StackQL is now available for ARM64-based Linux systems. To download the ARM64 binary, please visit our downloads page, where you can find the appropriate version for your system.

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stackql-deploy is now available in the GitHub Actions Marketplace.

Given this example stackql-deploy stack definition in a GitHub repo, you would simply add the following to your GitHub Actions workflow:

...
jobs:
  stackql-actions-test:
    name: StackQL Actions Test
    runs-on: ubuntu-latest
    env:
      GOOGLE_CREDENTIALS: ${{ secrets.GOOGLE_CREDENTIALS }}
    
    steps:
      - name: Checkout
        uses: actions/checkout@v4

      - name: Deploy a Stack
        uses: stackql/setup-deploy@v1.0.1
        with:
          command: build
          stack-dir: examples/k8s-the-hard-way
          stack-env: dev
          env-vars: GOOGLE_PROJECT=stackql-k8s-the-hard-way-demo

Example output is shown here:

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stackql-deploy is a multi-cloud resource provisioning framework using stackql. It is inspired by dbt (data build tool), which manages data transformation workflows in analytics engineering by treating SQL scripts as models that can be built, tested, and materialized incrementally. With StackQL, you can create a similar framework for cloud and SaaS provisioning. The goal is to treat infrastructure-as-code (IaC) queries as models that can be deployed, managed, and interconnected.

This ELT/model-based framework for IaC allows you to provision, test, update, and tear down multi-cloud stacks, similar to how dbt manages data transformation projects, with the benefits of version control, peer review, and automation. This approach enables you to deploy complex, dependent infrastructure components in a reliable and repeatable manner.

Features​

StackQL simplifies the interaction with cloud resources by using SQL-like syntax, making it easier to define and execute complex cloud management operations. Resources are provisioned with INSERT statements, and tests are structured around SELECT statements.

Features include:

• Dynamic state determination (eliminating the need for state files)
• Pre-flight and post-deploy assurance tests for resources
• Simple flow control with rollback capabilities
• Single code base for multiple target environments
• SQL-based definitions for resources and tests

Installing stackql-deploy​

To get started with stackql-deploy, run the following:

pip install stackql-deploy

stackql-deploy will automatically download the latest release of stackql using the pystackql Python package. You can then use the info command to get runtime information:

$ stackql-deploy info
stackql-deploy version: 1.1.0
pystackql version     : 3.6.1
stackql version       : v0.5.612
stackql binary path   : /home/javen/.local/stackql
platform              : Linux x86_64 (Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35), Python 3.10.12

Project structure​

A stackql-deploy project is a directory with declarative SQL definitions to provision, de-provision, or test resources in a stack. The key components and their definitions are listed here:

• stackql_manifest.yml : The manifest file for your project, defining resources and properties in your stack.
• stackql_resources directory : Contains StackQL queries to provision and de-provision resources in your stack.
• stackql_tests directory : Contains StackQL queries to test the desired state for resources in your stack.

Getting started​

Use the init command to create a starter project directory:

stackql-deploy init activity_monitor

You will now have a directory named activity_monitor with stackql_resources and stackql_tests directories and a sample stackql_manifest.yml file, which will help you to get started.

Usage​

The general syntax for stackql-deploy is described here:

stackql-deploy [OPTIONS] COMMAND [ARGS]...

Commands include:

• build: Create or update resources based on the defined stack.
• teardown: Remove or decommission resources that were previously deployed.
• test: Execute test queries to verify the current state of resources against the expected state.
• info: Display the version information of the stackql-deploy tool and current configuration settings.
• init: Initialize a new project structure for StackQL deployments.

Optional global options (for all commands) include:

• --custom-registry TEXT: Specify a custom registry URL for StackQL. This URL will be used by all commands for registry interactions.
• --download-dir TEXT: Define a download directory for StackQL where all files will be stored.
• --help: Show the help message and exit.

Options for build, test, and teardown include:

• --on-failure [rollback|ignore|error]: Define the action to be taken if the operation fails. Options include rollback, ignore, or treat as an error.
• --dry-run: Perform a simulation of the operation without making any actual changes.
• -e <TEXT TEXT>...: Specify additional environment variables in key-value pairs.
• --env-file TEXT: Path to a file containing environment variables.
• --log-level [DEBUG|INFO|WARNING|ERROR|CRITICAL]: Set the logging level to control the verbosity of logs during execution.

Example​

Using the activity_monitor stack we created previously using the init command, we can start defining a stack and defining the associated queries; here is the manifest file:

version: 1
name: activity_monitor
description: oss activity monitor stack
providers:
  - azure
globals:
  - name: subscription_id
    description: azure subscription id
    value: "{{ vars.AZURE_SUBSCRIPTION_ID }}"
  - name: location
    value: eastus
  - name: resource_group_name_base
    value: "activity-monitor"
resources:
  - name: monitor_resource_group
    description: azure resource group for activity monitor
    props:
      - name: resource_group_name
        description: azure resource group name
        value: "{{ globals.resource_group_name_base }}-{{ globals.stack_env }}"
	# more resources would go here...

globals.stack_env is a variable referencing the user-specified environment label.

Our stackql_resources directory must contain a .iql file (StackQL query file) with the same name as each resource defined in the resources key in the manifest file. Here is an example for stackql_resources/monitor_resource_group.iql:

/*+ createorupdate */
INSERT INTO azure.resource.resource_groups(
  resource_group_name,
  subscription_id,
  data__location
)
SELECT
  '{{ resource_group_name }}',
  '{{ subscription_id }}',
  '{{ location }}'

/*+ delete */
DELETE FROM azure.resource.resource_groups 
WHERE resource_group_name = '{{ resource_group_name }}' AND subscription_id = '{{ subscription_id }}'

Similarly, our stackql_tests directory must contain a .iql file (StackQL query file) with the same name as each resource defined in the stack. Here is an example for stackql_tests/monitor_resource_group.iql:

/*+ preflight */
SELECT COUNT(*) as count FROM azure.resource.resource_groups
WHERE subscription_id = '{{ subscription_id }}'
AND resource_group_name = '{{ resource_group_name }}'

/*+ postdeploy, retries=2, retry_delay=2 */
SELECT COUNT(*) as count FROM azure.resource.resource_groups
WHERE subscription_id = '{{ subscription_id }}'
AND resource_group_name = '{{ resource_group_name }}'
AND location = '{{ location }}'
AND JSON_EXTRACT(properties, '$.provisioningState') = 'Succeeded'

Now we can build, test, and teardown our example stack using these commands (starting with a dry-run, which will render the target queries without executing them):

# stackql-deploy build|test|teardown {stack_name} {stack_env} [{options}]
stackql-deploy build example_stack prd -e AZURE_SUBSCRIPTION_ID 00000000-0000-0000-0000-000000000000 --dry-run
stackql-deploy build example_stack prd -e AZURE_SUBSCRIPTION_ID 00000000-0000-0000-0000-000000000000
stackql-deploy test example_stack prd -e AZURE_SUBSCRIPTION_ID 00000000-0000-0000-0000-000000000000
stackql-deploy teardown example_stack prd -e AZURE_SUBSCRIPTION_ID 00000000-0000-0000-0000-000000000000

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An exciting new feature to cap off 2023! Parallel query execution in StackQL. With the latest release of StackQL, parameters in WHERE IN clauses are fetched asyncronously.

This query for example, queries lambda functions across 17 AWS regions in under 1.5 sec, technically these are 17 parallel queries to 17 different endpoints.

SELECT region, function_name
FROM aws.lambda.functions
WHERE region IN ( 'us-east-1','us-east-2','us-west-1','us-west-2','ap-south-1','ap-northeast-3','ap-northeast-2','ap-southeast-1','ap-southeast-2','ap-northeast-1','ca-central-1','eu-central-1','eu-west-1','eu-west-2','eu-west-3','eu-north-1','sa-east-1'
)

You could do something similar for other hyperscalars, for example querying resources across projects in GCP asynchronously, or querying across resource groups in Azure asynchronously.

This capability was previously available using the pystackql package, as discussed in the Query Resources Across AWS Regions Asynchronously blog post, but is now available natively in the StackQL query optimizer.

You just need to add --execution.concurrency.limit=-1 to your stackql exec or stackql shell commands or when starting a StackQL Server using stackql srv. More query optimizations coming! Happy New Year! 🎉 🎉 🎉

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Materialized Views are now available in StackQL. Materialized Views can be used to improve performance for dependent or repetetive queries within StackQL provisioning or analytics routines.

Refresher on Materialized Views​

Unlike standard views that provide a virtual representation of data, a Materialized View physically stores the result set of a query. This implies that the data is pre-computed and stored, which can lead to performance gains as the data doesn't need to be fetched from the underlying resource(s) every time it is queried.

Benefits of Materialized Views in StackQL​

1. Performance Boost: With data already stored and readily available, Materialized Views can substantially reduce StackQL query execution time, especially for complex and frequently-run queries.

2. Data Consistency: Since Materialized Views provide a snapshot of the data at a specific point in time, it ensures consistent data is returned every time it is accessed until it is refreshed.

3. Flexibility: You have the flexibility to refresh the Materialized View as needed usign the REFRESH MATERIALIZED VIEW lifecycle operation in StackQL. This is particularly useful when working with rapidly changing data.

Using Materialized Views in StackQL​

Here's a step-by-step guide on how you to use this new feature in StackQL:

1. Create a Materialized View:

CREATE MATERIALIZED VIEW vw_ec2_instance_types AS 
SELECT 
 memoryInfo, 
 hypervisor, 
 autoRecoverySupported, 
 instanceType, 
 SPLIT_PART(processorInfo, '\n', 3) as processorArch, 
 currentGeneration, 
 freeTierEligible, 
 hibernationSupported,
 SPLIT_PART(vCpuInfo, '\n', 2) as vCPUs, 
 bareMetal, 
 burstablePerformanceSupported, 
 dedicatedHostsSupported 
FROM aws.ec2.instance_types 
WHERE region = 'us-east-1';

2. Refresh the Materialized View:

REFRESH MATERIALIZED VIEW vw_ec2_instance_types;

3. Use the Materialized View in a StackQL Query:

SELECT 
 i.instanceId, 
 i.instanceType, 
 it.vCPUs, 
 it.memoryInfo 
FROM aws.ec2.instances i 
 INNER JOIN vw_ec2_instance_types it 
 ON i.instanceType = it.instanceType 
WHERE i.region = 'us-east-1';

3. Drop the Materialized View:

DROP MATERIALIZED VIEW vw_ec2_instance_types;

More information on Materialized Views in StackQL can be found here.