Real-time Data Preprocessing with GCP Cloud Functions

Question

Pulumi · Accepted Answer

To create a real-time data preprocessing system on Google Cloud Platform (GCP), we can make use of GCP Cloud Functions. Here's why and how to do it:

### Why GCP Cloud Functions?
Cloud Functions is a serverless execution environment on Google Cloud. It's ideal for real-time data preprocessing because:

- **Scalability**: Cloud Functions automatically scales depending on the workload, which is great for handling varying volumes of data.
- **Event-driven**: It can be triggered by events from your cloud infrastructure, such as changes in data storage or messages on a pub/sub queue.
- **No Server Management**: You don't have to manage infrastructure, Google Cloud handles it.
- **Pay for What You Use**: You are only billed for your function's execution time, measured to the nearest 100 milliseconds.

### Setting Up a Cloud Function for Real-time Data Preprocessing
The Pulumi program below creates a simple Cloud Function in Python that gets triggered by HTTP requests. This function could preprocess data received in the request:

1. **Define the main Cloud Function**: Using `gcp.cloudfunctions.Function`, which represents a function that can be triggered in response to various events, including HTTP requests, pub/sub messages, etc.
2. **Set up the Trigger**: We'll make it an HTTP-triggered function using `trigger_http` property.
3. **Runtime**: Choose the correct runtime that matches the environment your function runs in. For example, Python 3.9.
4. **Function source**: The source code can be uploaded directly in a zip file or using Cloud Source Repositories. We'll use inline deployment for simplicity.
5. **Environment Variables**: Optionally, you can set environment variables for your function that might be required for processing work.

Please replace `inline_source` with the actual preprocessing logic you want to execute.

```python
import pulumi
import pulumi_gcp as gcp

# Define a new Cloud Function triggered by HTTP
real_time_preprocessing_fn = gcp.cloudfunctions.Function("real-time-preprocessing-fn",
    entry_point="preprocess_data",        # Name of the function in your Python file
    runtime="python39",                   # The runtime environment for the function
    trigger_http=True,                    # Make the function HTTP-triggered
    region="us-central1",                 # The GCP region where the function will be hosted
    source_archive_bucket=gcp.storage.Bucket("source-bucket").name,
    source_archive_object=gcp.storage.BucketObject("archive-object",
        bucket=gcp.storage.Bucket("source-bucket").name,
        source=pulumi.AssetArchive({
            '.': pulumi.FileArchive("./function_source")
        })
    ).name,
    available_memory_mb=256               # Adjust memory based on the function's requirement
)

# The function's endpoint will be available as an output once deployed
pulumi.export("function_endpoint", real_time_preprocessing_fn.https_trigger_url)
```

Make sure you have the preprocessing function `preprocess_data` defined within the `./function_source` directory.

### What to do next?

- Place your Python function in the `./function_source` directory. This directory should contain a `main.py` file with a `preprocess_data` function defined.
- Replace `"us-central1"` with the desired GCP region.
- Modify the `available_memory_mb` property and other settings according to your needs.
- Deploy the function using Pulumi CLI. Run `pulumi up` to start the deployment process.

Once the function is deployed, it will preprocess data in real-time as requests hit the function's endpoint. You can test it using `curl` or any HTTP client.

Keep in mind that the above code is for HTTP-triggered functions. If you want to trigger your function in response to other events, such as changes in a Cloud Storage bucket or incoming Pub/Sub messages, you would use the corresponding trigger properties instead of `trigger_http`.