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用于 Python 的 Azure 监视库

概述

监视可以为用户提供数据，确保应用程序始终处于健康运行状态。 监视还有助于避免潜在问题，或者解决过去的问题。 此外，还可以利用监视数据深入了解应用程序的情况。 了解这些情况有助于改进应用程序的性能或可维护性，或者实现本来需要手动干预的操作的自动化。

在此处详细了解 Azure Monitor。

安装

pip install azure-mgmt-monitor

示例 - 指标

此示例获取 Azure 上的资源（VM 等）的指标。 此示例至少需要 Python 程序包的 0.4.0 版。

此处提供了筛选器可用关键字的完整列表。

此处提供了每个资源类型支持的指标。

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

示例 - 警报

此示例演示如何在创建资源时自动对资源设置警报，确保正确监视所有资源。

在 VM 上创建一个数据源用于针对 CPU 使用率发出警报：

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'
)

创建当 VM 的平均 CPU 使用率在过去 5 分钟高于 90% 时触发的阈值条件（使用前面的数据源）：

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'
)

创建电子邮件操作：

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'
    ]
)

创建警报：

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
        ]
    }
)

了解管理 API