Agent skill

azure-monitor-query-py

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".

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SKILL.md

Azure Monitor Query SDK for Python

Query logs and metrics from Azure Monitor and Log Analytics workspaces.

Installation

bash
pip install azure-monitor-query

Environment Variables

bash
# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>

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

Authentication

python
from azure.identity import DefaultAzureCredential

credential = DefaultAzureCredential()

Logs Query Client

Basic Query

python
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)

Query with Time Range

python
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)
    )
)

Convert to DataFrame

python
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())

Batch Query

python
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)}")

Handle Partial Results

python
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}")

Metrics Query Client

Query Resource Metrics

python
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}")

Aggregations

python
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
    ]
)

Filter by Dimension

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

List Metric Definitions

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

List Metric Namespaces

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

Async Clients

python
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

Common Kusto Queries

kusto
// 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_

Client Types

Client Purpose
LogsQueryClient Query Log Analytics workspaces
MetricsQueryClient Query Azure Monitor metrics

Best Practices

  1. Use timedelta for relative time ranges
  2. Handle partial results for large queries
  3. Use batch queries when running multiple queries
  4. Set appropriate granularity for metrics to reduce data points
  5. Convert to DataFrame for easier data analysis
  6. Use aggregations to summarize metric data
  7. Filter by dimensions to narrow metric results

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