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Accessing diagnostic logs for Azure Data Lake Storage Gen1

  • บทความ

Learn to enable diagnostic logging for your Azure Data Lake Storage Gen1 account and how to view the logs collected for your account.

Organizations can enable diagnostic logging for their Azure Data Lake Storage Gen1 account to collect data access audit trails that provides information such as list of users accessing the data, how frequently the data is accessed, how much data is stored in the account, etc. When enabled, the diagnostics and/or requests are logged on a best-effort basis. Both Requests and Diagnostics log entries are created only if there are requests made against the service endpoint.

Prerequisites

Enable diagnostic logging for your Data Lake Storage Gen1 account

  1. Sign on to the new Azure portal.

  2. Open your Data Lake Storage Gen1 account, and from your Data Lake Storage Gen1 account blade, click Diagnostic settings.

  3. In the Diagnostics settings blade, click Turn on diagnostics.

    Screenshot of the Data Lake Storage Gen 1 account with the Diagnostic setting option and the Turn on diagnostics option called out.

  4. In the Diagnostics settings blade, make the following changes to configure diagnostic logging.

    Screenshot of the Diagnostic setting section with the Name text box and the Save option called out.

    • For Name, enter a value for the diagnostic log configuration.

    • You can choose to store/process the data in different ways.

      • Select the option to Archive to a storage account to store logs to an Azure Storage account. You use this option if you want to archive the data that will be batch-processed at a later date. If you select this option you must provide an Azure Storage account to save the logs to.

      • Select the option to Stream to an event hub to stream log data to an Azure Event Hub. Most likely you will use this option if you have a downstream processing pipeline to analyze incoming logs at real time. If you select this option, you must provide the details for the Azure Event Hub you want to use.

      • Select the option to Send to Log Analytics to use the Azure Monitor service to analyze the generated log data. If you select this option, you must provide the details for the Log Analytics workspace that you would use the perform log analysis. See View or analyze data collected with Azure Monitor logs search for details on using Azure Monitor logs.

    • Specify whether you want to get audit logs or request logs or both.

    • Specify the number of days for which the data must be retained. Retention is only applicable if you are using Azure storage account to archive log data.

    • Click Save.

Once you have enabled diagnostic settings, you can watch the logs in the Diagnostic Logs tab.

View diagnostic logs for your Data Lake Storage Gen1 account

There are two ways to view the log data for your Data Lake Storage Gen1 account.

  • From the Data Lake Storage Gen1 account settings view
  • From the Azure Storage account where the data is stored

Using the Data Lake Storage Gen1 Settings view

  1. From your Data Lake Storage Gen1 account Settings blade, click Diagnostic Logs.

    View diagnostic logs

  2. In the Diagnostics Logs blade, you should see the logs categorized by Audit Logs and Request Logs.

    • Request logs capture every API request made on the Data Lake Storage Gen1 account.
    • Audit Logs are similar to request Logs but provide a much more detailed breakdown of the operations being performed on the Data Lake Storage Gen1 account. For example, a single upload API call in request logs might result in multiple "Append" operations in the audit logs.

  3. To download the logs, click the Download link against each log entry.

From the Azure Storage account that contains log data

  1. Open the Azure Storage account blade associated with Data Lake Storage Gen1 for logging, and then click Blobs. The Blob service blade lists two containers.

    Screenshot of the Data Lake Storage Gen 1 blade the the Blobs option selected and the Blog service blade with the names of the two blob services called out.

    • The container insights-logs-audit contains the audit logs.
    • The container insights-logs-requests contains the request logs.

  2. Within these containers, the logs are stored under the following structure.

    Screenshot of the log structure as it is stored in the container.

    As an example, the complete path to an audit log could be https://adllogs.blob.core.windows.net/insights-logs-audit/resourceId=/SUBSCRIPTIONS/<sub-id>/RESOURCEGROUPS/myresourcegroup/PROVIDERS/MICROSOFT.DATALAKESTORE/ACCOUNTS/mydatalakestorage/y=2016/m=07/d=18/h=04/m=00/PT1H.json

    Similarly, the complete path to a request log could be https://adllogs.blob.core.windows.net/insights-logs-requests/resourceId=/SUBSCRIPTIONS/<sub-id>/RESOURCEGROUPS/myresourcegroup/PROVIDERS/MICROSOFT.DATALAKESTORE/ACCOUNTS/mydatalakestorage/y=2016/m=07/d=18/h=14/m=00/PT1H.json

Understand the structure of the log data

The audit and request logs are in a JSON format. In this section, we look at the structure of JSON for request and audit logs.

Request logs

Here's a sample entry in the JSON-formatted request log. Each blob has one root object called records that contains an array of log objects.

{
"records": 
  [        
    . . . .
    ,
    {
        "time": "2016-07-07T21:02:53.456Z",
        "resourceId": "/SUBSCRIPTIONS/<subscription_id>/RESOURCEGROUPS/<resource_group_name>/PROVIDERS/MICROSOFT.DATALAKESTORE/ACCOUNTS/<data_lake_storage_gen1_account_name>",
        "category": "Requests",
        "operationName": "GETCustomerIngressEgress",
        "resultType": "200",
        "callerIpAddress": "::ffff:1.1.1.1",
        "correlationId": "4a11c709-05f5-417c-a98d-6e81b3e29c58",
        "identity": "1808bd5f-62af-45f4-89d8-03c5e81bac30",
        "properties": {"HttpMethod":"GET","Path":"/webhdfs/v1/Samples/Outputs/Drivers.csv","RequestContentLength":0,"StoreIngressSize":0 ,"StoreEgressSize":4096,"ClientRequestId":"3b7adbd9-3519-4f28-a61c-bd89506163b8","StartTime":"2016-07-07T21:02:52.472Z","EndTime":"2016-07-07T21:02:53.456Z","QueryParameters":"api-version=<version>&op=<operationName>"}
    }
    ,
    . . . .
  ]
}

Request log schema

Name Type Description
time String The timestamp (in UTC) of the log
resourceId String The ID of the resource that operation took place on
category String The log category. For example, Requests.
operationName String Name of the operation that is logged. For example, getfilestatus.
resultType String The status of the operation, For example, 200.
callerIpAddress String The IP address of the client making the request
correlationId String The ID of the log that can used to group together a set of related log entries
identity Object The identity that generated the log
properties JSON See below for details

Request log properties schema

Name Type Description
HttpMethod String The HTTP Method used for the operation. For example, GET.
Path String The path the operation was performed on
RequestContentLength int The content length of the HTTP request
ClientRequestId String The ID that uniquely identifies this request
StartTime String The time at which the server received the request
EndTime String The time at which the server sent a response
StoreIngressSize Long Size in bytes ingressed to Data Lake Store
StoreEgressSize Long Size in bytes egressed from Data Lake Store
QueryParameters String Description: These are the http query parameters. Example 1: api-version=2014-01-01&op=getfilestatus Example 2: op=APPEND&append=true&syncFlag=DATA&filesessionid=bee3355a-4925-4435-bb4d-ceea52811aeb&leaseid=bee3355a-4925-4435-bb4d-ceea52811aeb&offset=28313319&api-version=2017-08-01

Audit logs

Here's a sample entry in the JSON-formatted audit log. Each blob has one root object called records that contains an array of log objects

{
"records": 
  [        
    . . . .
    ,
    {
        "time": "2016-07-08T19:08:59.359Z",
        "resourceId": "/SUBSCRIPTIONS/<subscription_id>/RESOURCEGROUPS/<resource_group_name>/PROVIDERS/MICROSOFT.DATALAKESTORE/ACCOUNTS/<data_lake_storage_gen1_account_name>",
        "category": "Audit",
        "operationName": "SeOpenStream",
        "resultType": "0",
        "resultSignature": "0",
        "correlationId": "381110fc03534e1cb99ec52376ceebdf;Append_BrEKAmg;25.66.9.145",
        "identity": "A9DAFFAF-FFEE-4BB5-A4A0-1B6CBBF24355",
        "properties": {"StreamName":"adl://<data_lake_storage_gen1_account_name>.azuredatalakestore.net/logs.csv"}
    }
    ,
    . . . .
  ]
}

Audit log schema

Name Type Description
time String The timestamp (in UTC) of the log
resourceId String The ID of the resource that operation took place on
category String The log category. For example, Audit.
operationName String Name of the operation that is logged. For example, getfilestatus.
resultType String The status of the operation, For example, 200.
resultSignature String Additional details on the operation.
correlationId String The ID of the log that can used to group together a set of related log entries
identity Object The identity that generated the log
properties JSON See below for details

Audit log properties schema

Name Type Description
StreamName String The path the operation was performed on

Samples to process the log data

When sending logs from Azure Data Lake Storage Gen1 to Azure Monitor logs (see View or analyze data collected with Azure Monitor logs search for details on using Azure Monitor logs), the following query will return a table containing a list of user display names, the time of the events, and the count of events for the time of the event along with a visual chart. It can easily be modified to show user GUID or other attributes:

search *
| where ( Type == "AzureDiagnostics" )
| summarize count(TimeGenerated) by identity_s, TimeGenerated

Azure Data Lake Storage Gen1 provides a sample on how to process and analyze the log data. You can find the sample at https://github.com/Azure/AzureDataLake/tree/master/Samples/AzureDiagnosticsSample.

See also