Use existing staging client for dataproc staging

sdk/python/feast/pyspark/launchers/gcloud/dataproc.py

@@ -4,7 +4,7 @@
 from urllib.parse import urlparse
 
 from google.api_core.operation import Operation
-from google.cloud import dataproc_v1, storage
+from google.cloud import dataproc_v1
 from google.cloud.dataproc_v1 import Job as DataprocJob
 from google.cloud.dataproc_v1 import JobStatus
 
@@ -18,6 +18,7 @@
     SparkJobParameters,
     SparkJobStatus,
 )
+from feast.staging.storage_client import get_staging_client
 
 
 class DataprocJobMixin:
@@ -113,13 +114,11 @@ def __init__(
         )
 
     def _stage_files(self, pyspark_script: str, job_id: str) -> str:
-        client = storage.Client()
-        bucket = client.get_bucket(self.staging_bucket)
+        staging_client = get_staging_client("gs")
         blob_path = os.path.join(
             self.remote_path, job_id, os.path.basename(pyspark_script),
         )
-        blob = bucket.blob(blob_path)
-        blob.upload_from_filename(pyspark_script)
+        staging_client.upload_file(blob_path, self.staging_bucket, pyspark_script)
 
         return f"gs://{self.staging_bucket}/{blob_path}"
 

sdk/python/feast/staging/storage_client.py

@@ -12,8 +12,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
-
+import hashlib
 import os
 import re
 import shutil
@@ -192,6 +191,13 @@ def list_files(self, bucket: str, path: str) -> List[str]:
         else:
             return [f"{S3}://{bucket}/{path.lstrip('/')}"]
 
+    def _hash_file(self, local_path: str):
+        h = hashlib.sha256()
+        with open(local_path, "rb") as f:
+            for block in iter(lambda: f.read(2 ** 20), b""):
+                h.update(block)
+        return h.hexdigest()
+
     def upload_file(self, local_path: str, bucket: str, remote_path: str):
         """
         Uploads file to s3.
@@ -201,8 +207,36 @@ def upload_file(self, local_path: str, bucket: str, remote_path: str):
             bucket (str): s3 Bucket name
             remote_path (str): relative path to the folder to which the files need to be uploaded
         """
-        with open(local_path, "rb") as file:
-            self.s3_client.upload_fileobj(file, bucket, remote_path)
+
+        sha256sum = self._hash_file(local_path)
+
+        import botocore
+
+        try:
+            head_response = self.s3_client.head_object(Bucket=bucket, Key=remote_path)
+            if head_response["Metadata"]["sha256sum"] == sha256sum:
+                # File already exists
+                return remote_path
+            else:
+                print(f"Uploading {local_path} to {remote_path}")
+                self.s3_client.upload_file(
+                    local_path,
+                    bucket,
+                    remote_path,
+                    ExtraArgs={"Metadata": {"sha256sum": sha256sum}},
+                )
+                return remote_path
+        except botocore.exceptions.ClientError as e:
+            if e.response["Error"]["Code"] != "404":
+                raise
+
+        self.s3_client.upload_file(
+            local_path,
+            bucket,
+            remote_path,
+            ExtraArgs={"Metadata": {"sha256sum": sha256sum}},
+        )
+        return remote_path
 
 
 class LocalFSClient(AbstractStagingClient):