feat(parquet): Add next_row_group API for ParquetRecordBatchStream

parquet/src/arrow/async_reader/mod.rs

@@ -613,6 +613,9 @@ impl<T> std::fmt::Debug for StreamState<T> {
 
 /// An asynchronous [`Stream`](https://docs.rs/futures/latest/futures/stream/trait.Stream.html) of [`RecordBatch`]
 /// for a parquet file that can be constructed using [`ParquetRecordBatchStreamBuilder`].
+///
+/// `ParquetRecordBatchStream` also provides [`ParquetRecordBatchStream::next_row_group`] for fetching row groups,
+/// allowing users to decode record batches separately from I/O.
 pub struct ParquetRecordBatchStream<T> {
     metadata: Arc<ParquetMetaData>,
 
@@ -654,6 +657,66 @@ impl<T> ParquetRecordBatchStream<T> {
     }
 }
 
+impl<T> ParquetRecordBatchStream<T>
+where
+    T: AsyncFileReader + Unpin + Send + 'static,
+{
+    /// Fetches the next row group from the stream.
+    ///
+    /// Users can continue to call this function to get row groups and decode them concurrently.
+    ///
+    /// ## Notes
+    ///
+    /// ParquetRecordBatchStream should be used either as a `Stream` or with `next_row_group`; they should not be used simultaneously.
+    ///
+    /// ## Returns
+    ///
+    /// - `Ok(None)` if the stream has ended.
+    /// - `Err(error)` if the stream has errored. All subsequent calls will return `Ok(None)`.
+    /// - `Ok(Some(reader))` which holds all the data for the row group.
+    pub async fn next_row_group(&mut self) -> Result<Option<ParquetRecordBatchReader>> {
+        loop {
+            match &mut self.state {
+                StreamState::Decoding(_) | StreamState::Reading(_) => unreachable!(),
+                StreamState::Init => {
+                    let row_group_idx = match self.row_groups.pop_front() {
+                        Some(idx) => idx,
+                        None => return Ok(None),
+                    };
+
+                    let row_count = self.metadata.row_group(row_group_idx).num_rows() as usize;
+
+                    let selection = self.selection.as_mut().map(|s| s.split_off(row_count));
+
+                    let reader_factory = self.reader.take().expect("lost reader");
+
+                    let (reader_factory, maybe_reader) = reader_factory
+                        .read_row_group(
+                            row_group_idx,
+                            selection,
+                            self.projection.clone(),
+                            self.batch_size,
+                        )
+                        .await
+                        .map_err(|err| {
+                            self.state = StreamState::Error;
+                            err
+                        })?;
+                    self.reader = Some(reader_factory);
+
+                    if let Some(reader) = maybe_reader {
+                        return Ok(Some(reader));
+                    } else {
+                        // All rows skipped, read next row group
+                        continue;
+                    }
+                }
+                StreamState::Error => return Ok(None), // Ends the stream as error happens.
+            }
+        }
+    }
+}
+
 impl<T> Stream for ParquetRecordBatchStream<T>
 where
     T: AsyncFileReader + Unpin + Send + 'static,
@@ -1020,6 +1083,71 @@ mod tests {
         );
     }
 
+    #[tokio::test]
+    async fn test_async_reader_with_next_row_group() {
+        let testdata = arrow::util::test_util::parquet_test_data();
+        let path = format!("{testdata}/alltypes_plain.parquet");
+        let data = Bytes::from(std::fs::read(path).unwrap());
+
+        let metadata = ParquetMetaDataReader::new()
+            .parse_and_finish(&data)
+            .unwrap();
+        let metadata = Arc::new(metadata);
+
+        assert_eq!(metadata.num_row_groups(), 1);
+
+        let async_reader = TestReader {
+            data: data.clone(),
+            metadata: metadata.clone(),
+            requests: Default::default(),
+        };
+
+        let requests = async_reader.requests.clone();
+        let builder = ParquetRecordBatchStreamBuilder::new(async_reader)
+            .await
+            .unwrap();
+
+        let mask = ProjectionMask::leaves(builder.parquet_schema(), vec![1, 2]);
+        let mut stream = builder
+            .with_projection(mask.clone())
+            .with_batch_size(1024)
+            .build()
+            .unwrap();
+
+        let mut readers = vec![];
+        while let Some(reader) = stream.next_row_group().await.unwrap() {
+            readers.push(reader);
+        }
+
+        let async_batches: Vec<_> = readers
+            .into_iter()
+            .flat_map(|r| r.map(|v| v.unwrap()).collect::<Vec<_>>())
+            .collect();
+
+        let sync_batches = ParquetRecordBatchReaderBuilder::try_new(data)
+            .unwrap()
+            .with_projection(mask)
+            .with_batch_size(104)
+            .build()
+            .unwrap()
+            .collect::<ArrowResult<Vec<_>>>()
+            .unwrap();
+
+        assert_eq!(async_batches, sync_batches);
+
+        let requests = requests.lock().unwrap();
+        let (offset_1, length_1) = metadata.row_group(0).column(1).byte_range();
+        let (offset_2, length_2) = metadata.row_group(0).column(2).byte_range();
+
+        assert_eq!(
+            &requests[..],
+            &[
+                offset_1 as usize..(offset_1 + length_1) as usize,
+                offset_2 as usize..(offset_2 + length_2) as usize
+            ]
+        );
+    }
+
     #[tokio::test]
     async fn test_async_reader_with_index() {
         let testdata = arrow::util::test_util::parquet_test_data();