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Create a user that has read access to the Informatica Server.

Generate runtime mappings

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

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

Info

The session logs can take a log time to generate so it is recommended that this process be generated infrequently. Generally runtime overrides will only change when there are changes to informatica jobs.

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

These logs can then be parsed with the kada-collector parse_runtime_logs to generate a runtime_session_overrides.json. This file is used when running the Informatica extractor

Step 1: Create the Source in K

Create a Informatica source in K

  • Go to Settings, Select Sources and click Add Source

  • Select “Load from File” option

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

  • Add the Host name for the Informatica 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.

Run the following command to install the collector

Code Block
pip install kada_collectors_extractors_informatica-2.0.0-py3-none-any.whl

You may require an ODBC package for the OS to be installed as well as an oracle client library package if do you not have one already, see https://www.oracle.com/au/database/technologies/instant-client.html

Step 4: Configure the Collector

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

...

FIELD

...

FIELD TYPE

...

DESCRIPTION

...

EXAMPLE

...

username

...

string

...

Username to log into Oracle

...

“myuser”

...

password

...

string

...

Password to log into Oracle

...

dsn

...

string

...

Datasource Name for Oracle, this can be one of the following forms

<tnsname>
<host/servicename>

...

“preprod”

local.example.com/oraservice”

...

repo_owner

...

string

...

This is the owner of all the tables required by the extractor

...

“inf”

...

oracle_client_path

...

string

...

Full path to the location of the Oracle Client libraries

...

“/tmp/drivers/lib/oracleinstantclient_11_9”

...

cached

...

boolean

...

If set to true if will prevent re-extracting data

...

false

...

input_path

...

string

...

Absolute path to the input location where files are to be read

...

“/tmp/input”

...

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

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

kada_informatica_extractor_config.json

Code Block
languagejson
{
    "username": "",
    "password": "",
    "dsn": "",
    "repo_owner": "",
    "oracle_client_path": "",
    "cached": false,
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": 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 code sample uses the kada_informatica_extractor.py for handling the configuration details

Code Block
languagepy
import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.informatica import Extractor

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

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

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

start_hwm, end_hwm = get_hwm(_type)

ext = Extractor(**load_config(args.config))
ext.test_connection()
ext.run(**{"start_hwm": start_hwm, "end_hwm": end_hwm})

publish_hwm(_type, end_hwm)

There is also a wrapper script to parse runtime logs

kada_informatica_runtime_parser.py

Code Block
languagepy
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"]})

This is used to produce a session.json file which is used in the input folder for the extractor.

Info

The runtime parser can also be called in isolation using the below code.

...

languagepy

...

...

Step 1: Create the Source in K

Create a Informatica source in K

  • Go to Settings, Select Sources and click Add Source

  • Select “Load from File” option

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

  • Add the Host name for the Informatica Server

  • Click Finish Setup

...

Step 2: Getting Access to the Source Landing Directory

Insert excerpt
Collectors
Collectors
namelanding

...

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.

Run the following command to install the collector

Code Block
pip install kada_collectors_extractors_informatica-2.0.0-py3-none-any.whl

You may require an ODBC package for the OS to be installed as well as an oracle client library package if do you not have one already, see https://www.oracle.com/au/database/technologies/instant-client.html

...

Step 4: Generate runtime mappings

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.

Info

Replace any < > with values for your Informatica environment.

Code Block
languagesql
select 
'call infacmd.bat isp getsessionlog -dn DOMAIN_PRODUCTION -hp <HOST>:<PORT> -un <SERVER USERNAME> -pd <SERVER PASSWORD> -is <SERVERNAME> -rs <REPO NAME> -ru <REPO USERNAME> -rp <REPO PASSWORD> -fm xml -fn ' || ws.subject_area || ' -wf ' || ws.workflow_name || ' -ss ' || CASE WHEN hierarchy_structure is null then ws.instance_name ELSE '"' || substr(hierarchy_structure, 2) || '"' END || ' -lo <C:\\output\\path\\for\\logs\\>' || ws.workflow_id || '_' || ws.task_id || '_' || ws.instance_id as cmd
from (
    SELECT ti.instance_name,
        ti.task_id,
        ti.version_number,
        wws.instance_id,
        wf.workflow_id,
        wf.workflow_name,
        wf.workflow_comments,
        wf.server_name,
        wf.subject_area,
        hierarchy_structure,
        path
FROM (
        select path 
        , TO_NUMBER(substr(path, 2, instr(path,'/',1, 2)-2)) as workflow_id
        , TO_NUMBER(substr(path, -instr(reverse(path),'/', 1, 2)+1, instr(reverse(path),'/', 1, 2)-2)) as task_id
        , hierarchy_structure
        , instance_id
        from (SELECT DISTINCT '/' || temp1.task_id AS path
                , temp1.task_name AS hierarchy_structure
                , 0 as instance_id
                FROM opb_task temp1, opb_subject temp2
                WHERE temp1.subject_id = temp2.subj_id
                AND temp1.task_type = 71 -- workflows
                UNION ALL
                SELECT DISTINCT temp1.path
                    , temp1.task_name AS hierarchy_structure
                    , instance_id
                FROM (SELECT opb_task_inst.workflow_id, opb_task_inst.task_id, opb_task_inst.instance_id, LEVEL depth,
                        SYS_CONNECT_BY_PATH(opb_task_inst.workflow_id ,'/') || '/' || opb_task_inst.task_id || '/' path,
                        SYS_CONNECT_BY_PATH(opb_task_inst.instance_name ,'/') task_name
                        FROM opb_task_inst
                        WHERE opb_task_inst.task_type IN (68,70)
                        START WITH workflow_id IN (select distinct w.workflow_id
                                                        from rep_workflows w
                                                        join rep_task_inst ti on w.workflow_id = ti.workflow_id
                                                        where ti.task_type_name = 'Worklet'
                                                        and w.subject_area not in ('<SUBJECT_AREAS_TO_EXCLUDE>')
                                                    )
                        CONNECT BY PRIOR opb_task_inst.task_id = opb_task_inst.workflow_id
                    ) temp1,
                    opb_task temp2,
                    opb_subject temp3
                WHERE temp2.subject_id = temp3.subj_id
                AND temp2.task_id = SUBSTR(temp1.path,2, INSTR(temp1.path,'/', 1, 2) -2 )
                ORDER BY path ASC )
        where instance_id <> 0
) wws
    JOIN rep_task_inst ti on ti.task_id = wws.task_id and ti.task_type = 68
    JOIN REP_WORKFLOWS wf on wws.workflow_id = wf.workflow_id
UNION
SELECT ti.instance_name,
    ti.task_id,
    ti.version_number,
    ti.instance_id,
    wf.workflow_id,
    wf.workflow_name,
    wf.workflow_comments,
    wf.server_name,
    wf.subject_area,
    '' as hierarchy_structure,
    '' as path
FROM REP_WORKFLOWS wf
    JOIN rep_task_inst ti on ti.workflow_id = wf.workflow_id and ti.task_type = 68
where wf.subject_area not in ('<SUBJECT_AREAS_TO_EXCLUDE>')
) ws
    join (select distinct workflow_id as workflow_id from rep_wflow_run) active_wflows on ws.workflow_id = active_wflows.workflow_id 

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

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

The session logs can take a long time to generate. We recommended that you run this step on an adhoc frequency when your Informatica jobs change.

These logs can then be parsed with the kada_informatica_runtime_parser.py to generate a runtime_session_overrides.json which is used when running the Informatica extractor

kada_informatica_runtime_parser.py

Code Block
languagepy
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"]})

...

Step 5: Configure the Collector

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

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

username

string

Username to log into Oracle

“myuser”

password

string

Password to log into Oracle

dsn

string

Datasource Name for Oracle, this can be one of the following forms

<tnsname>
<host/servicename>

“preprod”

local.example.com/oraservice”

repo_owner

string

This is the owner of all the tables required by the extractor

“inf”

oracle_client_path

string

Full path to the location of the Oracle Client libraries

“/tmp/drivers/lib/oracleinstantclient_11_9”

cached

boolean

If set to true if will prevent re-extracting data

false

input_path

string

Absolute path to the input location where runtime_session_overrides.json is placed

“/tmp/input”

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

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

kada_informatica_extractor_config.json

Code Block
languagejson
{
    "username": "",
    "password": "",
    "dsn": "",
    "repo_owner": "",
    "oracle_client_path": "",
    "cached": false,
    "input_path": "/tmp/input",
    "output_path": "/tmp/output",
    "mask": true
}

...

Step 6: 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 code sample uses the kada_informatica_extractor.py for handling the configuration details

Code Block
languagepy
import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.informatica import Extractor

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

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

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

start_hwm, end_hwm = get_hwm(_type)

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 else where 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.

...

To edit the internal SQL being run refer to https://kadaai.atlassian.net/wiki/spaces/DAT/pages/1894318152/Notes+v2.0.0#Adding-Custom-SQL

...

Step

...

7: 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

...

If you want prefer file managed hwm, you can edit the location of the hwn by following these instructions https://kadaai.atlassian.net/wiki/spaces/DAT/pages/1894318152/Notes+v2.0.0#Storing-HWM-in-another-location

...

Step

...

8: Push the Extracts to K

Once the files have been validated, you can push the files to the K landing directory.

...