Scalable NoSQL Database to Serve LLM Predictions

Question

Pulumi · Accepted Answer

To serve LLM (Large Language Model) predictions, it is critical to have a highly performant and scalable NoSQL database that can handle both the throughput requirements and the potentially unstructured nature of the data. Among the various NoSQL database solutions available, Google Cloud's Bigtable and Azure Cosmos DB are well-regarded for their ability to scale and perform under demanding workloads.

In the context of Pulumi and based on the Pulumi Registry Results provided, I will showcase how to provision a Google Cloud Bigtable instance using Pulumi's Python SDK. Google Cloud Bigtable is designed to handle massive workloads at consistent low latency and high throughput, making it a suitable choice for predictive analytics workloads, which could include serving real-time predictions from a large language model.

Below is a Pulumi program in Python that sets up a Google Cloud Bigtable instance, which is suitable for serving LLM predictions:

```python
import pulumi
import pulumi_gcp as gcp

# Create a Bigtable instance that can serve LLM predictions
# Documentation: https://www.pulumi.com/docs/reference/pkg/gcp/bigtable/instance/
bigtable_instance = gcp.bigtable.Instance("llm-predictions-instance",
    # Choose "PRODUCTION" for high availability and durability.
    instance_type="PRODUCTION",
    # Number of nodes to serve and process data, can be adjusted for scalability.
    clusters=[{
        "clusterId": "llm-predictions-cluster",
        "numNodes": 3,
        "zone": "us-central1-b",  # Choose an appropriate zone
        "storageType": "SSD",  # SSD storage for better performance
    }],
    # Labels help organize and filter resources
    labels={
        "purpose": "llm-predictions"
    })

pulumi.export('bigtable_instance_name', bigtable_instance.name)
pulumi.export('bigtable_instance_id', bigtable_instance.instance_id)
```

In this program, we are:

- Importing the required Pulumi packages for Google Cloud (`pulumi_gcp`).
- Creating an instance of `gcp.bigtable.Instance` named `llm-predictions-instance`. This resource is a Google Cloud Bigtable instance that is suitable for a production environment (`instance_type="PRODUCTION"`).
- Configuring the instance with one cluster (which is required to create a Bigtable instance). This cluster has `3` nodes (`numNodes`) to begin with, indicating how scalable the database is from the start. You may adjust the number of nodes based on your expected workload.
- Specifying that the database uses SSD storage (`storageType="SSD"`) for better performance.
- Using labels to categorize the Bigtable instance with the intended purpose (e.g., for LLM predictions).

Finally, the program exports the Bigtable instance name and ID so you can easily reference it later, perhaps in other parts of your Pulumi stack or in other systems that need to interact with the database.

To run this Pulumi program, you will need to have Pulumi installed and configured for Google Cloud credentials. You can then run `pulumi up` in the directory containing your Pulumi program, and Pulumi will handle the provisioning and configuration of the Google Cloud resources as defined in the code.