SQL Server (via Collector method) - v2.0.0

About Collectors

Pre-requisites

• Python 3.6 - 3.10

• Access to K landing directory

• Access to SQL Server (see section below)

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

    CODE

    USE master 
    GO 
    GRANT VIEW ANY DEFINITION TO Kadauser

  • Selected databases. Repeat for each database

    CODE

    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.

--Query To Create Extended Events Session
CREATE EVENT SESSION [KADA] ON SERVER ADD EVENT sqlserver.sp_statement_completed (
	ACTION(package0.collect_system_time, package0.event_sequence, sqlos.task_time, sqlserver.client_app_name, sqlserver.client_hostname, sqlserver.database_id, sqlserver.database_name, sqlserver.nt_username, sqlserver.query_hash, sqlserver.server_instance_name, sqlserver.server_principal_name, sqlserver.server_principal_sid, sqlserver.session_id, sqlserver.session_nt_username, sqlserver.transaction_id, sqlserver.username) WHERE (
		(
			[statement] LIKE '%CREATE %'
			OR [statement] LIKE '%DROP %'
			OR [statement] LIKE '%MERGE %'
			OR [statement] LIKE '%FROM %'
			)
		AND [sqlserver].[server_principal_name] <> N'USERS_TO_EXCLUDE'
		AND [sqlserver].[is_system] = (0)
		AND NOT [statement] LIKE 'Insert into % Values %'
		AND [sqlserver].[Query_hash] <> (0)
		)
	), ADD EVENT sqlserver.sql_statement_completed (
	SET collect_statement = (1) ACTION(package0.collect_system_time, package0.event_sequence, sqlos.task_time, sqlserver.client_app_name, sqlserver.client_hostname, sqlserver.database_id, sqlserver.database_name, sqlserver.nt_username, sqlserver.query_hash, sqlserver.server_instance_name, sqlserver.server_principal_name, sqlserver.session_id, sqlserver.session_nt_username, sqlserver.transaction_id, sqlserver.username) WHERE (
		(
			[statement] LIKE '%CREATE %'
			OR [statement] LIKE '%DROP %'
			OR [statement] LIKE '%MERGE %'
			OR [statement] LIKE '%FROM %'
			)
		AND [sqlserver].[server_principal_name] <> N'USERS_TO_EXCLUDE'
		AND [sqlserver].[is_system] = (0)
		AND NOT [statement] LIKE 'Insert into % Values %'
		AND [sqlserver].[Query_hash] <> (0)
		)
	) ADD TARGET package0.event_file (SET filename = N'G:\extended events\Extendedevents.xel', max_file_size = (20), max_rollover_files = (100))
	WITH (MAX_MEMORY = 4096 KB, EVENT_RETENTION_MODE = ALLOW_MULTIPLE_EVENT_LOSS, MAX_DISPATCH_LATENCY = 30 SECONDS, MAX_EVENT_SIZE = 0 KB, MEMORY_PARTITION_MODE = NONE, TRACK_CAUSALITY = ON, STARTUP_STATE = ON)
GO


-- Check if the session is dropping events and see other data about the session
-- https://sqlperformance.com/2019/10/extended-events/understanding-event-loss-with-extended-events
SELECT
   s.name, 
   s.total_regular_buffers,
   s.regular_buffer_size,
   s.total_large_buffers,
   s.large_buffer_size,
   s.dropped_event_count,
   s.dropped_buffer_count,
   s.largest_event_dropped_size
FROM sys.dm_xe_sessions AS s;


-- Also check log growth rate. Apply filters to remove noise.
-- some filters:
-- [sqlserver].[server_principal_name] = N'name of principal'
-- [sqlserver].[is_system] = (0)
-- [sqlserver].[client_app_name] = N'name of app'

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
  

• 

  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 SQL Server whl via Platform Settings → Sources → Download Collectors

Run the following command to install the collector

pip install kada_collectors_extractors_sqlserver-#.#.#-py3-none-any.whl

Note that 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 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

{
    "server": "",
    "username": "",
    "password": "",
    "databases": [""],
    "sqlserver_version": "2016",
    "driver": "ODBC Driver 17 for SQL Server",
    "events_path": "/tmp/Extendedevents*.xel",
    "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_sqlserver_extractor_config.json for handling the configuration details

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

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

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

parser = argparse.ArgumentParser(description='KADA SqlServer 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 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 Collector Integration General 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 Collector Integration General Notes | The-run-method

class Extractor(username: str = None, password: str = None, server: str = None, \
    driver: str = None, events_path: str = None, databases: list = [], \
    output_path: str = './output', mask: bool = False) -> None

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
output_path: full or relative path to where the outputs should go
mask: To mask the META/DATABASE_LOG 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

# 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.
# Change '.csv' to '.csv.gz' if you set compress = true in the config
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