perf(reader): Avoid second create_parquet_record_batch_stream_builder() call for migrated tables

crates/iceberg/src/arrow/delete_file_loader.rs

@@ -18,6 +18,7 @@
 use std::sync::Arc;
 
 use futures::{StreamExt, TryStreamExt};
+use parquet::arrow::ParquetRecordBatchStreamBuilder;
 
 use crate::arrow::ArrowReader;
 use crate::arrow::reader::ParquetReadOptions;
@@ -61,16 +62,20 @@ impl BasicDeleteFileLoader {
            Essentially a super-cut-down ArrowReader. We can't use ArrowReader directly
            as that introduces a circular dependency.
         */
-        let record_batch_stream = ArrowReader::create_parquet_record_batch_stream_builder(
+        let parquet_read_options = ParquetReadOptions::builder().build();
+
+        let (parquet_file_reader, arrow_metadata) = ArrowReader::open_parquet_file(
             data_file_path,
-            self.file_io.clone(),
-            None,
+            &self.file_io,
             file_size_in_bytes,
-            ParquetReadOptions::builder().build(),
+            parquet_read_options,
         )
-        .await?
-        .build()?
-        .map_err(|e| Error::new(ErrorKind::Unexpected, format!("{e}")));
+        .await?;
+
+        let record_batch_stream =
+            ParquetRecordBatchStreamBuilder::new_with_metadata(parquet_file_reader, arrow_metadata)
+                .build()?
+                .map_err(|e| Error::new(ErrorKind::Unexpected, format!("{e}")));
 
         Ok(Box::pin(record_batch_stream) as ArrowRecordBatchStream)
     }

crates/iceberg/src/arrow/reader.rs

@@ -35,7 +35,7 @@ use fnv::FnvHashSet;
 use futures::future::BoxFuture;
 use futures::{FutureExt, StreamExt, TryFutureExt, TryStreamExt};
 use parquet::arrow::arrow_reader::{
-    ArrowPredicateFn, ArrowReaderOptions, RowFilter, RowSelection, RowSelector,
+    ArrowPredicateFn, ArrowReaderMetadata, ArrowReaderOptions, RowFilter, RowSelection, RowSelector,
 };
 use parquet::arrow::async_reader::AsyncFileReader;
 use parquet::arrow::{PARQUET_FIELD_ID_META_KEY, ParquetRecordBatchStreamBuilder, ProjectionMask};
@@ -320,12 +320,10 @@ impl ArrowReader {
         let delete_filter_rx =
             delete_file_loader.load_deletes(&task.deletes, Arc::clone(&task.schema));
 
-        // Migrated tables lack field IDs, requiring us to inspect the schema to choose
-        // between field-ID-based or position-based projection
-        let initial_stream_builder = Self::create_parquet_record_batch_stream_builder(
+        // Open the Parquet file once, loading its metadata
+        let (parquet_file_reader, arrow_metadata) = Self::open_parquet_file(
             &task.data_file_path,
-            file_io.clone(),
-            None,
+            &file_io,
             task.file_size_in_bytes,
             parquet_read_options,
         )
@@ -334,7 +332,7 @@ impl ArrowReader {
         // Check if Parquet file has embedded field IDs
         // Corresponds to Java's ParquetSchemaUtil.hasIds()
         // Reference: parquet/src/main/java/org/apache/iceberg/parquet/ParquetSchemaUtil.java:118
-        let missing_field_ids = initial_stream_builder
+        let missing_field_ids = arrow_metadata
             .schema()
             .fields()
             .iter()
@@ -356,38 +354,42 @@ impl ArrowReader {
         // - Branch 1: hasIds(fileSchema) → trust embedded field IDs, use pruneColumns()
         // - Branch 2: nameMapping present → applyNameMapping(), then pruneColumns()
         // - Branch 3: fallback → addFallbackIds(), then pruneColumnsFallback()
-        let mut record_batch_stream_builder = if missing_field_ids {
+        let arrow_metadata = if missing_field_ids {
             // Parquet file lacks field IDs - must assign them before reading
             let arrow_schema = if let Some(name_mapping) = &task.name_mapping {
                 // Branch 2: Apply name mapping to assign correct Iceberg field IDs
                 // Per spec rule #2: "Use schema.name-mapping.default metadata to map field id
                 // to columns without field id"
                 // Corresponds to Java's ParquetSchemaUtil.applyNameMapping()
                 apply_name_mapping_to_arrow_schema(
-                    Arc::clone(initial_stream_builder.schema()),
+                    Arc::clone(arrow_metadata.schema()),
                     name_mapping,
                 )?
             } else {
                 // Branch 3: No name mapping - use position-based fallback IDs
                 // Corresponds to Java's ParquetSchemaUtil.addFallbackIds()
-                add_fallback_field_ids_to_arrow_schema(initial_stream_builder.schema())
+                add_fallback_field_ids_to_arrow_schema(arrow_metadata.schema())
             };
 
             let options = ArrowReaderOptions::new().with_schema(arrow_schema);
-
-            Self::create_parquet_record_batch_stream_builder(
-                &task.data_file_path,
-                file_io.clone(),
-                Some(options),
-                task.file_size_in_bytes,
-                parquet_read_options,
-            )
-            .await?
+            ArrowReaderMetadata::try_new(Arc::clone(arrow_metadata.metadata()), options).map_err(
+                |e| {
+                    Error::new(
+                        ErrorKind::Unexpected,
+                        "Failed to create ArrowReaderMetadata with field ID schema",
+                    )
+                    .with_source(e)
+                },
+            )?
         } else {
             // Branch 1: File has embedded field IDs - trust them
-            initial_stream_builder
+            arrow_metadata
         };
 
+        // Build the stream reader, reusing the already-opened file reader
+        let mut record_batch_stream_builder =
+            ParquetRecordBatchStreamBuilder::new_with_metadata(parquet_file_reader, arrow_metadata);
+
         // Filter out metadata fields for Parquet projection (they don't exist in files)
         let project_field_ids_without_metadata: Vec<i32> = task
             .project_field_ids
@@ -579,30 +581,32 @@ impl ArrowReader {
         Ok(Box::pin(record_batch_stream) as ArrowRecordBatchStream)
     }
 
-    pub(crate) async fn create_parquet_record_batch_stream_builder(
+    /// Opens a Parquet file and loads its metadata, returning both the reader and metadata.
+    /// The reader can be reused to build a `ParquetRecordBatchStreamBuilder` without
+    /// reopening the file.
+    pub(crate) async fn open_parquet_file(
         data_file_path: &str,
-        file_io: FileIO,
-        arrow_reader_options: Option<ArrowReaderOptions>,
+        file_io: &FileIO,
         file_size_in_bytes: u64,
         parquet_read_options: ParquetReadOptions,
-    ) -> Result<ParquetRecordBatchStreamBuilder<ArrowFileReader>> {
-        // Get the metadata for the Parquet file we need to read and build
-        // a reader for the data within
+    ) -> Result<(ArrowFileReader, ArrowReaderMetadata)> {
         let parquet_file = file_io.new_input(data_file_path)?;
         let parquet_reader = parquet_file.reader().await?;
-        let parquet_file_reader = ArrowFileReader::new(
+        let mut reader = ArrowFileReader::new(
             FileMetadata {
                 size: file_size_in_bytes,
             },
             parquet_reader,
         )
         .with_parquet_read_options(parquet_read_options);
 
-        // Create the record batch stream builder, which wraps the parquet file reader
-        let options = arrow_reader_options.unwrap_or_default();
-        let record_batch_stream_builder =
-            ParquetRecordBatchStreamBuilder::new_with_options(parquet_file_reader, options).await?;
-        Ok(record_batch_stream_builder)
+        let arrow_metadata = ArrowReaderMetadata::load_async(&mut reader, Default::default())
+            .await
+            .map_err(|e| {
+                Error::new(ErrorKind::Unexpected, "Failed to load Parquet metadata").with_source(e)
+            })?;
+
+        Ok((reader, arrow_metadata))
     }
 
     /// computes a `RowSelection` from positional delete indices.