feat(python,rust!): Allow specifying Hive schema in read/scan_parquet

crates/polars-io/src/predicates.rs

@@ -38,24 +38,24 @@ pub fn apply_predicate(
     Ok(())
 }
 
-/// The statistics for a column in a Parquet file
-/// or Hive partition.
-/// they typically hold
-/// - max value
-/// - min value
-/// - null_count
+/// Statistics of the values in a column.
+///
+/// The following statistics are tracked for each row group:
+/// - Null count
+/// - Minimum value
+/// - Maximum value
 #[derive(Debug)]
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
 pub struct ColumnStats {
     field: Field,
-    // The array may hold the null count for every row group,
-    // or for a single row group.
+    // Each Series contains the stats for each row group.
     null_count: Option<Series>,
     min_value: Option<Series>,
     max_value: Option<Series>,
 }
 
 impl ColumnStats {
+    /// Constructs a new [`ColumnStats`].
     pub fn new(
         field: Field,
         null_count: Option<Series>,
@@ -70,6 +70,17 @@ impl ColumnStats {
         }
     }
 
+    /// Constructs a new [`ColumnStats`] with only the [`Field`] information and no statistics.
+    pub fn from_field(field: Field) -> Self {
+        Self {
+            field,
+            null_count: None,
+            min_value: None,
+            max_value: None,
+        }
+    }
+
+    /// Constructs a new [`ColumnStats`] from a single-value Series.
     pub fn from_column_literal(s: Series) -> Self {
         debug_assert_eq!(s.len(), 1);
         Self {
@@ -80,16 +91,33 @@ impl ColumnStats {
         }
     }
 
+    /// Returns the [`DataType`] of the column.
     pub fn dtype(&self) -> &DataType {
         self.field.data_type()
     }
 
+    /// Returns the null count of each row group of the column.
+    pub fn get_null_count_state(&self) -> Option<&Series> {
+        self.null_count.as_ref()
+    }
+
+    /// Returns the minimum value of each row group of the column.
+    pub fn get_min_state(&self) -> Option<&Series> {
+        self.min_value.as_ref()
+    }
+
+    /// Returns the maximum value of each row group of the column.
+    pub fn get_max_state(&self) -> Option<&Series> {
+        self.max_value.as_ref()
+    }
+
+    /// Returns the null count of the column.
     pub fn null_count(&self) -> Option<usize> {
-        match self.field.data_type() {
+        match self.dtype() {
             #[cfg(feature = "dtype-struct")]
             DataType::Struct(_) => None,
             _ => {
-                let s = self.null_count.as_ref()?;
+                let s = self.get_null_count_state()?;
                 // if all null, there are no statistics.
                 if s.null_count() != s.len() {
                     s.sum().ok()
@@ -100,34 +128,33 @@ impl ColumnStats {
         }
     }
 
+    /// Returns the minimum and maximum values of the column as a single [`Series`].
     pub fn to_min_max(&self) -> Option<Series> {
-        let max_val = self.max_value.as_ref()?;
-        let min_val = self.min_value.as_ref()?;
+        let min_val = self.get_min_state()?;
+        let max_val = self.get_max_state()?;
+        let dtype = self.dtype();
 
-        let dtype = min_val.dtype();
+        if !use_min_max(dtype) {
+            return None;
+        }
 
-        if Self::use_min_max(dtype) {
-            let mut min_max_values = min_val.clone();
-            min_max_values.append(max_val).unwrap();
-            if min_max_values.null_count() > 0 {
-                None
-            } else {
-                Some(min_max_values)
-            }
-        } else {
+        let mut min_max_values = min_val.clone();
+        min_max_values.append(max_val).unwrap();
+        if min_max_values.null_count() > 0 {
             None
+        } else {
+            Some(min_max_values)
         }
     }
 
-    pub fn get_min_state(&self) -> Option<&Series> {
-        self.min_value.as_ref()
-    }
-
+    /// Returns the minimum value of the column as a single-value [`Series`].
+    ///
+    /// Returns `None` if no maximum value is available.
     pub fn to_min(&self) -> Option<&Series> {
         let min_val = self.min_value.as_ref()?;
         let dtype = min_val.dtype();
 
-        if !Self::use_min_max(dtype) || min_val.len() != 1 {
+        if !use_min_max(dtype) || min_val.len() != 1 {
             return None;
         }
 
@@ -138,11 +165,14 @@ impl ColumnStats {
         }
     }
 
+    /// Returns the maximum value of the column as a single-value [`Series`].
+    ///
+    /// Returns `None` if no maximum value is available.
     pub fn to_max(&self) -> Option<&Series> {
         let max_val = self.max_value.as_ref()?;
         let dtype = max_val.dtype();
 
-        if !Self::use_min_max(dtype) || max_val.len() != 1 {
+        if !use_min_max(dtype) || max_val.len() != 1 {
             return None;
         }
 
@@ -152,14 +182,15 @@ impl ColumnStats {
             Some(max_val)
         }
     }
+}
 
-    fn use_min_max(dtype: &DataType) -> bool {
-        dtype.is_numeric()
-            || matches!(
-                dtype,
-                DataType::String | DataType::Binary | DataType::Boolean
-            )
-    }
+/// Returns whether the [`DataType`] supports minimum/maximum operations.
+fn use_min_max(dtype: &DataType) -> bool {
+    dtype.is_numeric()
+        || matches!(
+            dtype,
+            DataType::String | DataType::Binary | DataType::Boolean
+        )
 }
 
 /// A collection of column stats with a known schema.
@@ -168,12 +199,14 @@ impl ColumnStats {
 pub struct BatchStats {
     schema: SchemaRef,
     stats: Vec<ColumnStats>,
-    // This might not be available,
-    // as when prunnign hive partitions.
+    // This might not be available, as when pruning hive partitions.
     num_rows: Option<usize>,
 }
 
 impl BatchStats {
+    /// Constructs a new [`BatchStats`].
+    ///
+    /// The `stats` should match the order of the `schema`.
     pub fn new(schema: SchemaRef, stats: Vec<ColumnStats>, num_rows: Option<usize>) -> Self {
         Self {
             schema,
@@ -182,19 +215,27 @@ impl BatchStats {
         }
     }
 
-    pub fn get_stats(&self, column: &str) -> polars_core::error::PolarsResult<&ColumnStats> {
-        self.schema.try_index_of(column).map(|i| &self.stats[i])
-    }
-
-    pub fn num_rows(&self) -> Option<usize> {
-        self.num_rows
-    }
-
+    /// Returns the [`Schema`] of the batch.
     pub fn schema(&self) -> &SchemaRef {
         &self.schema
     }
 
+    /// Returns the [`ColumnStats`] of all columns in the batch, if known.
     pub fn column_stats(&self) -> &[ColumnStats] {
         self.stats.as_ref()
     }
+
+    /// Returns the [`ColumnStats`] of a single column in the batch.
+    ///
+    /// Returns an `Err` if no statistics are available for the given column.
+    pub fn get_stats(&self, column: &str) -> PolarsResult<&ColumnStats> {
+        self.schema.try_index_of(column).map(|i| &self.stats[i])
+    }
+
+    /// Returns the number of rows in the batch.
+    ///
+    /// Returns `None` if the number of rows is unknown.
+    pub fn num_rows(&self) -> Option<usize> {
+        self.num_rows
+    }
 }

crates/polars-lazy/src/scan/parquet.rs

@@ -18,6 +18,7 @@ pub struct ScanArgsParquet {
     pub cloud_options: Option<CloudOptions>,
     pub use_statistics: bool,
     pub hive_partitioning: bool,
+    pub hive_schema: Option<SchemaRef>,
 }
 
 impl Default for ScanArgsParquet {
@@ -32,6 +33,7 @@ impl Default for ScanArgsParquet {
             cloud_options: None,
             use_statistics: true,
             hive_partitioning: false,
+            hive_schema: None,
         }
     }
 }
@@ -84,6 +86,7 @@ impl LazyFileListReader for LazyParquetReader {
             self.args.cloud_options,
             self.args.use_statistics,
             self.args.hive_partitioning,
+            self.args.hive_schema,
         )?
         .build()
         .into();

crates/polars-plan/src/logical_plan/builder.rs

@@ -116,7 +116,9 @@ impl LogicalPlanBuilder {
             row_index: None,
             rechunk: false,
             file_counter: Default::default(),
+            // TODO: Support Hive partitioning.
             hive_partitioning: false,
+            hive_schema: None,
         };
 
         Ok(LogicalPlan::Scan {
@@ -148,6 +150,7 @@ impl LogicalPlanBuilder {
         cloud_options: Option<CloudOptions>,
         use_statistics: bool,
         hive_partitioning: bool,
+        hive_schema: Option<SchemaRef>,
     ) -> PolarsResult<Self> {
         use polars_io::{is_cloud_url, SerReader as _};
 
@@ -197,10 +200,8 @@ impl LogicalPlanBuilder {
             (num_rows, num_rows.unwrap_or(0)),
         );
 
-        // We set the hive partitions of the first path to determine the schema.
-        // On iteration the partition values will be re-set per file.
         if hive_partitioning {
-            file_info.init_hive_partitions(path.as_path())?;
+            file_info.init_hive_partitions(path.as_path(), hive_schema.clone())?
         }
 
         let options = FileScanOptions {
@@ -211,6 +212,7 @@ impl LogicalPlanBuilder {
             row_index,
             file_counter: Default::default(),
             hive_partitioning,
+            hive_schema,
         };
         Ok(LogicalPlan::Scan {
             paths,
@@ -285,7 +287,9 @@ impl LogicalPlanBuilder {
                 rechunk,
                 row_index,
                 file_counter: Default::default(),
+                // TODO: Support Hive partitioning.
                 hive_partitioning: false,
+                hive_schema: None,
             },
             predicate: None,
             scan_type: FileScan::Ipc {
@@ -393,8 +397,9 @@ impl LogicalPlanBuilder {
             rechunk,
             row_index,
             file_counter: Default::default(),
-            // TODO! add
+            // TODO: Support Hive partitioning.
             hive_partitioning: false,
+            hive_schema: None,
         };
         Ok(LogicalPlan::Scan {
             paths,