feat: Add ArrivalOrder to ArrowScan for bounded-memory concurrent reads

mkdocs/docs/api.md

@@ -355,6 +355,52 @@ for buf in tbl.scan().to_arrow_batch_reader():
     print(f"Buffer contains {len(buf)} rows")
 ```
 
+You can control the number of rows per batch using the `batch_size` parameter:
+
+```python
+for buf in tbl.scan().to_arrow_batch_reader(batch_size=1000):
+    print(f"Buffer contains {len(buf)} rows")
+```
+
+By default, each file's batches are materialized in memory before being yielded (`TaskOrder()`). For large files that may exceed available memory, use `ArrivalOrder()` to yield batches as they are produced without materializing entire files:
+
+```python
+from pyiceberg.table import ArrivalOrder
+
+for buf in tbl.scan().to_arrow_batch_reader(order=ArrivalOrder(), batch_size=1000):
+    print(f"Buffer contains {len(buf)} rows")
+```
+
+For maximum throughput, use `concurrent_streams` to read multiple files in parallel with arrival order. Batches are yielded as they arrive from any file — ordering across files is not guaranteed:
+
+```python
+from pyiceberg.table import ArrivalOrder
+
+for buf in tbl.scan().to_arrow_batch_reader(order=ArrivalOrder(concurrent_streams=4), batch_size=1000):
+    print(f"Buffer contains {len(buf)} rows")
+```
+
+**Ordering semantics:**
+
+| Configuration | File ordering | Within-file ordering |
+|---|---|---|
+| `TaskOrder()` (default) | Batches grouped by file, in task submission order | Row order |
+| `ArrivalOrder()` | Interleaved across files (no grouping guarantee) | Row order within each file |
+
+Within each file, batch ordering always follows row order. The `limit` parameter is enforced correctly regardless of configuration.
+
+**Which configuration should I use?**
+
+| Use case | Recommended config |
+|---|---|
+| Small tables, simple queries | Default — no extra args needed |
+| Large tables, memory-constrained | `order=ArrivalOrder()` — one file at a time, minimal memory |
+| Maximum throughput with bounded memory | `order=ArrivalOrder(concurrent_streams=N)` — tune N to balance throughput vs memory |
+| Fine-grained memory control | `order=ArrivalOrder(concurrent_streams=N, max_buffered_batches=M)` — tune both parameters |
+| Fine-grained batch control | Add `batch_size=N` to any of the above |
+
+**Note:** `ArrivalOrder()` yields batches in arrival order (interleaved across files when `concurrent_streams > 1`). For deterministic file ordering, use the default `TaskOrder()` mode. `batch_size` is usually an advanced tuning knob — the PyArrow default of 131,072 rows works well for most workloads.
+
 To avoid any type inconsistencies during writing, you can convert the Iceberg table schema to Arrow:
 
 ```python
@@ -1619,6 +1665,39 @@ table.scan(
 ).to_arrow_batch_reader()
 ```
 
+The `batch_size` parameter controls the maximum number of rows per RecordBatch (default is PyArrow's 131,072 rows):
+
+```python
+table.scan(
+    row_filter=GreaterThanOrEqual("trip_distance", 10.0),
+    selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
+).to_arrow_batch_reader(batch_size=1000)
+```
+
+Use `order=ScanOrder.ARRIVAL` to avoid materializing entire files in memory. This yields batches as they are produced by PyArrow, one file at a time:
+
+```python
+from pyiceberg.table import ScanOrder
+
+table.scan(
+    row_filter=GreaterThanOrEqual("trip_distance", 10.0),
+    selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
+).to_arrow_batch_reader(order=ScanOrder.ARRIVAL)
+```
+
+For concurrent file reads with arrival order, use `concurrent_files`. Note that batch ordering across files is not guaranteed:
+
+```python
+from pyiceberg.table import ScanOrder
+
+table.scan(
+    row_filter=GreaterThanOrEqual("trip_distance", 10.0),
+    selected_fields=("VendorID", "tpep_pickup_datetime", "tpep_dropoff_datetime"),
+).to_arrow_batch_reader(order=ScanOrder.ARRIVAL, concurrent_files=4)
+```
+
+When using `concurrent_files > 1`, batches from different files may be interleaved. Within each file, batches are always in row order. See the ordering semantics table in the [Apache Arrow section](#apache-arrow) above for details.
+
 ### Pandas
 
 <!-- prettier-ignore-start -->

pyiceberg/io/pyarrow.py

@@ -33,11 +33,14 @@
 import logging
 import operator
 import os
+import queue
 import re
+import threading
 import uuid
 import warnings
 from abc import ABC, abstractmethod
-from collections.abc import Callable, Iterable, Iterator
+from collections.abc import Callable, Generator, Iterable, Iterator
+from concurrent.futures import ThreadPoolExecutor
 from copy import copy
 from dataclasses import dataclass
 from enum import Enum
@@ -141,7 +144,7 @@
     visit,
     visit_with_partner,
 )
-from pyiceberg.table import DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE, TableProperties
+from pyiceberg.table import DOWNCAST_NS_TIMESTAMP_TO_US_ON_WRITE, ScanOrder, TaskOrder, ArrivalOrder, TableProperties
 from pyiceberg.table.locations import load_location_provider
 from pyiceberg.table.metadata import TableMetadata
 from pyiceberg.table.name_mapping import NameMapping, apply_name_mapping
@@ -1581,6 +1584,7 @@ def _task_to_record_batches(
     partition_spec: PartitionSpec | None = None,
     format_version: TableVersion = TableProperties.DEFAULT_FORMAT_VERSION,
     downcast_ns_timestamp_to_us: bool | None = None,
+    batch_size: int | None = None,
 ) -> Iterator[pa.RecordBatch]:
     arrow_format = _get_file_format(task.file.file_format, pre_buffer=True, buffer_size=(ONE_MEGABYTE * 8))
     with io.new_input(task.file.file_path).open() as fin:
@@ -1612,14 +1616,18 @@ def _task_to_record_batches(
 
         file_project_schema = prune_columns(file_schema, projected_field_ids, select_full_types=False)
 
-        fragment_scanner = ds.Scanner.from_fragment(
-            fragment=fragment,
-            schema=physical_schema,
+        scanner_kwargs: dict[str, Any] = {
+            "fragment": fragment,
+            "schema": physical_schema,
             # This will push down the query to Arrow.
             # But in case there are positional deletes, we have to apply them first
-            filter=pyarrow_filter if not positional_deletes else None,
-            columns=[col.name for col in file_project_schema.columns],
-        )
+            "filter": pyarrow_filter if not positional_deletes else None,
+            "columns": [col.name for col in file_project_schema.columns],
+        }
+        if batch_size is not None:
+            scanner_kwargs["batch_size"] = batch_size
+
+        fragment_scanner = ds.Scanner.from_fragment(**scanner_kwargs)
 
         next_index = 0
         batches = fragment_scanner.to_batches()
@@ -1677,6 +1685,76 @@ def _read_all_delete_files(io: FileIO, tasks: Iterable[FileScanTask]) -> dict[st
     return deletes_per_file
 
 
+_QUEUE_SENTINEL = object()
+
+
+def _bounded_concurrent_batches(
+    tasks: list[FileScanTask],
+    batch_fn: Callable[[FileScanTask], Iterator[pa.RecordBatch]],
+    concurrent_streams: int,
+    max_buffered_batches: int = 16,
+) -> Generator[pa.RecordBatch, None, None]:
+    """Read batches from multiple files concurrently with bounded memory.
+
+    Uses a per-scan ThreadPoolExecutor(max_workers=concurrent_streams) to naturally
+    bound concurrency. Workers push batches into a bounded queue which provides
+    backpressure when the consumer is slower than the producers.
+
+    Args:
+        tasks: The file scan tasks to process.
+        batch_fn: A callable that takes a FileScanTask and returns an iterator of RecordBatches.
+        concurrent_streams: Maximum number of concurrent read streams.
+        max_buffered_batches: Maximum number of batches to buffer in the queue.
+    """
+    if not tasks:
+        return
+
+    batch_queue: queue.Queue[pa.RecordBatch | BaseException | object] = queue.Queue(maxsize=max_buffered_batches)
+    cancel = threading.Event()
+    remaining = len(tasks)
+    remaining_lock = threading.Lock()
+
+    def worker(task: FileScanTask) -> None:
+        nonlocal remaining
+        try:
+            for batch in batch_fn(task):
+                if cancel.is_set():
+                    return
+                batch_queue.put(batch)
+        except BaseException as e:
+            if not cancel.is_set():
+                batch_queue.put(e)
+        finally:
+            with remaining_lock:
+                remaining -= 1
+                if remaining == 0:
+                    batch_queue.put(_QUEUE_SENTINEL)
+
+    with ThreadPoolExecutor(max_workers=concurrent_streams) as executor:
+        for task in tasks:
+            executor.submit(worker, task)
+
+        try:
+            while True:
+                item = batch_queue.get()
+
+                if item is _QUEUE_SENTINEL:
+                    break
+
+                if isinstance(item, BaseException):
+                    raise item
+
+                yield item
+        finally:
+            cancel.set()
+            # Drain the queue to unblock any workers stuck on put()
+            while not batch_queue.empty():
+                try:
+                    batch_queue.get_nowait()
+                except queue.Empty:
+                    break
+
+
 class ArrowScan:
     _table_metadata: TableMetadata
     _io: FileIO
@@ -1756,54 +1834,109 @@ def to_table(self, tasks: Iterable[FileScanTask]) -> pa.Table:
 
         return result
 
-    def to_record_batches(self, tasks: Iterable[FileScanTask]) -> Iterator[pa.RecordBatch]:
+    def to_record_batches(
+        self,
+        tasks: Iterable[FileScanTask],
+        batch_size: int | None = None,
+        order: ScanOrder = TaskOrder(),
+    ) -> Iterator[pa.RecordBatch]:
         """Scan the Iceberg table and return an Iterator[pa.RecordBatch].
 
         Returns an Iterator of pa.RecordBatch with data from the Iceberg table
         by resolving the right columns that match the current table schema.
         Only data that matches the provided row_filter expression is returned.
 
+        Ordering semantics:
+            - TaskOrder() (default): Yields batches one file at a time in task submission order.
+            - ArrivalOrder(): Batches may be interleaved across files as they arrive.
+              Within each file, batch ordering follows row order.
+
         Args:
             tasks: FileScanTasks representing the data files and delete files to read from.
+            batch_size: The number of rows per batch. If None, PyArrow's default is used.
+            order: Controls the order in which record batches are returned.
+                TaskOrder() (default) yields batches one file at a time in task order.
+                ArrivalOrder(concurrent_streams=N, max_buffered_batches=M) yields batches
+                as they are produced without materializing entire files into memory.
 
         Returns:
             An Iterator of PyArrow RecordBatches.
             Total number of rows will be capped if specified.
 
         Raises:
             ResolveError: When a required field cannot be found in the file
-            ValueError: When a field type in the file cannot be projected to the schema type
+            ValueError: When a field type in the file cannot be projected to the schema type,
+                or when an invalid order value is provided, or when concurrent_streams < 1.
         """
-        deletes_per_file = _read_all_delete_files(self._io, tasks)
+        if not isinstance(order, ScanOrder):
+            raise ValueError(f"Invalid order: {order!r}. Must be a ScanOrder instance (TaskOrder() or ArrivalOrder()).")
 
-        total_row_count = 0
+        task_list, deletes_per_file = self._prepare_tasks_and_deletes(tasks)
+
+        if isinstance(order, ArrivalOrder):
+            if order.concurrent_streams < 1:
+                raise ValueError(f"concurrent_streams must be >= 1, got {order.concurrent_streams}")
+            return self._apply_limit(self._iter_batches_arrival(task_list, deletes_per_file, batch_size, order.concurrent_streams, order.max_buffered_batches))
+
+        return self._apply_limit(self._iter_batches_materialized(task_list, deletes_per_file, batch_size))
+
+    def _prepare_tasks_and_deletes(
+        self, tasks: Iterable[FileScanTask]
+    ) -> tuple[list[FileScanTask], dict[str, list[ChunkedArray]]]:
+        """Resolve delete files and return tasks as a list."""
+        task_list = list(tasks)
+        deletes_per_file = _read_all_delete_files(self._io, task_list)
+        return task_list, deletes_per_file
+
+    def _iter_batches_arrival(
+        self,
+        task_list: list[FileScanTask],
+        deletes_per_file: dict[str, list[ChunkedArray]],
+        batch_size: int | None,
+        concurrent_streams: int,
+        max_buffered_batches: int = 16,
+    ) -> Iterator[pa.RecordBatch]:
+        """Yield batches using bounded concurrent streaming in arrival order."""
+
+        def batch_fn(task: FileScanTask) -> Iterator[pa.RecordBatch]:
+            return self._record_batches_from_scan_tasks_and_deletes([task], deletes_per_file, batch_size)
+
+        yield from _bounded_concurrent_batches(task_list, batch_fn, concurrent_streams, max_buffered_batches)
+
+    def _iter_batches_materialized(
+        self,
+        task_list: list[FileScanTask],
+        deletes_per_file: dict[str, list[ChunkedArray]],
+        batch_size: int | None,
+    ) -> Iterator[pa.RecordBatch]:
+        """Yield batches using executor.map with full file materialization."""
         executor = ExecutorFactory.get_or_create()
 
         def batches_for_task(task: FileScanTask) -> list[pa.RecordBatch]:
-            # Materialize the iterator here to ensure execution happens within the executor.
-            # Otherwise, the iterator would be lazily consumed later (in the main thread),
-            # defeating the purpose of using executor.map.
-            return list(self._record_batches_from_scan_tasks_and_deletes([task], deletes_per_file))
+            return list(self._record_batches_from_scan_tasks_and_deletes([task], deletes_per_file, batch_size))
 
-        limit_reached = False
-        for batches in executor.map(batches_for_task, tasks):
-            for batch in batches:
-                current_batch_size = len(batch)
-                if self._limit is not None and total_row_count + current_batch_size >= self._limit:
-                    yield batch.slice(0, self._limit - total_row_count)
+        for batches in executor.map(batches_for_task, task_list):
+            yield from batches
 
-                    limit_reached = True
-                    break
-                else:
-                    yield batch
-                    total_row_count += current_batch_size
+    def _apply_limit(self, batches: Iterator[pa.RecordBatch]) -> Iterator[pa.RecordBatch]:
+        """Apply row limit across batches."""
+        if self._limit is None:
+            yield from batches
+            return
 
-            if limit_reached:
-                # This break will also cancel all running tasks in the executor
-                break
+        total_row_count = 0
+        for batch in batches:
+            remaining = self._limit - total_row_count
+            if remaining <= 0:
+                return
+            if len(batch) > remaining:
+                yield batch.slice(0, remaining)
+                return
+            yield batch
+            total_row_count += len(batch)
 
     def _record_batches_from_scan_tasks_and_deletes(
-        self, tasks: Iterable[FileScanTask], deletes_per_file: dict[str, list[ChunkedArray]]
+        self, tasks: Iterable[FileScanTask], deletes_per_file: dict[str, list[ChunkedArray]], batch_size: int | None = None
     ) -> Iterator[pa.RecordBatch]:
         total_row_count = 0
         for task in tasks:
@@ -1822,6 +1955,7 @@ def _record_batches_from_scan_tasks_and_deletes(
                 self._table_metadata.specs().get(task.file.spec_id),
                 self._table_metadata.format_version,
                 self._downcast_ns_timestamp_to_us,
+                batch_size,
             )
             for batch in batches:
                 if self._limit is not None: