Several projection improvements

polars/clippy.toml

@@ -0,0 +1 @@
+disallowed-types = ["std::collections::HashMap", "std::collections::HashSet"]

polars/polars-core/src/frame/hash_join/mod.rs

@@ -16,7 +16,6 @@ use ahash::RandomState;
 use hashbrown::hash_map::{Entry, RawEntryMut};
 use hashbrown::HashMap;
 use rayon::prelude::*;
-use std::collections::HashSet;
 use std::fmt::Debug;
 use std::hash::{BuildHasher, Hash, Hasher};
 
@@ -1017,7 +1016,7 @@ impl DataFrame {
         mut df_right: DataFrame,
         suffix: Option<String>,
     ) -> Result<DataFrame> {
-        let mut left_names = HashSet::with_capacity_and_hasher(df_left.width(), RandomState::new());
+        let mut left_names = PlHashSet::with_capacity(df_left.width());
 
         df_left.columns.iter().for_each(|series| {
             left_names.insert(series.name());
@@ -1036,6 +1035,7 @@ impl DataFrame {
             df_right.rename(&name, &format!("{}{}", name, suffix))?;
         }
 
+        drop(left_names);
         df_left.hstack_mut(&df_right.columns)?;
         Ok(df_left)
     }

polars/polars-core/src/frame/mod.rs

@@ -1,6 +1,5 @@
 //! DataFrame module.
 use std::borrow::Cow;
-use std::collections::HashSet;
 use std::iter::{FromIterator, Iterator};
 use std::mem;
 use std::ops;
@@ -33,7 +32,6 @@ use crate::vector_hasher::boost_hash_combine;
 #[cfg(feature = "row_hash")]
 use crate::vector_hasher::df_rows_to_hashes_threaded;
 use crate::POOL;
-use hashbrown::HashMap;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 use std::hash::{BuildHasher, Hash, Hasher};
@@ -155,14 +153,6 @@ impl DataFrame {
         }
     }
 
-    fn hash_names(&self) -> HashSet<String, RandomState> {
-        let mut set = HashSet::with_capacity_and_hasher(self.columns.len(), RandomState::default());
-        for s in &self.columns {
-            set.insert(s.name().to_string());
-        }
-        set
-    }
-
     /// Create a DataFrame from a Vector of Series.
     ///
     /// # Example
@@ -673,7 +663,11 @@ impl DataFrame {
     /// }
     /// ```
     pub fn hstack_mut(&mut self, columns: &[Series]) -> Result<&mut Self> {
-        let mut names = self.hash_names();
+        let mut names = PlHashSet::with_capacity(self.columns.len());
+        for s in &self.columns {
+            names.insert(s.name());
+        }
+
         let height = self.height();
         // first loop check validity. We don't do this in a single pass otherwise
         // this DataFrame is already modified when an error occurs.
@@ -693,8 +687,9 @@ impl DataFrame {
                     .into(),
                 ));
             }
-            names.insert(name.to_string());
+            names.insert(name);
         }
+        drop(names);
         Ok(self.hstack_mut_no_checks(columns))
     }
 
@@ -1280,10 +1275,10 @@ impl DataFrame {
 
     /// A non generic implementation to reduce compiler bloat.
     fn select_series_impl(&self, cols: &[String]) -> Result<Vec<Series>> {
-        let selected = if cols.len() > 1 && self.columns.len() > 300 {
+        let selected = if cols.len() > 1 && self.columns.len() > 10 {
             // we hash, because there are user that having millions of columns.
             // # https://github.com/pola-rs/polars/issues/1023
-            let name_to_idx: HashMap<&str, usize> = self
+            let name_to_idx: PlHashMap<&str, usize> = self
                 .columns
                 .iter()
                 .enumerate()

polars/polars-lazy/Cargo.toml

@@ -86,6 +86,7 @@ test = [
 [dependencies]
 ahash = "0.7"
 glob = "0.3"
+parking_lot = "0.12"
 rayon = "1.5"
 regex = { version = "1.5", optional = true }
 

polars/polars-lazy/src/logical_plan/alp.rs

@@ -5,10 +5,8 @@ use crate::logical_plan::ParquetOptions;
 use crate::logical_plan::{det_melt_schema, Context, CsvParserOptions};
 use crate::prelude::*;
 use crate::utils::{aexprs_to_schema, PushNode};
-use ahash::RandomState;
 use polars_core::prelude::*;
 use polars_utils::arena::{Arena, Node};
-use std::collections::HashSet;
 #[cfg(any(feature = "csv-file", feature = "parquet"))]
 use std::path::PathBuf;
 use std::sync::Arc;
@@ -669,18 +667,16 @@ impl<'a> ALogicalPlanBuilder<'a> {
         let schema_right = self.lp_arena.get(other).schema(self.lp_arena);
 
         // column names of left table
-        let mut names: HashSet<&str, RandomState> = HashSet::with_capacity_and_hasher(
-            schema_left.len() + schema_right.len(),
-            Default::default(),
-        );
+        let mut names: PlHashSet<&str> =
+            PlHashSet::with_capacity(schema_left.len() + schema_right.len());
         let mut new_schema = Schema::with_capacity(schema_left.len() + schema_right.len());
 
         for (name, dtype) in schema_left.iter() {
             names.insert(name.as_str());
             new_schema.with_column(name.to_string(), dtype.clone())
         }
 
-        let right_names: HashSet<_, RandomState> = right_on
+        let right_names: PlHashSet<_> = right_on
             .iter()
             .map(|e| match self.expr_arena.get(*e) {
                 AExpr::Alias(_, name) => name.clone(),
@@ -710,6 +706,7 @@ impl<'a> ALogicalPlanBuilder<'a> {
             right_on,
             options,
         };
+        drop(names);
         let root = self.lp_arena.add(lp);
         Self::new(root, self.expr_arena, self.lp_arena)
     }

polars/polars-lazy/src/logical_plan/builder.rs

@@ -2,7 +2,6 @@ use crate::logical_plan::projection::rewrite_projections;
 use crate::prelude::*;
 use crate::utils;
 use crate::utils::{combine_predicates_expr, has_expr};
-use ahash::RandomState;
 use polars_core::prelude::*;
 use polars_io::csv::CsvEncoding;
 #[cfg(feature = "csv-file")]
@@ -17,7 +16,6 @@ use polars_io::{
     csv::NullValues,
     csv_core::utils::{get_reader_bytes, is_compressed},
 };
-use std::collections::HashSet;
 use std::io::{Read, Seek, SeekFrom};
 use std::path::PathBuf;
 
@@ -378,22 +376,22 @@ impl LogicalPlanBuilder {
         let schema_right = other.schema();
 
         // column names of left table
-        let mut names: HashSet<&String, RandomState> = HashSet::default();
+        let mut names: PlHashSet<&str> = PlHashSet::default();
         let mut new_schema = Schema::with_capacity(schema_left.len() + schema_right.len());
 
         for (name, dtype) in schema_left.iter() {
             names.insert(name);
             new_schema.with_column(name.to_string(), dtype.clone())
         }
 
-        let right_names: HashSet<_, RandomState> = right_on
+        let right_names: PlHashSet<_> = right_on
             .iter()
             .map(|e| utils::expr_output_name(e).expect("could not find name"))
             .collect();
 
         for (name, dtype) in schema_right.iter() {
             if !right_names.iter().any(|s| s.as_ref() == name) {
-                if names.contains(name) {
+                if names.contains(&**name) {
                     let new_name = format!("{}{}", name, options.suffix.as_ref());
                     new_schema.with_column(new_name, dtype.clone())
                 } else {
@@ -404,6 +402,7 @@ impl LogicalPlanBuilder {
 
         let schema = Arc::new(new_schema);
 
+        drop(names);
         LogicalPlan::Join {
             input_left: Box::new(self.0),
             input_right: Box::new(other),

polars/polars-lazy/src/physical_plan/executors/groupby.rs

@@ -13,6 +13,7 @@ pub struct GroupByExec {
     aggs: Vec<Arc<dyn PhysicalExpr>>,
     apply: Option<Arc<dyn DataFrameUdf>>,
     maintain_order: bool,
+    input_schema: SchemaRef,
 }
 
 impl GroupByExec {
@@ -22,13 +23,15 @@ impl GroupByExec {
         aggs: Vec<Arc<dyn PhysicalExpr>>,
         apply: Option<Arc<dyn DataFrameUdf>>,
         maintain_order: bool,
+        input_schema: SchemaRef,
     ) -> Self {
         Self {
             input,
             keys,
             aggs,
             apply,
             maintain_order,
+            input_schema,
         }
     }
 }
@@ -44,6 +47,7 @@ fn groupby_helper(
     let gb = df.groupby_with_series(keys, true, maintain_order)?;
 
     if let Some(f) = apply {
+        state.clear_schema_cache();
         return gb.apply(|df| f.call_udf(df));
     }
 
@@ -74,6 +78,7 @@ fn groupby_helper(
     let agg_columns = agg_columns?;
 
     columns.extend(agg_columns.into_iter().flatten());
+    state.clear_schema_cache();
     DataFrame::new(columns)
 }
 
@@ -83,6 +88,7 @@ impl Executor for GroupByExec {
             eprintln!("aggregates are not partitionable: running default HASH AGGREGATION")
         }
         let df = self.input.execute(state)?;
+        state.set_schema(self.input_schema.clone());
         let keys = self
             .keys
             .iter()
@@ -289,6 +295,7 @@ impl Executor for PartitionGroupByExec {
         // MERGE phase
         // merge and hash aggregate again
         let df = accumulate_dataframes_vertical(dfs)?;
+        // the partitioned groupby has added columns so we must update the schema.
         let key = self.key.evaluate(&df, state)?;
 
         // first get mutable access and optionally sort
@@ -321,6 +328,7 @@ impl Executor for PartitionGroupByExec {
             POOL.install(|| rayon::join(get_columns, get_agg));
 
         columns.extend(agg_columns);
+        state.clear_schema_cache();
 
         let df = DataFrame::new_no_checks(columns);
         Ok(df)

polars/polars-lazy/src/physical_plan/executors/groupby_dynamic.rs

@@ -10,13 +10,15 @@ pub(crate) struct GroupByDynamicExec {
     pub(crate) keys: Vec<Arc<dyn PhysicalExpr>>,
     pub(crate) aggs: Vec<Arc<dyn PhysicalExpr>>,
     pub(crate) options: DynamicGroupOptions,
+    pub(crate) input_schema: SchemaRef,
 }
 
 impl Executor for GroupByDynamicExec {
     fn execute(&mut self, state: &ExecutionState) -> Result<DataFrame> {
         #[cfg(feature = "dynamic_groupby")]
         {
             let df = self.input.execute(state)?;
+            state.set_schema(self.input_schema.clone());
             let keys = self
                 .keys
                 .iter()
@@ -44,6 +46,7 @@ impl Executor for GroupByDynamicExec {
                     .collect::<Result<Vec<_>>>()
             })?;
 
+            state.clear_schema_cache();
             let mut columns = Vec::with_capacity(agg_columns.len() + 1 + keys.len());
             columns.extend(keys);
             columns.push(time_key);

polars/polars-lazy/src/physical_plan/executors/groupby_rolling.rs

@@ -7,13 +7,15 @@ pub(crate) struct GroupByRollingExec {
     pub(crate) input: Box<dyn Executor>,
     pub(crate) aggs: Vec<Arc<dyn PhysicalExpr>>,
     pub(crate) options: RollingGroupOptions,
+    pub(crate) input_schema: SchemaRef,
 }
 
 impl Executor for GroupByRollingExec {
     fn execute(&mut self, state: &ExecutionState) -> Result<DataFrame> {
         #[cfg(feature = "dynamic_groupby")]
         {
             let df = self.input.execute(state)?;
+            state.set_schema(self.input_schema.clone());
 
             let (time_key, groups) = df.groupby_rolling(&self.options)?;
 
@@ -36,6 +38,7 @@ impl Executor for GroupByRollingExec {
                         .collect::<Result<Vec<_>>>()
                 })?;
 
+            state.clear_schema_cache();
             let mut columns = Vec::with_capacity(agg_columns.len() + 1);
             columns.push(time_key);
             columns.extend(agg_columns.into_iter().flatten());

polars/polars-lazy/src/physical_plan/executors/mod.rs

@@ -143,6 +143,7 @@ pub(crate) fn evaluate_physical_expressions(
                 .collect::<Result<_>>()
         })?
     };
+    state.clear_schema_cache();
 
     check_expand_literals(selected_columns, zero_length)
 }

polars/polars-lazy/src/physical_plan/executors/projection.rs

@@ -9,13 +9,15 @@ pub struct ProjectionExec {
     pub(crate) input: Box<dyn Executor>,
     pub(crate) expr: Vec<Arc<dyn PhysicalExpr>>,
     pub(crate) has_windows: bool,
+    pub(crate) input_schema: SchemaRef,
     #[cfg(test)]
     pub(crate) schema: SchemaRef,
 }
 
 impl Executor for ProjectionExec {
     fn execute(&mut self, state: &ExecutionState) -> Result<DataFrame> {
         let df = self.input.execute(state)?;
+        state.set_schema(self.input_schema.clone());
 
         let df = evaluate_physical_expressions(&df, &self.expr, state, self.has_windows);
 

polars/polars-lazy/src/physical_plan/executors/scan.rs

@@ -264,6 +264,7 @@ impl Executor for DataFrameExec {
         // projection should be before selection as those are free
         // TODO: this is only the case if we don't create new columns
         if let Some(projection) = &self.projection {
+            state.may_set_schema(&df, projection.len());
             df = evaluate_physical_expressions(&df, projection, state, self.has_windows)?;
         }
 
@@ -274,6 +275,7 @@ impl Executor for DataFrameExec {
             })?;
             df = df.filter(mask)?;
         }
+        state.clear_schema_cache();
 
         if let Some(limit) = set_n_rows(None) {
             Ok(df.head(Some(limit)))

polars/polars-lazy/src/physical_plan/executors/stack.rs

@@ -8,12 +8,14 @@ pub struct StackExec {
     pub(crate) input: Box<dyn Executor>,
     pub(crate) has_windows: bool,
     pub(crate) expr: Vec<Arc<dyn PhysicalExpr>>,
+    pub(crate) input_schema: SchemaRef,
 }
 
 impl Executor for StackExec {
     fn execute(&mut self, state: &ExecutionState) -> Result<DataFrame> {
         let mut df = self.input.execute(state)?;
 
+        state.set_schema(self.input_schema.clone());
         let res = if self.has_windows {
             // we have a different run here
             // to ensure the window functions run sequential and share caches
@@ -26,12 +28,13 @@ impl Executor for StackExec {
                     .collect::<Result<Vec<_>>>()
             })?
         };
+        state.clear_schema_cache();
+        state.clear_expr_cache();
 
         for s in res {
             df.with_column(s)?;
         }
 
-        state.clear_expr_cache();
         Ok(df)
     }
 }

polars/polars-lazy/src/physical_plan/expressions/column.rs

@@ -17,14 +17,16 @@ impl PhysicalExpr for ColumnExpr {
     fn as_expression(&self) -> &Expr {
         &self.1
     }
-    fn evaluate(&self, df: &DataFrame, _state: &ExecutionState) -> Result<Series> {
-        let column = match &*self.0 {
-            "" => df.select_at_idx(0).ok_or_else(|| {
-                PolarsError::NoData("could not select a column from an empty DataFrame".into())
-            })?,
-            _ => df.column(&self.0)?,
-        };
-        Ok(column.clone())
+    fn evaluate(&self, df: &DataFrame, state: &ExecutionState) -> Result<Series> {
+        match state.get_schema() {
+            None => df.column(&self.0).cloned(),
+            Some(schema) => {
+                let (idx, _, _) = schema
+                    .get_full(&self.0)
+                    .ok_or_else(|| PolarsError::NotFound(self.0.to_string()))?;
+                Ok(df.get_columns()[idx].clone())
+            }
+        }
     }
     #[allow(clippy::ptr_arg)]
     fn evaluate_on_groups<'a>(