remove top level folder

README.md

@@ -71,11 +71,16 @@ datacustomcode run ./payload/entrypoint.py
 After modifying the `entrypoint.py` as needed, using any dependencies you add in the `.venv` virtual environment, you can run this script in Data Cloud:
 ```zsh
 datacustomcode scan ./payload/entrypoint.py
-datacustomcode deploy --path ./payload --name my_custom_script
+datacustomcode deploy --path ./payload --name my_custom_script --compute-type CPU_L
 ```
 
 > [!TIP]
 > The `deploy` process can take several minutes.  If you'd like more feedback on the underlying process, you can add `--debug` to the command like `datacustomcode --debug deploy --path ./payload --name my_custom_script`
+>
+> [!NOTE]
+> **Compute Types**: Choose the appropriate compute type based on your workload requirements:
+> - **CPU_L/CPU_XL/CPU_2XL/CPU_4XL**: Large, X-Large, 2X-Large and 4X-Large CPU instances for data processing
+> - Default is `CPU_2XL` which provides a good balance of performance and cost for most use cases
 
 You can now use the Salesforce Data Cloud UI to find the created Data Transform and use the `Run Now` button to run it.
 Once the Data Transform run is successful, check the DLO your script is writing to and verify the correct records were added.
@@ -139,6 +144,7 @@ Options:
 - `--name TEXT`: Name of the transformation job [required]
 - `--version TEXT`: Version of the transformation job (default: "0.0.1")
 - `--description TEXT`: Description of the transformation job (default: "")
+- `--compute-type TEXT`: Compute type for the deployment (default: "CPU_XL"). Available options: CPU_L(Large), CPU_XL (Extra Large), CPU_2XL (2X Large), CPU_4XL (4X Large)
 
 #### `datacustomcode init`
 Initialize a new development environment with a template.

src/datacustomcode/cli.py

@@ -83,16 +83,43 @@ def zip(path: str):
 @click.option("--name", required=True)
 @click.option("--version", default="0.0.1")
 @click.option("--description", default="Custom Data Transform Code")
-def deploy(path: str, name: str, version: str, description: str):
+@click.option(
+    "--cpu-size",
+    default="CPU_2XL",
+    help="""CPU size for deployment. Available options:
+
+    \b
+    CPU_L     - Large CPU instance
+    CPU_XL    - X-Large CPU instance
+    CPU_2XL   - 2X-Large CPU instance [DEFAULT]
+    CPU_4XL   - 4X-Large CPU instance
+
+    Choose based on your workload requirements.""",
+)
+def deploy(path: str, name: str, version: str, description: str, cpu_size: str):
     from datacustomcode.credentials import Credentials
     from datacustomcode.deploy import TransformationJobMetadata, deploy_full
 
     logger.debug("Deploying project")
 
+    # Validate compute type
+    from datacustomcode.deploy import COMPUTE_TYPES
+
+    if cpu_size not in COMPUTE_TYPES.keys():
+        click.secho(
+            f"Error: Invalid CPU size '{cpu_size}'. "
+            f"Available options: {', '.join(COMPUTE_TYPES.keys())}",
+            fg="red",
+        )
+        raise click.Abort()
+
+    logger.debug(f"Deploying with CPU size: {cpu_size}")
+
     metadata = TransformationJobMetadata(
         name=name,
         version=version,
         description=description,
+        computeType=COMPUTE_TYPES[cpu_size],
     )
     try:
         credentials = Credentials.from_available()

src/datacustomcode/deploy.py

@@ -44,11 +44,25 @@
 AUTH_PATH = "services/oauth2/token"
 WAIT_FOR_DEPLOYMENT_TIMEOUT = 3000
 
+# Available compute types for Data Cloud deployments.
+# Nomenclature used by COMPUTE_TYPES keys align with
+# compute instances provisioned by Data Cloud.
+COMPUTE_TYPES = {
+    "CPU_L": "CPU_XS",  # Large CPU instance
+    "CPU_XL": "CPU_S",  # X-Large CPU instance
+    "CPU_2XL": "CPU_M",  # 2X-Large CPU instance (default)
+    "CPU_4XL": "CPU_L",  # 4X-Large CPU instance
+}
+
 
 class TransformationJobMetadata(BaseModel):
     name: str
     version: str
     description: str
+    computeType: str
+
+    def __init__(self, **data):
+        super().__init__(**data)
 
 
 def _join_strip_url(*args: str) -> str:
@@ -123,7 +137,7 @@ def create_deployment(
         "name": metadata.name,
         "description": metadata.description,
         "version": metadata.version,
-        "computeType": "CPU_M",
+        "computeType": metadata.computeType,
     }
     logger.debug(f"Creating deployment {metadata.name}...")
     try:
@@ -372,9 +386,9 @@ def zip(
             # Skip .DS_Store files when adding to zip
             for file in files:
                 if file != ".DS_Store":
-                    file_path = os.path.join(root, file)
-                    zipf.write(file_path)
-
+                    abs_path = os.path.join(root, file)
+                    arcname = os.path.relpath(abs_path, directory)
+                    zipf.write(abs_path, arcname)
     logger.debug(f"Created zip file: {ZIP_FILE_NAME}")
 
 

src/datacustomcode/scan.py

@@ -54,10 +54,6 @@ class DataAccessLayerCalls(pydantic.BaseModel):
     def validate_access_layer(self) -> DataAccessLayerCalls:
         if self.read_dlo and self.read_dmo:
             raise ValueError("Cannot read from DLO and DMO in the same file.")
-        if len(self.write_to_dlo) > 1 or len(self.write_to_dmo) > 1:
-            raise ValueError(
-                "Cannot write to more than one DLO or DMO in the same file."
-            )
         if not self.read_dlo and not self.read_dmo:
             raise ValueError("Must read from at least one DLO or DMO.")
         if self.read_dlo and self.write_to_dmo:

tests/test_deploy.py

@@ -434,7 +434,10 @@ def test_create_deployment_success(self, mock_make_api_call):
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         mock_make_api_call.return_value = {
@@ -454,7 +457,10 @@ def test_create_deployment_conflict(self, mock_make_api_call):
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         # Mock HTTP error with 409 Conflict
@@ -571,7 +577,10 @@ def test_get_deployments(self, mock_make_api_call):
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         mock_make_api_call.return_value = {"deploymentStatus": "Deployed"}
@@ -595,7 +604,10 @@ def test_wait_for_deployment_success(
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
         callback = MagicMock()
 
@@ -622,7 +634,10 @@ def test_wait_for_deployment_timeout(
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         # Mock time to simulate timeout
@@ -699,7 +714,10 @@ def test_create_data_transform(self, mock_make_api_call, mock_get_config):
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         mock_get_config.return_value = DataTransformConfig(
@@ -762,7 +780,10 @@ def test_deploy_full(
             login_url="https://example.com",
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
         callback = MagicMock()
 
@@ -799,7 +820,10 @@ def test_run_data_transform(self, mock_make_api_call):
             access_token="test_token", instance_url="https://instance.example.com"
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
 
         mock_make_api_call.return_value = {"status": "Running"}
@@ -839,7 +863,10 @@ def test_deploy_full_happy_path(
             login_url="https://example.com",
         )
         metadata = TransformationJobMetadata(
-            name="test_job", version="1.0.0", description="Test job"
+            name="test_job",
+            version="1.0.0",
+            description="Test job",
+            computeType="CPU_M",
         )
         callback = MagicMock()
 

tests/test_scan.py

@@ -247,7 +247,7 @@ def test_read_dmo_write_dlo_throws_error(self):
         finally:
             os.unlink(temp_path)
 
-    def test_invalid_multiple_writes(self):
+    def test_multiple_writes(self):
         """Test scanning a file with multiple write operations."""
         content = textwrap.dedent(
             """
@@ -258,15 +258,25 @@ def test_invalid_multiple_writes(self):
             # Read from DLO
             df = client.read_dlo("input_dlo")
 
-            # Write to multiple DLOs - invalid
-            client.write_to_dlo("output_dlo_1", df, "overwrite")
-            client.write_to_dlo("output_dlo_2", df, "overwrite")
+            # Transform data for different outputs
+            df_filtered = df.filter(df.col > 10)
+            df_aggregated = df.groupBy("category").agg({"value": "sum"})
+
+            # Write to multiple DLOs
+            client.write_to_dlo("output_filtered", df_filtered, "overwrite")
+            client.write_to_dlo("output_aggregated", df_aggregated, "overwrite")
         """
         )
         temp_path = create_test_script(content)
         try:
-            with pytest.raises(ValueError, match="Cannot write to more than one DLO"):
-                scan_file(temp_path)
+            result = scan_file(temp_path)
+            assert "input_dlo" in result.read_dlo
+            assert "output_filtered" in result.write_to_dlo
+            assert "output_aggregated" in result.write_to_dlo
+            assert len(result.read_dlo) == 1
+            assert len(result.write_to_dlo) == 2
+            assert len(result.read_dmo) == 0
+            assert len(result.write_to_dmo) == 0
         finally:
             os.unlink(temp_path)