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BigQuery (via Collector method) - v3.0.0

About Collectors

Collector Method

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


Step 1: Establish BigQuery Access

This step is performed by the Google Cloud Admin

  • Create a Service Account by going to the Google Cloud Admin or clicking on this link

    • Give the Service Account a name (e.g. KADA BQ Integration)

    • Select the Projects that include the BigQuery instance(s) that you want to catalog

    • Click Save

  • Create a Service Token

    • Click on the Service Account

    • Select the Keys tab. Click on Create new key

    • Select the JSON option. After clicking ‘CREATE’, the JSON file will automatically download to your device. Provide this to the user(s) that will complete the next steps

  • Add permission grants on the Service Account by going to IAM page or clicking on this link

    • Click on ADD

    • Add the Service Account to the ‘New principals’ field.

    • Grant the following roles this principal as shown in the following screenshot.

      • BigQuery Job User

      • BigQuery Metadata Viewer

      • BigQuery Read Session User

      • BigQuery Resource Viewer

    • Click SAVE


Step 2: Create the Source in K

Create a BigQuery 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. BigQuery Production

  • Add the Host name for the BigQuery Server

  • Click Finish Setup


Step 3: Getting Access to the Source Landing Directory

Collector Method

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

    1. If using Azure: storage_azure_storage_account

    2. if using AWS:

      1. storage_root_folder - the AWS s3 bucket

      2. 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 4: 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 → SourcesDownload Collectors

Run the following command to install the collector

CODE
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.

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

Under the covers this uses the BigQuery Client API and may have OS dependencies see https://cloud.google.com/bigquery/docs/reference/libraries


Step 5: Configure the Collector

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

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

regions

list<string>

List of valid regions to inspect against for data, see https://cloud.google.com/bigquery/docs/locations for list of valid regions

“us”

projects

list<string>

List of project ids to inspect across the regions specified

“kada-data”

host

string

This is the host that was onboarded in K for BigQuery

“bigquery”

json_credentials

JSON

See permissions section on how to download the credentials json to assign to this value

CODE
{
    "type": "service_account",
    "project_id": "kada-data",
    "private_key_id": "",
    "private_key": "",
    "client_email": "kada.iam.gserviceaccount.com",
    "client_id": "1234",
    "auth_uri": "https://accounts.google.com/o/oauth2/auth",
    "token_uri": "https://oauth2.googleapis.com/token",
    "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
    "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/kada.iam.gserviceaccount.com"
}

output_path

string

Absolute path to the output location where files are to be written

“/tmp/output”

mask

boolean

To enable masking or not

true

compress

boolean

To gzip the output or not

true

These parameters can be added directly into the run or you can use pass the parameters in via a JSON file.

KADA provides an out of the box script that reads a configuration JSON file and runs the extractor. Below is the configuration file.

kada_bigquery_extractor_config.json

CODE
{
    "regions": [],
    "projects": [],
    "host": "",
    "json_credentials": {},
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true
}

Step 6: 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. It will produce and read a high water mark file from the same directory as the execution called bigquery_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_bigquery_extractor.py

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

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

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

parser = argparse.ArgumentParser(description='KADA BigQuery 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(_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 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 Additional Notes | The-run-method

CODE
from kada_collectors.extractors.bigquery 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)

CODE
class Extractor(regions: list = [], projects: list = [], host: str = 'bigquery', \
  json_credentials: dict = {}, output_path: str = './output', mask: bool = False, \
  compress: bool = False) -> None

regions: The list of regions specified by user to extract
projects: The list of projects specified by user to extract
host: The host value onboarded in K
json_credentials: The json credentials for connection to BQ
sql: The list of SQL queries that will be executed by the program
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not


Step 7: 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 bigquery_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions Additional Notes | Storing-High-Water-Marks-(HWM)


Step 8: 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

Collector Method

The following example is how you can orchestrate the Tableau collector using Airflow and push the files to K hosted on Azure. The code is not expected to be used as-is but as a template for your own DAG.

PY
# 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.
# Change '.csv' to '.csv.gz' if you set compress = true in the config
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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