How do I deploy a GCP BigQuery routine using Pulumi?

Introduction

This guide will walk you through deploying a GCP BigQuery routine. BigQuery routines allow you to encapsulate logic that can be reused in your queries, making your workloads more efficient and your code easier to manage. In this example, we will define a BigQuery dataset and a routine within that dataset.

Detailed Explanation

1. Provider Configuration: First, we configure the provider to use the Google Cloud Platform. This involves specifying the project and region.
2. Dataset Creation: We create a BigQuery dataset which will contain the routine.
3. Routine Definition: We define a BigQuery routine that encapsulates our reusable logic. The routine can be defined in SQL.
4. Stack Exports: We export important output variables such as dataset ID and routine ID for reference.

Program

import * as pulumi from "@pulumi/pulumi";
import * as gcp from "@pulumi/gcp";

const exampleDataset = new gcp.bigquery.Dataset("example_dataset", {
    datasetId: "example_dataset",
    location: "US",
    labels: {
        env: "dev",
    },
});
const exampleRoutine = new gcp.bigquery.Routine("example_routine", {
    datasetId: exampleDataset.datasetId,
    routineId: "example_routine",
    routineType: "SCALAR_FUNCTION",
    definitionBody: `CREATE FUNCTION example_function(x INT64) AS (
  x + 1
)
`,
});
export const datasetId = exampleDataset.datasetId;
export const routineId = exampleRoutine.routineId;

Key Points

• Provider Configuration: Defines the project and region for GCP.
• Dataset Creation: Creates a BigQuery dataset with specified dataset id and location.
• Routine Definition: Defines a reusable SQL function within the dataset.
• Stack Exports: Exports dataset ID and routine ID for use in other resources or outputs.

Summary

In this example, we successfully deployed a BigQuery routine on GCP. We began by configuring the provider, created a BigQuery dataset, and then defined our routine within that dataset. We also exported the dataset ID and routine ID for potential future use. This approach helps in managing and reusing SQL logic efficiently in BigQuery.