Table of Contents

Best practices for developing GQI extensions

When developing a GQI extension, keep the following in mind:

Use DIS to create and publish extensions

Using DataMiner Integration Studio (DIS) in Visual Studio is the most efficient way to develop a GQI extension. You can use the DataMiner Automation Script Solution project template to get started or import an existing Automation script to edit.

This way of working gives you the best tools to write and maintain the required C# code. It also allows you to easily push your extension to a DataMiner Agent for testing.

Remember to compile the script as a library. You can configure this in your Automation Script project by adding the preCompile and libraryName parameters in the associated XML file:

<DMSScript>
 ...
 <Script>
  <Exe>
   ...
   <Param type="preCompile">true</Param>
   <Param type="libraryName">My GQI extension library name</Param>
  </Exe>
 </Script>
</DMSScript>

Do not use Skyline.DataMiner.Automation

Never use references to the Skyline.DataMiner.Automation namespace in your GQI extension code.

If you use DIS to create your extensions, these references are automatically available through the Skyline.DataMiner.Dev.Automation NuGet package. They should only ever be used in actual Automation scripts.

Types and methods in this namespace have no use in a GQI extension and, more importantly, will prevent GQI from loading your extension (see: Only use 64-bit DLL references).

Only use 64-bit assembly references

GQI runs in a 64-bit process and cannot load any extensions that require 32-bit assemblies. You should therefore only use 64-bit assembly references.

Make your ad hoc data source scalable

Each time a query is executed, a new instance of the data source is created. This makes scalability crucial, especially when building the underlying data source is time-consuming or resource-intensive. Keep in mind that the code you are writing could be executed concurrently by many users.

Tip

For an example of how to build a highly scalable data source capable of serving many concurrent users, see Scaling an ad hoc data source.

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