def api_entry():
start_time = time.time()
app.logger.info('getting root endpoint')
# return 'Entrypoint to the Application'
name = flask_request.args.get('name', str)
tracer.set_tags({'name': name})
mycursor.execute("SELECT Name, UUID, Number FROM kikeyama_table where name='%s'" % name)
myresult = mycursor.fetchall()
for x in myresult:
result = json.dumps(x)
return result
duration = time.time() - start_time
statsd.distribution('kikeyama.dogstatsd.distribution.latency', duration)
statsd.histogram('kikeyama.dogstatsd.histogram.latency', duration)
from datadog import initialize, statsd
import random
import time
options = {'statsd_host': '127.0.0.1', 'statsd_port': 8125}
initialize(**options)
namespace = "testing7"
# statsd.distribution('example_metric.distribution', random.randint(0, 20), tags=["environment:dev"])
statsd.timing("%s.timing" % namespace,
random.randint(1, 20),
tags=["environment:dev"])
statsd.distribution("%s.distribution" % namespace,
50 + random.randint(1, 20),
tags=["environment:dev"])
# time.sleep(5)
# statsd.timing("%s.timing"%namespace, random.randint(1, 20), tags=["environment:dev"])
# statsd.distribution("%s.distribution"%namespace, 50 + random.randint(1, 20), tags=["environment:dev"])
from datadog import initialize, statsd
import time
import random
options = {
'statsd_host': '127.0.0.1',
'statsd_port': 8125
}
initialize(**options)
statsd.increment('example_metric.increment', tags=["environment:dev"])
statsd.decrement('example_metric.decrement', tags=["environment:dev"])
statsd.gauge('example_metric.gauge', 40, tags=["environment:dev"])
statsd.set('example_metric.set', 40, tags=["environment:dev"])
statsd.histogram('example_metric.histogram', random.randint(0, 20), tags=["environment:dev"])
with statsd.timed('example_metric.timer', tags=["environment:dev"]):
# do something to be measured
time.sleep(random.randint(0, 10))
statsd.distribution('example_metric.distribution', random.randint(0, 20), tags=["environment:dev"])
def distribution(self, metric_name, value, tags=[], timestamp=None):
statsd.distribution(metric_name, value, tags=tags)
def gen_distribution_metric():
print("distribution_metric")
statsd.distribution('example_metric.distribution',
random.randint(0, 20),
tags=["environment:doghouse", "food:hotdogs"])