{"slug":"azure-monitor-query-py","title":"azure-monitor-query-py","summary":"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\".","platform":"GitHub Copilot","tags":[],"authorName":"Ciza","authorSlug":"ciza","score":0,"source":"github","price":null,"verified":false,"createdAt":"2026-08-12T21:05:09.326187Z","repo":{"url":"https://github.com/microsoft/skills","stars":3075,"forks":352,"license":"MIT","updatedAt":"2026-10-02T16:39:30Z"},"bodyHtml":"
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name: azure-monitor-query-py\ndescription: |\nAzure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.\nTriggers: \"azure-monitor-query\", \"LogsQueryClient\", \"MetricsQueryClient\", \"Log Analytics\", \"Kusto queries\", \"Azure metrics\".\nlicense: MIT\nmetadata:\nauthor: Microsoft\nversion: \"1.0.0\"\npackage: azure-monitor-query

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Azure Monitor Query SDK for Python

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

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Installation

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pip install azure-monitor-query\n
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Environment Variables

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# Log Analytics\nAZURE_LOG_ANALYTICS_WORKSPACE_ID=<workspace-id>  # Required for log queries\n\n# Metrics\nAZURE_METRICS_RESOURCE_URI=/subscriptions/<sub>/resourceGroups/<rg>/providers/<provider>/<type>/<name>  # Required for metric queries\nAZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production\n
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Authentication & Lifecycle

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\uD83D\uDD11 Two rules apply to every code sample below:

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  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.\n
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    • Local dev: DefaultAzureCredential works as-is.
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    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
      • \n
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    2. \n
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:\n
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    • Sync: with <Client>(...) as client:
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    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)
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    3. \n
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Snippets may abbreviate this setup, but production code should always follow both rules.

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from azure.identity import DefaultAzureCredential, ManagedIdentityCredential\n\n# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>\ncredential = DefaultAzureCredential(require_envvar=True)\n# Or use a specific credential directly in production:\n# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes\n# credential = ManagedIdentityCredential()\n
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Logs Query Client

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Basic Query

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from azure.monitor.query import LogsQueryClient\nfrom datetime import timedelta\n\nquery = \"\"\"\nAppRequests\n| where TimeGenerated > ago(1h)\n| summarize count() by bin(TimeGenerated, 5m), ResultCode\n| order by TimeGenerated desc\n\"\"\"\n\nwith LogsQueryClient(credential) as client:\n    response = client.query_workspace(\n        workspace_id=os.environ[\"AZURE_LOG_ANALYTICS_WORKSPACE_ID\"],\n        query=query,\n        timespan=timedelta(hours=1)\n    )\n\n    for table in response.tables:\n        for row in table.rows:\n            print(row)\n
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Query with Time Range

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from datetime import datetime, timezone\n\nresponse = client.query_workspace(\n    workspace_id=workspace_id,\n    query=\"AppRequests | take 10\",\n    timespan=(\n        datetime(2024, 1, 1, tzinfo=timezone.utc),\n        datetime(2024, 1, 2, tzinfo=timezone.utc)\n    )\n)\n
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Convert to DataFrame

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import pandas as pd\n\nresponse = client.query_workspace(workspace_id, query, timespan=timedelta(hours=1))\n\nif response.tables:\n    table = response.tables[0]\n    df = pd.DataFrame(data=table.rows, columns=[col.name for col in table.columns])\n    print(df.head())\n
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Batch Query

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from azure.monitor.query import LogsBatchQuery\n\nqueries = [\n    LogsBatchQuery(workspace_id=workspace_id, query=\"AppRequests | take 5\", timespan=timedelta(hours=1)),\n    LogsBatchQuery(workspace_id=workspace_id, query=\"AppExceptions | take 5\", timespan=timedelta(hours=1))\n]\n\nresponses = client.query_batch(queries)\n\nfor response in responses:\n    if response.tables:\n        print(f\"Rows: {len(response.tables[0].rows)}\")\n
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Handle Partial Results

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from azure.monitor.query import LogsQueryStatus\n\nresponse = client.query_workspace(workspace_id, query, timespan=timedelta(hours=24))\n\nif response.status == LogsQueryStatus.PARTIAL:\n    print(f\"Partial results: {response.partial_error}\")\nelif response.status == LogsQueryStatus.FAILURE:\n    print(f\"Query failed: {response.partial_error}\")\n
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Metrics Query Client

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Query Resource Metrics

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from azure.monitor.query import MetricsQueryClient\nfrom datetime import timedelta\n\nwith MetricsQueryClient(credential) as metrics_client:\n    response = metrics_client.query_resource(\n        resource_uri=os.environ[\"AZURE_METRICS_RESOURCE_URI\"],\n        metric_names=[\"Percentage CPU\", \"Network In Total\"],\n        timespan=timedelta(hours=1),\n        granularity=timedelta(minutes=5)\n    )\n\n    for metric in response.metrics:\n        print(f\"{metric.name}:\")\n        for time_series in metric.timeseries:\n            for data in time_series.data:\n                print(f\"  {data.timestamp}: {data.average}\")\n
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Aggregations

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from azure.monitor.query import MetricAggregationType\n\nresponse = metrics_client.query_resource(\n    resource_uri=resource_uri,\n    metric_names=[\"Requests\"],\n    timespan=timedelta(hours=1),\n    aggregations=[\n        MetricAggregationType.AVERAGE,\n        MetricAggregationType.MAXIMUM,\n        MetricAggregationType.MINIMUM,\n        MetricAggregationType.COUNT\n    ]\n)\n
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Filter by Dimension

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

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definitions = metrics_client.list_metric_definitions(resource_uri)\nfor definition in definitions:\n    print(f\"{definition.name}: {definition.unit}\")\n
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List Metric Namespaces

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namespaces = metrics_client.list_metric_namespaces(resource_uri)\nfor ns in namespaces:\n    print(ns.fully_qualified_namespace)\n
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Async Clients

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from azure.monitor.query.aio import LogsQueryClient, MetricsQueryClient\nfrom azure.identity.aio import DefaultAzureCredential\n\nasync def query_logs():\n    async with DefaultAzureCredential() as credential:\n        async with LogsQueryClient(credential) as client:\n            response = await client.query_workspace(\n                workspace_id=workspace_id,\n                query=\"AppRequests | take 10\",\n                timespan=timedelta(hours=1)\n            )\n            return response\n
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Common Kusto Queries

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// Requests by status code\nAppRequests\n| summarize count() by ResultCode\n| order by count_ desc\n\n// Exceptions over time\nAppExceptions\n| summarize count() by bin(TimeGenerated, 1h)\n\n// Slow requests\nAppRequests\n| where DurationMs > 1000\n| project TimeGenerated, Name, DurationMs\n| order by DurationMs desc\n\n// Top errors\nAppExceptions\n| summarize count() by ExceptionType\n| top 10 by count_\n
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Client Types

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ClientPurpose
LogsQueryClientQuery Log Analytics workspaces
MetricsQueryClientQuery Azure Monitor metrics
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Best Practices

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  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose one mode per module.
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  2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
    3. \n
  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
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  4. Use timedelta for relative time ranges
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  5. Handle partial results for large queries
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  6. Use batch queries when running multiple queries
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  7. Set appropriate granularity for metrics to reduce data points
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  8. Convert to DataFrame for easier data analysis
    9. \n
  9. Use aggregations to summarize metric data
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  10. Filter by dimensions to narrow metric results
    11. \n
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Reference Files

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FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.
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