azure-monitor-query-py
Cloud, DevOps & Systèmes|
Documentation
Azure Monitor Query SDK for Python
Query logs and metrics from Azure Monitor and Log Analytics workspaces.
Installation
pip install azure-monitor-queryEnvironment Variables
# Log Analytics
AZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>
# Metrics
AZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>Authentication
from azure.identity import DefaultAzureCredential
credential = DefaultAzureCredential()Logs Query Client
Basic Query
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
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
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
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
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
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
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
response = metrics_client.query_resource(
resource_uri=resource_uri,
metric_names=["Requests"],
timespan=timedelta(hours=1),
filter="ApiName eq 'GetBlob'"
)List Metric Definitions
definitions = metrics_client.list_metric_definitions(resource_uri)
for definition in definitions:
print(f"{definition.name}: {definition.unit}")List Metric Namespaces
namespaces = metrics_client.list_metric_namespaces(resource_uri)
for ns in namespaces:
print(ns.fully_qualified_namespace)Async Clients
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 responseCommon Kusto Queries
// 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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