Postgres (via Collector method) - v3.0.0

Owned by Sidney Chen
Last updated: Feb 01, 2024
7 min read

Open in new tab

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.

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_authid

  • 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. GRANT SELECT ON ALL TABLES IN SCHEMA <schema> TO <kada user>
  2. 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

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 → Sources → Download 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.

You will also need to install the common library kada_collectors_lib for this collector to function properly.

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

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

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

 

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

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

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