SSAS (Collector method) - v3.0.0

About Collectors

Collectors are extractors that are developed and managed by you (a customer of K).

KADA provides python libraries that customers can use to quickly deploy a Collector.

Why you should use a Collector

There are several reasons why you may use a collector vs the direct connect extractor:

1. You are using the KADA SaaS offering and it cannot connect to your sources due to firewall restrictions

2. You want to push metadata to KADA rather than allow it to pull data for security reasons

3. You want to inspect the metadata before pushing it to K

Using a collector requires you to manage:

1. Deploying and orchestrating the extract code

2. Managing a high water mark so the extract only pulls the latest metadata

3. Storing and pushing the extracts to your K instance

Pre-requisites

Collector Server Minimum Requirements

For the collector to operate effectively, it will need to be deployed on a server with the below minimum specifications:

• CPU: 2 vCPU

• Memory: 8GB

• Storage: 30GB (depends on historical data extracted)

• OS: unix distro e.g. RHEL preferred but can also work with Windows Server

• Python 3.10.x or later

• Access to K landing directory

SSAS Requirements

• Supports SQL SSAS 2016+ for Tabular Models.

Metadata Extract

• The KADA SSAS collector supports SQL Server Data Tools (SSDT) as the authoring tool for developing and deploying SSAS cubes.

• For each SSAS model, the collector will need access to the deployment metadata files produced by a standard SSDT deployment such as below:

Events Extract

To extract usage events for SSAS, a Database Administrator will need to setup an extended events process on the SSAS environment.

Some tuning of the logging parameters may be needed depending on processing volume of your SSAS instance.

Example XMLA script:

<Create xmlns="http://schemas.microsoft.com/analysisservices/2003/engine">
  <ObjectDefinition>
    <Trace>
      <ID>test</ID>
      <Name>test</Name>
      <XEvent xmlns="http://schemas.microsoft.com/analysisservices/2011/engine/300/300">
        <event_session name="test" dispatchLatency="0" maxEventSize="0" maxMemory="4" memoryPartition="none" eventRetentionMode="AllowSingleEventLoss" trackCausality="true" xmlns="http://schemas.microsoft.com/analysisservices/2003/engine">
          <event package="AS" name="DAXQueryPlan" />
          <event package="AS" name="QueryEnd" />
          <target package="PACKAGE0" name="asynchronous_file_target">  
           <parameter name="filename" value="data_filename.xel"/>  
           <parameter name="metadatafile" value="metadata_filename.xem"/>  
        </target>  
        </event_session>
      </XEvent>
    </Trace>
  </ObjectDefinition>
</Create>

The Event Type we are looking to extract is the DaxQueryPlan and QueryEnd attribute, exported to a file for the KADA collector to read. This can also be done through the Extended Events wizard.

Step 1: Create the Source in K

Create a SSAS source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from file system” option

  

• Give the source a Name - e.g. SSRS Production

• Add the Host name for the SSRS Server

• Click Finish Setup

Step 2: Getting Access to the Source Landing Directory

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to:

1. Go to Platform Settings - Settings. Note down the value of this setting:

   • If using Azure: storage_azure_storage_account

   • If using AWS:

     • storage_root_folder - the AWS s3 bucket

     • storage_aws_region - the region where the AWS s3 bucket is hosted

2. Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section.

To connect to the landing directory you will need:

• If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)

• If using AWS:

  • An Access key and Secret. Request this from KADA Support (support@kada.ai)

  • OR provide your IAM role to KADA Support to provision access.

Step 3: Install the Collector

It is recommended to use a python environment such as pyenv or pipenv if you are not intending to install this package at the system level.

Some python packages also have dependencies on the OS level packages, so you may be required to install additional OS packages if the below fails to install.

You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_<version>-none-any.whl

You will also need to install the common library kada_collectors_lib for this collector to function properly.

pip install kada_collectors_lib-<version>-none-any.whl

Step 4: Configure the Collector

The collector requires a set of parameters to connect to and extract metadata from SSRS.

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file. The following is an example you can use that is included in the example run code below.

kada_ssrs_extractor_config.json

{
    "host": "",
    "server": "",
    "username": "",
    "password": "",
    "driver": "ODBC Driver 17 for SQL Server",
    "events_path": "/tmp/Extendedevents*.xel",
    "use_ssl": false,
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": false,
    "events_only": false
}

Step 5: Run the Collector

The following code is an example of how to run the extractor. You may need to uplift this code to meet any code standards at your organisation.

This can be executed in any python environment where the whl has been installed.

This is the wrapper script: kada_ssas_extractor.py

import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.ssrs import Extractor

get_generic_logger('root') # Set to use the root logger, you can change the context accordingly or define your own logger

_type = 'ssrs'
dirname = os.path.dirname(__file__)
filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type))

parser = argparse.ArgumentParser(description='KADA SSAS Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename, help='Location of the configuration json, default is the config json in the same directory as the script.')
parser.add_argument('--name', '-n', dest='name', default=_type, help='Name of the collector instance.')
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(args.name)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(args.name, end_hwm)

This code will produce and read a high water mark file from the same directory as the execution called ssas_hwm.txt and produce files according to the configuration JSON.

Step 6: Check the Collector Outputs

K Extracts

A set of files (eg metadata, linkages, events etc) will be generated. These files will appear in the output_path directory you set in the configuration details

High Water Mark File

A high water mark file is created in the same directory as the execution called ssas_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method. Collector Integration General Notes

Step 7: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

You can use Azure Storage Explorer if you want to initially do this manually. You can push the files using python as well (see Airflow example below)

Example: Using Airflow to orchestrate the Extract and Push to K

# built-in
import os

# Installed
from airflow.operators.python_operator import PythonOperator
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

from plugins.utils.azure_blob_storage import AzureBlobStorage

from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.tableau import Extractor

# To be configed by the customer.
# Note variables may change if using a different object store.
KADA_SAS_TOKEN = os.getenv("KADA_SAS_TOKEN")
KADA_CONTAINER = ""
KADA_STORAGE_ACCOUNT = ""
KADA_LANDING_PATH = "lz/tableau/landing"
KADA_EXTRACTOR_CONFIG = {
    "host": "",
    "server": "",
    "username": "",
    "password": "",
    "driver": "ODBC Driver 17 for SQL Server",
    "events_path": "/tmp/Extendedevents*.xel",
    "use_ssl": false,
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": false,
    "events_only": false
}

# To be implemented by the customer. 
# Upload to your landing zone storage.
def upload():
  output = KADA_EXTRACTOR_CONFIG['output_path']
  for filename in os.listdir(output):
      if filename.endswith('.csv'):
        file_to_upload_path = os.path.join(output, filename)

        AzureBlobStorage.upload_file_sas_token(
            client=KADA_SAS_TOKEN,
            storage_account=KADA_STORAGE_ACCOUNT,
            container=KADA_CONTAINER, 
            blob=f'{KADA_LANDING_PATH}/{filename}', 
            local_path=file_to_upload_path
        )

with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:
  
    # To be implemented by the customer.
    # Retrieve the timestamp from the prior run
    start_hwm = 'YYYY-MM-DD HH:mm:SS'
    end_hwm = 'YYYY-MM-DD HH:mm:SS' # timestamp now
    
    ext = Extractor(**KADA_EXTRACTOR_CONFIG)
    
    start = DummyOperator(task_id="start")

    with TaskGroup("taskgroup_1", tooltip="extract tableau and upload") as extract_upload:
        task_1 = PythonOperator(
            task_id="extract_tableau",
            python_callable=ext.run, 
            op_kwargs={"start_hwm": start_hwm, "end_hwm": end_hwm},
            provide_context=True,
        )
        
        task_2 = PythonOperator(
            task_id="upload_extracts",
            python_callable=upload, 
            op_kwargs={},
            provide_context=True,
        )

        # To be implemented by the customer. 
        # Timestamp needs to be saved for next run
        task_3 = DummyOperator(task_id='save_hwm') 

    end = DummyOperator(task_id='end')

    start >> extract_upload >> end