Real-time Inference on Structured Streaming with Databricks

Question

Pulumi · Accepted Answer

Real-time inference on structured streaming is a powerful capability used for processing and analyzing streaming data in real-time to make predictions or decisions based on machine learning models. Databricks, a unified data analytics platform, provides support for structured streaming through its interactive workspaces, powerful compute clusters, and collaborative notebooks.

To implement real-time inference on structured streaming with Databricks using Pulumi, you would typically do the following:

1. Set up a Databricks workspace where you can write and execute your code.
2. Create a Databricks cluster that can run Apache Spark for stream processing.
3. Deploy notebooks or jobs containing your machine learning model and the logic to apply the model to streaming data.
4. Optionally, create Databricks SQL visualizations or tables to store or visualize results.

Below is a Pulumi program written in Python that demonstrates how to set up these resources with the `pulumi_databricks` provider.

```python
import pulumi
import pulumi_databricks as databricks

# Define a new Databricks workspace where ML models and streaming data processing will occur.
databricks_workspace = databricks.Workspace("my-databricks-workspace",
                                            region="us-west-2",
                                            sku="standard")

# Create a new Databricks cluster configured for running structured streaming jobs.
databricks_cluster = databricks.Cluster("my-streaming-cluster",
                                        # Depending on the processing needs, you may want to choose an appropriate node type and cluster size
                                        node_type_id="Standard_DS3_v2",
                                        spark_version="8.3.x-scala2.12",
                                        num_workers=2,  # assumes a simple stream processing job
                                        autoscale=databricks.ClusterAutoscaleArgs(
                                            min_workers=2,
                                            max_workers=10
                                        ))

# Deploy a Databricks notebook that contains Spark Structured Streaming logic
# and ML model inference code. You would replace this placeholder with the actual notebook path.
databricks_notebook = databricks.Notebook("my-inference-notebook",
                                          content_base64="base64-encoded-notebook-content",
                                          path="/Workspace/Path/For/MyInferenceNotebook",
                                          language="SCALA")  # Assuming the notebook is written in Scala

# (Optional) Create a Databricks SQL visualization based on the output of the streaming job.
# This would typically display real-time metrics or predictions.
# The actual creation would depend on the SQL queries running on Databricks.
databricks_visualization = databricks.SqlVisualization("my-visualization",
                                                       name="Prediction Visualization",
                                                       # other configuration may follow
                                                       )

# Make sure to output the relevant attributes that you might need to access these resources.
# For instance, you may want to output the workspace URL for direct access.
pulumi.export('workspace_url', databricks_workspace.workspace_url)
pulumi.export('cluster_id', databricks_cluster.id)
pulumi.export('notebook_path', databricks_notebook.path)

```

In the above program, here's the role of each entity:

- **Databricks Workspace**: A workspace is an environment for accessing all of Databricks’ services. It contains your notebooks, libraries, and dashboards.
- **Databricks Cluster**: The cluster is a set of computation resources where you run your data processing jobs. In the context of real-time streaming, this would be where you run Apache Spark streaming jobs.
- **Databricks Notebook**: Notebooks are collaborative documents where you can execute code, visualize data, and share the results. Here it's assumed to contain the logic for real-time streaming and inference.
- **Databricks Sql Visualization**: Although not strictly required for real-time inference, visualizations can help in creating real-time dashboards using the Databricks SQL service.

This is a simplification of a typical streaming setup. There may be essential additional components and configurations such as:
- Security configurations, including setting up access tokens and network permissions.
- Setting up logging and monitoring for the streaming jobs.
- Additional resources for storing and managing the data being processed.

You can refer to the [Databricks](https://www.pulumi.com/registry/packages/databricks/) provider in the Pulumi Registry for more details on configuring these components in a real-world application.