Power BI API Dataset Limit

Streaming real-time information to Power BI Service datasets is extremely easy but you need to understand its limitations. In this post we will hit the maximum number of Post Requests per minute which is 120 requests per dataset.

Before we start, one “must do” while you are working with IoT information is to store the raw information while you send it to the application.

You can find the Power BI Rest API limitations here.

If you are experiencing the following errors, you may be hitting these limits:.

• The remote server returned an error: (503) Server Unavailable.
• The remote server returned an error: (429)

Requirements:

• Good Internet Connection
• Access to Power BI Service
• Power Shell ISE (Optional)
• This Script (Optional), which you can copy from below.

Create the dataset with Historic Data Analysis

Checkbox enables the PushStreaming dataset.

Copy the following code into Power Shell ISE:.

#Include the Power BI Dataset Url in the following variable
$Endpoint = ""
$NumberofAttempts = 121
$RowId = 1

Do
{
# Define correct timestamp format
$date= $(Get-Date).ToString("yyyy-MM-ddTHH:MM:ss.fffZ")

# Incremental Number
$RowId = $RowId

# Json body request
$payload = @{
"Timestamp" =$date
"RowId" =$RowId
}
# Post the row
Invoke-RestMethod -Method Post -Uri "$Endpoint" -Body (ConvertTo-Json @($payload))

write-host $date"-"$RowId


} While (++$RowId -le $NumberofAttempts)

Let’s run the script!

Make it fail and verify our dataset.

FAQ

Why does the grid display only 120 rows and the card the value 121?

Both are different datasets behind the scene with different infrastructure, 1 is the push dataset (grid) and the other is the streaming dataset (card). Behind the scenes the architecture is different. More info here.

How can we solve this problem?

There are many ways to solve it:

• Wait before you send the following request (in Power Shell “Start-Sleep -m 500” will wait 500 milliseconds)
• Send multiple rows in a single request (up to 10.000) but be aware of the limitation per hour (1.000.000).
• Aggregate/Summarize the information before sending it to Power BI with time windows (must do!).

I hope you have enjoyed the post and found it helpful.