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GQI: Get trend data

Solved96 views5 hours agodashboards app GQI GQI queries
3
Yahya Mesmoudi [DevOps Advocate]297 2 days ago 3 Comments

Hi Dojo!

I am trying to retrieve a table of maximum and average values for a trended parameter over a custom period of time, across multiple elements, using a specific connector.

The goal is not to query a single element, but rather to dynamically get the top X elements, which may vary over time.

While exploring GitHub, I came across the GQI: SLC-GQIDS-GetParameterTrendAggregation, which seems to match what I need. However, I am currently using DataMiner version 10.4.0.0-14772-CU6, and this GQI requires 10.4.5 / 10.5.0 or higher, specifically for the IGQILogger interface.

I also came across some older questions referencing a get trend data query, but couldn’t find it.

Could you please point me to a solution that would work with my version, or suggest an alternative approach to achieve this?

Yahya Mesmoudi [DevOps Advocate] Posted new comment 5 hours ago
Yahya Mesmoudi [DevOps Advocate] commented 2 days ago

Just a quick correction, in my question I have put the wrong link for the query in GitHub, this is the link of the GQI I was actually referring to: https://github.com/SkylineCommunications/AggregatedTrendStatistics

Steve Purcell [SLC] [DevOps Advocate] commented 2 days ago

Hi Yahya, for the 'get trend data' query, you need to enable the 'GenericInterface' soft launch option described here. https://docs.dataminer.services/dataminer/Reference/Soft-launch_options/Activating_Soft_Launch_Options.html

Yahya Mesmoudi [DevOps Advocate] commented 5 hours ago

Thanks Steve!

1 Answer

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Sebastiaan Dumoulein [SLC] [DevOps Enabler]13.09K Posted 1 day ago 1 Comment

HI Yahya,

You have several options to pick from:

  1. Upgrading to the latest (web) version of DataMiner. In case of the web version, be sure to enable the GQI DxM which has all of the latest improvements and capabilities.
  2. Enabling the 'GenericInterface' soft launch option to get access to the native get trend data data source.
  3. (Advanced) Forking/Deploying the data source and commenting out the logging logic. This will make sure it can compile in the automation module and will make it available in dashboards and low-code apps.
Yahya Mesmoudi [DevOps Advocate] Posted new comment 5 hours ago
Yahya Mesmoudi [DevOps Advocate] commented 5 hours ago

Thank you for your suggestions!
I tried all of them in our staging environment, and they all work well.
I prefer option 3 as I was able to adapt it to suit my needs exactly.

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