fix: Scanning hive partitioned files where hive columns are partially included in the file

crates/polars-io/src/hive.rs

@@ -5,6 +5,8 @@ use polars_core::series::Series;
 /// We have a special num_rows arg, as df can be empty when a projection contains
 /// only hive partition columns.
 ///
+/// The `hive_partition_columns` must be ordered by their position in the `reader_schema`
+///
 /// # Safety
 ///
 /// num_rows equals the height of the df when the df height is non-zero.
@@ -15,27 +17,58 @@ pub(crate) fn materialize_hive_partitions<D>(
     num_rows: usize,
 ) {
     if let Some(hive_columns) = hive_partition_columns {
-        let Some(first) = hive_columns.first() else {
+        // Insert these hive columns in the order they are stored in the file.
+        if hive_columns.is_empty() {
             return;
-        };
-
-        if reader_schema.index_of(first.name()).is_some() {
-            // Insert these hive columns in the order they are stored in the file.
-            for s in hive_columns {
-                let i = match df.get_columns().binary_search_by_key(
-                    &reader_schema.index_of(s.name()).unwrap_or(usize::MAX),
-                    |s| reader_schema.index_of(s.name()).unwrap_or(usize::MIN),
-                ) {
-                    Ok(i) => i,
-                    Err(i) => i,
-                };
-
-                df.insert_column(i, s.new_from_index(0, num_rows)).unwrap();
-            }
-        } else {
-            for s in hive_columns {
-                unsafe { df.with_column_unchecked(s.new_from_index(0, num_rows)) };
+        }
+
+        let hive_columns_iter = hive_columns.iter().map(|s| s.new_from_index(0, num_rows));
+
+        if reader_schema.index_of(hive_columns[0].name()).is_none() || df.width() == 0 {
+            // Fast-path - all hive columns are at the end
+            unsafe { df.get_columns_mut() }.extend(hive_columns_iter);
+            return;
+        }
+
+        let out_width: usize = df.width() + hive_columns.len();
+        let df_columns = df.get_columns();
+        let mut out_columns = Vec::with_capacity(out_width);
+
+        // We have a slightly involved algorithm here because `reader_schema` may contain extra
+        // columns that were excluded from a projection pushdown.
+
+        let hive_columns = hive_columns_iter.collect::<Vec<_>>();
+        // Safety: These are both non-empty at the start
+        let mut series_arr = [df_columns, hive_columns.as_slice()];
+        let mut schema_idx_arr = [
+            reader_schema.index_of(series_arr[0][0].name()).unwrap(),
+            reader_schema.index_of(series_arr[1][0].name()).unwrap(),
+        ];
+
+        loop {
+            let arg_min = if schema_idx_arr[0] < schema_idx_arr[1] {
+                0
+            } else {
+                1
+            };
+
+            out_columns.push(series_arr[arg_min][0].clone());
+            series_arr[arg_min] = &series_arr[arg_min][1..];
+
+            if series_arr[arg_min].is_empty() {
+                break;
             }
+
+            let Some(i) = reader_schema.index_of(series_arr[arg_min][0].name()) else {
+                break;
+            };
+
+            schema_idx_arr[arg_min] = i;
         }
+
+        out_columns.extend_from_slice(series_arr[0]);
+        out_columns.extend_from_slice(series_arr[1]);
+
+        *unsafe { df.get_columns_mut() } = out_columns;
     }
 }

crates/polars-plan/src/plans/conversion/dsl_to_ir.rs

@@ -1068,27 +1068,26 @@ pub(crate) fn maybe_init_projection_excluding_hive(
     // Update `with_columns` with a projection so that hive columns aren't loaded from the
     // file
     let hive_parts = hive_parts?;
-
     let hive_schema = hive_parts.schema();
 
-    let (first_hive_name, _) = hive_schema.get_at_index(0)?;
-
-    // TODO: Optimize this
-    let names = match reader_schema {
-        Either::Left(ref v) => v
-            .contains(first_hive_name.as_str())
-            .then(|| v.iter_names_cloned().collect::<Vec<_>>()),
-        Either::Right(ref v) => v
-            .contains(first_hive_name.as_str())
-            .then(|| v.iter_names_cloned().collect()),
-    };
-
-    let names = names?;
-
-    Some(
-        names
-            .into_iter()
-            .filter(|x| !hive_schema.contains(x))
-            .collect::<Arc<[_]>>(),
-    )
+    match &reader_schema {
+        Either::Left(reader_schema) => hive_schema
+            .iter_names()
+            .any(|x| reader_schema.contains(x))
+            .then(|| {
+                reader_schema
+                    .iter_names_cloned()
+                    .filter(|x| !hive_schema.contains(x))
+                    .collect::<Arc<[_]>>()
+            }),
+        Either::Right(reader_schema) => hive_schema
+            .iter_names()
+            .any(|x| reader_schema.contains(x))
+            .then(|| {
+                reader_schema
+                    .iter_names_cloned()
+                    .filter(|x| !hive_schema.contains(x))
+                    .collect::<Arc<[_]>>()
+            }),
+    }
 }

crates/polars-plan/src/plans/hive.rs

@@ -57,6 +57,8 @@ impl HivePartitions {
     }
 }
 
+/// Note: Returned hive partitions are ordered by their position in the `reader_schema`
+///
 /// # Safety
 /// `hive_start_idx <= [min path length]`
 pub fn hive_partitions_from_paths(
@@ -198,10 +200,11 @@ pub fn hive_partitions_from_paths(
     }
 
     let mut hive_partitions = Vec::with_capacity(paths.len());
-    let buffers = buffers
+    let mut buffers = buffers
         .into_iter()
         .map(|x| x.into_series())
         .collect::<PolarsResult<Vec<_>>>()?;
+    buffers.sort_by_key(|s| reader_schema.index_of(s.name()).unwrap_or(usize::MAX));
 
     #[allow(clippy::needless_range_loop)]
     for i in 0..paths.len() {

py-polars/tests/unit/io/test_hive.py

@@ -554,6 +554,42 @@ def assert_with_projections(lf: pl.LazyFrame, df: pl.DataFrame) -> None:
     )
     assert_with_projections(lf, rhs)
 
+    # partial cols in file
+    partial_path = tmp_path / "a=1/b=2/partial_data.bin"
+    df = pl.DataFrame(
+        {"x": 1, "b": 2, "y": 1},
+        schema={"x": pl.Int32, "b": pl.Int16, "y": pl.Int32},
+    )
+    write_func(df, partial_path)
+
+    rhs = rhs.select(
+        pl.col("x").cast(pl.Int32),
+        pl.col("b").cast(pl.Int16),
+        pl.col("y").cast(pl.Int32),
+        pl.col("a").cast(pl.Int64),
+    )
+
+    lf = scan_func(partial_path, hive_partitioning=True)  # type: ignore[call-arg]
+    assert_frame_equal(lf.collect(projection_pushdown=projection_pushdown), rhs)
+    assert_with_projections(lf, rhs)
+
+    lf = scan_func(  # type: ignore[call-arg]
+        partial_path,
+        hive_schema={"a": pl.String, "b": pl.String},
+        hive_partitioning=True,
+    )
+    rhs = rhs.select(
+        pl.col("x").cast(pl.Int32),
+        pl.col("b").cast(pl.String),
+        pl.col("y").cast(pl.Int32),
+        pl.col("a").cast(pl.String),
+    )
+    assert_frame_equal(
+        lf.collect(projection_pushdown=projection_pushdown),
+        rhs,
+    )
+    assert_with_projections(lf, rhs)
+
 
 @pytest.mark.write_disk
 def test_hive_partition_dates(tmp_path: Path) -> None: