Bibliotecas de Azure Monitor para PythonAzure Monitoring libraries for python
Información generalOverview
La supervisión proporciona datos para garantizar que la aplicación permanece en funcionamiento en un estado correcto.Monitoring provides data to ensure that your application stays up and running in a healthy state. También ayuda a evitar posibles problemas o a solucionar los existentes.It also helps you to stave off potential problems or troubleshoot past ones. Además, puede usar datos de supervisión para obtener un conocimiento más profundo sobre su aplicación.In addition, you can use monitoring data to gain deep insights about your application. Este conocimiento puede ayudarle a mejorar el rendimiento o mantenimiento de la aplicación, o a automatizar acciones que de lo contrario requerirían intervención manual.That knowledge can help you to improve application performance or maintainability, or automate actions that would otherwise require manual intervention.
Más información sobre Azure Monitor.Learn more about Azure Monitor here.
InstalaciónInstallation
pip install azure-mgmt-monitor
Ejemplo: métricasExample - Metrics
Este ejemplo obtiene las métricas de un recurso en Azure (máquinas virtuales, etc.).This sample obtains the metrics of a resource on Azure (VMs, etc.). Este ejemplo requiere al menos la versión 0.4.0 del paquete de Python.This sample requires version 0.4.0 of the Python package at least.
Esta es la lista completa de las palabras clave disponibles para los filtros.A complete list of available keywords for filters is available here.
Estas son las métricas admitidas por tipo de recurso que hay disponibles.Supported metrics per resource type is available here.
import datetime
from azure.mgmt.monitor import MonitorManagementClient
# Get the ARM id of your resource. You might chose to do a "get"
# using the according management or to build the URL directly
# Example for a ARM VM
resource_id = (
"subscriptions/{}/"
"resourceGroups/{}/"
"providers/Microsoft.Compute/virtualMachines/{}"
).format(subscription_id, resource_group_name, vm_name)
# create client
client = MonitorManagementClient(
credentials,
subscription_id
)
# You can get the available metrics of this specific resource
for metric in client.metric_definitions.list(resource_id):
# azure.monitor.models.MetricDefinition
print("{}: id={}, unit={}".format(
metric.name.localized_value,
metric.name.value,
metric.unit
))
# Example of result for a VM:
# Percentage CPU: id=Percentage CPU, unit=Unit.percent
# Network In: id=Network In, unit=Unit.bytes
# Network Out: id=Network Out, unit=Unit.bytes
# Disk Read Bytes: id=Disk Read Bytes, unit=Unit.bytes
# Disk Write Bytes: id=Disk Write Bytes, unit=Unit.bytes
# Disk Read Operations/Sec: id=Disk Read Operations/Sec, unit=Unit.count_per_second
# Disk Write Operations/Sec: id=Disk Write Operations/Sec, unit=Unit.count_per_second
# Get CPU total of yesterday for this VM, by hour
today = datetime.datetime.now().date()
yesterday = today - datetime.timedelta(days=1)
metrics_data = client.metrics.list(
resource_id,
timespan="{}/{}".format(yesterday, today),
interval='PT1H',
metricnames='Percentage CPU',
aggregation='Total'
)
for item in metrics_data.value:
# azure.mgmt.monitor.models.Metric
print("{} ({})".format(item.name.localized_value, item.unit.name))
for timeserie in item.timeseries:
for data in timeserie.data:
# azure.mgmt.monitor.models.MetricData
print("{}: {}".format(data.time_stamp, data.total))
# Example of result:
# Percentage CPU (percent)
# 2016-11-16 00:00:00+00:00: 72.0
# 2016-11-16 01:00:00+00:00: 90.59
# 2016-11-16 02:00:00+00:00: 60.58
# 2016-11-16 03:00:00+00:00: 65.78
# 2016-11-16 04:00:00+00:00: 43.96
# 2016-11-16 05:00:00+00:00: 43.96
# 2016-11-16 06:00:00+00:00: 114.9
# 2016-11-16 07:00:00+00:00: 45.4
Ejemplo: alertasExample - Alerts
Este ejemplo muestra cómo configurar automáticamente las alertas en los recursos cuando se crean para asegurarse de que todos los recursos se supervisan correctamente.This example shows how to automatically set up alerts on your resources when they are created to ensure that all resources are monitored correctly.
Cree un origen de datos en una máquina virtual para que le alerte sobre el uso de la CPU:Create a data source on a VM to alert on CPU usage:
from azure.mgmt.monitor import MonitorMgmtClient
from azure.mgmt.monitor.models import RuleMetricDataSource
resource_id = (
"subscriptions/{}/"
"resourceGroups/MonitorTestsDoNotDelete/"
"providers/Microsoft.Compute/virtualMachines/MonitorTest"
).format(self.settings.SUBSCRIPTION_ID)
# create client
client = MonitorMgmtClient(
credentials,
subscription_id
)
# I need a subclass of "RuleDataSource"
data_source = RuleMetricDataSource(
resource_uri = resource_id,
metric_name = 'Percentage CPU'
)
Cree una condición de umbral que se desencadenará cuando el uso medio de la CPU de una máquina virtual durante los últimos 5 minutos sea superior al 90 % (con el origen de datos anterior):Create a threshold condition that triggers when the average CPU usage of a VM for the last 5 minutes is above 90% (using the preceding data source):
from azure.mgmt.monitor.models import ThresholdRuleCondition
# I need a subclasses of "RuleCondition"
rule_condition = ThresholdRuleCondition(
data_source = data_source,
operator = 'GreaterThanOrEqual',
threshold = 90,
window_size = 'PT5M',
time_aggregation = 'Average'
)
Cree una acción de correo electrónico:Create an email action:
from azure.mgmt.monitor.models import RuleEmailAction
# I need a subclass of "RuleAction"
rule_action = RuleEmailAction(
send_to_service_owners = True,
custom_emails = [
'monitoringemail@microsoft.com'
]
)
Cree la regla:Create the alert:
rule_name = 'MyPyTestAlertRule'
my_alert = client.alert_rules.create_or_update(
group_name,
rule_name,
{
'location': 'westus',
'alert_rule_resource_name': rule_name,
'description': 'Testing Alert rule creation',
'is_enabled': True,
'condition': rule_condition,
'actions': [
rule_action
]
}
)