut before_proj = Vec :: new ( ) ; let mut after_expr = Vec :: new ( ) ; let mut after_pred = Vec :: new ( ) ; let mut after_proj = Vec :: new ( ) ; // Track which output columns must be preserved in the output of `before`. let mut demanded = HashSet :: new ( ) ; demanded . extend ( 0 .. self . input_arity ) ; demanded . extend ( self . projection . iter ( ) ) ; // Determine which map expressions can be computed from the available subset. // Some expressions may depend on other expressions, but by evaluating them // in forward order we should accurately determine the available expressions. let mut available_expr = vec ! [ false self input_arity ] ; // Initialize available columns from `available`, which is then not used again. for index in available . keys ( ) { available_expr [ * index ] = true ; } for expr in self . expressions . into_iter ( ) { // We treat an expression as available if its supporting columns are available, // and if it is not a literal (we want to avoid pushing down literals). This // choice is ad-hoc, but the intent is that we partition the operators so // that we can reduce the row representation size and total computation. // Pushing down literals harms the former and does nothing for the latter. // In the future, we'll want to have a harder think about this trade-off, as // we are certainly making sub-optimal decisions by pushing down all available // work. // TODO(mcsherry): establish better principles about what work to push down. let is_available = expr . support ( ) . into_iter ( ) . all ( | i | available_expr [ i ] ) && ! expr . is_literal ( ) ; if is_available { before_expr . push ( expr ) ; } else { demanded . extend ( expr . support ( ) ) ; after_expr . push ( expr ) ; } available_expr . push ( is_available ) ; } // Determine which predicates can be computed from the available subset. for ( _when , pred ) in self . predicates . into_iter ( ) { let is_available = pred . support ( ) . into_iter ( ) . all ( | i | available_expr [ i ] ) ; if is_available { before_pred . push ( pred ) ; } else { demanded . extend ( pred . support ( ) ) ; after_pred . push ( pred ) ; } } // Map from prior output location to location in un-projected `before`. // This map is used to correct references in `before` but it should be // adjusted to reflect `before`s projection prior to use in `after`. let mut before_map = available ; // Input columns include any additional undescribed columns that may // not be captured by the `available` argument, so we must independently // track the current number of columns (vs relying on `before_map.len()`). let mut input_columns = input_arity ; for index in self . input_arity .. available_expr . len ( ) { if available_expr [ index ] { before_map . insert ( index , input_columns ) ; input_columns += 1 ; } } // Permute the column references in `before` expressions and predicates. for expr in before_expr . iter_mut ( ) { expr . permute_map ( & before_map ) ; } for pred in before_pred . iter_mut ( ) { pred . permute_map ( & before_map ) ; } // Demand information determines `before`s output projection. // Specifically, we produce all input columns in the output, as well as // any columns that are available and demanded. before_proj . extend ( 0 .. input_arity ) ; for index in self . input_arity .. available_expr . len ( ) { // If an intermediate result is both available and demanded, // we should produce it as output. if available_expr [ index ] && demanded . contains ( & index ) { // Use the new location of `index`. before_proj . push ( before_map [ & index ] ) ; } } // Map from prior output locations to location in post-`before` columns. // This map is used to correct references in `after`. // The presumption is that `after` will be presented with all input columns, // followed by the output columns introduced by `before` in order. let mut after_map = HashMap :: new ( ) ; for index in 0 .. self . input_arity { after_map . insert ( index , index ) ; } for index in self . input_arity .. available_expr . len ( ) { // If an intermediate result is both available and demanded, // it was produced as output. if available_expr [ index ] && demanded . contains ( & index ) { // We expect to find the output as far after `self.input_arity` as // it was produced after `input_arity` in the output of `before`. let location = self . input_arity + ( before_proj . iter ( ) . position ( | x | x == & before_map [ & index ] ) . unwrap ( ) - input_arity ) ; after_map . insert ( index , location ) ; } } // We must now re-map the remaining non-demanded expressions, which are // contiguous rather than potentially interspersed. for index in self . input_arity .. available_expr . len ( ) { if ! available_expr [ index ] { after_map . insert ( index , after_map . len ( ) ) ; } } // Permute the column references in `after` expressions and predicates. for expr in after_expr . iter_mut ( ) { expr . permute_map ( & after_map ) ; } for pred in after_pred . iter_mut ( ) { pred . permute_map ( & after_map ) ; } // Populate `after` projection with the new locations of `self.projection`. for index in self . projection { after_proj . push ( after_map [ & index ] ) ; } // Form and return the before and after MapFilterProject instances. let before = Self :: new ( input_arity ) . map ( before_expr ) . filter ( before_pred ) . project ( before_proj . clone ( ) ) ; let after = Self :: new ( self . input_arity + ( before_proj . len ( ) - input_arity ) ) . map ( after_expr ) . filter ( after_pred ) . project ( after_proj ) ; ( before , after ) }
Partitions `self` into two instances, one of which can be eagerly applied.
Partitions ` self ` into two instances one of which can be eagerly applied .
self
available
HashMap<usize, usize>
input_arity
usize
self
available
HashMap<usize, usize>
input_arity
usize
example
```rust
use expr::{BinaryFunc, MapFilterProject, MirScalarExpr}.

imagine an action on columns (a, b, c, d).
let original = MapFilterProject::new(4).map(vec![
MirScalarExpr::column(0).call_binary(MirScalarExpr::column(1), BinaryFunc::AddInt64),
MirScalarExpr::column(2).call_binary(MirScalarExpr::column(4), BinaryFunc::AddInt64),
MirScalarExpr::column(3).call_binary(MirScalarExpr::column(5), BinaryFunc::AddInt64),
]).project(vec![6]).

Imagine we start with columns (b, x, a, y, c).

The `partition` method requires a map from *expected* input columns to *actual
input columns. In the example above, the columns a, b, and c exist, and are at
locations 2, 0, and 4 respectively. We must construct a map to this effect.
let mut available_columns = std::collections::HashMap::new();
available_columns.insert(0, 2);
available_columns.insert(1, 0);
available_columns.insert(2, 4);
Partition `original` using the available columns and current input arity.
This informs `partition` which columns are available, where they can be found,
and how many columns are not relevant but should be preserved.
let (before, after) = original.partition(available_columns, 5).

`before` sees all five input columns, and should append `a + b + c`.
assert_eq!(before, MapFilterProject::new(5).map(vec![
MirScalarExpr::column(2).call_binary(MirScalarExpr::column(0), BinaryFunc::AddInt64),
MirScalarExpr::column(4).call_binary(MirScalarExpr::column(5), BinaryFunc::AddInt64),
]).project(vec![0, 1, 2, 3, 4, 6])).

`after` expects to see `(a, b, c, d, a + b + c)`.
assert_eq!(after, MapFilterProject::new(5).map(vec![
MirScalarExpr::column(3).call_binary(MirScalarExpr::column(4), BinaryFunc::AddInt64)
]).project(vec![5])).

To reconstruct `self`, we must introduce the columns that are not present,
and present them in the order intended by `self`. In this example, we must
introduce colunm d and permute the columns so that they begin (a, b, c, d).
The columns x and y must be projected away, and any columns introduced by
`begin` must be retained in their current order.

The `after` instance expects to be provided with all inputs, but it
may not need all inputs. The `demand()` and `permute()` methods can
optimize the representation.
` ` ` rust use expr :: { BinaryFunc MapFilterProject MirScalarExpr } . imagine an action on columns ( a b c d ) . let original = MapFilterProject :: new ( 4 ) . map ( vec! [ MirScalarExpr :: column ( 0 ) . call_binary ( MirScalarExpr :: column ( 1 ) BinaryFunc :: AddInt64 ) MirScalarExpr :: column ( 2 ) . call_binary ( MirScalarExpr :: column ( 4 ) BinaryFunc :: AddInt64 ) MirScalarExpr :: column ( 3 ) . call_binary ( MirScalarExpr :: column ( 5 ) BinaryFunc :: AddInt64 ) ] ) . project ( vec! [ 6 ] ) . Imagine we start with columns ( b x a y c ) . The ` partition ` method requires a map from * expected * input columns to * actual input columns . In the example above the columns a b and c exist and are at locations 2 0 and 4 respectively . We must construct a map to this effect . let mut available_columns = std :: collections :: HashMap :: new () ; available_columns . insert ( 0 2 ) ; available_columns . insert ( 1 0 ) ; available_columns . insert ( 2 4 ) ; Partition ` original ` using the available columns and current input arity . This informs ` partition ` which columns are available where they can be found and how many columns are not relevant but should be preserved . let ( before after ) = original . partition ( available_columns 5 ) . ` before ` sees all five input columns and should append ` a + b + c ` . assert_eq! ( before MapFilterProject :: new ( 5 ) . map ( vec! [ MirScalarExpr :: column ( 2 ) . call_binary ( MirScalarExpr :: column ( 0 ) BinaryFunc :: AddInt64 ) MirScalarExpr :: column ( 4 ) . call_binary ( MirScalarExpr :: column ( 5 ) BinaryFunc :: AddInt64 ) ] ) . project ( vec! [ 0 1 2 3 4 6 ] )) . ` after ` expects to see ` ( a b c d a + b + c ) ` . assert_eq! ( after MapFilterProject :: new ( 5 ) . map ( vec! [ MirScalarExpr :: column ( 3 ) . call_binary ( MirScalarExpr :: column ( 4 ) BinaryFunc :: AddInt64 ) ] ) . project ( vec! [ 5 ] )) . To reconstruct ` self ` we must introduce the columns that are not present and present them in the order intended by ` self ` . In this example we must introduce colunm d and permute the columns so that they begin ( a b c d ) . The columns x and y must be projected away and any columns introduced by ` begin ` must be retained in their current order . The ` after ` instance expects to be provided with all inputs but it may not need all inputs . The ` demand () ` and ` permute () ` methods can optimize the representation .