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SSIS (Collector method) - v3.1.0

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

  • Python 3.8 - 3.11

  • MSDB database / SQLServer DB access

    • if using SSISDB to store package you will need to download each project via SQL Server Management Studio. See SSIS (Collector method) - v3.1.0 | Downloading-SSISDB-packages

    • The collector will need access to the underlying SQLServer Database with permissions to read the following tables is the SSIS main databases:

      • MSDB.DBO.SYSSSISPACKAGES

      • <SSIS Logging Database>.DBO.SYSSSISLOG where <SSIS Logging Database> is the database configured for SSIS logging

  • Access to K landing directory

  • Check your SSIS instance port

    • Run the following query and note the local tcp port.

      CODE
      SELECT local_tcp_port
      FROM   sys.dm_exec_connections
      WHERE  session_id = @@SPID
      GO

Downloading SSISDB packages

  1. In SQL Server Studio Manager Go to Integration Services Catalog > Projects.

  2. Right click on a Project > Export…

  3. Save project file

  4. Change the file extraction from .ispac to .zip

  5. Repeat for each project.


Step 1: Create the Source in K

Create a SSIS source in K

  • Go to Settings, Select Sources and click Add Source

  • Select “Load from file system” option

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

  • Add the Host name for the SSIS Server

  • Click Finish Setup


Step 2: Getting Access to the Source Landing Directory

Collector Method

When using a Collector you will push metadata to a K landing directory.

To find your landing directory you will need to

  1. Go to Platform Settings - Settings. Note down the value of this setting

    1. If using Azure: storage_azure_storage_account

    2. if using AWS:

      1. storage_root_folder - the AWS s3 bucket

      2. storage_aws_region - the region where the AWS s3 bucket is hosted

  2. Go to Sources - Edit the Source you have configured. Note down the landing directory in the About this Source section

To connect to the landing directory you will need

  • If using Azure: a SAS token to push data to the landing directory. Request this from KADA Support (support@kada.ai)

  • if using AWS:

    • an Access key and Secret. Request this from KADA Support (support@kada.ai)

    • OR provide your IAM role to KADA Support to provision access.


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 and whl via Platform Settings → SourcesDownload Collectors

Run the following command to install the collector

CODE
pip install kada_collectors_extractors_<version>-none-any.whl

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

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

You will also need an ODBC package installed at the OS level for pyodbc to use as well as a SQLServer ODBC driver, refer to https://docs.microsoft.com/en-us/sql/connect/odbc/download-odbc-driver-for-sql-server?view=sql-server-ver15


Step 4: Configure the Collector

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

FIELD

FIELD TYPE

DESCRIPTION

EXAMPLE

server

string

SQLServer server host

Note if the default port is not used append the port to the server name. Example

10.123.123.123\\<SERVICE NAME>,<INSTANCE PORT>

“10.1.18.19”

username

string

Username to log into the SQLServer account

“myuser”

password

string

Password to log into the SQLServer account

 

logging_database

string

Database where the SSIS Logging has been setup

“ssis_logging”

mapping

JSON

Mapping file of data source names against the onboarded host and database name in K

Assuming I have a “myDSN” data source name in powerbi, I’ll map it to host “myhost” and database “mydatabase” onboarded in K, snowflake type references are handled automatically

CODE
{
        "myDSN": {
            "host": "myhost",
            "database": "mydatabase"
        }
    }

driver

string

This is the ODBC driver, generally its ODBC Driver 17 for SQL Server, if you another driver installed please use that instead

“ODBC Driver 17 for SQL Server”

output_path

string

Absolute path to the output location where files are to be written

“/tmp/output”

input_path

string

Absolute path to the input folder where SSISDB project zips are extract to. See SSIS (Collector method) - v3.1.0 | Downloading-SSISDB-packages

Note that within this folder there should be seperate folders for each project and the .manifest/.dtsx/.conmgr (conmgr may or may nor be applicable) files should sit within this folder, for e.g.


/tmp/input/project1/my.dtsx
/tmp/input/project1/@Project.manifest
/tmp/input/project1/mycon.conmgr

/tmp/input/project2/my.dtsx

“/tmp/input”

mask

boolean

To enable masking or not

true

compress

boolean

To gzip the output or not

true

uses_ssissdb

boolean

Are packages deployed to ssisdb or msdb?

false

meta_only

boolean

Do you wish to extract metadata only and don’t want to set up logging for SSIS packages?

false

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_ssis_extractor_config.json

JSON
{
    "server": "",
    "username": "",
    "password": "",
    "logging_database": "ssis_logging",
    "mapping": {},
    "driver": "ODBC Driver 17 for SQL Server",
    "output_path": "/tmp/output",
    "input_path": "/tmp/input",
    "mask": true,
    "compress": true,
    "uses_ssisdb": false,
    "meta_only": false
}

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_ssis_extractor.py

PY
import os
import argparse
from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.ssis import Extractor

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

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

parser = argparse.ArgumentParser(description='KADA SSIS 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(args.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)

This code will produce and read a high water mark file from the same directory as the execution called ssis_hwm.txt and produce files according to the configuration JSON.

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 Additional Notes | Storing-HWM-in-another-location

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 Additional Notes | The-run-method

PY
from kada_collectors.extractors.ssis 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)

PY
class Extractor(username: str = None, password: str = None, \
  server: str = None, driver: str = None, logging_database: str = None, \
  mapping: dict = {}, output_path: str = './output', mask: bool = False, \
  compress: bool = False, uses_ssisdb: bool = False, meta_only: bool = False) -> None

username: username to sign into sqlserver
password: password to sign into sqlserver
server: sqlserver host
driver: sqlserver driver name
logging_database: Logging Database name for SSIS
mapping: Dict of DNS to database and hostnames
output_path: full or relative path to where the outputs should go
input_path: full or relative path to where the dstx files should be
mask: To mask the META/DATABASE_LOG files or not
compress: To gzip output files or not
uses_ssisdb: Are packages published to ssisdb or msdb?
meta_only: Option to extract metadata only


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 ssis_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method. Additional Notes | Storing-the-HWM-using-the-K-Landing-Area


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

PY
# built-in
import os

# Installed
from airflow.operators.python_operator import PythonOperator
from airflow.models.dag import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

from plugins.utils.azure_blob_storage import AzureBlobStorage

from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger
from kada_collectors.extractors.tableau import Extractor

# To be configed by the customer.
# Note variables may change if using a different object store.
KADA_SAS_TOKEN = os.getenv("KADA_SAS_TOKEN")
KADA_CONTAINER = ""
KADA_STORAGE_ACCOUNT = ""
KADA_LANDING_PATH = "lz/tableau/landing"
KADA_EXTRACTOR_CONFIG = {
    "server_address": "http://tabserver",
    "username": "user",
    "password": "password",
    "sites": [],
    "db_host": "tabserver",
    "db_username": "repo_user",
    "db_password": "repo_password",
    "db_port": 8060,
    "db_name": "workgroup",
    "meta_only": False,
    "retries": 5,
    "dry_run": False,
    "output_path": "/set/to/output/path",
    "mask": True,
    "mapping": {}
}

# To be implemented by the customer. 
# Upload to your landing zone storage.
def upload():
  output = KADA_EXTRACTOR_CONFIG['output_path']
  for filename in os.listdir(output):
      if filename.endswith('.csv'):
        file_to_upload_path = os.path.join(output, filename)

        AzureBlobStorage.upload_file_sas_token(
            client=KADA_SAS_TOKEN,
            storage_account=KADA_STORAGE_ACCOUNT,
            container=KADA_CONTAINER, 
            blob=f'{KADA_LANDING_PATH}/{filename}', 
            local_path=file_to_upload_path
        )

with DAG(dag_id="taskgroup_example", start_date=days_ago(1)) as dag:
  
    # To be implemented by the customer.
    # Retrieve the timestamp from the prior run
    start_hwm = 'YYYY-MM-DD HH:mm:SS'
    end_hwm = 'YYYY-MM-DD HH:mm:SS' # timestamp now
    
    ext = Extractor(**KADA_EXTRACTOR_CONFIG)
    
    start = DummyOperator(task_id="start")

    with TaskGroup("taskgroup_1", tooltip="extract tableau and upload") as extract_upload:
        task_1 = PythonOperator(
            task_id="extract_tableau",
            python_callable=ext.run, 
            op_kwargs={"start_hwm": start_hwm, "end_hwm": end_hwm},
            provide_context=True,
        )
        
        task_2 = PythonOperator(
            task_id="upload_extracts",
            python_callable=upload, 
            op_kwargs={},
            provide_context=True,
        )

        # To be implemented by the customer. 
        # Timestamp needs to be saved for next run
        task_3 = DummyOperator(task_id='save_hwm') 

    end = DummyOperator(task_id='end')

    start >> extract_upload >> end

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