def test_logs_query_batch_additional_workspaces():
client = LogsQueryClient(_credential())
query = "union * | where TimeGenerated > ago(100d) | project TenantId | summarize count() by TenantId"
requests = [
LogsBatchQuery(
os.environ['LOG_WORKSPACE_ID'],
query,
timespan=timedelta(hours=1),
additional_workspaces=[os.environ['SECONDARY_WORKSPACE_ID']]),
LogsBatchQuery(
os.environ['LOG_WORKSPACE_ID'],
query,
timespan=timedelta(hours=1),
additional_workspaces=[os.environ['SECONDARY_WORKSPACE_ID']]),
LogsBatchQuery(
os.environ['LOG_WORKSPACE_ID'],
query,
timespan=timedelta(hours=1),
additional_workspaces=[os.environ['SECONDARY_WORKSPACE_ID']]),
]
response = client.query_batch(requests)
for resp in response:
assert len(resp.tables[0].rows) == 2
def test_logs_query_batch_default():
client = LogsQueryClient(_credential())
requests = [
LogsBatchQuery(query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="""AppRequests | take 10 |
summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
timespan=timedelta(hours=1),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="Wrong query | take 2",
workspace_id=os.environ['LOG_WORKSPACE_ID'],
timespan=None),
]
response = client.query_batch(requests)
assert len(response) == 3
r0 = response[0]
assert r0.tables[0].columns == ['count_']
r1 = response[1]
assert r1.tables[0].columns[0] == 'TimeGenerated'
assert r1.tables[0].columns[1] == '_ResourceId'
assert r1.tables[0].columns[2] == 'avgRequestDuration'
r2 = response[2]
assert r2.__class__ == LogsQueryError
def test_logs_query_batch_with_statistics_in_some():
client = LogsQueryClient(_credential())
requests = [
LogsBatchQuery(
query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id= os.environ['LOG_WORKSPACE_ID']
),
LogsBatchQuery(
query= """AppRequests|
summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
timespan=timedelta(hours=1),
workspace_id= os.environ['LOG_WORKSPACE_ID'],
include_statistics=True
),
LogsBatchQuery(
query= "AppRequests",
workspace_id= os.environ['LOG_WORKSPACE_ID'],
timespan=None,
include_statistics=True
),
]
response = client.query_batch(requests)
assert len(response) == 3
assert response[0].statistics is None
assert response[2].statistics is not None
def test_logs_batch_query_non_fatal_exception():
credential = _credential()
client = LogsQueryClient(credential)
requests = [
LogsBatchQuery(query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="""AppRequests | take 10""",
timespan=(datetime(2021, 6, 2), timedelta(days=1)),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="""Bad Query""",
workspace_id=os.environ['LOG_WORKSPACE_ID'],
timespan=(datetime(2021, 6, 2), datetime(2021, 6, 3)),
include_statistics=True),
]
responses = client.query_batch(requests)
r1, r2, r3 = responses[0], responses[1], responses[2]
assert r1.__class__ == LogsQueryResult
assert r2.__class__ == LogsQueryResult
assert r3.__class__ == LogsQueryError
def test_logs_batch_query_partial_exception():
credential = _credential()
client = LogsQueryClient(credential)
requests = [
LogsBatchQuery(query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="""AppRequests | take 10""",
timespan=(datetime(2021, 6, 2), timedelta(days=1)),
workspace_id=os.environ['LOG_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['LOG_WORKSPACE_ID'],
timespan=(datetime(2021, 6, 2), datetime(2021, 6, 3)),
include_statistics=True),
]
responses = client.query_batch(requests)
r1, r2, r3 = responses[0], responses[1], responses[2]
assert r1.__class__ == LogsQueryResult
assert r2.__class__ == LogsQueryResult
assert r3.__class__ == LogsQueryPartialResult
def test_logs_batch_query_fatal_exception():
credential = ClientSecretCredential(
client_id=os.environ['AZURE_CLIENT_ID'],
client_secret='bad_secret',
tenant_id=os.environ['AZURE_TENANT_ID'])
client = LogsQueryClient(credential)
requests = [
LogsBatchQuery(query="AzureActivity | summarize count()",
timespan=timedelta(hours=1),
workspace_id=os.environ['LOG_WORKSPACE_ID']),
LogsBatchQuery(query="""AppRequestsss | take 10""",
timespan=(datetime(2021, 6, 2), timedelta(days=1)),
workspace_id=os.environ['LOG_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['LOG_WORKSPACE_ID'],
timespan=(datetime(2021, 6, 2), datetime(2021, 6, 3)),
include_statistics=True),
]
with pytest.raises(HttpResponseError):
responses = client.query_batch(requests)
"body": {
"query": "AzureActivity | summarize count()",
"timespan": "PT1H"
},
"method": "POST",
"path": "/query",
"workspace": os.environ['LOG_WORKSPACE_ID']
},
{
"id": "2",
"headers": {
"Content-Type": "application/json"
},
"body": {
"query": "AzureActivity | summarize count()",
"timespan": "PT1H"
},
"method": "POST",
"path": "/fakePath",
"workspace": os.environ['LOG_WORKSPACE_ID']
}
]
responses = client.query_batch(requests)
for response in responses:
try:
table = response.tables[0]
df = pd.DataFrame(table.rows, columns=[col.name for col in table.columns])
print(df)
except TypeError:
print(response.error)
class LogsBatchPerfTest(PerfStressTest):
def __init__(self, arguments):
super().__init__(arguments)
# auth configuration
self.workspace_id = self.get_from_env('LOG_WORKSPACE_ID')
# Create clients
self.logs_client = SyncLogsQueryClient(
credential=SyncDefaultAzureCredential())
self.async_logs_client = AsyncLogsQueryClient(
credential=AsyncDefaultAzureCredential())
self.requests = [
LogsQueryRequest(query="AzureActivity | summarize count()",
start_time=datetime(2021,
7,
25,
0,
0,
0,
tzinfo=timezone.utc),
end_time=datetime(2021,
7,
26,
0,
0,
0,
tzinfo=timezone.utc),
workspace_id=self.workspace_id),
LogsQueryRequest(query="""AppRequests | take 10 |
summarize avgRequestDuration=avg(DurationMs) by bin(TimeGenerated, 10m), _ResourceId""",
start_time=datetime(2021,
7,
25,
0,
0,
0,
tzinfo=timezone.utc),
end_time=datetime(2021,
7,
26,
0,
0,
0,
tzinfo=timezone.utc),
workspace_id=self.workspace_id),
LogsQueryRequest(query="AppRequests | take 20",
workspace_id=self.workspace_id,
include_statistics=True),
]
async def close(self):
"""This is run after cleanup.
Use this to close any open handles or clients.
"""
await self.async_logs_client.close()
await super().close()
def run_sync(self):
"""The synchronous perf test.
Try to keep this minimal and focused. Using only a single client API.
Avoid putting any ancilliary logic (e.g. generating UUIDs), and put this in the setup/init instead
so that we're only measuring the client API call.
"""
self.logs_client.query_batch(self.requests)
async def run_async(self):
"""The asynchronous perf test.
Try to keep this minimal and focused. Using only a single client API.
Avoid putting any ancilliary logic (e.g. generating UUIDs), and put this in the setup/init instead
so that we're only measuring the client API call.
"""
await self.async_logs_client.query_batch(self.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),
]
results = client.query_batch(requests)
for res in results:
if res.status == LogsQueryStatus.FAILURE:
# this will be a LogsQueryError
print(res.message)
elif res.status == LogsQueryStatus.PARTIAL:
## this will be a LogsQueryPartialResult
print(res.partial_error.message)
for table in res.partial_data:
df = pd.DataFrame(table.rows, columns=table.columns)
print(df)
elif res.status == LogsQueryStatus.SUCCESS:
## this will be a LogsQueryResult
table = res.tables[0]
df = pd.DataFrame(table.rows, columns=table.columns)