opendatahub-io
diff --git a/‎docs/reference/offline-stores/spark.md‎
Lines changed: 5 additions & 1 deletion b/‎docs/reference/offline-stores/spark.md‎
Lines changed: 5 additions & 1 deletion
diff --git a/‎sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark.py‎
Lines changed: 161 additions & 47 deletions b/‎sdk/python/feast/infra/offline_stores/contrib/spark_offline_store/spark.py‎
Lines changed: 161 additions & 47 deletions
@@ -32,9 +32,13 @@ offline_store:
         spark.sql.session.timeZone: "UTC"
         spark.sql.execution.arrow.fallback.enabled: "true"
         spark.sql.execution.arrow.pyspark.enabled: "true"
+    # Optional: spill large materializations to the staging location instead of collecting in the driver
+    staging_location: "s3://my-bucket/tmp/feast"
 online_store:
     path: data/online_store.db
 ```
+
+> The `staging_location` can point to object storage (like S3, GCS, or Azure blobs) or a local filesystem directory (e.g., `/tmp/feast/staging`) to spill large materialization outputs before reading them back into Feast.
 {% endcode %}
 
 The full set of configuration options is available in [SparkOfflineStoreConfig](https://rtd.feast.dev/en/master/#feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkOfflineStoreConfig).
@@ -60,7 +64,7 @@ Below is a matrix indicating which functionality is supported by `SparkRetrieval
 | export to arrow table                                 | yes   |
 | export to arrow batches                               | no    |
 | export to SQL                                         | no    |
-| export to data lake (S3, GCS, etc.)                   | no    |
+| export to data lake (S3, GCS, etc.)                   | yes   |
 | export to data warehouse                              | no    |
 | export as Spark dataframe                             | yes   |
 | local execution of Python-based on-demand transforms  | no    |
 
@@ -16,6 +16,7 @@
     Union,
     cast,
 )
+from urllib.parse import urlparse
 
 if TYPE_CHECKING:
     from feast.saved_dataset import ValidationReference
@@ -24,6 +25,7 @@
 import pandas
 import pandas as pd
 import pyarrow
+import pyarrow.dataset as ds
 import pyarrow.parquet as pq
 import pyspark
 from pydantic import StrictStr
@@ -445,8 +447,43 @@ def _to_df_internal(self, timeout: Optional[int] = None) -> pd.DataFrame:
 
     def _to_arrow_internal(self, timeout: Optional[int] = None) -> pyarrow.Table:
         """Return dataset as pyarrow Table synchronously"""
+        if self._should_use_staging_for_arrow():
+            return self._to_arrow_via_staging()
+
         return pyarrow.Table.from_pandas(self._to_df_internal(timeout=timeout))
 
+    def _should_use_staging_for_arrow(self) -> bool:
+        offline_store = getattr(self._config, "offline_store", None)
+        return bool(
+            isinstance(offline_store, SparkOfflineStoreConfig)
+            and getattr(offline_store, "staging_location", None)
+        )
+
+    def _to_arrow_via_staging(self) -> pyarrow.Table:
+        paths = self.to_remote_storage()
+        if not paths:
+            return pyarrow.table({})
+
+        parquet_paths = _filter_parquet_files(paths)
+        if not parquet_paths:
+            return pyarrow.table({})
+
+        normalized_paths = self._normalize_staging_paths(parquet_paths)
+        dataset = ds.dataset(normalized_paths, format="parquet")
+        return dataset.to_table()
+
+    def _normalize_staging_paths(self, paths: List[str]) -> List[str]:
+        """Normalize staging paths for PyArrow datasets."""
+        normalized = []
+        for path in paths:
+            if path.startswith("file://"):
+                normalized.append(path[len("file://") :])
+            elif "://" in path:
+                normalized.append(path)
+            else:
+                normalized.append(path)
+        return normalized
+
     def to_feast_df(
         self,
         validation_reference: Optional["ValidationReference"] = None,
@@ -508,55 +545,53 @@ def supports_remote_storage_export(self) -> bool:
 
     def to_remote_storage(self) -> List[str]:
         """Currently only works for local and s3-based staging locations"""
-        if self.supports_remote_storage_export():
-            sdf: pyspark.sql.DataFrame = self.to_spark_df()
-
-            if self._config.offline_store.staging_location.startswith("/"):
-                local_file_staging_location = os.path.abspath(
-                    self._config.offline_store.staging_location
-                )
-
-                # write to staging location
-                output_uri = os.path.join(
-                    str(local_file_staging_location), str(uuid.uuid4())
-                )
-                sdf.write.parquet(output_uri)
-
-                return _list_files_in_folder(output_uri)
-            elif self._config.offline_store.staging_location.startswith("s3://"):
-                from feast.infra.utils import aws_utils
-
-                spark_compatible_s3_staging_location = (
-                    self._config.offline_store.staging_location.replace(
-                        "s3://", "s3a://"
-                    )
-                )
-
-                # write to staging location
-                output_uri = os.path.join(
-                    str(spark_compatible_s3_staging_location), str(uuid.uuid4())
-                )
-                sdf.write.parquet(output_uri)
-
-                return aws_utils.list_s3_files(
-                    self._config.offline_store.region, output_uri
-                )
-            elif self._config.offline_store.staging_location.startswith("hdfs://"):
-                output_uri = os.path.join(
-                    self._config.offline_store.staging_location, str(uuid.uuid4())
-                )
-                sdf.write.parquet(output_uri)
-                spark_session = get_spark_session_or_start_new_with_repoconfig(
-                    store_config=self._config.offline_store
-                )
-                return _list_hdfs_files(spark_session, output_uri)
-            else:
-                raise NotImplementedError(
-                    "to_remote_storage is only implemented for file://, s3:// and hdfs:// uri schemes"
-                )
+        if not self.supports_remote_storage_export():
+            raise NotImplementedError()
+
+        sdf: pyspark.sql.DataFrame = self.to_spark_df()
+        staging_location = self._config.offline_store.staging_location
+
+        if staging_location.startswith("/"):
+            local_file_staging_location = os.path.abspath(staging_location)
+            output_uri = os.path.join(local_file_staging_location, str(uuid.uuid4()))
+            sdf.write.parquet(output_uri)
+            return _list_files_in_folder(output_uri)
+        elif staging_location.startswith("s3://"):
+            from feast.infra.utils import aws_utils
 
+            spark_compatible_s3_staging_location = staging_location.replace(
+                "s3://", "s3a://"
+            )
+            output_uri = os.path.join(
+                spark_compatible_s3_staging_location, str(uuid.uuid4())
+            )
+            sdf.write.parquet(output_uri)
+            s3_uri_for_listing = output_uri.replace("s3a://", "s3://", 1)
+            return aws_utils.list_s3_files(
+                self._config.offline_store.region, s3_uri_for_listing
+            )
+        elif staging_location.startswith("gs://"):
+            output_uri = os.path.join(staging_location, str(uuid.uuid4()))
+            sdf.write.parquet(output_uri)
+            return _list_gcs_files(output_uri)
+        elif staging_location.startswith(("wasbs://", "abfs://", "abfss://")) or (
+            staging_location.startswith("https://")
+            and ".blob.core.windows.net" in staging_location
+        ):
+            output_uri = os.path.join(staging_location, str(uuid.uuid4()))
+            sdf.write.parquet(output_uri)
+            return _list_azure_files(output_uri)
+        elif staging_location.startswith("hdfs://"):
+            output_uri = os.path.join(staging_location, str(uuid.uuid4()))
+            sdf.write.parquet(output_uri)
+            spark_session = get_spark_session_or_start_new_with_repoconfig(
+                store_config=self._config.offline_store
+            )
+            return _list_hdfs_files(spark_session, output_uri)
         else:
-            raise NotImplementedError()
+            raise NotImplementedError(
+                "to_remote_storage is only implemented for file://, s3://, gs://, azure, and hdfs uri schemes"
+            )
 
     @property
     def metadata(self) -> Optional[RetrievalMetadata]:
@@ -789,6 +824,10 @@ def _list_files_in_folder(folder):
     return files
 
 
+def _filter_parquet_files(paths: List[str]) -> List[str]:
+    return [path for path in paths if path.endswith(".parquet")]
+
+
 def _list_hdfs_files(spark_session: SparkSession, uri: str) -> List[str]:
     jvm = spark_session._jvm
     jsc = spark_session._jsc
@@ -805,6 +844,81 @@ def _list_hdfs_files(spark_session: SparkSession, uri: str) -> List[str]:
     return files
 
 
+def _list_gcs_files(path: str) -> List[str]:
+    try:
+        from google.cloud import storage
+    except ImportError as e:
+        from feast.errors import FeastExtrasDependencyImportError
+
+        raise FeastExtrasDependencyImportError("gcp", str(e))
+
+    assert path.startswith("gs://"), "GCS path must start with gs://"
+    bucket_path = path[len("gs://") :]
+    if "/" in bucket_path:
+        bucket, prefix = bucket_path.split("/", 1)
+    else:
+        bucket, prefix = bucket_path, ""
+
+    client = storage.Client()
+    bucket_obj = client.bucket(bucket)
+    blobs = bucket_obj.list_blobs(prefix=prefix)
+
+    files = []
+    for blob in blobs:
+        if not blob.name.endswith("/"):
+            files.append(f"gs://{bucket}/{blob.name}")
+    return files
+
+
+def _list_azure_files(path: str) -> List[str]:
+    try:
+        from azure.identity import DefaultAzureCredential
+        from azure.storage.blob import BlobServiceClient
+    except ImportError as e:
+        from feast.errors import FeastExtrasDependencyImportError
+
+        raise FeastExtrasDependencyImportError("azure", str(e))
+
+    parsed = urlparse(path)
+    scheme = parsed.scheme
+
+    if scheme in ("wasbs", "abfs", "abfss"):
+        if "@" not in parsed.netloc:
+            raise ValueError("Azure staging URI must include container@account host")
+        container, account_host = parsed.netloc.split("@", 1)
+        account_url = f"https://{account_host}"
+        base = f"{scheme}://{container}@{account_host}"
+        prefix = parsed.path.lstrip("/")
+    else:
+        account_url = f"{parsed.scheme}://{parsed.netloc}"
+        container_and_prefix = parsed.path.lstrip("/").split("/", 1)
+        container = container_and_prefix[0]
+        base = f"{account_url}/{container}"
+        prefix = container_and_prefix[1] if len(container_and_prefix) > 1 else ""
+
+    credential = os.environ.get("AZURE_STORAGE_KEY") or os.environ.get(
+        "AZURE_STORAGE_ACCOUNT_KEY"
+    )
+    if credential:
+        client = BlobServiceClient(account_url=account_url, credential=credential)
+    else:
+        default_credential = DefaultAzureCredential(
+            exclude_shared_token_cache_credential=True
+        )
+        client = BlobServiceClient(
+            account_url=account_url, credential=default_credential
+        )
+
+    container_client = client.get_container_client(container)
+    blobs = container_client.list_blobs(name_starts_with=prefix if prefix else None)
+
+    files = []
+    for blob in blobs:
+        if not blob.name.endswith("/"):
+            files.append(f"{base}/{blob.name}")
+    return files
+
+
 def _cast_data_frame(
     df_new: pyspark.sql.DataFrame, df_existing: pyspark.sql.DataFrame
 ) -> pyspark.sql.DataFrame: