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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# cspell:ignore xtitle, ytitle, ymax
"""
FILE: sample_logs_query_visualization.py
DESCRIPTION:
This sample demonstrates how a user might visualize the results of a query using the "plotly" library.
Kusto queries can be visualized using the "render" operator.
See https://learn.microsoft.com/azure/data-explorer/kusto/query/renderoperator.
When executing a render operation in a query using the LogsQueryClient, visualization data can be returned in
the response by setting the "include_visualization" keyword argument to True. The data returned conveys
information about how the visualization should be rendered, but the interpretation of the data is generally
up to the user.
USAGE:
python sample_logs_query_visualization.py
Set the environment variables with your own values before running the sample:
1) LOGS_WORKSPACE_ID - The first (primary) workspace ID.
The "plotly" library can also be installed with `pip install plotly`.
This example uses DefaultAzureCredential, which requests a token from Azure Active Directory.
For more information on DefaultAzureCredential, see https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#defaultazurecredential.
"""
from datetime import timedelta
import os
from azure.core.exceptions import HttpResponseError
from azure.identity import DefaultAzureCredential
from azure.monitor.query import LogsQueryClient, LogsQueryStatus
import plotly.express as px
credential = DefaultAzureCredential()
client = LogsQueryClient(credential)
query = (
"AzureActivity "
"| where Level == 'Error' | summarize operation_count=count() by OperationName "
"| where operation_count > 10 | project OperationName, operation_count "
"| sort by operation_count desc "
"| render columnchart with (title='Operations with errors', xtitle='Operation', ytitle='Count', ymax=100)"
)
try:
response = client.query_workspace(
os.environ["LOGS_WORKSPACE_ID"], query, timespan=timedelta(days=1), include_visualization=True
)
if response.status == LogsQueryStatus.SUCCESS:
viz = response.visualization
if not viz:
print("No visualization data returned.")
exit()
# Examine the structure of the visualization data to see what information is provided.
# Generally, it reflects properties set using the "render" operator in the query.
print(viz)
tables = response.tables
for table in tables:
# For each entry, index 0 (OperationName) corresponds to the x-axis and index 1 (operation_count)
# corresponds to the y-axis. Some information from the "visualization" dict are used to help
# construct a bar chart.
fig = px.bar(
table.rows,
x=0,
y=1,
title=viz["title"],
labels={"0": viz["xTitle"], "1": viz["yTitle"]},
range_y=[0, viz["yMax"]],
)
# Output visualization to a file.
fig.write_html("query-visualization.html")
# To open in a browser, use:
# fig.show()
else:
error = response.partial_error
data = response.partial_data
print(error)
print(data)
except HttpResponseError as err:
print("something fatal happened")
print(err)
|
