Azure Compute – Linux-Diagnostiktillägg 3.0 – Azure Virtual Machines (2023)

This document describes Linux Diagnostics Extension (LAD) version 3.0 and later.

Introduction

The Linux diagnostic extension helps the user to monitor the status of a Linux virtual machine running on Microsoft Azure. It has the following characteristics:

• Collects virtual machine system performance metrics and stores them in a specific table in a specific storage account.
• Retrieves syslog log events and stores them in a specific table in the designated storage account.
• Allows users to customize the data metrics that are collected and uploaded.
• Allows users to customize syslog facilities and severity levels of events collected and uploaded.
• Allows users to upload specific log files to a specific storage table.
• Supports sending log events and metrics to arbitrary Azure Event Hub endpoints and JSON-formatted blobs in designated storage account.

This extension works with both Azure deployment models.

Install the extension in a virtual machine

You can enable the extension using Azure PowerShell cmdlets, Azure CLI scripts, Azure Resource Monitor templates (ARM templates), or the Azure portal. More information can be found atAdditional functions.

These installation instructions and aconfiguration example to downloadto configure LAD 3.0 to:

• Collect and store the same measurements as in LAD 2.3.
• Collect a useful set of file system metrics. This feature is new in LAD 3.0.
• Collect the default syslog collection that LAD 2.3 enabled.
• Enable the Azure portal for virtual machine metric charts and alerts.

The downloadable configuration is just an example. Modify it to suit your needs.

Supported Linux distributions

LAD supports the following distributions and versions. The list of distributions and versions only applies to Azure-approved Linux provider images. The extension does not typically support BYOL and BYOS images from third parties, such as devices.

A distribution that only lists major versions, such as Debian 7, also supports all minor versions. If a partial version is specified, only that version is supported. If a plus sign (+) is added, partial versions equal to or later than the specified version are supported.

Supported distributions and versions:

• Ubuntu 20.04, 18.04, 16.04, 14.04
• CentOS 7, 6.5+
• OracleLinux 7, 6.4+
• OpenSUSE 13.1+
• SUSE Linux Enterprise Server 12
• Debian 9, 8, 7
• Red Hat Enterprise Linux (RHEL) 7, 6.7+

Conditions

• Azure Linux Agent version 2.2.0 or later. Most Azure VM Linux gallery images contain version 2.2.7 or later. Lead/usr/sbin/waagent -versionto confirm the version installed on the virtual machine. If the virtual machine is running an older versionyou update the guest agent.
• CLI de Azure. If requiredyou set up the Azure CLI environmentnot computer.
• The wget command. If you haven't already, drivesudo apt-get install wget.
• an existingblue subscription.
• 1existing storage account for general useto store data. General storage accounts must support table storage. A blob storage account doesn't work.
• python 2.

Requirements for Python

The Linux diagnostic plugin requires Python 2. If the virtual machine uses a distribution that does not include Python 2 by default, you must install it. The following example commands install Python 2 on various distributions:

• Red Hat, CentOS, Oracle:yum install -y python2
• Book, Debian:apt-get install -y python2
• SUSE:zypper installation -y python2

opython2executable must have an alias forPiton. Here is a method to specify this alias:

1. Run the following command to remove all existing aliases.

   sudo update-alternatives --remove-all python

2. Run the following command to create the alias.

   sudo update-alternatives --install /usr/bin/python python /usr/bin/python2 1

Installation example

The sample configuration downloaded in the following example collects a standard data set and sends it to table storage. The sample configuration URL and its content can be changed.

In most cases, you should download a copy of the portal configuration JSON file and customize it to meet your needs. Then use templates or your own automation to use a custom version of the config file instead of downloading the URL each time.

Azure sample CLI

# Set your Azure VM diagnostic variables.my_resource_group=<your_azure_resource_group_name_containing_your_azure_linux_vm>my_linux_vm=<your_azure_linux_vm_name>my_diagnostic_storage_account=<your_azure_storage_account_for_storing_vm_diagnostic_data># Log in to Azure before doing anything else. subscription <your_azure_subscription_id># Download the sample public configuration. (You can also use curl or any web browser.) wget https://raw.githubusercontent.com/Azure/azure-linux-extensions/master/Diagnostic/tests/lad_2_3_compatible_portal_pub_settings.json -O portal_public_settings.json# Create the VM resource I WOULD LIKE. Substitute the storage account name and resource ID in the public settings. " portal_public_settings.jsonsed -i "s#__VM_RESOURCE_ID__#$my_vm_resource_id#g" portal_public_settings.json# Create protected settings (storage account SAS token). expire 2037-12-31T23:59:00Z --wlacu permissions -- resource-types co --services bt -o tsv)my_lad_protected_settings="{'storageAccountName': '$my_diagnostic_storage_account', 'storageAccountSasToken': '$my_diagnostic_storage_account_sastoken'} "# Finally, tell Azure to install and activate the extension suite .az vm --publisher Microsoft.Azure.Diagnostics --name LinuxDiagnostic --version 3.0 --resource-group $my_resource_group --vm-name $my_linux_vm --protected -settings "${my_lad_protected_settings}" --settings portal_public_settings.json

Azure CLI sample to install LAD 3.0 on a vm scale set instance

# Establezca las variables de diagnóstico de Azure Virtual Machine Scale Sets.$my_resource_group=<your_azure_resource_group_name_containing_your_azure_linux_vm>$my_linux_vmss=<your_azure_linux_vmss_name>$my_diagnostic_storage_account=<your_azure_storage_account_for_storing_vm_diagnostic_data># Inicie sesión en Azure antes de hacer cualquier otra cosa que contenga la suscripción. Set of accounts .az --subscription <your_azure_subscription_id># Download the sample public configuration. (You can also use curl or any web browser.) wget https://raw.githubusercontent.com/Azure/azure-linux-extensions/master/Diagnostic/tests/lad_2_3_compatible_portal_pub_settings.json -O portal_public_settings.json# Create the VM scale set resource id. Replace the storage account name and resource ID in the public configuration. $my_vmss_resource_id=$(az vmss show -g $my_resource_group -n $my_linux_vmss --query "id" -o tsv)sed -i "s# __DIAGNOSTIC_STORAGE_ACCOUNT__# $my_diagnostic_storage_account# g" portal_public_settings.jsonsed -i "s#__VM_RESOURCE_ID__#$my_vmss_resource_id#g" portal_public_settings.json# Create protected settings (storage account SAS token).$my_diagnostic_storage_account_sastoken=$(az account-sastokenstorage=$(az account-sastoken - account name $my_diagnostic_storage_account - -expiry 2037-12-31T23:59:00Z --permissions wlacu --resource-types co --services bt -o tsv)$my_lad_protected_settings="{'storageAccountName': '$my_diagnostic_storage_account', ' storageAccountSasToken': '$my_diagnostic_storage_account_sastoken '}"# Finally, tell Azure to install and activate the extension suite extension.az vmss --publisher Microsoft.Azure.Diagnostics --name LinuxDiagnostic --version 3.0 --r esource-group $my_res ource_group --vmss-name $my_linux_vmss --protected-settings "${my_lad_protected_settings}" --settings portal_public_settings.json

PowerShell Example

$storageAccountName = "yourStorageAccountName"$storageAccountResourceGroup = "yourStorageAccountResourceGroupName"$vmName = "yourVMName"$VMresourceGroup = "yourVMResourceGroupName"# Get VM object $vm = Get-AzVM -Name $vmName -ResourceGroupName $VMresourceGroup# Get configuration template GitHub public and update the template values ​​for the storage account and resource ID $publicSettings = (Invoke-WebRequest -Uri https://raw.githubusercontent.com/Azure/azure-linux-extensions/master/Diagnostic/ tests/lad_2_3_portal_compatible_pub_settings .json). Content$publicSettings = $publicSettings.Replace('__DIAGNOSTIC_STORAGE_ACCOUNT__', $storageAccountName)$publicSettings = $publicSettings.Replace('__VM_RESOURCE_ID__', $vm.Id)# If you have customized public settings, you can embed them instead of using the template above : $publicSettings = '{"ladCfg": { ... },}'# Generate a SAS token for the agent to use for authentication with the storage account $sasToken = New-AzStorageAccountSASToken -Service Blob,Table -ResourceType Service, Container, Object -Permission "racwdlup" -Context(Get-AzStorageAccount -ResourceGroupName $storageAccountResourceGroup -AccountName $storageAccountName).Context -ExpiryTime $([System.DateTime]::Now.AddYears(10) )# Create Protected Configuration ( storage account SAS token)$protectedSettings="{'storageAccountName': '$storageAccountName', 'storageAccountSasToken': '$sasToken'}"# Finally install the extension with the settings you created Set-AzVMExtension - ResourceGroupNa me $VMresourceGroup -VMName $vmName -Location $vm.Location -ExtensionType LinuxDiagnostic -Editor Microsoft.Azure.Diagnostics -Name LinuxDiagnostic -SettingString $publicSettings -ProtectedSettingString $protectedSettings -TypeHandlerVersion 3.0

Update extension settings

After changing your protected or public settings, deploy them to the virtual machine by running the same command. If the settings change, the updates will be pushed to the extension. LAD reloads the configuration and reboots.

Migrate from previous versions of the extension

The latest version of the extension is4.0.

Recommendations:

• Install the extension with automatic partial version upgrade enabled.
  • For classic deployment model virtual machines, you specify the version3.*if you install the extension via Azure XPLAT CLI or PowerShell.
  • For virtual machines in the Azure Resource Manager deployment model, include"autoUpgradeMinorVersion": truein the VM deployment model.
• Use a new or different storage account for LAD 3.0. LAD 2.3 and LAD 3.0 have several minor incompatibilities that make it difficult to share an account:
  • LAD 3.0 stores syslog events in a table that has a different name.
  • the ropescounter specifierforIncorporateddimensions differ in LAD 3.0.

protected settings

This set of configuration information contains sensitive information that must be protected from public view. For example, it contains credentials for storage. These settings are transferred and stored by the extension in encrypted form.

{ "storageAccountName": "the storage account to receive data", "storageAccountEndPoint": "the cloud hostname suffix for this account", "storageAccountSasToken": "SAS access token", "mdsdHttpProxy": " http proxy configuration", "sinksConfig ": {...}}

To retrieve a SAS token on an ARM model, uselistAccountSasthe function. You can find an example template atlist function example.

You can create the required SAS token through the Azure portal:

1. Select the general storage account that you want the extension to write to.
2. In the left menu belowsettingsYou chooseSubscription for shared access.
3. Make selections as described above.
4. Choosegenerate SAS.

Copy the generated SAS into the fieldstorage account SasToken. Removes the leading question mark (?).

sinksconfig

"sinksConfig": { "sink": [ { "name": "sinkname", "type": "sinktype", ... }, ... ]},

osinksconfigthe optional section defines more destinations to which the extension sends the information it collects. Matrixinsidecontains one - object for each additional data sink. the attributewritesdetermines the other attributes of the object.

LAD version 3.0 supports two types of receivers:event centermiJsonBlobNumber.

EventHub Recipient

"sink": [ { "name": "sinkname", "type": "EventHub", "sasURL": "https SAS URL" }, ...]

The mail"sasURL"contains the full URL, including the SAS token, of the event hub to which the data will be published. LAD requires a SAS to name a policy that enables claim forwarding.

An example:

• Create an Azure Event Hub namespace namedcontosohub.
• Create an event hub in the namespace namedsyslogmsgs.
• Create a shared access policy in the event hub that enables broadcast notification. institutewritername.

If your SAS is valid until midnight UTC on January 1, 2018, the sasURL value might look like this example:

https://contosohub.servicebus.windows.net/syslogmsgs?sr=contosohub.servicebus.windows.net%2fsyslogmsgs&sig=xxxxxxxxxxxxxxxxxxxxxxxx&se=1514764800&skn=writer

More information on generating and retrieving SAS token information for Event Hubs can be found atGenerate a SAS token.

Receptor JsonBlob

"sink": [ { "name": "sinkname", "type": "JsonBlob" }, ...]

Data sent to aJsonBlobNumberrecipients are stored in blobs in Azure Storage. Each LAD instance creates one blob every hour for each recipient name. Each blob always contains a syntactically valid array of JSON objects. New entries are atomically added to the array.

The blobs are stored in a container with the same name as the recipient. Azure storage rules for blob container names apply toJsonBlobNumberreceiver. The name must contain between 3 and 63 alphanumeric ASCII characters or hyphens.

public settings

The Public Settings tree contains several configuration blocks that control the information collected by the extension. Each setting is optional. if you specifyrapazcfgyou must also specifyAccountStorage.

{ "ladCfg": { ... }, "perfCfg": { ... }, "fileLogs": { ... }, "StorageAccount": "the storage account to receive data from", "mdsdHttpProxy" : " " }

The following sections contain information on the remaining items.

rapazcfg

"ladCfg": { "diagnosticMonitorConfiguration": { "eventVolume": "Medium", "metrics": { ... }, "performanceCounters": { ... }, "syslogEvents": { ... } }, " muestraRateInSeconds": 15}

structuresrapazcfgis optional Controls the collection of metrics and logs that are delivered to the Azure Monitor Metrics service and other data sinks. You must enter:

• Someperformance countersosystem log eventsor both.
• structuresMetric.

Metric

"métricas": { "resourceId": "/suscripciones/...", "metricAggregation" : [ { "scheduledTransferPeriod" : "PT1H" }, { "scheduledTransferPeriod" : "PT5M" } ]}

Examples of measures listed inperformance countersthe section is collected every 15 seconds or at the sample rate explicitly defined for the counter. Yes, severalscheduled transfer periodfrequencies are shown, as in the example, each aggregation is calculated independently.

performance counters

"performanceCounters": { "sinks": "", "performanceCounterConfiguration": [ { "type": "embedded", "class": "Procesador", "counter": "PercentIdleTime", "counterSpecifier": "/incorporated/ Processor/PercentIdleTime", "condition": "IsAggregate=TRUE", "sampleRate": "PT15S", "unit": "Percentage", "anotation": [ { "displayName" : "Aggregate CPU Idle Time", " locale" : "en-us" } ] } ]}

operformance countersThe optional section controls the collection of metrics. Raw samples are aggregated for eachscheduled transfer periodto generate the following values:

• Middle value
• Minimum
• maximum
• Last amount collected
• Number of raw samples used to calculate aggregation

counter specifieris an arbitrary identifier. Consumers of metrics such as Azure portal charts and notification functionality usagecounter specifieras a "key" that identifies a metric or an instance of a metric.

ForIncorporatedmeasurements recommendcounter specifierwe values ​​from/Incorporated/. If you are collecting a specific instance of a metric, we recommend that you add the instance identifiercounter specifierThe value that

Here are some examples:

• /embedded/Processor/Idle Time Percentage– Average idle time for all virtual processors
• /embedded/Disk/FreeSpace(/mnt)- Free space for/mntthe file system
• /embedded/disk/free space– Average free space for all mounted file systems

LAD and Azure portal awaitcounter specifierit's not that the value should match any pattern. Be consistent in the way you createcounter specifierhost.

when you enterperformance countersLAD always writes data to a table in Azure Storage. The same data can be written to JSON blobs, Event Hubs, or both. But you can't disable data storage in a table.

All LAD instances that use the same endpoint and storage account name add their metrics and logs to the same table. If many virtual machines write to the same table partition, Azure can limit writes to the partition.

The configurationevent volumecauses records to be spread across 1 (small), 10 (medium), or 100 (large) partitions. Usually, medium-sized partitions are enough to avoid throttling.

The Azure Monitor Metrics feature in the Azure portal uses the data in this table to create graphs or trigger alerts. The table name is the concatenation of these strings:

• WADMetrics
• "scheduled transfer period"For the aggregate values ​​stored in the table
• P10DV2S
• A date in the format "YYYYMMDD", which changes every ten days

The examples areWADMetricsPT1HP10DV2S20170410miWADMetricsPT1MP10DV2S20170609.

system log events

"syslogEvents": { "sinks": "", "syslogEventConfiguration": { "facilityName1": "minSeverity", "facilityName2": "minSeverity", ... }}

osystem log eventsThe optional section controls the collection of syslog log events. If the section is omitted, no syslog events are logged.

The collectionsyslogEventConfigurationhas an entry for each syslog resource of interest. Yeahmin gravityit is"NEITHER"for a specific facility, or if that facility does not appear in the element, no events are captured for that facility.

when you entersystem log eventsLAD always writes data to a table in Azure Storage. The same data can be written to JSON blobs, Event Hubs, or both. But you can't disable data storage in a table.

The table partition behavior is the same as described forperformance counters. The table name is the concatenation of these strings:

• LinuxSyslog
• A date in the format "YYYYMMDD", which changes every ten days

The examples areLinuxSyslog20170410miLinuxSyslog20170609.

performanceCfg

the episodeperformanceCfgIt is optional Control the driving of arbitraryOpen Management Infrastructure (OMI) issues.

"perfCfg": [ { "namespace": "root/scx", "query": "SELECT PercentAvailableMemory, PercentUsedSwap FROM SCX_MemoryStatisticalInformation", "table": "LinuxOldMemory", "frequency": 300, "sinks": "" } ]

Some"tabla"o"sinkholes"both must be specified.

log file

the episodelog filecontrols the collection of log files. LAD collects new lines of text as they are written to the file. Writes them to the table rows and/or to the specified recipients (JsonBlobNumberoevent center).

"fileLogs": [ { "file": "/var/log/mydaemonlog", "table": "MyDaemonEvents", "sinks": "" }]

Some"tabla"o"sinkholes", or both, must be specified.

Embedded Provider Supported Dimensions

The measurement providerIncorporatedit is a source of more interesting metrics for a broad set of users. These metrics fall into five broad classes:

• Processor
• Remember
• Red
• File System
• Disco

built-in metrics for processor class

The Processor metric class contains information about processor usage on the virtual machine. When the percentages are added, the result is the average of all processors.

If a virtual processor with two virtual processors is 100% busy and the other 100% idle, theIdle Time Percentage50. If each virtual processor is 50% busy during the same period, the reported result is also 50. If a virtual machine with four virtual processors is 100% busy and the others are idle, the reported valueIdle Time Percentage75.

The first four counters must add up to 100 percent. The last three counters also add to 100%. These three counters divide the sum byPercentProcessorTimePercentProcessorTime,PercentIOWaitTimemiOutage Time Percentage.

To add a single metric for all processors, enter"condition": "IsAdded=TRUE". To retrieve a metric for a specific processor, such as the second logical processor for a virtual machine with four virtual processors, enter"condition": "Name=\\"1\\"". The logical processor numbers are in the range[0..n-1].

internal metrics for memory class

The memory measurement class contains information about memory usage, paging, and switching.

This measure class has only one instance. the attribute"illness"it has no useful settings and should be omitted.

built-in metrics for network class

The network metric class contains information about network activity on a single network interface since its creation.

LAD does not expose bandwidth metrics. You can get these metrics from Host Metrics.

Although the network class is instantiated, LAD does not support the collection of aggregated network metrics on all network devices. To retrieve metrics for a specific interface, such as eth0, type the"condition": "instance id=\\"eth0\\"".

built-in metrics for the file system class

The Filesystem metric class contains information about file system usage. Absolute values ​​and percentage values ​​are reported as they would appear to a normal (non-root) user.

You can retrieve aggregate values ​​on any file system by specifying"condition": "IsAggregate=True". Get values ​​for a specific mounted file system, e.g."/mnt", specifying"condition": 'Name="/mnt"'.

built-in metrics for disk class

The Disk metric class contains information about disk drive usage. This statistic applies to the entire device.

When a device has multiple file systems, the counters for that device are effectively aggregated across all file systems.

You can retrieve aggregate values ​​across all disks by specifying"condition": "IsAggregate=True". If you want to retrieve information from a specific device (for example,/dev/sdf1) you regret"condition": "Name=\\"/dev/sdf1\\"".

Install and configure LAD 3.0

CLI de Azure

If your protected settings are in the fileProtected Configuration.jsonand the public configuration information is inPublicSettings.jsonrun the following command.

az vm extension set --publisher Microsoft.Azure.Diagnostics --name LinuxDiagnostic --version 3.0 --resource-group <resource_group_name> --vm-name <vm_name> --protected-settings ProtectedSettings.json --settings PublicSettings.json

The command assumes that you're using Azure Resource Manager mode in the Azure CLI. To configure LAD for VMs with classic deployment model, switch to "asm" mode (Azure asm configuration mode) and omits the resource group name from the command.

More information can be found atCLI documentation for various platforms.

Power Shell

If your protected setting is in the variable$protected settingsand the public configuration information is in the variable$publicsettingsrun this command:

Set-AzVMExtension -ResourceGroupName <nombre_grupo_recursos> -VMName <nombre_vm> -Ubicación <ubicación_vm> -ExtensionType LinuxDiagnostic -Publisher Microsoft.Azure.Diagnostics -Name LinuxDiagnostic -SettingString $publicSettings -ProtectedSettingString $protectedSettings -TypeHandlerVersion 3.0

Example of configuration LAD 3.0

Based on the above definitions, this section contains an example configuration of the LAD 3.0 extension and an explanation. To apply this example to your case, use your own storage account name, account SAS token, and Event Hub SAS token.

protected settings

Protected settings configure:

• A storage account.
• A corresponding SAS token for the account.
• Multiple recipients (JsonBlobNumberoevent centerwith the SAS token).

{ "storageAccountName": "yourdiagstgacct", "storageAccountSasToken": "sv=xxxx-xx-xx&ss=bt&srt=co&sp=wlacu&st=yyyy-yy-yyT21%3A22%3A00Z&se=zzzz-zz-zzT21%3A22%3A00Z&sig=falsa_firma" , "sinksConfig": { "sink": [ { "name": "SyslogJsonBlob", "type": "JsonBlob" }, { "name": "FilelogJsonBlob", "type": "JsonBlob" }, { "name ": "LinuxCpuJsonBlob", "tipo": "JsonBlob" }, { "nombre": "MyJsonMetricsBlob", "tipo": "JsonBlob" }, { "nombre": "LinuxCpuEventHub", "tipo": "EventHub", "sasURL": "https://youreventhubnamespace.servicebus.windows.net/yourreventhubpublisher?sr=https%3a%2f%2fyoureventhubnamespace.servicebus.windows.net%2fyoureventhubpublisher%2f&sig=fake_signature&se=1808096361&skn=yourehpolicy" }, { "nome ": "MyMetricEventHub", "type": "EventHub", "sasURL": "https://yourreventhubnamespace.servicebus.windows.net/yourreventhubpublisher?sr=https%3a%2f%2fyoureventhubnamespace.servicebus.windows.net%2fyoureventhubpublisher %2f&sig=yourehpolicy&skn=yourehpolicy" }, { "name": "LoggingEventHub", "type": " Mesmo tHub", "sasURL": "https://youreventhubnamespace.servicebus.windows.net/youreventhubpublisher?sr=https%3a%2f%2fyoureventhubnamespace.servicebus.windows.net%2fyoureventhubpublisher%2f&sig=yourehpolicy&se=1808096361&skn=yourehpolicy" } ] }}

public settings

The public configuration allows LAD to:

• Load metrics by percentage of processor time and metrics by used disk space into the tableWAD metrics*.
• Load messages from the syslog resource"From the user"and gravity"information"untilLinuxSyslog*table.
• Load the raw results of the OMI question (PercentProcessorTimePercentProcessorTimemiIdle Time Percentage) for the candidateLinuxCPUtable.
• Load added lines in the file/var/log/myladtestlogto the tableMyLadTestLog.

In any case, the data is also sent to:

• Azure blob storage. The container name is as defined inJsonBlobNumbero receptor.
• The Event Hubs endpoint, as set out inevent centero receptor.

{ "StorageAccount": "yourdiagstgacct", "ladCfg": { "sampleRateInSeconds": 15, "diagnosticMonitorConfiguration": { "performanceCounters": { "sinks": "MyMetricEventHub,MyJsonMetricsBlob", "performanceCounterConfiguration": [ { "unit": "Percentage", "type": "incorporado", "counter": "PercentProcessorTime", "counterSpecifier": "/incorporado/Procesador/PercentProcessorTime", "anotación": [ { "locale": "en-us", " displayName": "%aggregated CPU utilization" } ], "condition": "IsAggregate=TRUE", "class": "Procesador" }, { "unit": "Bytes", "type": "integrated", " counter ": "UsedSpace", "counterSpecifier": "/incorporado/FileSystem/UsedSpace", "anotación": [ { "locale": "en-us", "displayName": "Disk space used in /" } ] , " condition": "Name=\"/\"", "class": "Filesystem" } ] }, "metrics": { "metricAggregation": [ { "scheduledTransferPeriod": "PT1H" }, { "scheduledTransferPeriod" : " PT1M" } ], "resourceId": "/subscriptions/your_azure_subscription_id/resourceGroups/your_resource_group_name /providers/Microsoft.Com pute/virtualMachines/your_vm_name" }, "eventVolume": "Large", "syslogEvents": { "sinks": "SyslogJsonBlob,LoggingEventHub", "syslogEventConfiguration": { "LOG_USER": "LOG_INFO" } } } }, " perfCfg": [ { "query": "SELECT PercentProcessorTime, PercentIdleTime FROM SCX_ProcessorStatisticalInformation WHERE Name='_TOTAL'", "table": "LinuxCpu", "frequency": 60, "sinks": "LinuxCpuJsonBlob ,LinuxCpuEventHub" } ], "fileLogs": [ { "file": "/var/log/myladtestlog", "table": "MyLadTestLog", "sinks": "FilelogJsonBlob,LoggingEventHub" } ]}

id make resourceIn the configuration, it must match the virtual machine or virtual machine scale set.

• Graphs and notifications for the Azure platform know thisid make resourcevirtual machine you are working with. Hope to find the virtual machine data with help.id make resourceof the search key.
• If you use Azure Autoscale, the configuration mustid make resourcein autoscaling, matchid make resourcewhich or LAD uses.
• id make resourceit is embedded in JSON blob names written by LAD.

Datos View Yours

Use the Azure portal to view performance data or set notifications:

Dataperformance countersit is always stored in an Azure storage table. Azure Storage APIs are available for multiple languages ​​and platforms.

data sent toJsonBlobNumberrecipients are stored in blobs in the storage account named in deprotected settings. You can consume blob data with all Azure blob storage APIs.

You can also use these interface tools to access data in Azure Storage:

• Visual Studio Server Explorer
• Azure Storage Explorer

The following screenshot of an Azure Storage Explorer session shows the Azure Storage containers and tables generated from a successfully configured LAD 3.0 extension on a test VM. The photo does not match the boy.Sample Configuration 3.0exactly.

For more information about using messages published to an Event Hubs endpoint, seeDocumentation of event centers.

Next step

• Create notifications for the metrics you collectoversee azure.
• In tearstracking chartfor your measurements.
• Create a virtual machine scale setusing your measurements to control autoscaling.