Greenplum (via Collector method) - v3.0.0

About Collectors

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 to 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 pulls the latest metadata

3. Storing and pushing the extracts to your K instance

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

Greenplum Requirements

User access to Greenplum database(s). For each database, the user will need access to a set of PG Catalog tables and GP Metrics tables outlined below.

• Connection to each DB

The user to be configured must be able to connect to each database

CREATE USER kadauser WITH PASSWORD 'complexpassword';
GRANT CONNECT ON DATABASE testdatabase TO kadauser;

• Access to PG Catalog

Generally all users should have access to the pg_catalog tables on database creation. In the event the user doesn't have access, explicit grants will need to be done per new database in Greenplum.

GRANT USAGE ON SCHEMA pg_catalog TO <kada user>;
GRANT SELECT ON ALL TABLES IN SCHEMA pg_catalog TO <kada user>;

Alternatively you may choose to be specific with the SELECT grant based on the tables in the list below:

• pg_attribute

• pg_class

• pg_namespace

• pg_proc

• pg_database

• pg_language

• pg_type

• pg_collation

• pg_depend

• pg_constraint

• pg_roles

• pg_auth_members

• Access to Query history

This step assumes you have configured gpperfmon for query history logs.

The user must have read access to the gpcc_queries_history table in the gpperform database.

• gpcc_queries_history

Step 1: Create the Source in K

Create a Greenplum source in K

• Go to Settings, Select Sources and click Add Source

• Select "Load from File" option

• Give the source a Name - e.g. Greenplum Production

• Add the Host name for the Greenplum Server

• Click Finish Setup

Step 2: Getting Access to the Source Landing Directory

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:

   • If using Azure: storage_azure_storage_account

   • If using AWS:

     • storage_root_folder - the AWS s3 bucket

     • 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 3: Install the Collector

You can download the latest Core Library 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

Step 4: Configure the Collector

kada_greenplum_extractor_config.json

{
    "host": "",
    "server": "",
    "username": "",
    "password": "",
    "databases": [],
    "port": 5432,
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true,
    "meta_only": true,
    "audit_database": "gpperfmon"
}

Step 5: Run the Collector

This is the wrapper script: kada_greenplum_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.greenplum import Extractor

get_generic_logger('root')

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

parser = argparse.ArgumentParser(description='KADA Greenplum Extractor.')
parser.add_argument('--config', '-c', dest='config', default=filename)
parser.add_argument('--name', '-n', dest='name', default=_type)
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(args.name, end_hwm)

Step 6: Check the Collector Outputs

K Extracts

A set of files (eg metadata, databaselog, linkages, events etc) will be generated in the output_path directory.

High Water Mark File

A high water mark file is created called greenplum_hwm.txt.

Refer to Collector Integration General Notes for more information.

Step 7: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

Example: Using Airflow to orchestrate the Extract and Push to K

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.

# 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 configured 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