Azure Logs Ingestion API
Send logs to Azure Log Analytics using Logs Ingestion API with DCE and DCR
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Azure Logs Ingestion plugin allows you ingest your records using Logs Ingestion API in Azure Monitor to supported Azure tables or to custom tables that you create.
The Logs ingestion API requires the following components:
A Data Collection Endpoint (DCE)
A Data Collection Rule (DCR) and
A Log Analytics Workspace
Note: According to this document, all resources should be in the same region.
To visualize basic Logs Ingestion operation, see the following image:
To get more details about how to setup these components, please refer to the following documentations:
Configuration Parameters
Key | Description | Default |
---|---|---|
tenant_id | Required - The tenant ID of the AAD application. | |
client_id | Required - The client ID of the AAD application. | |
client_secret | Required - The client secret of the AAD application (App Secret). | |
dce_url | Required - Data Collection Endpoint(DCE) URL. | |
dcr_id | Required - Data Collection Rule (DCR) immutable ID (see this document to collect the immutable id) | |
table_name | Required - The name of the custom log table (include the | |
time_key | Optional - Specify the key name where the timestamp will be stored. |
|
time_generated | Optional - If enabled, will generate a timestamp and append it to JSON. The key name is set by the 'time_key' parameter. |
|
compress | Optional - Enable HTTP payload gzip compression. |
|
workers | The number of workers to perform flush operations for this output. |
|
Getting Started
To send records into an Azure Log Analytics using Logs Ingestion API the following resources needs to be created:
A Data Collection Endpoint (DCE) for ingestion
A Data Collection Rule (DCR) for data transformation
Either an Azure tables or custom tables
An app registration with client secrets (for DCR access).
You can follow this guideline to setup the DCE, DCR, app registration and a custom table.
Configuration File
Use this configuration to quickly get started:
Setup your DCR transformation accordingly based on the json output from fluent-bit's pipeline (input, parser, filter, output).
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