feat: reduce memory impact of chunked reads

Author: jaidisido

awswrangler/s3/_read_parquet.py

@@ -247,40 +247,33 @@ def _read_parquet_chunked(
             if pq_file is None:
                 continue
 
-            schema = pq_file.schema.to_arrow_schema()
+            metadata = pq_file.metadata
+            schema = metadata.schema.to_arrow_schema()
             if columns:
                 schema = pa.schema([schema.field(column) for column in columns], schema.metadata)
 
             use_threads_flag: bool = use_threads if isinstance(use_threads, bool) else bool(use_threads > 1)
-            iterate_at_least_once = False
-            for chunk in pq_file.iter_batches(
-                batch_size=batch_size, columns=columns, use_threads=use_threads_flag, use_pandas_metadata=False
-            ):
-                iterate_at_least_once = True
-                table = _add_table_partitions(
-                    table=pa.Table.from_batches([chunk], schema=schema),
-                    path=path,
-                    path_root=path_root,
-                )
-                df = _table_to_df(table=table, kwargs=arrow_kwargs)
-                if chunked is True:
-                    yield df
-                else:
-                    if next_slice is not None:
-                        df = pd.concat(objs=[next_slice, df], sort=False, copy=False)
-                    while len(df.index) >= chunked:
-                        yield df.iloc[:chunked, :].copy()
-                        df = df.iloc[chunked:, :]
-                    if df.empty:
-                        next_slice = None
+            table_kwargs = {"path": path, "path_root": path_root}
+            if metadata.num_rows > 0:
+                for chunk in pq_file.iter_batches(
+                    batch_size=batch_size, columns=columns, use_threads=use_threads_flag, use_pandas_metadata=False
+                ):
+                    table = _add_table_partitions(table=pa.Table.from_batches([chunk], schema=schema), **table_kwargs)
+                    df = _table_to_df(table=table, kwargs=arrow_kwargs)
+                    if chunked is True:
+                        yield df
                     else:
-                        next_slice = df
-            if not iterate_at_least_once:
-                table = _add_table_partitions(
-                    table=pa.Table.from_batches([], schema=schema),
-                    path=path,
-                    path_root=path_root,
-                )
+                        if next_slice is not None:
+                            df = pd.concat(objs=[next_slice, df], sort=False, copy=False)
+                        while len(df.index) >= chunked:
+                            yield df.iloc[:chunked, :].copy()
+                            df = df.iloc[chunked:, :]
+                        if df.empty:
+                            next_slice = None
+                        else:
+                            next_slice = df
+            else:
+                table = _add_table_partitions(table=pa.Table.from_batches([], schema=schema), **table_kwargs)
                 df = _table_to_df(table=table, kwargs=arrow_kwargs)
                 yield df