Metadata-Version: 2.4
Name: salesforce-data-customcode
Version: 0.1.16
Summary: Data Cloud Custom Code SDK
License-Expression: Apache-2.0
License-File: LICENSE.txt
Requires-Python: >=3.10,<3.12
Classifier: Development Status :: 4 - Beta
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: click (>=8.1.8,<9.0.0)
Requires-Dist: loguru (>=0.7.3,<0.8.0)
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: pydantic (>=1.8.2,<3.0.0)
Requires-Dist: pyspark (==3.5.1)
Requires-Dist: pyyaml (>=6.0,<7.0)
Requires-Dist: salesforce-cdp-connector
Requires-Dist: setuptools_scm (>=7.1.0,<8.0.0)
Description-Content-Type: text/markdown

# Data Cloud Custom Code SDK (BETA)

This package provides a development kit for creating custom data transformations in [Data Cloud](https://www.salesforce.com/data/). It allows you to write your own data processing logic in Python while leveraging Data Cloud's infrastructure for data access and running data transformations, mapping execution into Data Cloud data structures like [Data Model Objects](https://help.salesforce.com/s/articleView?id=data.c360_a_data_model_objects.htm&type=5) and [Data Lake Objects](https://help.salesforce.com/s/articleView?id=sf.c360_a_data_lake_objects.htm&language=en_US&type=5).

More specifically, this codebase gives you ability to test code locally before pushing to Data Cloud's remote execution engine, greatly reducing how long it takes to develop.

Use of this project with Salesforce is subject to the [TERMS OF USE](./TERMS_OF_USE.md)

## Prerequisites

- **Python 3.11 only** (currently supported version - if your system version is different, we recommend using [pyenv](https://github.com/pyenv/pyenv) to configure 3.11)
- [Azul Zulu OpenJDK 17.x](https://www.azul.com/downloads/?version=java-17-lts&package=jdk#zulu)
- Docker support like [Docker Desktop](https://docs.docker.com/desktop/)
- A salesforce org with some DLOs or DMOs with data and this feature enabled (it is not GA)
- A [connected app](#creating-a-connected-app)

## Installation
The SDK can be downloaded directly from PyPI with `pip`:
```
pip install salesforce-data-customcode
```

You can verify it was properly installed via CLI:
```
datacustomcode version
```

## Quick start
Ensure you have all the [prerequisites](#prerequisites) prepared on your machine.

To get started, create a directory and initialize a new project with the CLI:
```zsh
mkdir datacloud && cd datacloud
python3.11 -m venv .venv
source .venv/bin/activate
pip install salesforce-data-customcode
datacustomcode init my_package
```

This will yield all necessary files to get started:
```
.
├── Dockerfile
├── README.md
├── requirements.txt
├── requirements-dev.txt
├── payload
│   ├── config.json
│   ├── entrypoint.py
├── jupyterlab.sh
└── requirements.txt
```
* `Dockerfile` <span style="color:grey;font-style:italic;">(Do not update)</span> – Development container emulating the remote execution environment.
* `requirements-dev.txt` <span style="color:grey;font-style:italic;">(Do not update)</span> – These are the dependencies for the development environment.
* `jupyterlab.sh` <span style="color:grey;font-style:italic;">(Do not update)</span> – Helper script for setting up Jupyter.
* `requirements.txt` – Here you define the requirements that you will need for your script.
* `payload` – This folder will be compressed and deployed to the remote execution environment.
  * `config.json` – This config defines permissions on the back and can be generated programmatically with `scan` CLI method.
  * `entrypoint.py` – The script that defines the data transformation logic.

A functional entrypoint.py is provided so you can run once you've configured your connected app:
```zsh
cd my_package
datacustomcode configure
datacustomcode run ./payload/entrypoint.py
```

> [!IMPORTANT]
> The example entrypoint.py requires a `Account_Home__dll` DLO to be present.  And in order to deploy the script (next step), the output DLO (which is `Account_Home_copy__dll` in the example entrypoint.py) also needs to exist and be in the same dataspace as `Account_Home__dll`.

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:

**To Add New Dependencies**:
1. Make sure your virtual environment is activated
2. Add dependencies to `requirements.txt`
3. Run `pip install -r requirements.txt`
4. The SDK automatically packages all dependencies when you run `datacustomcode zip`

```zsh
cd my_package
datacustomcode scan ./payload/entrypoint.py
datacustomcode deploy --path ./payload --name my_custom_script --cpu-size 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]
> **CPU Size**: Choose the appropriate CPU/Compute Size 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.

## Dependency Management

The SDK automatically handles all dependency packaging for Data Cloud deployment. Here's how it works:

1. **Add dependencies to `requirements.txt`** - List any Python packages your script needs
2. **Install locally** - Use `pip install -r requirements.txt` in your virtual environment
3. **Automatic packaging** - When you run `datacustomcode zip`, the SDK automatically:
   - Packages all dependencies from `requirements.txt`
   - Uses the correct platform and architecture for Data Cloud

**No need to worry about platform compatibility** - the SDK handles this automatically through the Docker-based packaging process.

## files directory

```
.
├── payload
│   ├── config.json
│   ├── entrypoint.py
├── files
│   ├── data.csv
```

## py-files directory

Your Python dependencies can be packaged as .py files, .zip archives (containing multiple .py files or a Python package structure), or .egg files.

```
.
├── payload
│   ├── config.json
│   ├── entrypoint.py
├── py-files
│   ├── moduleA
│   │   ├── __init__.py
│   │   ├── moduleA.py
```

## API

Your entry point script will define logic using the `Client` object which wraps data access layers.

You should only need the following methods:
* `find_file_path(file_name)` - Returns a file path
* `read_dlo(name)` – Read from a Data Lake Object by name
* `read_dmo(name)` – Read from a Data Model Object by name
* `write_to_dlo(name, spark_dataframe, write_mode)` – Write to a Data Model Object by name with a Spark dataframe
* `write_to_dmo(name, spark_dataframe, write_mode)` – Write to a Data Lake Object by name with a Spark dataframe

For example:
```
from datacustomcode import Client

client = Client()

sdf = client.read_dlo('my_DLO')
# some transformations
# ...
client.write_to_dlo('output_DLO')
```


> [!WARNING]
> Currently we only support reading from DMOs and writing to DMOs or reading from DLOs and writing to DLOs, but they cannot mix.


## CLI

The Data Cloud Custom Code SDK provides a command-line interface (CLI) with the following commands:

### Global Options
- `--debug`: Enable debug-level logging

### Commands

#### `datacustomcode version`
Display the current version of the package.

#### `datacustomcode configure`
Configure credentials for connecting to Data Cloud.

Options:
- `--profile TEXT`: Credential profile name (default: "default")
- `--username TEXT`: Salesforce username
- `--password TEXT`: Salesforce password
- `--client-id TEXT`: Connected App Client ID
- `--client-secret TEXT`: Connected App Client Secret
- `--login-url TEXT`: Salesforce login URL


#### `datacustomcode init`
Initialize a new development environment with a code package template.

Argument:
- `DIRECTORY`: Directory to create project in (default: ".")


#### `datacustomcode scan`
Scan a Python file to generate a Data Cloud configuration.

Argument:
- `FILENAME`: Python file to scan

Options:
- `--config TEXT`: Path to save the configuration file (default: same directory as FILENAME)
- `--dry-run`: Preview the configuration without saving to a file


#### `datacustomcode run`
Run an entrypoint file locally for testing.

Argument:
- `ENTRYPOINT`: Path to entrypoint Python file

Options:
- `--config-file TEXT`: Path to configuration file
- `--dependencies TEXT`: Additional dependencies (can be specified multiple times)
- `--profile TEXT`: Credential profile name (default: "default")


#### `datacustomcode zip`
Zip a transformation job in preparation to upload to Data Cloud. Make sure to change directory into your code package folder (e.g., `my_package`) before running this command.

Arguments:
- `PATH`: Path to the code directory i.e. the payload folder (default: "payload")

Options:
- `--network TEXT`: docker network (default: "default")


#### `datacustomcode deploy`
Deploy a transformation job to Data Cloud. Note that this command takes care of creating a zip file from provided path before deployment. Make sure to change directory into your code package folder (e.g., `my_package`) before running this command.

Options:
- `--profile TEXT`: Credential profile name (default: "default")
- `--path TEXT`: Path to the code directory i.e. the payload folder (default: ".")
- `--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: "")
- `--network TEXT`: docker network (default: "default")
- `--cpu-size TEXT`: CPU size for the deployment (default: "CPU_XL"). Available options: CPU_L(Large), CPU_XL(Extra Large), CPU_2XL(2X Large), CPU_4XL(4X Large)


## Docker usage

The SDK provides Docker-based development options that allow you to test your code in an environment that closely resembles Data Cloud's execution environment.

### How Docker Works with the SDK

When you initialize a project with `datacustomcode init my_package`, a `Dockerfile` is created automatically. This Dockerfile:

- **Isn't used during local development** with virtual environments
- **Becomes active during packaging** when you run `datacustomcode zip` or `deploy`
- **Ensures compatibility** by using the same base image as Data Cloud
- **Handles dependencies automatically** regardless of platform differences

### VS Code Dev Containers

Within your `init`ed package, you will find a `.devcontainer` folder which allows you to run a docker container while developing inside of it.

Read more about Dev Containers here: https://code.visualstudio.com/docs/devcontainers/containers.
#### Setup Instructions

1. Install the VS Code extension "Dev Containers" by microsoft.com.
2. Open your package folder in VS Code, ensuring that the `.devcontainer` folder is
at the root of the File Explorer
3. Bring up the Command Palette (on mac: Cmd + Shift + P), and select "Dev
Containers: Rebuild and Reopen in Container"
4. Allow the docker image to be built, then you're ready to develop

#### Development Workflow

Once inside the Dev Container:
- **Terminal access**: Open a terminal within the container
- **Run your code**: Execute `datacustomcode run ./payload/entrypoint.py`
- **Environment consistency**: Your code will run inside a docker container that more closely resembles Data Cloud compute than your machine

> [!TIP]
> **IDE Configuration**: Use `CMD+Shift+P` (or `Ctrl+Shift+P` on Windows/Linux), then select "Python: Select Interpreter" to configure the correct Python Interpreter

> [!IMPORTANT]
> Dev Containers get their own tmp file storage, so you'll need to re-run `datacustomcode configure` every time you "Rebuild and Reopen in Container".

### JupyterLab

Within your `init`ed package, you will find a `jupyterlab.sh` file that can open a jupyter notebook for you.  Jupyter notebooks, in
combination with Data Cloud's [Query Editor](https://help.salesforce.com/s/articleView?id=data.c360_a_add_queries_to_a_query_workspace.htm&type=5)
and [Data Explorer](https://help.salesforce.com/s/articleView?id=data.c360_a_data_explorer.htm&type=5), can be extremely helpful for data
exploration.  Instead of running an entire script, one can run one code cell at a time as they discover and experiment with the DLO or DMO data.

You can read more about Jupyter Notebooks here: https://jupyter.org/

1. Within the root project of your package folder, run `./jupyterlab.sh start`
1. Double-click on "account.ipynb" file, which provides a starting point for a notebook
1. Use shift+enter to execute each cell within the notebook.  Add/edit/delete cells of code as needed for your data exploration.
1. Don't forget to run `./jupyterlab.sh stop` to stop the docker container

> [!IMPORTANT]
> JupyterLab uses its own tmp file storage, so you'll need to re-run `datacustomcode configure` each time you `./jupyterlab.sh start`.

## Prerequisite details

### Creating a connected app

1. Log in to salesforce as an admin. In the top right corner, click on the gear icon and go to `Setup`
2. In the left hand column search for `oauth`
3. Select `OAuth and OpenID Connect Settings`
4. Toggle on `Allow OAuth Username-Password Flows` and accept the dialog box that pops up
5. Clear the search bar
7. Expand `Apps`, expand `External Client Apps`, click `Settings`
8. Toggle on `Allow access to External Client App consumer secrets via REST API`
9. Toggle on `Allow creation of connected apps`
10. Click `Enable` in the warning box
11. Click `New Connected App` button
12. Fill in the required fields within the `Basic Information` section
13. Under the `API (Enable OAuth Settings)` section:
    a. Click on the checkbox to Enable OAuth Settings.
    b. Provide a callback URL like http://localhost:55555/callback
    c. In the Selected OAuth Scopes, make sure that `refresh_token`, `api`, `cdp_query_api`, `cdp_profile_api` is selected.
    d. Click on Save to save the connected app
14. From the detail page that opens up afterwards, click the `Manage Consumer Details` button to find your client id and client secret
15. Click `Cancel` button once complete
16. Click `Manage` button
17. Click `Edit Policies`
18. Under `IP Relaxation` select `Relax IP restrictions`
19. Click `Save`
20. Logout
21. Use the URL of the login page as the `login_url` value when setting up the SDK

7. Go back to `Setup`, then `OAuth and OpenID Connect Settings`, and enable the "Allow OAuth Username-Password Flows" option

You now have all fields necessary for the `datacustomcode configure` command.

## Other docs

- [Troubleshooting](./docs/troubleshooting.md)
- [For Contributors](./FOR_CONTRIBUTORS.md)

