Ponysearch/searx/metrics/__init__.py
2021-04-21 16:24:46 +02:00

206 lines
7.6 KiB
Python

# SPDX-License-Identifier: AGPL-3.0-or-later
import typing
import math
import contextlib
from timeit import default_timer
from operator import itemgetter
from searx.engines import engines
from .models import HistogramStorage, CounterStorage
from .error_recorder import count_error, count_exception, errors_per_engines
__all__ = ["initialize",
"get_engines_stats", "get_engine_errors",
"histogram", "histogram_observe", "histogram_observe_time",
"counter", "counter_inc", "counter_add",
"count_error", "count_exception"]
ENDPOINTS = {'search'}
histogram_storage: typing.Optional[HistogramStorage] = None
counter_storage: typing.Optional[CounterStorage] = None
@contextlib.contextmanager
def histogram_observe_time(*args):
h = histogram_storage.get(*args)
before = default_timer()
yield before
duration = default_timer() - before
if h:
h.observe(duration)
else:
raise ValueError("histogram " + repr((*args,)) + " doesn't not exist")
def histogram_observe(duration, *args):
histogram_storage.get(*args).observe(duration)
def histogram(*args, raise_on_not_found=True):
h = histogram_storage.get(*args)
if raise_on_not_found and h is None:
raise ValueError("histogram " + repr((*args,)) + " doesn't not exist")
return h
def counter_inc(*args):
counter_storage.add(1, *args)
def counter_add(value, *args):
counter_storage.add(value, *args)
def counter(*args):
return counter_storage.get(*args)
def initialize(engine_names=None):
"""
Initialize metrics
"""
global counter_storage, histogram_storage
counter_storage = CounterStorage()
histogram_storage = HistogramStorage()
# max_timeout = max of all the engine.timeout
max_timeout = 2
for engine_name in (engine_names or engines):
if engine_name in engines:
max_timeout = max(max_timeout, engines[engine_name].timeout)
# histogram configuration
histogram_width = 0.1
histogram_size = int(1.5 * max_timeout / histogram_width)
# engines
for engine_name in (engine_names or engines):
# search count
counter_storage.configure('engine', engine_name, 'search', 'count', 'sent')
counter_storage.configure('engine', engine_name, 'search', 'count', 'successful')
# global counter of errors
counter_storage.configure('engine', engine_name, 'search', 'count', 'error')
# score of the engine
counter_storage.configure('engine', engine_name, 'score')
# result count per requests
histogram_storage.configure(1, 100, 'engine', engine_name, 'result', 'count')
# time doing HTTP requests
histogram_storage.configure(histogram_width, histogram_size, 'engine', engine_name, 'time', 'http')
# total time
# .time.request and ...response times may overlap .time.http time.
histogram_storage.configure(histogram_width, histogram_size, 'engine', engine_name, 'time', 'total')
def get_engine_errors(engline_list):
result = {}
engine_names = list(errors_per_engines.keys())
engine_names.sort()
for engine_name in engine_names:
if engine_name not in engline_list:
continue
error_stats = errors_per_engines[engine_name]
sent_search_count = max(counter('engine', engine_name, 'search', 'count', 'sent'), 1)
sorted_context_count_list = sorted(error_stats.items(), key=lambda context_count: context_count[1])
r = []
for context, count in sorted_context_count_list:
percentage = round(20 * count / sent_search_count) * 5
r.append({
'filename': context.filename,
'function': context.function,
'line_no': context.line_no,
'code': context.code,
'exception_classname': context.exception_classname,
'log_message': context.log_message,
'log_parameters': context.log_parameters,
'secondary': context.secondary,
'percentage': percentage,
})
result[engine_name] = sorted(r, reverse=True, key=lambda d: d['percentage'])
return result
def to_percentage(stats, maxvalue):
for engine_stat in stats:
if maxvalue:
engine_stat['percentage'] = int(engine_stat['avg'] / maxvalue * 100)
else:
engine_stat['percentage'] = 0
return stats
def get_engines_stats(engine_list):
global counter_storage, histogram_storage
assert counter_storage is not None
assert histogram_storage is not None
list_time = []
list_time_http = []
list_time_total = []
list_result_count = []
list_error_count = []
list_scores = []
list_scores_per_result = []
max_error_count = max_http_time = max_time_total = max_result_count = max_score = None # noqa
for engine_name in engine_list:
error_count = counter('engine', engine_name, 'search', 'count', 'error')
if counter('engine', engine_name, 'search', 'count', 'sent') > 0:
list_error_count.append({'avg': error_count, 'name': engine_name})
max_error_count = max(error_count, max_error_count or 0)
successful_count = counter('engine', engine_name, 'search', 'count', 'successful')
if successful_count == 0:
continue
result_count_sum = histogram('engine', engine_name, 'result', 'count').sum
time_total = histogram('engine', engine_name, 'time', 'total').percentage(50)
time_http = histogram('engine', engine_name, 'time', 'http').percentage(50)
result_count = result_count_sum / float(successful_count)
if result_count:
score = counter('engine', engine_name, 'score') # noqa
score_per_result = score / float(result_count_sum)
else:
score = score_per_result = 0.0
max_time_total = max(time_total, max_time_total or 0)
max_http_time = max(time_http, max_http_time or 0)
max_result_count = max(result_count, max_result_count or 0)
max_score = max(score, max_score or 0)
list_time.append({'total': round(time_total, 1),
'http': round(time_http, 1),
'name': engine_name,
'processing': round(time_total - time_http, 1)})
list_time_total.append({'avg': time_total, 'name': engine_name})
list_time_http.append({'avg': time_http, 'name': engine_name})
list_result_count.append({'avg': result_count, 'name': engine_name})
list_scores.append({'avg': score, 'name': engine_name})
list_scores_per_result.append({'avg': score_per_result, 'name': engine_name})
list_time = sorted(list_time, key=itemgetter('total'))
list_time_total = sorted(to_percentage(list_time_total, max_time_total), key=itemgetter('avg'))
list_time_http = sorted(to_percentage(list_time_http, max_http_time), key=itemgetter('avg'))
list_result_count = sorted(to_percentage(list_result_count, max_result_count), key=itemgetter('avg'), reverse=True)
list_scores = sorted(list_scores, key=itemgetter('avg'), reverse=True)
list_scores_per_result = sorted(list_scores_per_result, key=itemgetter('avg'), reverse=True)
list_error_count = sorted(to_percentage(list_error_count, max_error_count), key=itemgetter('avg'), reverse=True)
return {
'time': list_time,
'max_time': math.ceil(max_time_total or 0),
'time_total': list_time_total,
'time_http': list_time_http,
'result_count': list_result_count,
'scores': list_scores,
'scores_per_result': list_scores_per_result,
'error_count': list_error_count,
}