{"id":102666,"date":"2021-12-19T15:37:03","date_gmt":"2021-12-19T13:37:03","guid":{"rendered":"https:\/\/prohoster.info\/blog\/novosti-interneta\/vypusk-cache-bench-0-2-0-dlya-issledovaniya-effektivnosti-keshirovaniya-fajlov"},"modified":"2021-12-19T15:37:03","modified_gmt":"2021-12-19T13:37:03","slug":"vypusk-cache-bench-0-2-0-dlya-issledovaniya-effektivnosti-keshirovaniya-fajlov","status":"publish","type":"post","link":"https:\/\/prohoster.info\/de\/blog\/news\/vypusk-cache-bench-0-2-0-dlya-issledovaniya-effektivnosti-keshirovaniya-fajlov","title":{"rendered":"Ver\u00f6ffentlichung von cache-bench 0.2.0 zur Untersuchung der Effektivit\u00e4t von Dateicaching","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Sieben Monate nach der vorherigen Ver\u00f6ffentlichung ist cache-bench 0.2.0 erschienen. Cache-bench ist ein Python-Skript, das die Auswirkungen von Einstellungen des virtuellen Speichers (vm.swappiness, vm.watermark_scale_factor, Multigenerational LRU Framework und andere) auf die Leistung von Aufgaben bewertet, deren Ausf\u00fchrung von der Zwischenspeicherung von Lese-Dateioperationen abh\u00e4ngt, insbesondere unter Bedingungen von speicherknappheit. Der Code ist unter der CC0-Lizenz offen.  <\/p>\n<p>  Der Code des Skripts in der Version 0.2.0 wurde nahezu vollst\u00e4ndig neu geschrieben. Anstelle des Lesens von Dateien aus dem angegebenen Verzeichnis (in der neuen Version wurde die Option -d entfernt) erfolgt nun das Lesen aus einer Datei in zuf\u00e4llig angeordneten Fragmenten der angegebenen Gr\u00f6\u00dfe.  <\/p>\n<p>  Hinzugef\u00fcgte Optionen:  <\/p>\n<ul>\n<li class=\"l\">  --file -- der Pfad zur Datei, aus der gelesen werden soll.\n<li class=\"l\">  --chunk -- die Gr\u00f6\u00dfe des Fragments in Kibibyte, standardm\u00e4\u00dfig 64.\n<li class=\"l\">  --mmap -- aus einem speicherabgebildeten Dateiwrapper lesen, anstatt aus einem Dateideskriptor zu lesen.\n<li class=\"l\">  --preread -- vor Beginn des Tests die angegebene Datei durch sequenzielles Lesen in Fragmenten von 1 MiB vorab lesen (cachen).\n<li class=\"l\">  --bloat -- die gelesenen Fragmente in eine Liste hinzuf\u00fcgen, um den Speicherverbrauch des Prozesses zu erh\u00f6hen und so sp\u00e4ter einen Mangel an RAM zu schaffen.\n<li class=\"l\">  --interval -- das Intervall f\u00fcr die Ausgabe (Protokollierung) der Ergebnisse in Sekunden.    <\/ul>\n<p>Quelle: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/www.opennet.ru\/opennews\/art.shtml?num=56376\">opennet.ru<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0421\u043f\u0443\u0441\u0442\u044f 7 \u043c\u0435\u0441\u044f\u0446\u0435\u0432 \u043f\u043e\u0441\u043b\u0435 \u043f\u0440\u0435\u0434\u044b\u0434\u0443\u0449\u0435\u0433\u043e \u0440\u0435\u043b\u0438\u0437\u0430 \u0432\u044b\u0448\u0435\u043b cache-bench 0.2.0. Cache-bench &#8212; \u044d\u0442\u043e Python \u0441\u043a\u0440\u0438\u043f\u0442, \u043f\u043e\u0437\u0432\u043e\u043b\u044f\u044e\u0449\u0438\u0439 \u043e\u0446\u0435\u043d\u0438\u0442\u044c \u0432\u043b\u0438\u044f\u043d\u0438\u0435 \u043d\u0430\u0441\u0442\u0440\u043e\u0435\u043a \u0432\u0438\u0440\u0442\u0443\u0430\u043b\u044c\u043d\u043e\u0439 \u043f\u0430\u043c\u044f\u0442\u0438 (vm.swappiness, vm.watermark_scale_factor, Multigenerational LRU Framework \u0438 \u043f\u0440\u043e\u0447\u0438\u0445) \u043d\u0430 \u043f\u0440\u043e\u0438\u0437\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u044c\u043d\u043e\u0441\u0442\u044c \u0437\u0430\u0434\u0430\u0447, \u0432\u044b\u043f\u043e\u043b\u043d\u0435\u043d\u0438\u0435 \u043a\u043e\u0442\u043e\u0440\u044b\u0445 \u0437\u0430\u0432\u0438\u0441\u0438\u0442 \u043e\u0442 \u043a\u044d\u0448\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u044f \u0444\u0430\u0439\u043b\u043e\u0432\u044b\u0445 \u043e\u043f\u0435\u0440\u0430\u0446\u0438\u0439 \u0447\u0442\u0435\u043d\u0438\u044f, \u043e\u0441\u043e\u0431\u0435\u043d\u043d\u043e \u0432 \u0443\u0441\u043b\u043e\u0432\u0438\u044f\u0445 \u043d\u0435\u0445\u0432\u0430\u0442\u043a\u0438 \u043f\u0430\u043c\u044f\u0442\u0438. \u041a\u043e\u0434 \u043e\u0442\u043a\u0440\u044b\u0442 \u043f\u043e\u0434 \u043b\u0438\u0446\u0435\u043d\u0437\u0438\u0435\u0439 CC0. \u041a\u043e\u0434 \u0441\u043a\u0440\u0438\u043f\u0442\u0430 \u0432 \u0432\u0435\u0440\u0441\u0438\u0438 0.2.0 \u043f\u043e\u0447\u0442\u0438 \u043f\u043e\u043b\u043d\u043e\u0441\u0442\u044c\u044e [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[702],"tags":[],"class_list":["post-102666","post","type-post","status-publish","format-standard","hentry","category-news"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One 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