Databricks (via Collector method) - v3.0.0

About Collectors

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

Databricks Requirements

1. Unity enabled catalogue. Hive catalogues are not supported currently.

   1. https://community.databricks.com/t5/bangalore/how-do-we-enable-unity-catalog-for-our-workspace/td-p/73258

2. Enable System Schemas for

   1. access

   2. query

   3. Follow the following documentation to enable

      1. https://docs.databricks.com/en/admin/system-tables/index.html#enable

      2. https://docs.databricks.com/en/dev-tools/auth/pat.html

      3. https://kb.databricks.com/unity-catalog/find-your-metastore-id

         CODE

         curl -v -X PUT -H "Authorization: Bearer <PAT TOKEN>" "https://<YOUR WORKSPACE>.cloud.databricks.com/api/2.0/unity-catalog/metastores/<METASTORE ID>/systemschemas/access"
         curl -v -X PUT -H "Authorization: Bearer <PAT TOKEN>" "https://<YOUR WORKSPACE>.cloud.databricks.com/api/2.0/unity-catalog/metastores/<METASTORE ID>/systemschemas/query"

Step 1: Create the Source in K

Create a source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option
  

• 

  Give the source a Name - e.g. Databricks Production

• Add the Host name for the Databricks Instance

• Click Next & Finish Setup

Step 2: Getting Access to the Source Landing Directory

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 Databricks whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_databricks-3.0.0-py3-none-any.whl

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

pip install kada_collectors_lib-x.x.x-py3-none-any.whl

Step 4: Configure the Collector

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

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_databricks_extractor_config.json

{
    "access_token": "",
    "server_hostname": "",
    "http_path": "",
    "statement_timeout": 600,
    "host": "",
    "databases": [],
    "information_catalog": "system",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": true
}

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 code sample uses the kada_databricks_extractor.py for handling the configuration details

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

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

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

parser = argparse.ArgumentParser(description='KADA Databricks 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)

# Initialize and run the extractor
ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

# Publish high watermark
publish_hwm(args.name, 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 Collector Integration General 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 Collector Integration General Notes | The-run-method

class Extractor(access_token: str, server_hostname: str, http_path: str, \
    statement_timeout: int = 600, host: str = None, databases: list = [], \
    information_catalog: str = 'system', output_path: str = './output', \
    mask: bool = False, compress: bool = False, meta_only: bool = False) -> None

access_token: Databricks personal access token authentication
server_hostname: Server address to the Databricks Service
http_path: Http path either to a DBSQL endpoint or to a DBR interactive cluster
statement_timeout: Query time limit (in seconds). Query execution will be timed out after this duration, if set. Default is 600s.
host: the onboarded host value in K, generally it will be the same as the server
databases: list of databases to extract (catalogs in Databricks)
information_catalog: The catalog to extract information. by default is 'system' which hold all the metadata for all the databases
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
meta_only: To extract without extended events or not

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

High Water Mark File

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

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 = {
    "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