Azure Monitor Query client library for Python

Azure Monitor helps you maximize the availability and performance of your applications and services. It delivers a comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments.

All data collected by Azure Monitor fits into one of two fundamental types, metrics and logs. Metrics are numerical values that describe some aspect of a system at a particular point in time. They are lightweight and capable of supporting near real-time scenarios. Logs contain different kinds of data organized into records with different sets of properties for each type. Telemetry such as events and traces are stored as logs in addition to performance data so that it can all be combined for analysis.

Source code | Package (PyPI) | API reference documentation | Product documentation | Samples | Changelog

Getting started

Prerequisites

• Python 2.7, or 3.6 or later is required to use this package.
• You must have an Azure subscription.

Install the package

Install the Azure Monitor Query client library for Python with pip:

pip install azure-monitor-query --pre

Authenticate the client

A token credential is necessary to instantiate both the LogsQueryClient and the MetricsQueryClient object.

from azure.monitor.query import LogsQueryClient
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = LogsQueryClient(credential)

from azure.monitor.query import MetricsQueryClient
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = MetricsQueryClient(credential)

Key concepts

Logs

Azure Monitor Logs is a feature of Azure Monitor that collects and organizes log and performance data from monitored resources. Data from different sources such as platform logs from Azure services, log and performance data from virtual machines agents, and usage and performance data from applications can be consolidated into a single workspace so they can be analyzed together using a sophisticated query language that's capable of quickly analyzing millions of records. You may perform a simple query that just retrieves a specific set of records or perform sophisticated data analysis to identify critical patterns in your monitoring data.

Log Analytics workspaces

Data collected by Azure Monitor Logs is stored in one or more Log Analytics workspaces. The workspace defines the geographic location of the data, access rights defining which users can access data, and configuration settings such as the pricing tier and data retention.

You must create at least one workspace to use Azure Monitor Logs. A single workspace may be sufficient for all of your monitoring data, or may choose to create multiple workspaces depending on your requirements. For example, you might have one workspace for your production data and another for testing.

Log queries

Data is retrieved from a Log Analytics workspace using a log query which is a read-only request to process data and return results. Log queries are written in Kusto Query Language (KQL), which is the same query language used by Azure Data Explorer. You can write log queries in Log Analytics to interactively analyze their results, use them in alert rules to be proactively notified of issues, or include their results in workbooks or dashboards. Insights include prebuilt queries to support their views and workbooks.

Metrics

Azure Monitor Metrics is a feature of Azure Monitor that collects numeric data from monitored resources into a time series database. Metrics are numerical values that are collected at regular intervals and describe some aspect of a system at a particular time. Metrics in Azure Monitor are lightweight and capable of supporting near real-time scenarios making them particularly useful for alerting and fast detection of issues. You can analyze them interactively with metrics explorer, be proactively notified with an alert when a value crosses a threshold, or visualize them in a workbook or dashboard.

Metrics data structure

Data collected by Azure Monitor Metrics is stored in a time-series database which is optimized for analyzing time-stamped data. Each set of metric values is a time series with the following properties:

• The time the value was collected
• The resource the value is associated with
• A namespace that acts like a category for the metric
• A metric name
• The value itself
• Some metrics may have multiple dimensions as described in Multi-dimensional metrics. Custom metrics can have up to 10 dimensions.

Examples

Get logs for a query

This sample shows getting a log query. to handle the response and view it in a tabular form, the pandas library is used. Please look at the samples if you don't want to use the pandas library.

import os
import pandas as pd
from azure.monitor.query import LogsQueryClient
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = LogsQueryClient(credential)

# Response time trend 
# request duration over the last 12 hours. 
query = """AppRequests | 
where TimeGenerated > ago(12h) | 
summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId"""

# returns LogsQueryResults 
response = client.query(
    os.environ['LOG_WORKSPACE_ID'],
    query,
    start_time=datetime(2021, 6, 2),
    end_time=datetime.now()
    )

if not response.tables:
    print("No results for the query")

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

Get Logs for multiple queries

This sample shows sending multiple queries at the same time using batch query API. For each query, a LogQueryRequest object can be used. Alternatively, a dictionary can be used as well.

import os
import pandas as pd
from azure.monitor.query import LogsQueryClient, LogsQueryRequest
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = LogsQueryClient(credential)

requests = [
    LogsQueryRequest(
        query="AzureActivity | summarize count()",
        duration="PT1H",
        workspace= os.environ['LOG_WORKSPACE_ID']
    ),
    LogsQueryRequest(
        query= """AppRequests | take 10  |
            summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
        duration="PT1H",
        start_time=datetime(2021, 6, 2),
        workspace= os.environ['LOG_WORKSPACE_ID']
    ),
    LogsQueryRequest(
        query= "AppRequests | take 2",
        workspace= os.environ['LOG_WORKSPACE_ID']
    ),
]
response = client.batch_query(requests)

for response in response.responses:
    body = response.body
    if not body.tables:
        print("Something is wrong")
    else:
        for table in body.tables:
            df = pd.DataFrame(table.rows, columns=[col.name for col in table.columns])
            print(df)

Get logs with server timeout

This sample shows setting a server timeout in seconds. A GateWay timeout is raised if the query takes more time than the mentioned timeout. The default is 180 seconds and can be set uptio 10 minutes (600 seconds).

import os
import pandas as pd
from azure.monitor.query import LogsQueryClient
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = LogsQueryClient(credential)

response = client.query(
    os.environ['LOG_WORKSPACE_ID'],
    "range x from 1 to 10000000000 step 1 | count",
    server_timeout=1,
    )

Get Metrics

This example shows getting the metrics for an EventGrid subscription. The resource URI is that of an eventgrid topic.

import os
from azure.monitor.query import MetricsQueryClient
from azure.identity import ClientSecretCredential


credential  = ClientSecretCredential(
        client_id = os.environ['AZURE_CLIENT_ID'],
        client_secret = os.environ['AZURE_CLIENT_SECRET'],
        tenant_id = os.environ['AZURE_TENANT_ID']
    )

client = MetricsQueryClient(credential)

metrics_uri = os.environ['METRICS_RESOURCE_URI']
response = client.query(
    metrics_uri,
    metric_names=["PublishSuccessCount"],
    start_time=datetime(2021, 5, 25),
    duration='P1D'
    )

for metric in response.metrics:
    print(metric.name)
    for time_series_element in metric.timeseries:
        for metric_value in time_series_element.data:
            print(metric_value.time_stamp)

Troubleshooting

• Enable azure.monitor.query logger to collect traces from the library.

General

Monitor Query client library will raise exceptions defined in Azure Core.

Logging

This library uses the standard logging library for logging. Basic information about HTTP sessions (URLs, headers, etc.) is logged at INFO level.

Optional Configuration

Optional keyword arguments can be passed in at the client and per-operation level. The azure-core reference documentation describes available configurations for retries, logging, transport protocols, and more.

Next steps

Additional documentation

For more extensive documentation on Azure Monitor Query, see the Monitor Query documentation on docs.microsoft.com.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Release History

1.0.0b1 (2021-06-10)

Features

• Version (1.0.0b1) is the first preview of our efforts to create a user-friendly and Pythonic client library for Azure Monitor Query. For more information about this, and preview releases of other Azure SDK libraries, please visit https://azure.github.io/azure-sdk/releases/latest/python.html.
• Added ~azure.monitor.query.LogsQueryClient to query log analytics along with ~azure.monitor.query.aio.LogsQueryClient.
• Implements the ~azure.monitor.query.MetricsQueryClient for querying metrics, listing namespaces and metric definitions along with ~azure.monitor.query.aio.MetricsQueryClient.