Sample code to send data to Azure Monitor using Logs ingestion API

The following script uses the Azure Monitor Ingestion client library for .NET.

1. Install the Azure Monitor Ingestion client library and the Azure Identity library. The Azure Identity library is required for the authentication used in this sample.

   dotnet add package Azure.Identity
   dotnet add package Azure.Monitor.Ingestion
   

2. Create the following environment variables with values for your Microsoft Entra application. These values are used by DefaultAzureCredential in the Azure Identity library.

   • AZURE_TENANT_ID
   • AZURE_CLIENT_ID
   • AZURE_CLIENT_SECRET

3. Replace the variables in the following sample code with values from your DCR. You may also want to replace the sample data with your own.

using Azure;
using Azure.Core;
using Azure.Identity;
using Azure.Monitor.Ingestion;

// Initialize variables
var endpoint = new Uri("https://my-url.monitor.azure.com");
var ruleId = "dcr-00000000000000000000000000000000";
var streamName = "Custom-MyTableRawData";

// Create credential and client
var credential = new DefaultAzureCredential();
LogsIngestionClient client = new LogsIngestionClient(endpoint, credential);

DateTime currentTime = DateTime.UtcNow;

// Use BinaryData to serialize instances of an anonymous type into JSON
BinaryData data = BinaryData.FromObjectAsJson(
   new[] {
	new
	{
	   Time = currentTime,
	   Computer = "Computer1",
	   AdditionalContext = new
	   {
	 	InstanceName = "user1",
		TimeZone = "Pacific Time",
		Level = 4,
		CounterName = "AppMetric1",
		CounterValue = 15.3
	   }
	},
	new
	{
	   Time = currentTime,
	   Computer = "Computer2",
	   AdditionalContext = new
	   {
		InstanceName = "user2",
		TimeZone = "Central Time",
		Level = 3,
		CounterName = "AppMetric1",
		CounterValue = 23.5
	   }
	},
   }
);

// Upload logs
try
{
   // ===== START: Use this block of code to upload compressed data
   byte[] dataBytes = data.ToArray();
   
   string contentEncoding = "gzip"; // Specify gzip if the content is already compressed

   using (MemoryStream memoryStream = new MemoryStream())
   {
	using (GZipStream gzipStream = new GZipStream(memoryStream, CompressionMode.Compress))
	{
	   gzipStream.Write(dataBytes, 0, dataBytes.Length);
	}
	byte[] gzipBytes = memoryStream.ToArray();

	var response = await client.UploadAsync(ruleId, streamName, RequestContent.Create(gzipBytes), contentEncoding).ConfigureAwait(false);
	if (response.IsError)
	{
	   throw new Exception(response.ToString());
	}
   }
   // ===== End: code block to upload compressed data
 
   //** ===== START: Use this block of code to upload uncompressed data.
   var response = await client.UploadAsync(ruleId, streamName, RequestContent.Create(data)).ConfigureAwait(false);
   if (response.IsError)
   {
	throw new Exception(response.ToString());
   }
   //** ===== End: code block to upload uncompressed data.

}
catch (Exception ex)
{
	Console.WriteLine("Upload failed with Exception: " + ex.Message);
}

4. Execute the code, and the data should arrive in your Log Analytics workspace within a few minutes.

The following sample code uses the Azure Monitor Ingestion Logs client module for Go.

1. Use go get to install the Azure Monitor Ingestion Logs and Azure Identity client modules for Go. The Azure Identity module is required for the authentication used in this sample.

   go get github.com/Azure/azure-sdk-for-go/sdk/monitor/ingestion/azlogs
   go get github.com/Azure/azure-sdk-for-go/sdk/azidentity
   

2. Create the following environment variables with values for your Microsoft Entra application. These values are used by DefaultAzureCredential in the Azure Identity module.

   • AZURE_TENANT_ID
   • AZURE_CLIENT_ID
   • AZURE_CLIENT_SECRET

3. Replace the variables in the following sample code with values from your DCR. You might also want to replace the sample data with your own.

   package main
   
   import (
       "context"
       "encoding/json"
       "strconv"
       "time" 
   
       "github.com/Azure/azure-sdk-for-go/sdk/azidentity"
       "github.com/Azure/azure-sdk-for-go/sdk/monitor/ingestion/azlogs"
   )
   
   // logs ingestion URI 
   const endpoint = "https://my-url.monitor.azure.com"
   // data collection rule (DCR) immutable ID
   const ruleID = "dcr-00000000000000000000000000000000"
   // stream name in the DCR that represents the destination table
   const streamName = "Custom-MyTableRawData"                 
   
   type Computer struct {
       Time              time.Time
       Computer          string
       AdditionalContext string
   }
   
   func main() {
       // creating the client using DefaultAzureCredential
       cred, err := azidentity.NewDefaultAzureCredential(nil)
   
       if err != nil {
           //TODO: handle error
       }
   
       client, err := azlogs.NewClient(endpoint, cred, nil)
   
       if err != nil {
           //TODO: handle error
       }
   
       // generating logs
       // logs should match the schema defined by the provided stream
       var data []Computer
   
       for i := 0; i < 10; i++ {
           data = append(data, Computer{
               Time:              time.Now().UTC(),
               Computer:          "Computer" + strconv.Itoa(i),
               AdditionalContext: "context",
           })
       }
   
       // marshal data into []byte
       logs, err := json.Marshal(data)
   
       if err != nil {
           panic(err)
       }
   
       // upload logs
       _, err = client.Upload(context.TODO(), ruleID, streamName, logs, nil)
   
       if err != nil {
           //TODO: handle error
       }
   }
   

4. Execute the code, and the data should arrive in your Log Analytics workspace within a few minutes.

The following sample code uses the Azure Monitor Ingestion client library for Java.

1. Include the Logs ingestion package and the azure-identity package from the Azure Identity library. The Azure Identity library is required for the authentication used in this sample.

   Note

   See the Maven repositories for Microsoft Azure Client Library For Identity and Microsoft Azure SDK For Azure Monitor Data Ingestion for the latest versions.

   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-monitor-ingestion</artifactId>
       <version>{get-latest-version}</version>    
   <dependency>
       <groupId>com.azure</groupId>
       <artifactId>azure-identity</artifactId>
       <version>{get-latest-version}</version>
   </dependency>
   

2. Create the following environment variables with values for your Microsoft Entra application. These values are used by DefaultAzureCredential in the Azure Identity library.

   • AZURE_TENANT_ID
   • AZURE_CLIENT_ID
   • AZURE_CLIENT_SECRET

3. Replace the variables in the following sample code with values from your DCR. You may also want to replace the sample data with your own.

   import com.azure.identity.DefaultAzureCredentialBuilder; 
   import com.azure.monitor.ingestion.models.LogsUploadException;
   
   import java.time.OffsetDateTime; 
   import java.util.Arrays; 
   import java.util.List; 
   
   public class LogsUploadSample {
       public static void main(String[] args) { 
   
           LogsIngestionClient client = new LogsIngestionClientBuilder()
               .endpoint("https://my-url.monitor.azure.com") 
               .credential(new DefaultAzureCredentialBuilder().build()) 
               .buildClient(); 
   
           List<Object> dataList = Arrays.asList( 
               new Object() { 
                   OffsetDateTime time = OffsetDateTime.now(); 
                   String computer = "Computer1"; 
                   Object additionalContext = new Object() { 
                       String instanceName = "user4"; 
                       String timeZone = "Pacific Time"; 
                       int level = 4; 
                       String counterName = "AppMetric1"; 
                       double counterValue = 15.3; 
                   }; 
               }, 
               new Object() { 
                   OffsetDateTime time = OffsetDateTime.now(); 
                   String computer = "Computer2"; 
                   Object additionalContext = new Object() { 
                       String instanceName = "user2"; 
                       String timeZone = "Central Time"; 
                       int level = 3; 
                       String counterName = "AppMetric2"; 
                       double counterValue = 43.5; 
                   }; 
               }); 
   
           try { 
               client.upload("dcr-00000000000000000000000000000000", "Custom-MyTableRawData", dataList);  
               System.out.println("Logs uploaded successfully"); 
           } catch (LogsUploadException exception) { 
               System.out.println("Failed to upload logs "); 
               exception.getLogsUploadErrors() 
                   .forEach(httpError -> System.out.println(httpError.getMessage())); 
           } 
       }
   }
   

4. Execute the code, and the data should arrive in your Log Analytics workspace within a few minutes.

The following sample code uses the Azure Monitor Ingestion client library for JavaScript.

1. Use npm to install the Azure Monitor Ingestion and Azure Identity client libraries for JavaScript. The Azure Identity library is required for the authentication used in this sample.

   npm install --save @azure/monitor-ingestion
   npm install --save @azure/identity
   

2. Create the following environment variables with values for your Microsoft Entra application. These values are used by DefaultAzureCredential in the Azure Identity library.

   • AZURE_TENANT_ID
   • AZURE_CLIENT_ID
   • AZURE_CLIENT_SECRET

3. Replace the variables in the following sample code with values from your DCR. You might also want to replace the sample data with your own.

   const { DefaultAzureCredential } = require("@azure/identity");
   const { LogsIngestionClient, isAggregateLogsUploadError } = require("@azure/monitor-ingestion");
   
   require("dotenv").config();
   
   async function main() {
     const logsIngestionEndpoint = "https://my-url.monitor.azure.com";
     const ruleId = "dcr-00000000000000000000000000000000";
     const streamName = "Custom-MyTableRawData";
     const credential = new DefaultAzureCredential();
     const client = new LogsIngestionClient(logsIngestionEndpoint, credential);
     const logs = [
       {
         Time: "2021-12-08T23:51:14.1104269Z",
         Computer: "Computer1",
         AdditionalContext: {
             "InstanceName": "user1",
             "TimeZone": "Pacific Time",
             "Level": 4,
             "CounterName": "AppMetric2",
             "CounterValue": 35.3    
         }
       },
       {
         Time: "2021-12-08T23:51:14.1104269Z",
         Computer: "Computer2",
         AdditionalContext: {
             "InstanceName": "user2",
             "TimeZone": "Pacific Time",
             "Level": 4,
             "CounterName": "AppMetric2",
             "CounterValue": 43.5    
         }
       },
     ];
     try{
       await client.upload(ruleId, streamName, logs);
     }
     catch(e){
       let aggregateErrors = isAggregateLogsUploadError(e) ? e.errors : [];
       if (aggregateErrors.length > 0) {
         console.log("Some logs have failed to complete ingestion");
         for (const error of aggregateErrors) {
           console.log(`Error - ${JSON.stringify(error.cause)}`);
           console.log(`Log - ${JSON.stringify(error.failedLogs)}`);
         }
       } else {
         console.log(e);
       }
     }
   }
   
   main().catch((err) => {
     console.error("The sample encountered an error:", err);
     process.exit(1);
   });
   

4. Execute the code, and the data should arrive in your Log Analytics workspace within a few minutes.

The following PowerShell code sends data to the endpoint by using HTTP REST fundamentals.

1. Run the following sample PowerShell command, which adds a required assembly for the script.

   Add-Type -AssemblyName System.Web
   

2. Replace the parameters in the Step 0 section with values from your application and DCR. You might also want to replace the sample data in the Step 2 section with your own.

   ### Step 0: Set variables required for the rest of the script.
   
   # information needed to authenticate to AAD and obtain a bearer token
   $tenantId = "00000000-0000-0000-00000000000000000" #Tenant ID the data collection endpoint resides in
   $appId = " 000000000-0000-0000-00000000000000000" #Application ID created and granted permissions
   $appSecret = "0000000000000000000000000000000000000000" #Secret created for the application
   
   # information needed to send data to the DCR endpoint
   $endpoint_uri = "https://my-url.monitor.azure.com" #Logs ingestion URI for the DCR
   $dcrImmutableId = "dcr-00000000000000000000000000000000" #the immutableId property of the DCR object
   $streamName = "Custom-MyTableRawData" #name of the stream in the DCR that represents the destination table
   
   
   ### Step 1: Obtain a bearer token used later to authenticate against the DCR.
   
   $scope= [System.Web.HttpUtility]::UrlEncode("https://monitor.azure.com//.default")   
   $body = "client_id=$appId&scope=$scope&client_secret=$appSecret&grant_type=client_credentials";
   $headers = @{"Content-Type"="application/x-www-form-urlencoded"};
   $uri = "https://login.microsoftonline.com/$tenantId/oauth2/v2.0/token"
   
   $bearerToken = (Invoke-RestMethod -Uri $uri -Method "Post" -Body $body -Headers $headers).access_token
   
   
   ### Step 2: Create some sample data. 
   
   $currentTime = Get-Date ([datetime]::UtcNow) -Format O
   $staticData = @"
   [
   {
       "Time": "$currentTime",
       "Computer": "Computer1",
       "AdditionalContext": {
           "InstanceName": "user1",
           "TimeZone": "Pacific Time",
           "Level": 4,
           "CounterName": "AppMetric1",
           "CounterValue": 15.3    
       }
   },
   {
       "Time": "$currentTime",
       "Computer": "Computer2",
       "AdditionalContext": {
           "InstanceName": "user2",
           "TimeZone": "Central Time",
           "Level": 3,
           "CounterName": "AppMetric1",
           "CounterValue": 23.5     
       }
   }
   ]
   "@;
   
   
   ### Step 3: Send the data to the Log Analytics workspace.
   
   $body = $staticData;
   $headers = @{"Authorization"="Bearer $bearerToken";"Content-Type"="application/json"};
   $uri = "$endpoint_uri/dataCollectionRules/$dcrImmutableId/streams/$($streamName)?api-version=2023-01-01"
   
   $uploadResponse = Invoke-RestMethod -Uri $uri -Method "Post" -Body $body -Headers $headers
   

   Note

   If you receive an Unable to find type [System.Web.HttpUtility]. error, run the last line in section 1 of the script for a fix and execute it. Executing it uncommented as part of the script won't resolve the issue. The command must be executed separately.

3. Execute the script, and you should see an HTTP - 204 response. The data should arrive in your Log Analytics workspace within a few minutes.

The following sample code uses the Azure Monitor Ingestion client library for Python.

1. Use pip to install the Azure Monitor Ingestion and Azure Identity client libraries for Python. The Azure Identity library is required for the authentication used in this sample.

   pip install azure-monitor-ingestion
   pip install azure-identity
   

2. Create the following environment variables with values for your Microsoft Entra application. These values are used by DefaultAzureCredential in the Azure Identity library.

   • AZURE_TENANT_ID
   • AZURE_CLIENT_ID
   • AZURE_CLIENT_SECRET

3. Replace the variables in the following sample code with values from your DCR. You might also want to replace the sample data in the Step 2 section with your own.

   # information needed to send data to the DCR endpoint
   endpoint_uri = "https://my-url.monitor.azure.com" # logs ingestion endpoint of the DCR
   dcr_immutableid = "dcr-00000000000000000000000000000000" # immutableId property of the Data Collection Rule
   stream_name = "Custom-MyTableRawData" #name of the stream in the DCR that represents the destination table
   
   # Import required modules
   import os
   from azure.identity import DefaultAzureCredential
   from azure.monitor.ingestion import LogsIngestionClient
   from azure.core.exceptions import HttpResponseError
   
   credential = DefaultAzureCredential()
   client = LogsIngestionClient(endpoint=endpoint_uri, credential=credential, logging_enable=True)
   
   body = [
           {
           "Time": "2023-03-12T15:04:48.423211Z",
           "Computer": "Computer1",
               "AdditionalContext": {
                   "InstanceName": "user1",
                   "TimeZone": "Pacific Time",
                   "Level": 4,
                   "CounterName": "AppMetric2",
                   "CounterValue": 35.3    
               }
           },
           {
               "Time": "2023-03-12T15:04:48.794972Z",
               "Computer": "Computer2",
               "AdditionalContext": {
                   "InstanceName": "user2",
                   "TimeZone": "Central Time",
                   "Level": 3,
                   "CounterName": "AppMetric2",
                   "CounterValue": 43.5     
               }
           }
       ]
   
   try:
       client.upload(rule_id=dcr_immutableid, stream_name=stream_name, logs=body)
   except HttpResponseError as e:
       print(f"Upload failed: {e}")
   

4. Execute the code, and the data should arrive in your Log Analytics workspace within a few minutes.