Glue (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

Glue Requirements

• Access to Glue

Step 1: Establish Glue Access

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

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

1. Ability to GET and LIST s3 resources that you want the user to have access to if required.

2. Call the following Glue APIs

   1. get_tables

   2. get_databases

Example Role Policy to allow Glue Access with least privileges for actions, this example allows the ACCOUNT ARN to assume the role. 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. Note the YOUR-REGION and AWS-ACCOUNT-ID. You may be more broad and allow all regions with *

Note this is a Cloudformation Template and is a YAML not JSON file

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

  KadaGluePolicy: 
    Type: 'AWS::IAM::Policy'
    Properties:
      PolicyName: root
      PolicyDocument:
        Version: "2012-10-17"
        Statement:
          - Effect: Allow
            Action: 
              - glue:GetTables
              - glue:GetDatabases
            Resource:
              - 'arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:catalog'
              - 'arn:aws:glue:your-region:your-account-id:database/*'
              - 'arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:table/*/*'
      Roles:
        - !Ref KadaGlueRole

Alternatively you may wish to just create the Policy using this example JSON

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "KadaGluePolicy",
            "Effect": "Allow",
            "Action": [
                "glue:GetDatabases",
                "glue:GetTables"
            ],
            "Resource": [
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:catalog",
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:database/*",
                "arn:aws:glue:YOUR-REGION:AWS-ACCOUNT-ID:table/*/*"
            ]
        }
    ]
}

Step 1 Optional: Creating Glue Crawlers over S3

See https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html for more details.

You may also wish to set up a crawler over your s3 data to ingest into Glue.

1. The crawler will need an IAM role with the direct policies attached

   1. AWSGlueServiceRole

   2. AWSS3FullAccessRole

2. Create a Database in the Glue Console

   1. In the left navigation pane, choose “Databases”

   2. Click “Add database” and provide a name for the database

3. Create a s3 Crawler

   1. In the left navigation pane, choose “Crawlers”

   2. Click “Add crawler” and provide a name for the crawler

   3. Choose “Data stores” and select “S3” as the data store type

   4. Specify the S3 path to the bucket you want to crawl

   5. Choose “Next” and select the IAM role you created earlier

   6. Choose “Next” and select the Database created in Step 2.

   7. Configure other settings like frequency etc.

   8. Choose “Next” to review settings then “Finish”

4. Run the Crawler

   1. Select the Crawler you created and click “Run Crawler” and wait for completion, once it’s finished you should be able to see the data in the Database with tables created based on the data in S3

Crawler costs can be controlled by sampling and reducing the frequency that the crawler runs.
https://repost.aws/knowledge-center/long-running-glue-crawler

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. Glue Production

• Add the Host name for the Athena Server, recommended to use the convention [AWS ACCOUNT ID]_glue e.g. 3255667_glue

• 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_glue_extractor_config.json

{
    "key": "",
    "secret": "",
    "server": "43234234_glue",
    "regions": ["ap-southeast-2"],
    "catalogId": "43234234",
    "role": "",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": 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 glue_hwm.txt and produce files according to the configuration JSON.

This is the wrapper script: kada_glue_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.glue import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Glue 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, \
    catalogId: str=None, regions: list=['ap-southeast-2'], role: str=None, \
    output_path: str='./output', mask: bool=False, compress: bool=False, \
    meta_only: bool=True) -> None

key: AWS Access Key.
secret: AWS Secret.
server: Glue host that was onboarded on K.
catalogId: The Glue catalog Id which is generally the Account Id.
regions: The regions in which glue exists and should be extracted.
role: AWS Role ARN if required to assume a role.
output_path: full or relative path to where the outputs should go.
compress: To gzip output files or not.
meta_only: To extract metadata only, Glue currently only supports True.

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 glue_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