Cognos (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 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 pull the latest metadata

3. Storing and pushing the extracts to your K instance.

Pre-requisites

• Python 3.7 - 3.11

• Access to K landing directory.

• Cognos access

  • Cognos Analytics user that has the ability to read all objects in Cognos

  • A SQL Authenticated user Database User for the underlying Audit Database configured for Cognos

• Cognos auditing must be enabled (Log level - Basic)

  • See https://www.ibm.com/support/pages/how-configure-audit-reporting-cognos-analytics

Step 1) Setup KADA user configuration in Cognos

• Log into your Cognos instance.

  • Note down the URL you use e.g. https://kada-cognos.cloudapp.net/ to be used in Step 3

• Create a new KADA user.

  • Follow the steps here - https://www.ibm.com/docs/en/cognos-analytics/11.2.0?topic=namespace-creating-managing-users

  • Add the user to a role that has read access to objects to be profiled/monitored.

  • To enable K to monitor ALL objects, the user will need read access to ALL Cognos objects.

  • Note down the Namespace ID for the namespace where the user was created. This can be found in IBM Cognos Configuration tool.

    

Step 2) Setup KADA user in the Cognos Audit Database

• Log into your Cognos Audit Database e.g SQL Server

• Create a new KADA database user

• Give the KADA database user READ ONLY access to the following tables in the Audit Database (Schema is dependent on where you initialised the Audit tables for Cognos)

  • COGIPF_VIEWREPORT

  • COGIPF_USERLOGON

  • COGIPF_RUNREPORT

  • COGIPF_RUNJOB

Step 3: Create the Source in K

Create a Cognos source in K

• Log into your K instance

• Go to Platform Settings, select Sources and click Add Source

• Select Cognos

• Select “Load from File” option

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

• Add the Host name - use the cognos URL from Step 1

• Click Finish Setup

Step 4: Getting Access to the Source Landing Directory

Step 5: 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 6: Configure the Collector

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

{
"somehost.adw": "analytics.adw"
}

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_cognos_extractor_config.json

{
    "server_url": "http://xxx:9300",
    "username": "",
    "password": "",
    "namespace": "",
    "timeout": 20,
    "db_host": "",
    "db_username": "",
    "db_password": "",
    "db_port": 8060,
    "db_name": "",
    "db_schema": "",
    "meta_only": false,
    "output_path": "/tmp/output",
    "mask": false,
    "mapping": {},
    "compress": false
}

Step 7: 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_cognos_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.cognos import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Cognos 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.')
args = parser.parse_args()

start_hwm, end_hwm = get_hwm(_type)

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

publish_hwm(_type, end_hwm)

Advance options:

If you wish to maintain your own high water mark files elsewhere you can use the above section’s script as a guide on how to call the extractor. The configuration file is simply the keyword arguments in JSON format. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Storing-HWM-in-another-location

If you are handling external arguments of the runner yourself, you’ll need to consider additional items for the run method. Refer to this document for more information https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method

from kada_collectors.extractors.cognos import Extractor

kwargs = {my args} # However you choose to construct your args
hwm_kwrgs = {"start_hwm": "end_hwm": } # The hwm values

ext = Extractor(**kwargs)
ext.run(**hwm_kwrgs)

class Extractor(
    server_url: str = None,
    username: str = None,
    password: str = None,
    namespace: str = None,
    timeout: int = 20,
    db_host: str = None,
    db_username: str = None,
    db_password: str = None,
    db_port: int = None,
    db_name: str = None,
    db_schema: str = None,
    db_driver: str = None,
    meta_only: bool = False,
    mapping: dict = {},
    output_path: str = './output',
    mask: bool = False,
    compress: bool = False
) -> None

server_url: Cognos API URL
username: Cognos API Username
password: Cognos API Password
namespace: Cognos API Namespace
timeout: Cognos API Timeout
db_host: Database host
db_username: Database username
db_password: Database password
db_port: Database port
db_name: Database name
db_schema: Database schema
meta_only: Only extract metadata
mapping: Mapping for the metadata
output_path: Output path for the files
mask: Mask the data
compress: Compress the data

Step 8: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, 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 cognos_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

Step 9: 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 = {
    "server_address": "http://tabserver",
    "username": "user",
    "password": "password",
    "sites": [],
    "db_host": "tabserver",
    "db_username": "repo_user",
    "db_password": "repo_password",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": False,
    "retries": 5,
    "dry_run": False,
    "output_path": "/set/to/output/path",
    "mask": True,
    "mapping": {}
}

# 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