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AWS RDS Postgres (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 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

Postgres Access

The user used for the extractor will need access to a number of pg_catalog tables outlined below

PG Catalog

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

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

The user used for the extraction must also be able to connect to the the databases needed for extraction.

PG Tables

These tables are per database in Postgres

  • pg_class

  • pg_namespace

  • pg_proc

  • pg_database

  • pg_language

  • pg_type

  • pg_collation

  • pg_depend

  • pg_sequence

  • pg_constraint

  • pg_auth_members

Databases

  • All other databases that you want onboarded

Note that visibility of entries in these tables will depend on if the user has SELECT access to the table, so make sure SELECT is granted to the <kada user> for all tables within the database. You may need to re-apply this grant if schemas are dropped, you may also wish to apply a default grant on the schema so future tables can be visible.

  1. SQL
    GRANT SELECT ON ALL TABLES IN SCHEMA <schema> TO <kada user>
  2. SQL
    ALTER DEFAULT PRIVILEGES IN SCHEMA <schema> public GRANT SELECT ON TABLES TO <kada user>

Step 1: Create the Source in K

Create a Postgres source in K

  • Go to Settings, Select Sources and click Add Source

  • Select “Load from File” option

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

  • Add the Host name for the Postgres Server

  • Click Finish Setup


Step 2: 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 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 via Platform Settings → SourcesDownload Collectors

You can request the whl from the Kada support team (support@kada.ai).

From 5.33 (Late October 2023) you can download the whl directly from the Platform

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

Step 4: Configure the Collector

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

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

host

string

Postgres host as per what was onboarded in the K platform, generally we onboard it as the same value as server, but if you did it differently, use that value

“example.postgres.localhost”

server

string

Postgres host to establish a connection

“example.postgres.localhost”

username

string

Username to log into Postgres

“postgres_user”

password

string

Password to log into the Postgres

databases

list<string>

A list of databases to extract from Postgres

[“dwh”, “adw”]

port

integer

Postgres port, general default is 5432

5432

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

meta_only

boolean

To extract metadata only or not, note as of this current version only metadata can be extracted regardless of this value

true

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_postgres_extractor_config.json

JSON
{
    "host": "",
    "server": "",
    "username": "",
    "password": "",
    "databases": [],
    "port": 5432,
    "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 is the wrapper script: kada_postgres_extractor.py

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

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

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

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

disable_roles_sql = """SELECT DISTINCT 'USER' AS "OBJECT_TYPE", '' AS "OBJECT_ID", '' AS "USER", '' AS "ROLE" where 1 = 2"""
ext.overwrite_sql('ROLES_SQL', disable_roles_sql)

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.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)

CODE
class Extractor(username: str = None, password: str = None, host: str = None, \
    server: str = None, databases: list = [], port: int = 5432, \
    output_path: str = './output', mask: bool = False, compress: bool = False, \
    meta_only: bool = False) -> None

username: username to sign into Postgres
password: password to sign into Postgres
host: Onboarded value for the Postgres server in K
server: Host address to the Postgres Service for a connection
databases: list of databases to extract, no spaces
port: postgres port
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 metadata only 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 postgres_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

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.
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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