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