How to create metrics for Application Insight with julia

While there is no official Azure SDK for the Julia programming language, it is possible to use Azure's REST API to interact with Azure services, including Azure Application Insights. Below, I'll provide a general approach on how to achieve this, including how to send custom metrics to Application Insights using Julia.

Step-by-Step Guide

1. Set Up Application Insights:

• Make sure you have an Application Insights resource created in your Azure portal.

• Note down your Instrumentation Key (also known as Connection String).

1. Install Required Julia Packages:

• You need to use HTTP and JSON packages to make HTTP requests and handle JSON data in Julia.

using Pkg
Pkg.add("HTTP")
Pkg.add("JSON")

1. Constructing the URL and Authentication:

• Application Insights metrics can be sent using a POST request to the following endpoint:

https://dc.services.visualstudio.com/v2/track

• Use your Instrumentation Key for authentication.

1. Creating the Payload:

• Construct a JSON payload with the required metric data.

• Here is an example of how to set up a custom metric payload:

using HTTP
using JSON

instrumentation_key = "your_instrumentation_key_here"

###  Construct the payload
payload = Dict(
    "name" => "CustomMetric",
    "time" => "2021-11-23T14:55:00.000Z",
    "iKey" => instrumentation_key,
    "data" => Dict(
        "baseType" => "MetricData",
        "baseData" => Dict(
                "metrics" => [
                        Dict(
                        "name" => "sample_metric",
                        "value" => 100,
                        "count" => 1 
                        )] 
                )
         )
)

headers = Dict("Content-Type" => "application/json")

# Convert the payload to JSON
json_payload = JSON.json(payload)

# Send the payload to Application Insights
response = HTTP.post("https://dc.services.visualstudio.com/v2/track", headers, json_payload)
println(response.status)
println(String(response.body))

1. Error Handling:

• Make sure to handle HTTP errors and possible retries.

• Logging the response status and body can help in debugging any issues.

Example: Julia Function to Send Metrics

Below is a more structured way to encapsulate the send metric functionality within a Julia function:

• metrics.jl

using HTTP
using JSON
using Dates
function send_metric(instrumentation_key, metric_name, metric_value)
    url = "https://dc.services.visualstudio.com/v2/track"
    timestamp = Dates.format(Dates.now(), "yyyy-mm-ddTHH:MM:SS.sssZ")
    payload = Dict(
        "name" => "CustomMetric",
        "time" => timestamp,
        "iKey" => instrumentation_key,
        "data" => Dict(
            "baseType" => "MetricData",
            "baseData" => Dict(
                "metrics" => [
                    Dict(
                        "name" => metric_name,
                        "value" => metric_value,
                        "count" => 1
                    )
                ]
            )
        )
    )
    headers = Dict("Content-Type" => "application/json")
    json_payload = JSON.json(payload)
    try
        response = HTTP.post(url, headers, json_payload)
        if response.status == 200
            println("Metric sent successfully.")
        else
            println("Failed to send metric. Status: ", response.status)
            println("Response body: ", String(response.body))
        end
    catch e
        println("Exception occurred: ", e)
    end
end

Example usage:

send_metric("your_instrumentation_key_here", "example_metric", 123)