...
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 |
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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 |
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@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
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 |
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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 | ||
---|---|---|
| ||
{
"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 | ||
---|---|---|
| ||
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 | ||
---|---|---|
| ||
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. |
...
language | py |
---|
...
...
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
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 | ||||||
---|---|---|---|---|---|---|
|
...
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 |
Code Block | ||
---|---|---|
| ||
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 | ||
---|---|---|
| ||
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> | “preprod” |
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 | “/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 | ||
---|---|---|
| ||
{
"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 | ||
---|---|---|
| ||
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.
...