Let’s consider a scenario, wherein the requirement is to find out the percentage of a particular type of values from a single input set.
Below can be considered as an example of input sample data and we need to find out what percentage of dev releases and what percentage of prod releases are present in the input data.
- let demoData = datatable(Environment: string, Feature:string)
- [
- "dev", "Feature1",
- "test", "Feature1",
- "prod", "Feature1",
- "Dev", "Feature2",
- "test", "Feature2",
- "dev", "Feature3",
- "test", "Feature3",
- "prod", "Feature3"
- ];
Approach
In order to achieve the solution, one has to go through various steps as mentioned below,
Step 1
Get the total number of records from the set.
- let totalRecords = demoData
- | count
- | project TotalRecords = Count;
Step 2
Get only those records which are of type ‘dev’
- let devRecords = demoData
- | where Environment =~ "dev"
- | count
- | project TotalDevRecords = Count;
Step 3
Get only those records which are of type ‘prod’
- let prodRecords = demoData
- | where Environment =~ "prod"
- | count
- | project TotalProdRecords=Count;
So far we have got all the individual parts. The next task is to combine all the above mentioned 3 steps and generate a single result set and here comes the challenge.
Challenge
As input set is holding only two columns, there is no common field in all the above mentioned three queries, and as there is no commonality it is significantly difficult to bring such a result set together to form a single result set.
Addressing the challenge
Can’t we go ahead and introduce some new column just for the sake of projection? Well, let’s see how that changes our above 3 steps now,


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