Azure Monitor Query Py

Azure Monitor Query SDK for Python

从 Azure Monitor 和 Log Analytics 工作区查询日志与指标。

安装

pip install azure-monitor-query

环境变量

# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>

# Metrics
AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>

身份认证

from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

日志查询客户端

基本查询

from azure.monitor.query import LogsQueryClient
from datetime import timedelta

client = LogsQueryClient(credential)

query = """
AppRequests
| where TimeGenerated > ago(1h)
| summarize count() by bin(TimeGenerated, 5m), ResultCode
| order by TimeGenerated desc
"""

response = client.query_workspace(
    workspace_id=os.environ["AZURE_LOG_ANALYTICS_WORKSPACE_ID"],
    query=query,
    timespan=timedelta(hours=1)
)

for table in response.tables:
    for row in table.rows:
        print(row)

带时间范围的查询

from datetime import datetime, timezone

response = client.query_workspace(
    workspace_id=workspace_id,
    query="AppRequests | take 10",
    timespan=(
        datetime(2024, 1, 1, tzinfo=timezone.utc),
        datetime(2024, 1, 2, tzinfo=timezone.utc)
    )
)

转换为 DataFrame

import pandas as pd

response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=1))

if response.tables:
    table = response.tables[0]
    df = pd.DataFrame(data=table.rows, columns=[col.name for col in table.columns])
    print(df.head())

批量查询

from azure.monitor.query import LogsBatchQuery

queries = [
    LogsBatchQuery(workspace_id=workspace_id, query="AppRequests | take 5", timespan=timedelta(hours=1)),
    LogsBatchQuery(workspace_id=workspace_id, query="AppExceptions | take 5", timespan=timedelta(hours=1))
]

responses = client.query_batch(queries)

for response in responses:
    if response.tables:
        print(f"Rows: {len(response.tables[0].rows)}")

处理部分结果

from azure.monitor.query import LogsQueryStatus

response = client.query_workspace(workspace_id, query, timespan=timedelta(hours=24))

if response.status == LogsQueryStatus.PARTIAL:
    print(f"Partial results: {response.partial_error}")
elif response.status == LogsQueryStatus.FAILURE:
    print(f"Query failed: {response.partial_error}")

指标查询客户端

查询资源指标

from azure.monitor.query import MetricsQueryClient
from datetime import timedelta

metrics_client = MetricsQueryClient(credential)

response = metrics_client.query_resource(
    resource_uri=os.environ["AZURE_METRICS_RESOURCE_URI"],
    metric_names=["Percentage CPU", "Network In Total"],
    timespan=timedelta(hours=1),
    granularity=timedelta(minutes=5)
)

for metric in response.metrics:
    print(f"{metric.name}:")
    for time_series in metric.timeseries:
        for data in time_series.data:
            print(f"  {data.timestamp}: {data.average}")

聚合

from azure.monitor.query import MetricAggregationType

response = metrics_client.query_resource(
    resource_uri=resource_uri,
    metric_names=["Requests"],
    timespan=timedelta(hours=1),
    aggregations=[
        MetricAggregationType.AVERAGE,
        MetricAggregationType.MAXIMUM,
        MetricAggregationType.MINIMUM,
        MetricAggregationType.COUNT
    ]
)

按维度筛选

response = metrics_client.query_resource(
    resource_uri=resource_uri,
    metric_names=["Requests"],
    timespan=timedelta(hours=1),
    filter="ApiName eq 'GetBlob'"
)

列出指标定义

definitions = metrics_client.list_metric_definitions(resource_uri)
for definition in definitions:
    print(f"{definition.name}: {definition.unit}")

列出指标命名空间

namespaces = metrics_client.list_metric_namespaces(resource_uri)
for ns in namespaces:
    print(ns.fully_qualified_namespace)

异步客户端

from azure.monitor.query.aio import LogsQueryClient, MetricsQueryClient
from azure.identity.aio import DefaultAzureCredential

async def query_logs():
    credential = DefaultAzureCredential()
    client = LogsQueryClient(credential)
    
    response = await client.query_workspace(
        workspace_id=workspace_id,
        query="AppRequests | take 10",
        timespan=timedelta(hours=1)
    )
    
    await client.close()
    await credential.close()
    return response

常用 Kusto 查询

// Requests by status code
AppRequests
| summarize count() by ResultCode
| order by count_ desc

// Exceptions over time
AppExceptions
| summarize count() by bin(TimeGenerated, 1h)

// Slow requests
AppRequests
| where DurationMs > 1000
| project TimeGenerated, Name, DurationMs
| order by DurationMs desc

// Top errors
AppExceptions
| summarize count() by ExceptionType
| top 10 by count_

客户端类型

最佳实践

1. 使用 timedelta 表示相对时间范围
2. 处理部分结果 以应对大型查询
3. 使用批量查询 执行多个查询时
4. 设置合适的粒度 以减少指标数据点
5. 转换为 DataFrame 便于数据分析
6. 使用聚合 汇总指标数据
7. 按维度筛选 缩小指标结果范围

适用场景

本技能适用于执行概述中描述的工作流或操作。

限制

• 仅当任务明确匹配上述范围时使用本技能。
• 不要将输出视为环境特定验证、测试或专家审查的替代。
• 如果缺少必要的输入、权限、安全边界或成功标准，请停下来请求澄清。