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 <https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/monitor/azure-monitor-query/>`_ | `Package (PyPI) <https://aka.ms/azsdk-python-monitor-query-pypi>`_ | `API reference documentation <https://docs.microsoft.com/python/api/overview/azure/monitor-query-readme?view=azure-python-preview>`_ | `Product documentation <https://docs.microsoft.com/azure/azure-monitor/>`_ | `Samples <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-query/samples>`_ | `Changelog <https://github.com/Azure/azure-sdk-for-python/tree/main/sdk/monitor/azure-monitor-query/CHANGELOG.md>`_

Getting started
---------------

Prerequisites
^^^^^^^^^^^^^


* Python 2.7, or 3.6 or later is required to use this package.
* You must have an `Azure subscription <https://azure.microsoft.com/free/>`_.

Install the package
^^^^^^^^^^^^^^^^^^^

Install the Azure Monitor Query client library for Python with `pip <https://pypi.org/project/pip/>`_\ :

.. code-block:: bash

   pip install azure-monitor-query --pre

Authenticate the client
^^^^^^^^^^^^^^^^^^^^^^^

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

.. code-block:: Python

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


   credential  = DefaultAzureCredential()

   client = LogsQueryClient(credential)

.. code-block:: Python

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


   credential  = DefaultAzureCredential()

   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) <https://docs.microsoft.com/azure/data-explorer/kusto/query/>`_\ , 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 <https://pypi.org/project/pandas/>`_ library is used. Please look at the samples if you don't want to use the pandas library.

.. code-block:: Python

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


   credential  = DefaultAzureCredential()

   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.

.. code-block:: Python

   import os
   from datetime import timedelta
   import pandas as pd
   from azure.monitor.query import LogsQueryClient, LogsQueryRequest
   from azure.identity import DefaultAzureCredential


   credential  = DefaultAzureCredential()

   client = LogsQueryClient(credential)

   requests = [
       LogsQueryRequest(
           query="AzureActivity | summarize count()",
           duration=timedelta(hours=1),
           workspace_id=os.environ['LOG_WORKSPACE_ID']
       ),
       LogsQueryRequest(
           query= """AppRequests | take 10  |
               summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
           duration=timedelta(hours=1),
           start_time=datetime(2021, 6, 2),
           workspace_id=os.environ['LOG_WORKSPACE_ID']
       ),
       LogsQueryRequest(
           query= "AppRequests | take 2",
           workspace_id=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).

.. code-block:: Python

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


   credential  = DefaultAzureCredential()

   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.

.. code-block:: Python

   import os
   from datetime import timedelta
   from azure.monitor.query import MetricsQueryClient
   from azure.identity import DefaultAzureCredential


   credential  = DefaultAzureCredential()

   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=timedelta(days=1),
       )

   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 <https://aka.ms/azsdk/python/core/docs#module-azure.core.exceptions>`_.

Logging
^^^^^^^

This library uses the standard
`logging <https://docs.python.org/3/library/logging.html>`_ 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 <https://aka.ms/azsdk/python/core/docs>`_
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 <https://docs.microsoft.com/azure/azure-monitor/>`_ 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 <https://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 <https://opensource.microsoft.com/codeofconduct/>`_. For more information see the `Code of Conduct FAQ <https://opensource.microsoft.com/codeofconduct/faq/>`_ or contact `opencode@microsoft.com <mailto:opencode@microsoft.com>`_ with any additional questions or comments.


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Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

.. toctree::
  :maxdepth: 5
  :glob:
  :caption: Developer Documentation

  azure.monitor.query.rst