Athena (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

Athena Requirements

• Access to Athena

Step 1: Establish Athena Access

It is advised you create a new Role and a separate s3 bucket for the service user provided to KADA and have a policy that allows the below, see Identity and access management in Athena - Amazon Athena

The service user/account/role will require permissions to the following

1. Execute queries against Athena with access to the INFORMATION_SCHEMA in particular the following tables:

   1. information_schema.views

   2. information_schema.tables

   3. information_schema.columns

2. Executing queries in Athena requires an s3 bucket to temporary store results.
   The policy must also allow Read Write Listing access to objects within that bucket, conversely, the bucket must also have policy to allow to do the same.

3. Call the following Athena APIs

   1. list_databases

   2. list_table_metadata

   3. list_query_executions

   4. list_work_groups

   5. batch_get_query_executions

   6. start_query_execution

   7. get_query_execution

4. The service user/account/role will need permissions to access all workgroups to be able to extract all data, if you omit workgroups, that information will not be extracted and you may not see the complete picture in K.

5. See IAM policies for accessing workgroups - Amazon Athena on how to add policy entries to have fine grain control at the workgroup level. Note that the extractor runs queries on Athena, If you do choose to restrict workgroup access, ensure that Query based actions (e.g. StartQueryExecution) are allowed for the workgroup the service user/account/role is associated to.

Note that user usage will be associated to the workgroup level rather than individual users, these workgroups are published as users in K in the form “athena_workgroup_<name>”

Example Role Policy to allow Athena Access with least privileges for actions, this example allows the ACCOUNT ARN to assume the role. Note the variables ATHENA RESULTS BUCKET NAME. You may also choose to just assign the policy directly to a new user and use that user without assuming roles. In the scenario you do wish to assume a role, please note down the role ARN to be used when onbaording/extracting

AWSTemplateFormatVersion: "2010-09-09"
Description: 'AWS IAM Role - Athena and Cloudtrail Access to KADA'
Resources: 
  KadaAthenaRole: 
    Type: "AWS::IAM::Role"
    Properties: 
      RoleName: "KadaAthenaRole"
      MaxSessionDuration: 43200
      Path: "/"
      AssumeRolePolicyDocument: 
        Version: "2012-10-17"
        Statement: 
        - Effect: "Allow"
          Principal:
            AWS: "[ACCOUNT ARN]"
          Action: "sts:AssumeRole"

  KadaAthenaPolicy: 
    Type: 'AWS::IAM::Policy'
    Properties:
      PolicyName: root
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Action: 
              - athena:BatchGetQueryExecution
              - athena:GetQueryExecution
              - athena:GetQueryResults
              - athena:GetQueryResultsStream
              - athena:ListQueryExecutions
              - athena:StartQueryExecution
              - athena:ListWorkGroups
              - athena:ListDataCatalogs
              - athena:ListDatabases
              - athena:ListTableMetadata
            Resource: '*'
          - Effect: Allow
            Action: 
              - s3:GetBucketLocation
              - s3:GetObject
              - s3:ListBucket
              - s3:ListBucketMultipartUploads
              - s3:ListMultipartUploadParts
              - s3:AbortMultipartUpload
              - s3:PutObject
              - s3:PutBucketPublicAccessBlock
              - s3:DeleteObject
            Resource:
              - arn:aws:s3:::[ATHENA RESULTS BUCKET NAME]
      Roles:
        - !Ref KadaAthenaRole

Step 2: Create the Source in K

Create an Athena 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. Athena Production

• Add the Host name for the Athena Server

• Click Finish Setup

Step 3: Getting Access to the Source Landing Directory

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 Athena 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

Under the covers this uses boto3 and may have OS dependencies see https://boto3.amazonaws.com/v1/documentation/api/latest/guide/quickstart.html

Step 5: Configure the Collector

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

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_athena_extractor_config.json

{
    "key": "",
    "secret": "",
    "server": "athena",
    "bucket": "s3://examplebucket/examplefolder",
    "catalogs": ["AwsDataCatalog"],
    "region": "ap-southeast-2",
    "role": "",
    "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 athena_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_athena_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.athena import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Athena 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 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

from kada_collectors.extractors.snowflake 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(key: str = None, secret: str = None, server: str = None, \
      bucket: str = None, catalogs: list = ['AwsDataCatalog'], \
      region: str = 'ap-southeast-2', role: str = None, \
      output_path: str = './output', mask: bool = False, compress: bool = False) -> None

key: AWS Access Key.
secret: AWS Secret.
region: Region.
server: Athena host that was onboarded on K.
role: AWS Role ARN if required to assume a role. bucket: s3 bucket used to temporary store results in the form s3://xxx.
catalogs: list of Catalogs from Athena to extract, by default this is just AwsDataCatalog.
output_path: full or relative path to where the outputs should go
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 athena_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 Collector Integration General 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

# 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