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About Collectors

Collectors are extractors that are developed and managed by you (A customer of K).

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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.

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Pre-requisites

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Python 3.6 - 3.9

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Collector Server Minimum Requirements

Postgres Requirements

• Access to Postgres (see section below)

Postgres 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.)

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

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

Be a Database user with:

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.

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

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

List all existing Schema in the Database by running

SELECT DISTINCT schema_name FROM svv_all_tables; -- LIST ALL SCHEMAS

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.

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• 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

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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 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_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 Postgres

• 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

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

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Step 1: Create the Source in K

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• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option

  Image RemovedImage Added

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

• Add the Host name for the Postgres Server

• Click Finish Setup

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You can download the latest Core Library and whl via Platform Settings → Sources → Download Collectors

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via Platform Settings → Sources → Download Collectors

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

Run the following command to install the collector.

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

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.

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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 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(_typeargs.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)

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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

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Step 6: Check the Collector Outputs

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