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##############################################################################
# Copyright (c) 2016 Dell EMC and others.
#
# All rights reserved. This program and the accompanying materials
# are made available under the terms of the Apache License, Version 2.0
# which accompanies this distribution, and is available at
# http://www.apache.org/licenses/LICENSE-2.0
##############################################################################
import logging
import os
from storperf.db import test_results_db
from storperf.db.graphite_db import GraphiteDB
from storperf.utilities import data_treatment as DataTreatment
from storperf.utilities import dictionary
from storperf.utilities import math as math
from storperf.utilities import steady_state as SteadyState
from time import sleep
import time
class DataHandler(object):
def __init__(self):
self.logger = logging.getLogger(__name__)
self.samples = 11
"""
"""
def data_event(self, executor):
self.logger.debug("Event received")
if executor.terminated:
self._push_to_db(executor)
else:
steady_state = True
metrics = {}
for metric in ('lat.mean', 'iops', 'bw'):
metrics[metric] = {}
for io_type in ('read', 'write'):
metrics[metric][io_type] = {}
series = self._lookup_prior_data(executor, metric, io_type)
steady = self._evaluate_prior_data(series)
self.logger.debug("Steady state for %s %s: %s"
% (io_type, metric, steady))
metrics[metric][io_type]['series'] = series
metrics[metric][io_type]['steady_state'] = steady
treated_data = DataTreatment.data_treatment(series)
metrics[metric][io_type]['slope'] = \
math.slope(treated_data['slope_data'])
metrics[metric][io_type]['range'] = \
math.range_value(treated_data['range_data'])
metrics[metric][io_type]['average'] = \
math.average(treated_data['average_data'])
if not steady:
steady_state = False
executor.metadata['report_data'] = metrics
executor.metadata['steady_state'] = steady_state
workload_name = executor.current_workload.split('.')[1]
if steady_state and not workload_name.startswith('_'):
executor.terminate()
def _lookup_prior_data(self, executor, metric, io_type):
workload = executor.current_workload
graphite_db = GraphiteDB()
# A bit of a hack here as Carbon might not be finished storing the
# data we just sent to it
now = int(time.time())
backtime = 60 * (self.samples + 2)
data_series = graphite_db.fetch_series(workload,
metric,
io_type,
now,
backtime)
most_recent_time = now
if len(data_series) > 0:
most_recent_time = data_series[-1][0]
delta = now - most_recent_time
self.logger.debug("Last update to graphite was %s ago" % delta)
while (delta < 5 or (delta > 60 and delta < 120)):
sleep(5)
data_series = graphite_db.fetch_series(workload,
metric,
io_type,
now,
backtime)
if len(data_series) > 0:
most_recent_time = data_series[-1][0]
delta = time.time() - most_recent_time
self.logger.debug("Last update to graphite was %s ago" % delta)
return data_series
def _evaluate_prior_data(self, data_series):
self.logger.debug("Data series: %s" % data_series)
if len(data_series) == 0:
return False
earliest_timestamp = data_series[0][0]
latest_timestamp = data_series[-1][0]
duration = latest_timestamp - earliest_timestamp
if (duration < 60 * self.samples):
self.logger.debug("Only %s minutes of samples, ignoring" %
(duration / 60,))
return False
return SteadyState.steady_state(data_series)
def _push_to_db(self, executor):
test_db = os.environ.get('TEST_DB_URL')
if test_db is not None:
pod_name = dictionary.get_key_from_dict(executor.metadata,
'pod_name',
'Unknown')
version = dictionary.get_key_from_dict(executor.metadata,
'version',
'Unknown')
scenario = dictionary.get_key_from_dict(executor.metadata,
'scenario_name',
'Unknown')
build_tag = dictionary.get_key_from_dict(executor.metadata,
'build_tag',
'Unknown')
duration = executor.end_time - executor.start_time
self.logger.info("Pushing results to %s" % (test_db))
payload = executor.metadata
payload['timestart'] = executor.start_time
payload['duration'] = duration
payload['status'] = 'OK'
graphite_db = GraphiteDB()
payload['metrics'] = graphite_db.fetch_averages(
executor.job_db.job_id)
criteria = {}
criteria['block_sizes'] = executor.block_sizes
criteria['queue_depths'] = executor.queue_depths
try:
test_results_db.push_results_to_db(test_db,
"storperf",
"Latency Test",
executor.start_time,
executor.end_time,
self.logger,
pod_name,
version,
scenario,
criteria,
build_tag,
payload)
except:
self.logger.exception("Error pushing results into Database")
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