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Azure Monitor Query Champion Scenarios

This document covers the basic champion Scenarios to use the package.

Authenticate the client

Consider the following example, which creates and authenticates clients for both logs and metrics querying:

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

credential = DefaultAzureCredential()
logs_client = LogsQueryClient(credential)
metrics_client = MetricsQueryClient(credential)

Make a simple query to the service

  • Each row is converted into a native python data type. For example, time is a datetime object instead of string.

Results in tabular form

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

def query():
    query = """AppRequests |
    summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId"""

    response = client.query(os.environ['LOG_WORKSPACE_ID'], query, 
    timespan=timedelta(days=1))

    if not response.tables:
        return None

    primary_table = response.tables[0]
    df = pd.DataFrame(table.rows, columns=table.columns)
    return df

if __name__ == '__main__':
    print(query())

"""
    TimeGenerated                                        _ResourceId          avgRequestDuration
0   2021-05-27T08:40:00Z  /subscriptions/<subscription id>...  27.307699999999997
1   2021-05-27T08:50:00Z  /subscriptions/<subscription id>...            18.11655
2   2021-05-27T09:00:00Z  /subscriptions/<subscription id>...             24.5271
"""

Results in Key Value form

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

def query():
    query = """AppRequests |
    summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId"""

    response = client.query(os.environ['LOG_WORKSPACE_ID'], query, 
    timespan=timedelta(days=1))

    if not response.tables:
        return None

    primary_table = response.tables[0]
    df = pd.DataFrame(table.rows, columns=table.columns)
    return df.to_dict(orient='records')

if __name__ == '__main__':
    print(query())


"""
[
    {
        'TimeGenerated': Timestamp('2021-08-24 01:10:00+0000'),
        '_ResourceId': '/subscriptions/faa080af....',
        'avgRequestDuration': 19.7987
    },
    {
        'TimeGenerated': Timestamp('2021-08-24 01:10:00+0000'),
        '_ResourceId': '/subscriptions/faa08....',
        'avgRequestDuration': 33.9654
    },
    {
        'TimeGenerated': Timestamp('2021-08-24 01:10:00+0000'),
        '_ResourceId': '/subscriptions/faa080....',
        'avgRequestDuration': 44.13115
    }
]
"""

Run multiple queries in 1 api call

  • batch_query returns the results as a list in the same order in which the requests were sent.
  • Each item in the result will have an error attribute if there is an error.

Results in tabular form

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


credential  = DefaultAzureCredential()

client = LogsQueryClient(credential)

requests = [
    LogsBatchQuery(
        query="AzureActivity | summarize count()",
        timespan=timedelta(hours=1),
        workspace_id= os.environ['LOG_WORKSPACE_ID']
    ),
    LogsBatchQuery(
        query= """AppRequests | take 5  |
            summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
        timespan=(datetime(2021, 6, 2), timedelta(hours=1)),
        workspace_id= os.environ['LOG_WORKSPACE_ID']
    ),
    LogsBatchQuery(
        query= """AppRequests | take 5  |
            summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
        workspace_id= os.environ['LOG_WORKSPACE_ID'],
        timespan=(datetime(2021, 6, 2), datetime(2021, 6, 3)),
        include_statistics=True
    ),
]
results = client.query_batch(requests)

for response in results:
    if response.error is not None:
        error = response.error.innererror
        print(error)
    
    table = response.tables[0]
    df = pd.DataFrame(table.rows, columns=table.columns)
    print(df)
    print("\n\n-------------------------\n\n")

"""
   count_
0       2


-------------------------


              TimeGenerated                                        _ResourceId  avgRequestDuration
0 2021-06-02 00:20:00+00:00  /subscriptions/<subscription id>...            18.12380
1 2021-06-02 00:00:00+00:00  /subscriptions/<subscription id>...            20.84805
2 2021-06-02 00:10:00+00:00  /subscriptions/<subscription id>...            19.72410
3 2021-06-02 00:30:00+00:00  /subscriptions/<subscription id>...            19.41265
4 2021-06-02 00:40:00+00:00  /subscriptions/<subscription id>...            19.17145


-------------------------



              TimeGenerated                                        _ResourceId  avgRequestDuration
0 2021-06-02 00:20:00+00:00  /subscriptions/<subscription id>...            18.12380
1 2021-06-02 00:00:00+00:00  /subscriptions/<subscription id>...            20.84805
2 2021-06-02 00:10:00+00:00  /subscriptions/<subscription id>...            19.72410
3 2021-06-02 00:30:00+00:00  /subscriptions/<subscription id>...            19.41265
4 2021-06-02 00:40:00+00:00  /subscriptions/<subscription id>...            19.17145



-------------------------
"""

Results in Key Value form

Very Simlar to above:

for response in results:
    if response.error is not None:
        error = response.error.innererror
        print(error)
    
    table = response.tables[0]
    df = pd.DataFrame(table.rows, columns=table.columns)
    print(df.to_dict(orient='records'))
    print("\n\n-------------------------\n\n")

Run a complex query to set server timeout for more than 3 minutes.

import os
import pandas as pd
from azure.core.serialization import NULL
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",
    timespan=NULL, # can pass None too
    server_timeout=600
    )

### results in server timeout

Run a metrics Query

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

credential  = DefaultAzureCredential()

client = MetricsQueryClient(credential)

metrics_uri = os.environ['METRICS_RESOURCE_URI']
response = client.query(
    metrics_uri,
    metric_names=["Ingress"],
    timespan=timedelta(hours=2),
    granularity=timedelta(minutes=5),
    aggregations=[MetricAggregationType.AVERAGE],
    )

for metric in response.metrics:
    print(metric.name + ' -- ' + metric.display_description)
    for time_series_element in metric.timeseries:
        for metric_value in time_series_element.data:
            print('The ingress at {} is {}'.format(
                metric_value.timestamp,
                metric_value.average
            ))

"""
Ingress -- The amount of ingress data, in bytes. This number includes ingress from an external client into Azure Storage as well as ingress within Azure.
The ingress at 2021-08-23 23:58:00+00:00 is 567.4285714285714 
The ingress at 2021-08-24 00:03:00+00:00 is 812.0
The ingress at 2021-08-24 00:08:00+00:00 is 812.0
The ingress at 2021-08-24 00:13:00+00:00 is 812.0
The ingress at 2021-08-24 00:18:00+00:00 is 812.0
The ingress at 2021-08-24 00:23:00+00:00 is 3623.3333333333335
The ingress at 2021-08-24 00:28:00+00:00 is 1082.75
The ingress at 2021-08-24 00:33:00+00:00 is 1160.6666666666667
The ingress at 2021-08-24 00:38:00+00:00 is 1060.75
The ingress at 2021-08-24 00:43:00+00:00 is 1081.75
The ingress at 2021-08-24 00:48:00+00:00 is 1061.25
The ingress at 2021-08-24 00:53:00+00:00 is 1160.3333333333333
The ingress at 2021-08-24 00:58:00+00:00 is 1082.0
The ingress at 2021-08-24 01:03:00+00:00 is 1628.6666666666667
The ingress at 2021-08-24 01:08:00+00:00 is 794.6666666666666
The ingress at 2021-08-24 01:13:00+00:00 is 1060.25
The ingress at 2021-08-24 01:18:00+00:00 is 1160.0
The ingress at 2021-08-24 01:23:00+00:00 is 1082.0
The ingress at 2021-08-24 01:28:00+00:00 is 1060.5
The ingress at 2021-08-24 01:33:00+00:00 is 1630.0
The ingress at 2021-08-24 01:38:00+00:00 is 795.0
The ingress at 2021-08-24 01:43:00+00:00 is 827.6
The ingress at 2021-08-24 01:48:00+00:00 is 1250.5
The ingress at 2021-08-24 01:53:00+00:00 is 1061.75
"""