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About Collectors

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About Collectors

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Pre-requisites

• Informatica 9.1+ with repository hosted in Oracle.

• Python 3.6 - 3.10

• Access to K landing directory

• Access to Informatica Repository (see section below)

Establish Informatica Repository Access

Create an Oracle user with read access to all tables in the Informatica repository database.

Establish Informatica Server Access

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Pre-requisites

Collector Server Minimum Requirements

IICS Requirements

• IICS with access to both V2 and V3 APIs

• Establish IICS Access

  Dean Nguyen need to populate this part with screenshots of the check boxes

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Step 1: Create the Source in K

Create a Informatica IICS source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option
  

  

• Give the source a Name - e.g. Informatica IICS Production

• Add the Host name for the Informatica ServerIICS Production

• Click Finish Setup

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Step 2: Getting Access to the Source Landing Directory

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pip install kada_collectors_lib-<version>-none-any.whl

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Step 4

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In your environment you maybe using runtime overrides for parameters in your Informatica jobs. KADA uses the runtime overrides to resolve lineage for parameter driven jobs.

Use the script below to generate infacmd commands to extract session logs in XML format.

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The commands can be be combined in a bat script like the example below to dump out the latest log per session.

@echo off
cd /d C:
cd "C:\Informatica\9.1.0\clients\DeveloperClient\infacmd"
echo %cd%
<ADD CALLS from SQL here>
pause

Use kada_informatica_runtime_parser.py to generate a runtime_session_overrides.json which will be used by the Informatica extractor.

kada_informatica_runtime_parser.py

import os
import argparse
from kada_collectors.extractors.utils import load_config, get_generic_logger
from kada_collectors.extractors.informatica import runtime_parser

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

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

parser = argparse.ArgumentParser(description='KADA Informatica Runtime Parser.')
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.')
args = parser.parse_args()

config_args = load_config(args.config)

runtime_parser(**{"input_path": config_args["input_path"], "output_path": config_args["output_path"]})

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Configure the Collector

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

KADA provides an out of the box script that reads a configuration JSON file and runs the extractor. Below is the configuration file.

kada_informaticaiics_extractor_config.json

{
    "username": "",
    "password": "",
    "dsnlogin_url": "",
    "repo_ownertimeout": ""30,
    "oracleactive_client_pathdays": ""60,
    "cachedmapping": false{},
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": true,
    "compress": true
}

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Step

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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.

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This is the wrapper script: kada_informaticaiics_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.informaticaiics import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA InformaticaIICS 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(_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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If you are handling external arguments of the runner yourself, you’ll need to consider the following for the run method https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Extractor-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)

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class Extractor(username: str = None, password: str = None, dsnlogin_url: str = None, \
    repoactive_ownerdays: str int= None60, oracle_client_pathmapping: str = None, \
    cached: bool = False, input_path: str = './input', \
    dict={}, timeout: int=30, 
  output_path: str = './output', mask: bool = False, compress: bool = False) -> None

username: username to sign into server
password: password to sign into server
dsn: server login_url: IICS login address
repoactive_owner: Oracle table owner
oracle_client_path: library path for the Oracle Instant Client
cached: Set to prevent re-extracting data
input_path: full or relative path to the directory containing the input filesdays: assessment window in days to consider tasks to be active for lineage resolution
timeout: timeout in seconds for API responses
output_path: full or relative path to where the outputs should go
compress: To gzip output mask: to mask files or not

The runtime parser can also be called in isolation

from kada_collectors.extractors.informatica import runtime_parser

kwargs = {my args} # However you choose to construct your args

runtime_parser(**kwargs)

def runtime_parser: (input_path: str = './input', output_path: str = './output') -> None

input_path: full or relative path to the directory containing the input files
output_path: full or relative path to where the outputs should go

To edit the internal SQL being run refer to https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#Adding-Custom-SQL

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compress: To gzip output files or not

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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

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A high water mark file is created in the same directory as the execution called informaticaiics_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions Additional Notes

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Step 8: Push the Extracts to K

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