instrumentation_key=os.environ['APPINSIGHTS_INSTRUMENTATION_KEY'])
metrics.set_meter_provider(MeterProvider())
meter = metrics.get_meter(__name__)
PushController(meter, metrics_exporter, 10)
tfgen_counter = meter.create_metric(
name="tfgen_counter",
description="mydemo namespace",
unit="1",
value_type=int,
metric_type=Counter,
)
# Define cloud role
def callback_function(envelope):
envelope.tags['ai.cloud.role'] = os.getenv('APP_NAME')
return True
trace_exporter.add_telemetry_processor(callback_function)
metrics_exporter.add_telemetry_processor(callback_function)
while True:
tfgen_counter.add(1, {"destination": "endpoint1"})
requests.get(os.getenv('REMOTE_ENDPOINT1'))
time.sleep(random.random() * 60)
tfgen_counter.add(1, {"destination": "endpoint2"})
requests.get(os.getenv('REMOTE_ENDPOINT2'))
time.sleep(random.random() * 60)
'CPU_LIMIT')
envelope.data.base_data.properties['kube_memory_limit'] = os.getenv(
'MEMORY_LIMIT')
# Read labels
f = open("/podinfo/labels")
for line in f:
key, value = line.partition("=")[::2]
envelope.data.base_data.properties['labels.%s' % key] = value.replace(
'"', '')
return True
# Add Azure Monitor exporter
exporterAzure = AzureMonitorSpanExporter(
connection_string=appInsightsConnectionString)
exporterAzure.add_telemetry_processor(azure_monitor_metadata)
trace.get_tracer_provider().add_span_processor(
SimpleExportSpanProcessor(exporterAzure))
# Create Flask object
app = flask.Flask(__name__)
# Add automatic instrumentation
RequestsInstrumentor().instrument()
FlaskInstrumentor().instrument_app(app)
PyMySQLInstrumentor().instrument()
# Prepare database
conn = pymysql.connect(host=mySqlHost,
user=mySqlUsername,
password=mySqlPassword)
