SQL Server (via Collector method) - v3.0.0

About Collectors

Pre-requisites

• Python 3.6 - 3.10

• Access to K landing directory

• Access to SQL Server (see section below)

• Check the SQLServer instance port

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

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

SQL Server Access

Setting up SQL Server for metadata extraction is a 2 step process.

Step 1: Establish SQLServer Access

Create a SqlServer user with read access per SQLServer database.

• INFORMATION_SCHEMA.ROUTINES

• INFORMATION_SCHEMA.VIEWS

• INFORMATION_SCHEMA.TABLE_CONSTRAINTS

• INFORMATION_SCHEMA.CONSTRAINT_COLUMN_USAGE

• INFORMATION_SCHEMA.TABLES

• INFORMATION_SCHEMA.COLUMNS

• INFORMATION_SCHEMA.VIEWS

• sys.foreign_key_columns

• sys.objects

• sys.tables

• sys.schemas

• sys.columns

• VIEW SERVER STATE permission on the server

  • Required for Extended Event log

• VIEW Definition

  • All databases

    USE master GO GRANT VIEW ANY DEFINITION TO Kadauser

  • Selected databases. Repeat for each database

    USE <REPLACE WITH A DATABASE> GO GRANT VIEW ANY DEFINITION TO Kadauser

Step 2: Setup Extended Event Logging

A SQLServer Admin will need to setup an extended events process to capture Query Execution in SQLServer.

Some tuning of the logging parameters may be needed depending on processing volume on your SQLServer instance.

Example script to setup Extended Events logging.

Step 1: Create the Source in K

Create a source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from File” option
  
  

• Open

  

  Give the source a Name - e.g. SQL Server Production

• Add the Host name for the SQL Server instance

• Click Next & 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.

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

Open

Run the following command to install the collector

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

Step 4: Configure the Collector

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

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_sqlserver_extractor_config.json

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_sqlserver_extractor.py for handling the configuration details

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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/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 https://kadaai.atlassian.net/wiki/spaces/KSL/pages/1902411777/Additional+Notes#The-run-method

username: username to sign into sqlserver
password: password to sign into sqlserver
server: sqlserver host
driver: sqlserver driver name
events_path: regex location of the events files on the server
databases: list of databases to extract
sqlserver_version: Release name for the SQLServer supported is 2012, 2016, 2017, 2019
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

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 sqlserver_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method.

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