Splunk® Data Stream Processor

Function Reference

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DSP 1.2.0 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.
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Get data from Amazon CloudWatch

Use the Amazon CloudWatch source function to get metric events data from Amazon CloudWatch.

Prerequisites

Before you can use this function, you must create a connection. See Create a DSP connection to Amazon CloudWatch 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.

For each metric in the body field, the function includes the following dimensions in addition to the ones that are part of the original payload:

  • AccountID: The ID of the AWS account associated with the metric. This dimension is omitted if the account ID cannot be retrieved.
  • Region: The AWS region associated with the metric.

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

{
"attributes": {
      "default_dimensions": {}
      },
"body": [
      {
      "dimensions": {
           "AccountID": "123412341234",
           "BucketName": "my-test-bucket",
           "FilterId": "EntireBucket",
           "Region": "us-east-1"
           },
      "name": "AWS/S3.4xxErrors.Maximum",
      "type": "g",
      "unit": "Count",
      "value": 0
      },
      {
      "dimensions": {
           "AccountID": "123412341234",
           "BucketName": "my-test-bucket",
           "FilterId": "EntireBucket",
           "Region": "us-east-1"
           },
      "name": "AWS/S3.4xxErrors.Sum",
      "type": "g",
      "unit": "Count",
      "value": 0
      }
      ],
"host": "aws_test_server",
"id": "2823738566644596",
"nanos": 0,
"source": "us-east-1:AWS/S3",
"sourcetype": "aws:cloudwatch:metrics",
"timestamp": 1598819400000
}

Required arguments

connection_id
Syntax: string
Description: The ID of your Amazon CloudWatch connection.
Example: "576205b3-f6f5-4ab7-8ffc-a4089a95d0c4"

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: "LATEST"

SPL2 example

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

| from read_from_aws_cloudwatch_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_aws_cloudwatch_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 need to list all unnamed arguments in the correct order before providing the named arguments.

Limitations of the Amazon CloudWatch source function

The Amazon CloudWatch 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 14 April, 2021
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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


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