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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
Python 3.6 8 - 3.1011
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
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
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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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