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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""
FILE: sample_batch_query.py
DESCRIPTION:
This sample demonstrates querying multiple queries in a batch.
USAGE:
python sample_batch_query.py
Set the environment variables with your own values before running the sample:
1) LOGS_WORKSPACE_ID - The The first (primary) workspace ID.
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.
**Note** - Although this example uses pandas to print the response, it's optional and
isn't a required package for querying. Alternatively, native Python can be used as well.
"""
# [START send_query_batch]
from datetime import datetime, timedelta, timezone
import os
from azure.core.exceptions import HttpResponseError
from azure.identity import DefaultAzureCredential
from azure.monitor.query import LogsQueryClient, LogsBatchQuery, LogsQueryStatus
import pandas as pd
credential = DefaultAzureCredential()
client = LogsQueryClient(credential)
requests = [
LogsBatchQuery(
query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id=os.environ["LOGS_WORKSPACE_ID"],
),
LogsBatchQuery(query="bad query", timespan=timedelta(days=1), workspace_id=os.environ["LOGS_WORKSPACE_ID"]),
LogsBatchQuery(
query="""let Weight = 92233720368547758;
range x from 1 to 3 step 1
| summarize percentilesw(x, Weight * 100, 50)""",
workspace_id=os.environ["LOGS_WORKSPACE_ID"],
timespan=(datetime(2021, 6, 2, tzinfo=timezone.utc), datetime(2021, 6, 5, tzinfo=timezone.utc)), # (start, end)
include_statistics=True,
),
]
try:
results = client.query_batch(requests)
for res in results:
if res.status == LogsQueryStatus.SUCCESS:
# This will be a LogsQueryResult
table = res.tables[0]
df = pd.DataFrame(table.rows, columns=table.columns)
print(df)
elif res.status == LogsQueryStatus.PARTIAL:
# This will be a LogsQueryPartialResult
print(res.partial_error)
for table in res.partial_data:
df = pd.DataFrame(table.rows, columns=table.columns)
print(df)
else:
# This will be a LogsQueryError
print(res)
except HttpResponseError as err:
print("something fatal happened")
print(err)
# [END send_query_batch]
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