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Add Fixed-rate sampling logic for Azure Log Exporter #848

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merged 5 commits into from
Jan 28, 2020
Merged

Add Fixed-rate sampling logic for Azure Log Exporter #848

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507face
Add filter for log sampling
lzchen Jan 21, 2020
12e6e83
add sampling rate validation
lzchen Jan 21, 2020
596038c
Address comments
lzchen Jan 28, 2020
62c528e
Merge branch 'master' of https://github.com/census-instrumentation/op…
lzchen Jan 28, 2020
db967ab
Fix lint
lzchen Jan 28, 2020
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1 change: 1 addition & 0 deletions contrib/opencensus-ext-azure/opencensus/ext/azure/common/__init__.py
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Expand Up @@ -92,6 +92,7 @@ def __init__(self, *args, **kwargs):
export_interval=15.0,
grace_period=5.0,
instrumentation_key=None,
logging_sampling_rate=1.0,
max_batch_size=100,
minimum_retry_interval=60, # minimum retry interval in seconds
proxy=None,
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15 changes: 15 additions & 0 deletions contrib/opencensus-ext-azure/opencensus/ext/azure/log_exporter/__init__.py
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Expand Up @@ -13,6 +13,7 @@
# limitations under the License.

import logging
import random
import threading
import time
import traceback
Expand Down Expand Up @@ -107,6 +108,16 @@ def stop(self, timeout=None): # pragma: NO COVER
return time.time() - start_time # time taken to stop


class SamplingFilter(logging.Filter):
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Is there any desire to support adaptive sampling for Application Insights as well? If so, it may be worth it to name this FixedRateSamplingFilter to be more specific.

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Currently in the roadmap there is no plan to add adaptive sampling. If this is ever added, it will be simple to iterate on this since the class is not exposed to customers.


def __init__(self, probability=1.0):
super(SamplingFilter, self).__init__()
self.probability = probability

def filter(self, record):
return random.random() < self.probability


class AzureLogHandler(TransportMixin, BaseLogHandler):
"""Handler for logging to Microsoft Azure Monitor.

Expand All @@ -116,6 +127,9 @@ class AzureLogHandler(TransportMixin, BaseLogHandler):
def __init__(self, **options):
self.options = Options(**options)
utils.validate_instrumentation_key(self.options.instrumentation_key)
if self.options.logging_sampling_rate < 0 or \
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nit:You can move this at the beginning of the method to avoid doing anything else if sampling rate is incorrect

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Validating the instrumentation key is also a validation and fast fail in itself.

self.options.logging_sampling_rate > 1.0:
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raise ValueError('Sampling must be in the range: [0,1]')
self.export_interval = self.options.export_interval
self.max_batch_size = self.options.max_batch_size
self.storage = LocalFileStorage(
Expand All @@ -125,6 +139,7 @@ def __init__(self, **options):
retention_period=self.options.storage_retention_period,
)
super(AzureLogHandler, self).__init__()
self.addFilter(SamplingFilter(self.options.logging_sampling_rate))

def close(self):
self.storage.close()
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40 changes: 40 additions & 0 deletions contrib/opencensus-ext-azure/tests/test_azure_log_exporter.py
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Expand Up @@ -78,6 +78,12 @@ def test_ctor(self):
self.assertRaises(ValueError, lambda: log_exporter.AzureLogHandler())
Options._default.instrumentation_key = instrumentation_key

def test_invalid_sampling_rate(self):
self.assertRaises(ValueError, lambda: log_exporter.AzureLogHandler(
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instrumentation_key='12345678-1234-5678-abcd-12345678abcd',
logging_sampling_rate=4.0,
))

@mock.patch('requests.post', return_value=mock.Mock())
def test_exception(self, requests_mock):
logger = logging.getLogger(self.id())
Expand Down Expand Up @@ -207,3 +213,37 @@ def test_log_with_invalid_custom_properties(self, requests_mock):

self.assertFalse('not_a_dict' in post_body)
self.assertFalse('key_1' in post_body)

@mock.patch('requests.post', return_value=mock.Mock())
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Surprising to see post patched here, but the only easy alternative I see is TransportMixin._transmit, which isn't obviously a better target.

def test_log_record_sampled(self, requests_mock):
logger = logging.getLogger(self.id())
handler = log_exporter.AzureLogHandler(
instrumentation_key='12345678-1234-5678-abcd-12345678abcd',
logging_sampling_rate=1.0,
)
logger.addHandler(handler)
logger.warning('Hello_World')
logger.warning('Hello_World2')
logger.warning('Hello_World3')
logger.warning('Hello_World4')
handler.close()
post_body = requests_mock.call_args_list[0][1]['data']
self.assertTrue('Hello_World' in post_body)
self.assertTrue('Hello_World2' in post_body)
self.assertTrue('Hello_World3' in post_body)
self.assertTrue('Hello_World4' in post_body)

@mock.patch('requests.post', return_value=mock.Mock())
def test_log_record_not_sampled(self, requests_mock):
logger = logging.getLogger(self.id())
handler = log_exporter.AzureLogHandler(
instrumentation_key='12345678-1234-5678-abcd-12345678abcd',
logging_sampling_rate=0.0,
)
logger.addHandler(handler)
logger.warning('Hello_World')
logger.warning('Hello_World2')
logger.warning('Hello_World3')
logger.warning('Hello_World4')
handler.close()
self.assertFalse(requests_mock.called)