22 posts tagged with "analytics"

You can leverage the powerful combination of StackQL and PowerBI to create comprehensive dashboard interfaces. These dashboards are perfect for reporting on various aspects such as cloud security, inventory, and configuration.

Quick Start Guide​

Set Up StackQL Server​

To get started, you can run a StackQL server container on port 7432. Use the following project for easy setup: StackQL Server on GitHub.

Create an ODBC Connection​

Next, set up an ODBC connection using the PostgreSQL ODBC driver. You can download the latest driver from the PostgreSQL ODBC Driver Versions. Install this driver on your local machine to proceed.

Integrating with PowerBI​

Once your ODBC connection is ready, you can move on to PowerBI. Here’s how you can integrate StackQL queries into PowerBI:

1. Create Data Sources in PowerBI: For each StackQL query that you want to visualize, create a new data source in PowerBI.

2. Test Queries Locally: Before integrating with PowerBI, you can test your StackQL queries locally using psql. For example:

   $ psql -h localhost -p 7432 -U stackql -d stackql
   psql (14.9 (Ubuntu 14.9-0ubuntu0.22.04.1), server 0.0.0)
   Type "help" for help.
   
   stackql=> select name, stargazers FROM
   (select name, stargazers_count as stargazers
   from github.repos.repos 
   where org = 'stackql' 
   and visibility = 'public'
   order by stargazers_count desc) t
   limit 3;
                  name                | stargazers 
   -----------------------------------+------------
    stackql                           | 179
    stackql-provider-registry         | 21
    google-discovery-to-openapi       | 18
   (3 rows)
   

3. Visualize with PowerBI: With your named data sources created, you can now visualize the result sets in PowerBI. Use various visualization tools like bar charts, pie charts, and line charts to create rich and insightful dashboards.

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Most AWS services and resources are regionally scoped, meaning the UI, CLI, SDKs, and all other methods of querying the aws provider give you a regional view (us-east-1 or ap-southeast-2, for instance). Many customer AWS estates span multiple regions - for multinational organizations, for example, or organizations with numerous dispersed locations within the US.

Sure, you could write custom scripts wrapping the CLI or SDKs - which would require development effort (not reusable for other providers); or get an abstract view with tools like AWS Config or Systems Manager, which requires these services to be enabled and configured (not flexible and not extendible to other providers). In either case:

1. You can't write and run customized queries and generate custom reports - as you can do in SQL
2. Any solutions you build will have to be rebuilt entirely for other providers

Using the latest (AWS provider for StackQL - which leverages the AWS Cloud Control API) and the executeQueriesAsync method in the pystackql Python package, I've put together an example here which runs a query to bring back attributes from all AWS Lambda functions deployed across 17 different AWS regions asynchronously. Results can be returned as a list of Python dictionaries or a Pandas dataframe. I am doing the former here, which took less than 10s.

from pystackql import StackQL
from pprint import pprint
from asyncio import run
stackql = StackQL()
stackql.executeStmt("REGISTRY PULL aws") # not required if the aws provider is already installed

async def stackql_async_queries(queries):
    return await stackql.executeQueriesAsync(queries)

regions= ["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"]

# list functions from all regions asynchronously
get_fns = [
    f"""
    SELECT *
    FROM aws.lambda.functions
    WHERE region = '{region}'
    """
    for region in regions
]

functions = run(stackql_async_queries(get_fns))

# get function details for all functions across all regions asynchronously
get_fn_details = [
    f"""
    SELECT 
    function_name,
    region,
    arn,
    description,
    architectures,
    memory_size,
    runtime
    FROM aws.lambda.function
    WHERE region = '{function['region']}'
    AND data__Identifier = '{function['function_name']}'
    """
    for function in functions
]

function_details = run(stackql_async_queries(get_fn_details))
pprint(function_details)

which returns...

[{'architectures': '["x86_64"]',
  'arn': 'arn:aws:lambda:us-east-1:824532806693:function:stackql-helloworld-fn',
  'description': '',
  'function_name': 'stackql-helloworld-fn',
  'memory_size': '128',
  'region': 'us-east-1',
  'runtime': 'nodejs18.x'},
 {'architectures': '["x86_64"]',
  'arn': 'arn:aws:lambda:us-east-2:824532806693:function:stackql-helloworld-fn',
  'description': '',
  'function_name': 'stackql-helloworld-fn',
  'memory_size': '128',
  'region': 'us-east-2',
  'runtime': 'nodejs18.x'},
 {'architectures': '["x86_64"]',
  'arn': 'arn:aws:lambda:us-west-1:824532806693:function:stackql-helloworld-fn',
  'description': '',
  'function_name': 'stackql-helloworld-fn',
  'memory_size': '128',
  'region': 'us-west-1',
  'runtime': 'nodejs18.x'},
...

You could customize the StackQL query to run specific reports and visualize the results in a Jupyter notebook, for example:

• Functions by runtimes
• Function by memory size
• Functions by tags
• etc...

You could do something similar for other hyperscalars, for example, GCP, which scopes resources by projects, or Azure, which scopes resources by resource groups.

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GitHub Codespaces is a development environment completely hosted online, enabling seamless development without setting up local machines. One of the great features of Codespaces is its compatibility with IPython, providing an interactive computing environment to test and prototype StackQL queries.

Codespaces and the power of IPython and notebooks provide a quick and easy way to use StackQL to analyze and report on your cloud estate and resource configuration. No software necessary!

Setting Up StackQL in Codespaces​

See the stackql-codespaces-notebook repository as an example.

With the example devcontainer.json configuration file shown below, you can use the stackql/stackql-codespaces-base image, which includes stackql and the pystackql package (which provides the IPython magic extension used to run stackql queries and return Pandas dataframes for inspection or visualization).

{
  "image": "stackql/stackql-codespaces-base",
  "containerEnv": {
    "STACKQL_GITHUB_PASSWORD": "${secrets:STACKQL_GITHUB_PASSWORD}",
    "STACKQL_GITHUB_USERNAME": "${secrets:STACKQL_GITHUB_USERNAME}"
  },
  "hostRequirements": {
    "cpus": 2
  },  
  "customizations": {
    "vscode": {
      "extensions": [
        "ms-toolsai.jupyter",
        "ms-python.python"
      ]
    }
  }
}

Note that the devcontainer configuration includes the essential extensions for Jupyter and Python; you can also optionally specify host requirements for the Codespaces machine.

Provider Authentication​

The environment variables required to authenticate to your specific provider or providers can be supplied using Codespaces secrets and passed securely to the Codespaces container using the containerEnv object in the example above. For specifics about variables required for different providers, see the provider documentation for your provider in the StackQL Provider Registry.

Launching the Codespaces Environment​

Given the devcontainer configuration shown above in the root of your repository at .devcontainer/devcontainer.json, you can launch codespaces from <your_repo_slug>/codespaces for example https://github.com/stackql/stackql-codespaces-notebook/codespaces. You can start a codespaces environment on any branch of your repo.

Running queries and visualizing output​

When the environment is provisioned (usually takes a minute or two), you can use the StackQL magic extension and the %%stackql magic decorator to seamlessly run stackql queries, including variable substitution, for example.

%load_ext pystackql.magic

(loads the Stackql magic extension, making the %%stackql decorator available)

region = 'us-east-1'

(set some notebook variables for reusability)

%%stackql
SELECT instanceType, COUNT(*) as num_instances 
FROM aws.ec2.instances
WHERE region = '$region'
GROUP BY instanceType

(run a query)

_.plot(kind='pie', y='num_instances', labels=_['instanceType'], title='Instances by Type', autopct='%1.1f%%')

(visualize the results - using matplotlib, plotly, or any other visualization package)

heres an example:

Using the pystackql package, you can also run asynchronous queries such as querying assets across AWS regions, Azure resource groups (or subscriptions) or Google projects in one statement, for example:

# get multiple regions asynchronously
regions = ["ap-southeast-2", "us-east-1"]

queries = [
    f"""
    SELECT '{region}' as region, instanceType, COUNT(*) as num_instances
    FROM aws.ec2.instances
    WHERE region = '{region}'
    GROUP BY instanceType
    """
    for region in regions
]

instances_df = await stackql.executeQueriesAsync(queries)

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Five minute dashboards with StackQL and Superset, check it out!

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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.