Splunk® Data Stream Processor

Function Reference

DSP 1.2.1 is impacted by the CVE-2021-44228 and CVE-2021-45046 security vulnerabilities from Apache Log4j. To fix these vulnerabilities, you must upgrade to DSP 1.2.4. See Upgrade the Splunk Data Stream Processor to 1.2.4 for upgrade instructions.

On October 30, 2022, all 1.2.x versions of the Splunk Data Stream Processor will reach its end of support date. See the Splunk Software Support Policy for details.
This documentation does not apply to the most recent version of Splunk® Data Stream Processor. For documentation on the most recent version, go to the latest release.

Get data from Microsoft Azure Monitor

Use the Microsoft Azure Monitor source function to get data from Microsoft Azure Monitor.

Prerequisites

Before you can use this function, you must create a connection. See Create a DSP connection to Microsoft Azure Monitor in the Connect to Data Sources and Destinations with the manual. When configuring this source function, set the connection_id argument to the ID of that connection.

Function output schema

This function outputs data pipeline metric events using the metrics schema.

Dimensions that apply to all of the metrics in the record rather than a specific metric are returned in the attributes field instead of the body field. Typically, the attributes field includes at least the following dimensions:

  • TenantId: The ID of the tenant or directory in Azure Active Directory (AD) containing the resource that the metric event belongs to.
  • ResourceId: The ID of the resource that the metric event belongs to.
  • Location: The location of the resource that the metric event belongs to.
  • One of the following dimensions:
    • SubscriptionId: The ID of the subscription that the metric event was delivered through.
    • ManagementGroup: The ID of the management group that the metric event was delivered through.

The following is an example of a typical record from the read_from_azure_monitor_metrics function:

{
"timestamp": 1562806833000,
"nanos": 0,
"id": "2823738566644596",
"host": "azure_test_server",
"source": "westus:Microsoft.Compute/virtualMachines",
"source_type": "azure:monitor:metrics",
"kind": "metric",
"body": {  
     "name": "Microsoft.Compute/virtualMachines.PercentageCPU.Average",
     "unit": "Percent",
     "type": "",
     "value": 0.7575,
     "dimensions": {
          "Interval": "PT1M"
          }
     },
"attributes": {
     "default_type": "g",
     "default_dimensions": {
          "Location": "eastus",
          "ManagementGroup": "",
          "ResourceId": "/subscriptions/ea1f75d9-5615-4390-c29f-a61f766eb4f3/resourceGroups/my-company/providers/Microsoft.EventHub/namespaces/myNamespace",
          "SubscriptionId": "ea1f75d9-5615-4390-c29f-a61f766eb4f3",
          "TenantId": "a7c29db7f-f1d3-c23b-1fd2-3ef17c529a0"
          }
     }
}

Required arguments

connection_id
Syntax: string
Description: The ID of your Azure Monitor connection.
Example in Canvas View: my-azure-monitor-connection

Optional arguments

initial_position
Syntax: LATEST | TRIM_HORIZON
Description: The position in the data stream where you want to start reading data. Defaults to LATEST.
  • LATEST: Start reading data from the latest position on the data stream.
  • TRIM_HORIZON: Start reading data from the very beginning of the data stream.
Example in Canvas View: LATEST

SPL2 example

When working in the SPL View, you can write the function using arguments in this exact order:

| from read_from_azure_monitor_metrics("my-connection-id", "TRIM_HORIZON") |... ;

Alternatively, you can use named arguments to declare arguments in any order. The following SPL2 example uses named arguments to specify the initial_position argument before the connection_id argument:

| from read_from_azure_monitor_metrics(initial_position: "TRIM_HORIZON", connection_id: "my-connection-id") |... ;

If you want to use a mix of unnamed and named arguments in your functions, you must list all unnamed arguments in the correct order before providing the named arguments.

Limitations of the Microsoft Azure Monitor source function

The Microsoft Azure Monitor source function uses scheduled data collection jobs to ingest data. See Limitations of scheduled data collection jobs for information about limitations that apply to all scheduled data collection jobs.

Last modified on 19 April, 2021
Get data from Microsoft Azure Event Hubs   Send data to a Splunk index with batching

This documentation applies to the following versions of Splunk® Data Stream Processor: 1.2.0, 1.2.1-patch02, 1.2.1, 1.2.2-patch02, 1.2.4, 1.2.5, 1.3.0, 1.3.1


Was this topic useful?







You must be logged into splunk.com in order to post comments. Log in now.

Please try to keep this discussion focused on the content covered in this documentation topic. If you have a more general question about Splunk functionality or are experiencing a difficulty with Splunk, consider posting a question to Splunkbase Answers.

0 out of 1000 Characters