Redshift (via Collector method) - v3.1.0

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

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

Redshift Requirements

• Access to Redshift

Redshift Access

Log into Redshift as a Superuser. Superuser access is required to complete the following steps.

Create a Redshift user. This user MUST be either (one or the other below, we generally recommend 2.)

1. Be a Superuser. Refer to https://docs.aws.amazon.com/redshift/latest/dg/r_superusers.html to view all required data.

   CODE

   ALTER USER <kada user> CREATEUSER; -- GRANTS SUPERUSER

2. Be a Database user with:

   1. Unrestricted SYSLOG ACCESS refer to https://docs.aws.amazon.com/redshift/latest/dg/c_visibility-of-data.html. This will allow full access to the STL tables for the user.

      SQL

      ALTER USER <kada user> SYSLOG ACCESS UNRESTRICTED; -- GRANTS READ ACCESS

   2. Select Access to existing and future tables in all Schemas for each Database you want K to ingest.

      1. List all existing Schema in the Database by running

         SQL

         SELECT DISTINCT schema_name FROM svv_all_tables; -- LIST ALL SCHEMAS

      2. For each schema above do the following to allow the user select access to all tables inside the Schema and any new tables created in the schema thereafter.

         You also must do this for ANY new schemas created in the Database to ensure K has view of it.

         SQL

         GRANT USAGE ON SCHEMA <schema name> TO <kada user>;
         GRANT SELECT ON ALL TABLES IN SCHEMA <schema name> TO <kada user>;
         ALTER DEFAULT PRIVILEGES IN SCHEMA <schema name> GRANT SELECT ON TABLES TO <kada user>;

PG Catalog

The PG tables are granted per database but generally all users should have access to them on DB creation. In the event the user doesn’t have access, explicit grants will need to be done per new DB in Redshift.

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 Redshift

• pg_class

• pg_user

• pg_group

• pg_namespace

• pg_proc

• pg_database

System Tables

These tables can be accessed in any database and reads from the leader node in Redshift

• svv_all_columns

• svv_all_tables

• svv_tables

• svv_external_tables

• svv_external_schemas

• stl_query

• stl_querytext

• stl_ddltext

• stl_utilitytext

• stl_query_metrics

• stl_sessions

• stl_connection_log

Databases

• dev (The extractor uses the dev database as a test access point)

• All other databases that you want onboarded

Step 1: Create the Source in K

Create a Redshift source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option
  

  

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

• Add the Host name for the Redshift Server

• Click 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 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

Step 4: Configure the Collector

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

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_redshift_extractor_config.json

{
    "host": "",
    "server":"",
    "username": "",
    "password": "",
    "databases": [],
    "port": 5439,
    "tunnel": false,
    "output_path": "/tmp/output",
    "mask": true,
    "compress": 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_redshift_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.redshift import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA Redshift 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(username: str = None, password: str = None, host: str = None, server: str = None, \
    databases: list = [], port: int = 5439, tunnel: bool = False, output_path: str = './output', \
    mask: bool = False, compress: bool = False) -> None

username: username to sign into Redshift
password: password to sign into Redshift
host: Host of the Redshift Service as onboarded in K
server: The host value to actually connect to Redshift, this can be an IP Address or the same value as server
databases: list of databases to extract, no spaces
port: redshift port
tunnel: Is a SSH tunnel being used? If yes then it will default to localhost
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

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 redshift_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.
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