SSRS (Collector method) - v3.0.0

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

Collector Server Minimum Requirements

SSRS Requirements

• Support SQL SSRS 2016+ where the database is called ReportServer$RS

  • if your SSRS databases differs from this, please Advise KADA of the SSRS version and what the database is called.

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

    • ReportServer$RS.DBO.CATALOG

    • ReportServer$RS.DBO.EXECUTIONLOG3

    • ReportServer$RS.DBO.USERS

• Check your SSRS 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

Known SSRS Collector limitations

The following connection types are NOT currently supported:

1. Teradata IP Reference Only Data Source

2. SAP NetWeaver Data Source

3. XML Data Source

4. Web Service Data Source

5. XML Document Data Source

6. Sharepoint Data Source

Step 1: Create the Source in K

Create a SSRS source in K

• Go to Settings, Select Sources and click Add Source

• Select “Load from file system” option
  
  

  Open

  

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

• Add the Host name for the SSRS 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.

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 SSRS.

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_ssrs_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 is the wrapper script: kada_ssrs_extractor.py

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

Advanced Usage

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.

If you are handling external arguments of the runner yourself, you’ll need to consider the following for the run method Collector Integration General Notes | Extractor run method

username: username to sign into sqlserver
password: password to sign into sqlserver
ssrs_database: Name of the SSRS database of the sqlserver host
server: sqlserver host
driver: sqlserver driver name
mapping: Dict of DNS to database and hostnames
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 ssrs_hwm.txt and produce files according to the configuration JSON. This file is only produced if you call the publish_hwm method. Collector Integration General 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