yandex-tank/Tank/Plugins/Aggregator.py
Andrey Pohilko 061c9f994f Add 85%
2013-11-19 16:48:01 +04:00

368 lines
13 KiB
Python

''' Core module to calculate aggregate data '''
from tankcore import AbstractPlugin
import copy
import datetime
import logging
import math
import tankcore
import time
class AggregateResultListener:
''' listener to be notified about aggregate data '''
def aggregate_second(self, second_aggregate_data):
''' notification about new aggregate data '''
raise NotImplementedError("Abstract method needs to be overridden")
class AggregatorPlugin(AbstractPlugin):
''' Plugin that manages aggregation '''
default_time_periods = "1ms 2 3 4 5 6 7 8 9 10 20 30 40 50 60 70 80 90 100 150 200 250 300 350 400 450 500 600 650 700 750 800 850 900 950 1s 1500 2s 2500 3s 3500 4s 4500 5s 5500 6s 6500 7s 7500 8s 8500 9s 9500 10s 11s"
SECTION = 'aggregator'
@staticmethod
def get_key():
return __file__
def __init__(self, core):
AbstractPlugin.__init__(self, core)
self.process = None
self.second_data_listeners = []
self.preproc_out_offset = 0
self.buffer = []
self.second_data_draft = []
self.preproc_out_filename = None
self.cumulative_data = SecondAggregateDataTotalItem()
self.reader = None
self.time_periods = [tankcore.expand_to_milliseconds(x)
for x in self.default_time_periods.split(' ')]
self.last_sample_time = 0
self.precise_cumulative = 1
def get_available_options(self):
return ["time_periods", "precise_cumulative"]
def configure(self):
periods = self.get_option(
"time_periods", self.default_time_periods).split(" ")
self.time_periods = [
tankcore.expand_to_milliseconds(x) for x in periods]
self.core.set_option(
self.SECTION, "time_periods", " ".join([str(x) for x in periods]))
self.precise_cumulative = int(
self.get_option("precise_cumulative", '1'))
def start_test(self):
if not self.reader:
self.log.warning("No one had set reader for aggregate data yet...")
def is_test_finished(self):
# read up to 2 samples in single pass
self.__read_samples(2)
return -1
def end_test(self, retcode):
self.__read_samples(force=True)
if self.reader:
self.reader.close_files()
return retcode
def add_result_listener(self, listener):
''' add object to data listeners '''
self.second_data_listeners.append(listener)
def __notify_listeners(self, data):
''' notify all listeners about aggregate data '''
self.log.debug(
"Notifying listeners about second: %s , %s/%s req/responses",
data.time, data.overall.planned_requests, data.overall.RPS)
for listener in self.second_data_listeners:
listener.aggregate_second(data)
def get_timeout(self):
''' get timeout based on time_periods last val '''
return self.time_periods[-1:][0]
def __generate_zero_samples(self, data):
''' fill timeline gaps with zero samples '''
if not data:
return
while self.last_sample_time and int(time.mktime(data.time.timetuple())) - self.last_sample_time > 1:
self.last_sample_time += 1
self.log.warning("Adding zero sample: %s", self.last_sample_time)
zero = self.reader.get_zero_sample(
datetime.datetime.fromtimestamp(self.last_sample_time))
self.__notify_listeners(zero)
self.last_sample_time = int(time.mktime(data.time.timetuple()))
def __read_samples(self, limit=0, force=False):
''' call reader object to read next sample set '''
if self.reader:
self.reader.check_open_files()
data = self.reader.get_next_sample(force)
count = 0
while data:
self.last_sample_time = int(time.mktime(data.time.timetuple()))
self.__generate_zero_samples(data)
self.__notify_listeners(data)
if limit < 1 or count < limit:
data = self.reader.get_next_sample(force)
else:
data = None
count += 1
# ===============================================================
class SecondAggregateData:
''' class holds aggregate data for the second '''
def __init__(self, cimulative_item=None):
self.cases = {}
self.time = None
self.overall = SecondAggregateDataItem()
self.cumulative = cimulative_item
def __repr__(self):
return "SecondAggregateData[%s][%s]" % (self.time, time.mktime(self.time.timetuple()))
class SecondAggregateDataItem:
''' overall and case items has this type '''
QUANTILES = [0.25, 0.50, 0.75, 0.80, 0.85, 0.90, 0.95, 0.98, 0.99, 1.00]
def __init__(self):
self.log = logging.getLogger(__name__)
self.case = None
self.planned_requests = 0
self.active_threads = 0
self.selfload = 0
self.RPS = 0
self.http_codes = {}
self.net_codes = {}
self.times_dist = []
self.quantiles = {}
self.dispersion = 0
self.input = 0
self.output = 0
self.avg_connect_time = 0
self.avg_send_time = 0
self.avg_latency = 0
self.avg_receive_time = 0
self.avg_response_time = 0
class SecondAggregateDataTotalItem:
''' total cumulative data item '''
def __init__(self):
self.avg_connect_time = 0
self.avg_send_time = 0
self.avg_latency = 0
self.avg_receive_time = 0
self.avg_response_time = 0
self.total_count = 0
self.times_dist = {}
self.quantiles = {}
def add_data(self, overall_item):
''' add data to total '''
for time_item in overall_item.times_dist:
self.total_count += time_item['count']
timing = int(time_item['from'])
if timing in self.times_dist.keys():
self.times_dist[timing]['count'] += time_item['count']
else:
self.times_dist[timing] = time_item
def add_raw_data(self, times_dist):
''' add data to total '''
for time_item in times_dist:
self.total_count += 1
timing = int(time_item)
dist_item = self.times_dist.get(
timing, {'from': timing, 'to': timing, 'count': 0})
dist_item['count'] += 1
self.times_dist[timing] = dist_item
logging.debug("Total times len: %s", len(self.times_dist))
def calculate_total_quantiles(self):
''' calculate total quantiles based on times dist '''
self.quantiles = {}
quantiles = reversed(copy.copy(SecondAggregateDataItem.QUANTILES))
timings = sorted(self.times_dist.keys(), reverse=True)
level = 1.0
for quan in quantiles:
while level >= quan:
timing = timings.pop(0)
level -= float(
self.times_dist[timing]['count']) / self.total_count
self.quantiles[quan * 100] = self.times_dist[timing]['to']
logging.debug("Total quantiles: %s", self.quantiles)
return self.quantiles
# ===============================================================
class AbstractReader:
'''
Parent class for all source reading adapters
'''
def __init__(self, owner):
self.aggregator = owner
self.log = logging.getLogger(__name__)
self.cumulative = SecondAggregateDataTotalItem()
self.data_queue = []
self.data_buffer = {}
def check_open_files(self):
''' open files if necessary '''
pass
def close_files(self):
''' Close opened handlers to avoid fd leak '''
pass
def get_next_sample(self, force):
''' read next sample from file '''
pass
def parse_second(self, next_time, data):
''' parse buffered data to aggregate item '''
self.log.debug("Parsing second: %s", next_time)
result = self.get_zero_sample(
datetime.datetime.fromtimestamp(next_time))
time_before = time.time()
for item in data:
self.__append_sample(result.overall, item)
marker = item[0]
if marker:
if not marker in result.cases.keys():
result.cases[marker] = SecondAggregateDataItem()
self.__append_sample(result.cases[marker], item)
# at this phase we have raw response times in result.overall.times_dist
if self.aggregator.precise_cumulative:
self.cumulative.add_raw_data(result.overall.times_dist)
self.log.debug(
"Calculate aggregates for %s requests", result.overall.RPS)
self.__calculate_aggregates(result.overall)
for case in result.cases.values():
self.__calculate_aggregates(case)
if not self.aggregator.precise_cumulative:
self.cumulative.add_data(result.overall)
self.cumulative.calculate_total_quantiles()
spent = time.time() - time_before
if spent:
self.log.debug(
"Aggregating speed: %s lines/sec", int(len(data) / spent))
return result
def __calculate_aggregates(self, item):
''' calculate aggregates on raw data '''
if item.RPS:
item.selfload = 100 * item.selfload / item.RPS
item.avg_connect_time /= item.RPS
item.avg_send_time /= item.RPS
item.avg_latency /= item.RPS
item.avg_receive_time /= item.RPS
item.avg_response_time /= item.RPS
item.times_dist.sort()
count = 0.0
quantiles = copy.copy(SecondAggregateDataItem.QUANTILES)
times = copy.copy(self.aggregator.time_periods)
time_from = 0
time_to = times.pop(0)
times_dist_draft = []
times_dist_item = {'from': time_from, 'to': time_to, 'count': 0}
deviation = 0.0
timing = 0
for timing in item.times_dist:
count += 1
if quantiles and (count / item.RPS) >= quantiles[0]:
level = quantiles.pop(0)
item.quantiles[level * 100] = timing
while times and timing > time_to:
time_from = time_to
time_to = times.pop(0)
if times_dist_item['count']:
times_dist_draft.append(times_dist_item)
times_dist_item = {
'from': time_from, 'to': time_to, 'count': 0}
times_dist_item['count'] += 1
deviation += math.pow(item.avg_response_time - timing, 2)
while quantiles:
level = quantiles.pop(0)
item.quantiles[level * 100] = timing
if times_dist_item['count']:
times_dist_draft.append(times_dist_item)
item.dispersion = deviation / item.RPS
item.times_dist = times_dist_draft
def __append_sample(self, result, item):
''' add single sample to aggregator buffer '''
for check in item:
if check < 0:
self.log.error("Problem item: %s", item)
raise ValueError("One of the sample items has negative value")
(marker, threads, overall_rt, http_code, net_code, sent_bytes,
received_bytes, connect, send, latency, receive, accuracy) = item
result.case = marker
result.active_threads = threads
result.planned_requests = 0
result.RPS += 1
if http_code and http_code != '0':
if not http_code in result.http_codes.keys():
result.http_codes[http_code] = 0
result.http_codes[http_code] += 1
if not net_code in result.net_codes.keys():
result.net_codes[net_code] = 0
result.net_codes[net_code] += 1
result.input += received_bytes
result.output += sent_bytes
result.avg_connect_time += connect
result.avg_send_time += send
result.avg_latency += latency
result.avg_receive_time += receive
result.avg_response_time += overall_rt
result.selfload += accuracy
result.times_dist.append(overall_rt)
def get_zero_sample(self, date_time):
''' instantiate new aggregate data item '''
res = SecondAggregateData(self.cumulative)
res.time = date_time
return res
def pop_second(self):
''' pop from out queue new aggregate data item '''
self.data_queue.sort()
next_time = self.data_queue.pop(0)
data = self.data_buffer[next_time]
del self.data_buffer[next_time]
res = self.parse_second(next_time, data)
return res