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About Collectors
Collectors are extractors that are developed and managed by you (A customer of K).
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Deploying and orchestrating the extract code
Managing a high water mark so the extract only pull the latest metadata
Storing and pushing the extracts to your K instance.
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Pre-requisites
Python 3.6 - 3.10
Tableau Server Version [2019.3] and above.
Enable the Tableau Metadata API for Tableau Server
This requires a server restart if not enabled
Tableau API access
An API user (record the username and password) needs to be created to access Tableau API.
The user cannot be a SSO user. This is a Tableau limitation. SSO users cannot access Tableau API
User needs
Site Administrator CreatororServer/Site Administratorrole. Roles are dependent on both Licensing and Server version see https://help.tableau.com/current/server/en-us/users_site_roles.htmSite Administrator Creatoris only available on Role Based Licensing ModelServer/Site Administratoris available on both Role Based and Core Based Licensing Model
Tableau Repository access
Follow the instructions to create a user that can access the Tableau repositoryhttps://help.tableau.com/current/server/en-us/perf_collect_server_repo.htm
This requires a server restart if not enabled
Note the Tableau repository default user is called
readonly
Access to the KADA Collector repository that contains the Tableau whl
The repository is currently hosted in KADA’s Azure Blob Storage. You will be given a SAS token to access the repository. Reach out to KADA Support (support@kada.ai) if you do not have access.
Download the Tableau whl (e.g. kada_collectors_extractors_tableau-#.#.#-py3-none-any.whl)
Access to K landing directory.
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Step 1: Create the Source in K
Create a Tableau source in K
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Give the source a Name - e.g. Tableau Production
Add the Host name for the Tableau server
Click Finish Setup
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Step 2: Getting Access to the Source Landing Directory
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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.
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pip install kada_collectors_extractors_tableau-2.0.0-py3-none-any.whl |
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Step 4: Configure the Collector
The collector requires a set of parameters to connect to and extract metadata from Tableau.
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{
"server_address": "",
"username": "",
"password": "",
"sites": [],
"db_host": "",
"db_username": "readonly",
"db_password": "",
"db_port": 8060,
"db_name": "workgroup",
"meta_only": false,
"retries": 5,
"dry_run": false,
"output_path": "/tmp/output",
"mask": true,
"mapping": {}
} |
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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.
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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
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Step 6: 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
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A high water mark file is created in the same directory as the execution called tableau_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.
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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)
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Example: Using Airflow to orchestrate the Extract and Push to K
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# 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 |
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