enables gcs staging databricks

dlt/destinations/impl/databricks/databricks.py

@@ -33,6 +33,7 @@
 from dlt.destinations.job_impl import ReferenceFollowupJobRequest
 
 AZURE_BLOB_STORAGE_PROTOCOLS = ["az", "abfss", "abfs"]
+SUPPORTED_BLOB_STORAGE_PROTOCOLS = AZURE_BLOB_STORAGE_PROTOCOLS + ["s3", "gs", "gcs"]
 
 
 class DatabricksLoadJob(RunnableLoadJob, HasFollowupJobs):
@@ -69,11 +70,12 @@ def run(self) -> None:
             bucket_url = urlparse(bucket_path)
             bucket_scheme = bucket_url.scheme
 
-            if bucket_scheme not in AZURE_BLOB_STORAGE_PROTOCOLS + ["s3"]:
+            if bucket_scheme not in SUPPORTED_BLOB_STORAGE_PROTOCOLS:
                 raise LoadJobTerminalException(
                     self._file_path,
-                    f"Databricks cannot load data from staging bucket {bucket_path}. Only s3 and"
-                    " azure buckets are supported",
+                    f"Databricks cannot load data from staging bucket {bucket_path}. Only s3, azure"
+                    " and gcs buckets are supported. Please note that gcs buckets are supported"
+                    " only via named credential",
                 )
 
             if self._job_client.config.is_staging_external_location:
@@ -106,6 +108,12 @@ def run(self) -> None:
                     bucket_path = self.ensure_databricks_abfss_url(
                         bucket_path, staging_credentials.azure_storage_account_name
                     )
+                else:
+                    raise LoadJobTerminalException(
+                        self._file_path,
+                        "You need to use Databricks named credential to use google storage."
+                        " Passing explicit Google credentials is not supported by Databricks.",
+                    )
 
             if bucket_scheme in AZURE_BLOB_STORAGE_PROTOCOLS:
                 assert isinstance(
@@ -125,7 +133,7 @@ def run(self) -> None:
             raise LoadJobTerminalException(
                 self._file_path,
                 "Cannot load from local file. Databricks does not support loading from local files."
-                " Configure staging with an s3 or azure storage bucket.",
+                " Configure staging with an s3, azure or google storage bucket.",
             )
 
         # decide on source format, stage_file_path will either be a local file or a bucket path

docs/website/docs/dlt-ecosystem/destinations/databricks.md

@@ -141,7 +141,7 @@ The `jsonl` format has some limitations when used with Databricks:
 
 ## Staging support
 
-Databricks supports both Amazon S3 and Azure Blob Storage as staging locations. `dlt` will upload files in `parquet` format to the staging location and will instruct Databricks to load data from there.
+Databricks supports both Amazon S3, Azure Blob Storage and Google GCS as staging locations. `dlt` will upload files in `parquet` format to the staging location and will instruct Databricks to load data from there.
 
 ### Databricks and Amazon S3
 
@@ -187,6 +187,11 @@ pipeline = dlt.pipeline(
 
 ```
 
+### Databricks and Google Cloud Storage
+
+In order to load from GCS stage you must set-up the credentials via **named credential**. See below. Databricks does not allow to pass Google Credentials
+explicitly in SQL Statements.
+
 ### Use external locations and stored credentials
 `dlt` forwards bucket credentials to the `COPY INTO` SQL command by default. You may prefer to use [external locations or stored credentials instead](https://docs.databricks.com/en/sql/language-manual/sql-ref-external-locations.html#external-location) that are stored on the Databricks side.
 

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "dlt"
-version = "1.1.0"
+version = "1.2.0"
 description = "dlt is an open-source python-first scalable data loading library that does not require any backend to run."
 authors = ["dltHub Inc. <[email protected]>"]
 maintainers = [ "Marcin Rudolf <[email protected]>", "Adrian Brudaru <[email protected]>", "Anton Burnashev <[email protected]>", "David Scharf <[email protected]>" ]

tests/load/pipeline/test_databricks_pipeline.py

@@ -2,7 +2,12 @@
 import os
 
 from dlt.common.utils import uniq_id
-from tests.load.utils import DestinationTestConfiguration, destinations_configs, AZ_BUCKET
+from tests.load.utils import (
+    GCS_BUCKET,
+    DestinationTestConfiguration,
+    destinations_configs,
+    AZ_BUCKET,
+)
 from tests.pipeline.utils import assert_load_info
 
 
@@ -13,7 +18,7 @@
 @pytest.mark.parametrize(
     "destination_config",
     destinations_configs(
-        default_sql_configs=True, bucket_subset=(AZ_BUCKET), subset=("databricks",)
+        default_sql_configs=True, bucket_subset=(AZ_BUCKET,), subset=("databricks",)
     ),
     ids=lambda x: x.name,
 )
@@ -62,7 +67,7 @@ def test_databricks_external_location(destination_config: DestinationTestConfigu
         in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message
     )
 
-    # # should fail on non existing stored credentials
+    # should fail on non existing stored credentials
     bricks = databricks(is_staging_external_location=False, staging_credentials_name="CREDENTIAL_X")
     pipeline = destination_config.setup_pipeline(
         "test_databricks_external_location",
@@ -90,3 +95,68 @@ def test_databricks_external_location(destination_config: DestinationTestConfigu
     assert (
         "credential_x" in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message
     )
+
+
+@pytest.mark.parametrize(
+    "destination_config",
+    destinations_configs(
+        default_sql_configs=True, bucket_subset=(AZ_BUCKET,), subset=("databricks",)
+    ),
+    ids=lambda x: x.name,
+)
+def test_databricks_gcs_external_location(destination_config: DestinationTestConfiguration) -> None:
+    # do not interfere with state
+    os.environ["RESTORE_FROM_DESTINATION"] = "False"
+    # let the package complete even with failed jobs
+    os.environ["RAISE_ON_FAILED_JOBS"] = "false"
+
+    dataset_name = "test_databricks_gcs_external_location" + uniq_id()
+
+    # swap AZ bucket for GCS_BUCKET
+    from dlt.destinations import databricks, filesystem
+
+    stage = filesystem(GCS_BUCKET)
+
+    # explicit cred handover should fail
+    bricks = databricks()
+    pipeline = destination_config.setup_pipeline(
+        "test_databricks_gcs_external_location",
+        dataset_name=dataset_name,
+        destination=bricks,
+        staging=stage,
+    )
+    info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs)
+    assert info.has_failed_jobs is True
+    assert (
+        "You need to use Databricks named credential or external location"
+        in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message
+    )
+
+    # should fail on internal config error as external location is not configured
+    # bricks = databricks(is_staging_external_location=True)
+    # pipeline = destination_config.setup_pipeline(
+    #     "test_databricks_gcs_external_location",
+    #     dataset_name=dataset_name,
+    #     destination=bricks,
+    #     staging=stage,
+    # )
+    # info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs)
+    # assert info.has_failed_jobs is True
+    # assert (
+    #     "Invalid configuration value detected"
+    #     in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message
+    # )
+
+    # should fail on non existing stored credentials
+    bricks = databricks(is_staging_external_location=False, staging_credentials_name="CREDENTIAL_X")
+    pipeline = destination_config.setup_pipeline(
+        "test_databricks_external_location",
+        dataset_name=dataset_name,
+        destination=bricks,
+        staging=stage,
+    )
+    info = pipeline.run([1, 2, 3], table_name="digits", **destination_config.run_kwargs)
+    assert info.has_failed_jobs is True
+    assert (
+        "credential_x" in pipeline.list_failed_jobs_in_package(info.loads_ids[0])[0].failed_message
+    )