แก้ไข

แชร์ผ่าน

Structure of a data collection rule in Azure Monitor

  • บทความ

Data collection rules (DCRs) are sets of instructions that determine how to collect and process telemetry sent to Azure Monitor. Some DCRs will be created and managed by Azure Monitor. This article describes the JSON structure of DCRs for creating and editing them in those cases where you need to work with them directly.

Properties

The following table describes properties at the top level of the DCR.

Property Description
description Optional description of the data collection rule defined by the user.
dataCollectionEndpointId Resource ID of the data collection endpoint (DCE) used by the DCR if you provided one when the DCR was created. This property isn't present in DCRs that don't use a DCE.
endpoints1 Contains the logsIngestion and metricsIngestion URL of the endpoints for the DCR. This section and its properties are automatically created when the DCR is created only if the kind attribute in the DCR is Direct.
immutableId A unique identifier for the data collection rule. This property and its value are automatically created when the DCR is created.
kind Specifies the data collection scenario the DCR is used for. This parameter is further described below.

1This property wasn't created for DCRs created before March 31, 2024. DCRs created before this date required a data collection endpoint (DCE) and the dataCollectionEndpointId property to be specified. If you want to use these embedded DCEs then you must create a new DCR.

Kind

The kind property in the DCR specifies the type of collection that the DCR is used for. Each kind of DCR has a different structure and properties.

The following table lists the different kinds of DCRs and their details.

Kind Description
Direct Direct ingestion using Logs ingestion API. Endpoints are created for the DCR only if this kind value is used.
AgentDirectToStore Send collected data to Azure Storage and Event Hubs.
AgentSettings Configure Azure Monitor agent parameters.
Linux Collect events and performance data from Linux machines.
PlatformTelemetry Export platform metrics.
Windows Collect events and performance data from Windows machines.
WorkspaceTransforms Workspace transformation DCR. This DCR doesn't include an input stream.

Overview of DCR data flow

The basic flow of a DCR is shown in the following diagram. Each of the components is described in the following sections.

Diagram that illustrates the relationship between the different sections of a DCR.

Input streams

The input stream section of a DCR defines the incoming data that's being collected. There are two types of incoming stream, depending on the particular data collection scenario. Most data collection scenarios use one of the input streams, while some may use both.

Note

Workspace transformation DCRs don't have an input stream.

Input stream Description
dataSources Known type of data. This is often data processed by Azure Monitor agent and delivered to Azure Monitor using a known data type.
streamDeclarations Custom data that needs to be defined in the DCR.

Data sent from the Logs ingestion API uses a streamDeclaration with the schema of the incoming data. This is because the API sends custom data that can have any schema.

Text logs from AMA are an example of data collection that requires both dataSources and streamDeclarations. The data source includes the configuration

Data sources

Data sources are unique sources of monitoring data that each has its own format and method of exposing its data. Each data source type has a unique set of parameters that must be configured for each data source. The data returned by the data source is typically a known type, so the schema doesn't need to be defined in the DCR.

For example, events and performance data collected from a VM with the Azure Monitor agent (AMA), use data sources such as windowsEventLogs and performanceCounters. You specify criteria for the events and performance counters that you want to collect, but you don't need to define the structure of the data itself since this is a known schema for potential incoming data.

Common parameters

All data source types share the following common parameters.

Parameter Description
name Name to identify the data source in the DCR.
streams List of streams that the data source will collect. If this is a standard data type such as a Windows event, then the stream will be in the form Microsoft-<TableName>. If it's a custom type, then it will be in the form Custom-<TableName>

Valid data source types

The data source types currently available are listed in the following table.

Data source type Description Streams Parameters
eventHub Data from Azure Event Hubs. Custom1 consumerGroup - Consumer group of event hub to collect from.
iisLogs IIS logs from Windows machines Microsoft-W3CIISLog logDirectories - Directory where IIS logs are stored on the client.
logFiles Text or json log on a virtual machine Custom1 filePatterns - Folder and file pattern for log files to be collected from client.
format - json or text
performanceCounters Performance counters for both Windows and Linux virtual machines Microsoft-Perf
Microsoft-InsightsMetrics		 samplingFrequencyInSeconds - Frequency that performance data should be sampled.
counterSpecifiers - Objects and counters that should be collected.
prometheusForwarder Prometheus data collected from Kubernetes cluster. Microsoft-PrometheusMetrics streams - Streams to collect
labelIncludeFilter - List of label inclusion filters as name-value pairs. Currently only 'microsoft_metrics_include_label' supported.
syslog Syslog events on Linux virtual machines Microsoft-Syslog facilityNames - Facilities to collect
logLevels - Log levels to collect
windowsEventLogs Windows event log on virtual machines Microsoft-Event xPathQueries - XPaths specifying the criteria for the events that should be collected.
extension Extension-based data source used by Azure Monitor agent. Varies by extension extensionName - Name of the extension
extensionSettings - Values for each setting required by the extension

1 These data sources use both a data source and a stream declaration since the schema of the data they collect can vary. The stream used in the data source should be the custom stream defined in the stream declaration.

Stream declarations

Declaration of the different types of data sent into the Log Analytics workspace. Each stream is an object whose key represents the stream name, which must begin with Custom-. The stream contains a full list of top-level properties that are contained in the JSON data that will be sent. The shape of the data you send to the endpoint doesn't need to match that of the destination table. Instead, the output of the transform that's applied on top of the input data needs to match the destination shape.

Data types

The possible data types that can be assigned to the properties are:

  • string
  • int
  • long
  • real
  • boolean
  • dynamic
  • datetime.

Destinations

The destinations section includes an entry for each destination where the data will be sent. These destinations are matched with input streams in the dataFlows section.

Common parameters

Parameters Description
name Name to identify the destination in the dataSources section.

Valid destinations

The destinations currently available are listed in the following table.

Destination Description Required parameters
logAnalytics Log Analytics workspace workspaceResourceId - Resource ID of the workspace.
workspaceID - ID of the workspace

This only specifies the workspace, not the table where the data will be sent. If it's a known destination, then no table needs to be specified. For custom tables, the table is specified in the data source.
azureMonitorMetrics Azure Monitor metrics No configuration is required since there is only a single metrics store for the subscription.
storageTablesDirect Azure Table storage storageAccountResourceId - Resource ID of the storage account
tableName - Name of the table
storageBlobsDirect Azure Blob storage storageAccountResourceId - Resource ID of the storage account
containerName - Name of the blob container
eventHubsDirect Event Hubs eventHubsDirect - Resource ID of the event hub.

Important

One stream can only send to one Log Analytics workspace in a DCR. You can have multiple dataFlow entries for a single stream if they are using different tables in the same workspace. If you need to send data to multiple Log Analytics workspaces from a single stream, create a separate DCR for each workspace.

Data flows

Data flows match input streams with destinations. Each data source may optionally specify a transformation and in some cases will specify a specific table in the Log Analytics workspace.

Data flow properties

Section Description
streams One or more streams defined in the input streams section. You may include multiple streams in a single data flow if you want to send multiple data sources to the same destination. Only use a single stream though if the data flow includes a transformation. One stream can also be used by multiple data flows when you want to send a particular data source to multiple tables in the same Log Analytics workspace.
destinations One or more destinations from the destinations section above. Multiple destinations are allowed for multi-homing scenarios.
transformKql Optional transformation applied to the incoming stream. The transformation must understand the schema of the incoming data and output data in the schema of the target table. If you use a transformation, the data flow should only use a single stream.
outputStream Describes which table in the workspace specified under the destination property the data will be sent to. The value of outputStream has the format Microsoft-[tableName] when data is being ingested into a standard table, or Custom-[tableName] when ingesting data into a custom table. Only one destination is allowed per stream.

This property isn't used for known data sources from Azure Monitor such as events and performance data since these are sent to predefined tables.

Next steps

Overview of data collection rules and methods for creating them