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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
Collector Server Minimum Requirements
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As of 3.1.0 the collector no longer supports Tableau Server Version lower than 3.5 which does not have the Metadata API Endpoint. The Metadata API Endpoint MUST BE ENABLED for the collector to work. |
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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_lib-<version>-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": [],
"ssl_verification": true,
"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": {},
"compress": true,
"use_token": false,
"use_cloud": false,
"token_name": "",
"token_secret": "",
"site_content_view_name": "kada_site_content",
"ts_events_view_name": "kada_ts_events",
"timeout": 120,
"timestamp_format": "%d/%m/%Y %H:%M:%S"
} |
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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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server_address: server address
username: username to sign into server
password: password to sign into server
sites: list of sites to extract.
ssl_verification: Should ssl verification be enabled for API requests.
db_host: Tableau database address
db_password: Tableau database password
db_port: Tableau database port
db_name: Tableau database name
db_username: Tableau database username
meta_only: extract metadata only
events_only: extract events only
retries: Number of attemps if an API fails on NonXMLResponse Error, default is 5
dry_run: If specified the extractor will do a dry run to produce a template mapping.
output_path: full or relative path to where the outputs should go
login_timeout: The timeout for snowflake Auth
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
use_cloud: Are you using Tableau Cloud? Note cloud will force use token authentication
use_token: Are you using a token for authentication? Token authentication is also available for Tableau Server
token_name: Token based authentication
token_secret: Token based authentication
site_content_view_name: The view name for Content View tab in the Kada workbook for events, defaults to kada_site_content
ts_events_view_name: The view name for TS Events View tab in the Kada workbook for events, defaults to kada_ts_events
timeout: The timeout value in seconds for Tableau API calls
timestamp_format: The format of the event timestamp for the TS Events View, if defaults to %d/%m/%Y %H:%M:%S, refer to python datetime formating
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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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