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About Collectors
Collectors are extractors that are developed and managed by you (A customer of K).
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Deploying and orchestrating the extract code
Managing a high water mark so the extract only pull the latest metadata
Storing and pushing the extracts to your K instance.
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Pre-requisites
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Collector Server Minimum Requirements
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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
Access to K landing directory
Access to the KADA Collector repository that contains the SSRS whl
The repository is currently hosted in KADA’s Azure Blob Storage. You will be given a SAS token to access the repository. Reach out to KADA Support (support@kada.ai) if you do not have access.
Download the SSRS whl (e.g. kada_collectors_extractors_ssrs-#.#.#-py3-none-any.whl)
Check if your SSRS instance port
Run the following query and note the local tcp port.
Code Block Check your SSRS instance port
Run the following query and note the local tcp port.
Code Block 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:
Teradata IP Reference Only Data Source
SAP NetWeaver Data Source
XML Data Source
Web Service Data Source
XML Document Data Source
Sharepoint Data Source
The following catalog item types are currently NOT supported:
Linked Reports
Files
Power BI Desktop Files
Report Models
Parameter resolution is not supported.
SSAS query syntax is not supported
Some TSQL syntax is not support. These are mostly statements that contain not standard ANSI SQL constructs. Examples include:
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Variables (DECLARE)
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connection types are NOT currently supported:
Teradata IP Reference Only Data Source
SAP NetWeaver Data Source
XML Data Source
Web Service Data Source
XML Document Data Source
Sharepoint Data Source
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Step 1: Create the Source in K
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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
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Run the following command to install the collector
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pip install kada_collectors_extractors_ssrs-3.0.0-py3-<version>-none-any.whl |
You will also need to install the common library kada_collectors_lib -1.0.0 for this collector to function properly.
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pip install kada_collectors_lib-1.0.0-py3<version>-none-any.whl |
Info |
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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 |
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The collector requires a set of parameters to connect to and extract metadata from SSRS.
FIELD | FIELD TYPE | DESCRIPTION | EXAMPLE | ||
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server | string | SQLServer server host Note if the default port is not used append the port to the server name. Example
| “10.1.18.19” | ||
username | string | Username to log into the SQLServer account | “myuser” | ||
password | string | Password to log into the SQLServer account |
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ssrs_database | string | The database which SSRS exists | ReportServer$RS | ||
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
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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” | ||
mask | boolean | To enable masking or not | true | ||
compress | boolean | To gzip the output or not | true |
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.
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import os import argparse from kada_collectors.extractors.utils import load_config, get_hwm, publish_hwm, get_generic_logger from kada_collectors.extractors.ssrs import Extractor get_generic_logger('root') # Set to use the root logger, you can change the context accordingly or define your own logger _type = 'ssrs' dirname = os.path.dirname(__file__) filename = os.path.join(dirname, 'kada_{}_extractor_config.json'.format(_type)) parser = argparse.ArgumentParser(description='KADA SSRS 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(_typeargs.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) |
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