{"id":35269,"date":"2025-11-27T18:31:23","date_gmt":"2025-11-27T15:31:23","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/asyncio-python-web-scraperda-tarih-tiplerini-postgresql-ile-yonetme\/"},"modified":"2025-11-27T18:31:23","modified_gmt":"2025-11-27T15:31:23","slug":"asyncio-python-web-scraperda-tarih-tiplerini-postgresql-ile-yonetme","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/asyncio-python-web-scraperda-tarih-tiplerini-postgresql-ile-yonetme\/","title":{"rendered":"Asyncio Python Web Scraper&#8217;da Tarih Tiplerini PostgreSQL ile Y\u00f6netme"},"content":{"rendered":"<p>Asyncio Python web scraper projelerinde PostgreSQL ile tarih tiplerini nas\u0131l etkili bir \u015fekilde y\u00f6netece\u011finizi ad\u0131m ad\u0131m \u00f6\u011frenin. Veri tutarl\u0131l\u0131\u011f\u0131 ve performans i\u00e7in en iyi uygulamalar.<\/p>\n<p>Web kaz\u0131ma (web scraping) projelerinde tarih ve saat verileriyle u\u011fra\u015fmak, ba\u015flang\u0131\u00e7ta basit g\u00f6r\u00fcnse de, derinlemesine bilgi ve dikkat gerektiren bir aland\u0131r. \u0130nternet d\u00fcnyas\u0131nda dola\u015fan verilerin \u00f6nemli bir k\u0131sm\u0131, olaylar\u0131n ne zaman ger\u00e7ekle\u015fti\u011fi, bir i\u00e7eri\u011fin ne zaman g\u00fcncellendi\u011fi veya bir kullan\u0131c\u0131n\u0131n ne zaman yorum yapt\u0131\u011f\u0131 gibi tarihsel bilgiler i\u00e7erir. Bu tarihsel veriler, sadece ham bilgi olmaktan \u00f6te, scraper&#8217;\u0131n\u0131z\u0131n elde etti\u011fi verinin ba\u011flam\u0131n\u0131, g\u00fcncelli\u011fini ve analiz edilebilirli\u011fini do\u011frudan etkiler. \u00d6rne\u011fin, bir e-ticaret sitesinden \u00fcr\u00fcn yorumlar\u0131n\u0131 topluyorsan\u0131z, yorumun ne zaman yap\u0131ld\u0131\u011f\u0131 bilgisi, \u00fcr\u00fcn\u00fcn pop\u00fclaritesini, trendleri veya yorumlar\u0131n g\u00fcncelli\u011fini anlamak i\u00e7in hayati \u00f6neme sahiptir. Ayn\u0131 \u015fekilde, haber sitelerinden makale \u00e7ekiyorsan\u0131z, haberin yay\u0131nlanma tarihi, kronolojik bir ak\u0131\u015f olu\u015fturman\u0131za ve eski, g\u00fcncelli\u011fini yitirmi\u015f i\u00e7erikleri filtrelemenize olanak tan\u0131r.<\/p>\n<p>Bu ba\u011flamda, tarih ve saat tiplerini do\u011fru bir \u015fekilde y\u00f6netmek, scraper&#8217;\u0131n\u0131z\u0131n toplad\u0131\u011f\u0131 verinin kalitesini ve g\u00fcvenilirli\u011fini art\u0131rmakla kalmaz, ayn\u0131 zamanda veri depolama ve analiz s\u00fcre\u00e7lerini de b\u00fcy\u00fck \u00f6l\u00e7\u00fcde kolayla\u015ft\u0131r\u0131r. Farkl\u0131 web siteleri, tarih ve saat bilgilerini say\u0131s\u0131z farkl\u0131 formatta sunabilir: &#8220;2 g\u00fcn \u00f6nce&#8221;, &#8220;12 May\u0131s 2023&#8221;, &#8220;2023-05-12T14:30:00Z&#8221;, &#8220;12\/05\/23 14:30&#8221; gibi. Bu \u00e7e\u015fitlilik, verileri standart bir bi\u00e7ime d\u00f6n\u00fc\u015ft\u00fcrme ihtiyac\u0131n\u0131 do\u011furur. Dahas\u0131, d\u00fcnya genelindeki farkl\u0131 zaman dilimleri (timezones) sorunu, verilerin tutarl\u0131 bir \u015fekilde depolanmas\u0131 ve kar\u015f\u0131la\u015ft\u0131r\u0131lmas\u0131 i\u00e7in merkezi bir UTC (Coordinated Universal Time) format\u0131na \u00e7evrilmesini neredeyse zorunlu k\u0131lar. Aksi takdirde, ayn\u0131 olay\u0131n farkl\u0131 zaman dilimlerinde farkl\u0131 kaydedilmesi, veri tutars\u0131zl\u0131klar\u0131na ve hatal\u0131 analizlere yol a\u00e7abilir.<\/p>\n<p>Asyncio tabanl\u0131 bir Python web scraper kullanmak, e\u015fzamanl\u0131 (concurrent) i\u015flemleri verimli bir \u015fekilde y\u00f6netmenizi sa\u011flar, ancak tarih veri tiplerinin i\u015flenmesi hala dikkatli bir yakla\u015f\u0131m gerektirir. Python&#8217;\u0131n kendi <code>datetime<\/code> mod\u00fcl\u00fc, bu t\u00fcr karma\u015f\u0131k tarih ve saat i\u015flemleri i\u00e7in g\u00fc\u00e7l\u00fc ara\u00e7lar sunar. Bu mod\u00fcl sayesinde, farkl\u0131 string formatlar\u0131ndaki tarihleri <code>datetime<\/code> objelerine d\u00f6n\u00fc\u015ft\u00fcrebilir, zaman dilimi d\u00f6n\u00fc\u015f\u00fcmleri yapabilir ve verileri PostgreSQL gibi bir veritaban\u0131na kaydetmeye haz\u0131r hale getirebilirsiniz. PostgreSQL ise, \u00e7e\u015fitli tarih ve saat tipleriyle (DATE, TIME, TIMESTAMP, TIMESTAMPTZ) bu d\u00f6n\u00fc\u015ft\u00fcr\u00fclm\u00fc\u015f verileri g\u00fcvenli ve etkin bir \u015fekilde depolama kapasitesi sunar. Bu entegrasyon, web kaz\u0131ma projelerinizin sadece h\u0131zl\u0131 ve verimli olmakla kalmay\u0131p, ayn\u0131 zamanda do\u011fru ve anlaml\u0131 veriler \u00fcretmesini de sa\u011flar. K\u0131sacas\u0131, tarih tiplerini y\u00f6netmek, bir web scraper projesinin temel ta\u015flar\u0131ndan biridir ve bu konuya hakim olmak, veri biliminden i\u015f zekas\u0131na kadar bir\u00e7ok alanda size avantaj sa\u011flayacakt\u0131r.<\/p>\n<h2>PostgreSQL&#8217;de Tarih ve Saat Tiplerini Tan\u0131ma: Hangisini Ne Zaman Kullanmal\u0131?<\/h2>\n<p>PostgreSQL, tarih ve saat verilerini depolamak i\u00e7in olduk\u00e7a zengin bir tip yelpazesi sunar. Bu tipleri do\u011fru anlamak ve projenizin ihtiya\u00e7lar\u0131na g\u00f6re se\u00e7mek, veri tutarl\u0131l\u0131\u011f\u0131, depolama alan\u0131 ve sorgu performans\u0131 a\u00e7\u0131s\u0131ndan kritik \u00f6neme sahiptir. Bir web scraper projesinde, genellikle kar\u015f\u0131la\u015f\u0131lan verilerin do\u011fas\u0131 gere\u011fi, zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip tipler b\u00fcy\u00fck avantaj sa\u011flar.<\/p>\n<p>PostgreSQL&#8217;in ba\u015fl\u0131ca tarih ve saat tipleri \u015funlard\u0131r:<\/p>\n<ul>\n<li><code>DATE<\/code>: Yaln\u0131zca tarihi (y\u0131l, ay, g\u00fcn) saklar. Saat bilgisi i\u00e7ermez. Do\u011fum tarihleri veya belirli bir olay\u0131n sadece g\u00fcn\u00fcn\u00fc kaydetmek gibi durumlarda idealdir.<\/li>\n<li><code>TIME<\/code>: Yaln\u0131zca saati (saat, dakika, saniye) saklar. Tarih bilgisi i\u00e7ermez. Bir ma\u011fazan\u0131n a\u00e7\u0131l\u0131\u015f saati gibi durumlarda kullan\u0131labilir.<\/li>\n<li><code>TIMESTAMP WITHOUT TIME ZONE<\/code> (k\u0131saca <code>TIMESTAMP<\/code>): Tarih ve saati saklar, ancak herhangi bir zaman dilimi bilgisi i\u00e7ermez. Veritaban\u0131na kaydedilen de\u011fer, tam olarak sizin belirtti\u011finiz de\u011ferdir ve bu, &#8220;hangi zaman dilimine ait oldu\u011fu&#8221; bilgisini d\u0131\u015far\u0131da b\u0131rak\u0131r. E\u011fer t\u00fcm tarih-saat verileriniz zaten UTC&#8217;ye d\u00f6n\u00fc\u015ft\u00fcr\u00fclm\u00fc\u015fse ve bu d\u00f6n\u00fc\u015f\u00fcm\u00fcn her zaman Python taraf\u0131nda garantilendi\u011fini d\u00fc\u015f\u00fcn\u00fcyorsan\u0131z kullan\u0131labilir. Ancak bu, potansiyel hatalara a\u00e7\u0131k bir yakla\u015f\u0131md\u0131r.<\/li>\n<li><code>TIMESTAMP WITH TIME ZONE<\/code> (k\u0131saca <code>TIMESTAMPTZ<\/code>): Bu tip, bir web scraper i\u00e7in genellikle en iyi se\u00e7imdir. Tarih ve saati saklar ve beraberinde zaman dilimi bilgisini de i\u00e7erir. PostgreSQL, bu tipi kullan\u0131rken, gelen t\u00fcm tarih-saat de\u011ferlerini veritaban\u0131n\u0131n kendi zaman dilimine (genellikle UTC olarak yap\u0131land\u0131r\u0131l\u0131r) d\u00f6n\u00fc\u015ft\u00fcrerek saklar. Sorgulama yap\u0131ld\u0131\u011f\u0131nda ise, istemcinin zaman dilimi ayar\u0131na g\u00f6re (veya belirtilen bir zaman dilimine g\u00f6re) tekrar d\u00f6n\u00fc\u015ft\u00fcr\u00fclerek g\u00f6sterilir. Bu \u00f6zellik, farkl\u0131 co\u011frafyalardan gelen veya farkl\u0131 zaman dilimlerinde olu\u015fturulmu\u015f verileri tutarl\u0131 bir \u015fekilde y\u00f6netmek i\u00e7in vazge\u00e7ilmezdir.<\/li>\n<li><code>INTERVAL<\/code>: \u0130ki tarih\/saat aras\u0131ndaki fark\u0131 saklar. \u00d6rne\u011fin, &#8220;5 g\u00fcn 3 saat 10 dakika&#8221; gibi bir s\u00fcreyi temsil eder. Bir i\u015flemin ne kadar s\u00fcrd\u00fc\u011f\u00fcn\u00fc veya belirli bir periyodu depolamak i\u00e7in kullan\u0131labilir.<\/li>\n<\/ul>\n<p>Peki, web kaz\u0131ma projelerinde hangi tipi ne zaman kullanmal\u0131y\u0131z?<\/p>\n<p>Genellikle <code>TIMESTAMPTZ<\/code>, web scraper&#8217;lar i\u00e7in alt\u0131n standartt\u0131r. Bunun ana nedeni, web&#8217;den \u00e7ekilen verilerin genellikle belirli bir zaman dilimine ait olmas\u0131 (\u00f6rne\u011fin, bir web sitesinin sunucusunun yerel saati) veya zaman dilimi bilgisinin belirsiz olmas\u0131d\u0131r. <code>TIMESTAMPTZ<\/code> kullanarak, t\u00fcm verilerinizi merkezi bir referans noktas\u0131na (UTC) g\u00f6re depolayabilir ve bu, gelecekteki analizlerinizde zaman dilimi karma\u015fas\u0131n\u0131 ortadan kald\u0131r\u0131r. \u00d6rne\u011fin, farkl\u0131 \u00fclkelerdeki e-ticaret sitelerinden yorum tarihleri \u00e7ekiyorsunuz. Her sitenin yorum zaman\u0131 kendi yerel zaman dilimindedir. E\u011fer hepsini UTC&#8217;ye d\u00f6n\u00fc\u015ft\u00fcr\u00fcp <code>TIMESTAMPTZ<\/code> olarak saklarsan\u0131z, t\u00fcm yorumlar\u0131 tek bir kronolojik s\u0131ralamada do\u011fru bir \u015fekilde g\u00f6rebilirsiniz. Aksi takdirde, \u00f6rne\u011fin \u0130stanbul&#8217;dan gelen &#8220;15:00&#8221; ve Londra&#8217;dan gelen &#8220;15:00&#8221; ayn\u0131 anda ger\u00e7ekle\u015fmi\u015f gibi g\u00f6r\u00fcnebilir, oysa ger\u00e7ekte aralar\u0131nda 3 saat fark vard\u0131r.<\/p>\n<aside class=\"expert-tip\">\n  Uzman \u0130pucu: Her zaman m\u00fcmk\u00fcn oldu\u011funca <code>TIMESTAMPTZ<\/code> kullanmaya \u00e7al\u0131\u015f\u0131n ve Python taraf\u0131nda verileri UTC&#8217;ye d\u00f6n\u00fc\u015ft\u00fcrd\u00fckten sonra veritaban\u0131na g\u00f6nderin. Bu, veri tutarl\u0131l\u0131\u011f\u0131n\u0131 garantilemenin ve gelecekteki zaman dilimi sorunlar\u0131n\u0131 \u00f6nlemenin en g\u00fcvenli yoludur.<br \/>\n<\/aside>\n<p>A\u015fa\u011f\u0131daki tablo, PostgreSQL tarih ve saat tiplerinin temel \u00f6zelliklerini ve kullan\u0131m senaryolar\u0131n\u0131 \u00f6zetlemektedir:<\/p>\n<div class=\"table-container\" style=\"overflow-x:auto;\">\n<table style=\"width:100%; border-collapse: collapse; margin-top: 1em;\">\n<thead>\n<tr>\n<th style=\"border: 1px solid #ddd; padding: 8px; text-align: left;\">Tip<\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; text-align: left;\">Depolad\u0131\u011f\u0131<\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; text-align: left;\">Zaman Dilimi Fark\u0131ndal\u0131\u011f\u0131<\/th>\n<th style=\"border: 1px solid #ddd; padding: 8px; text-align: left;\">Kullan\u0131m Senaryosu (Web Scraper)<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\"><code>DATE<\/code><\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Sadece tarih<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Yok<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Bir etkinli\u011fin sadece g\u00fcn\u00fc (saat \u00f6nemsizse)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\"><code>TIME<\/code><\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Sadece saat<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Yok<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Bir web sitesinin g\u00fcncelleme zaman\u0131 (tarih \u00f6nemsizse)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\"><code>TIMESTAMP<\/code><\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Tarih ve saat<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Yok (veritaban\u0131 zaman dilimine \u00e7evrilmez)<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">T\u00fcm verilerin zaten UTC oldu\u011fu garanti edilen \u00f6zel durumlar<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\"><code>TIMESTAMPTZ<\/code><\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Tarih ve saat (UTC&#8217;ye \u00e7evrilir)<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Var (\u00f6nerilen)<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Web kaz\u0131ma verileri (yorum tarihleri, yay\u0131nlanma zamanlar\u0131 vb.)<\/td>\n<\/tr>\n<tr>\n<td style=\"border: 1px solid #ddd; padding: 8px;\"><code>INTERVAL<\/code><\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Zaman aral\u0131\u011f\u0131<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">Uygulanamaz<\/td>\n<td style=\"border: 1px solid #ddd; padding: 8px;\">\u0130ki olay aras\u0131ndaki s\u00fcre, bir i\u015flemin s\u00fcresi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p>Bu se\u00e7imleri yaparken, projenizin gelecekteki ihtiya\u00e7lar\u0131n\u0131 ve verilerinizi kimlerin, hangi co\u011frafyada analiz edece\u011fini g\u00f6z \u00f6n\u00fcnde bulundurmak \u00f6nemlidir. Yanl\u0131\u015f tip se\u00e7imi, ileride b\u00fcy\u00fck veri d\u00fczeltme operasyonlar\u0131na veya hatal\u0131 raporlamalara yol a\u00e7abilir.<\/p>\n<h2>Python&#8217;da <code>datetime<\/code> Mod\u00fcl\u00fc ile Tarih Verilerini Haz\u0131rlama: Scraper Verileri Nas\u0131l D\u00f6n\u00fc\u015ft\u00fcr\u00fcl\u00fcr?<\/h2>\n<p>Web scraper&#8217;\u0131n\u0131z\u0131n toplad\u0131\u011f\u0131 ham tarih ve saat string&#8217;lerini, PostgreSQL&#8217;e do\u011fru ve tutarl\u0131 bir \u015fekilde kaydedebilmek i\u00e7in Python&#8217;\u0131n g\u00fc\u00e7l\u00fc <code>datetime<\/code> mod\u00fcl\u00fcn\u00fc kullanmam\u0131z gerekir. Bu mod\u00fcl, tarih string&#8217;lerini Python&#8217;\u0131n kendi <code>datetime<\/code> nesnelerine d\u00f6n\u00fc\u015ft\u00fcrme, bu nesneler \u00fczerinde aritmetik i\u015flemler yapma ve zaman dilimi d\u00f6n\u00fc\u015f\u00fcmleri ger\u00e7ekle\u015ftirme gibi yetenekler sunar.<\/p>\n<h3>Farkl\u0131 Tarih Formatlar\u0131n\u0131 Anlama ve D\u00f6n\u00fc\u015ft\u00fcrme<\/h3>\n<p>Web siteleri, tarihleri standart olmayan bir\u00e7ok farkl\u0131 bi\u00e7imde sunabilir. <code>datetime.strptime()<\/code> fonksiyonu, belirli bir format string&#8217;ini kullanarak bir tarih string&#8217;ini <code>datetime<\/code> nesnesine d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in kullan\u0131l\u0131r. \u0130\u015fte birka\u00e7 \u00f6rnek:<\/p>\n<pre><code class=\"language-python\">\nfrom datetime import datetime\n\n# \u00d6rnek 1: 'YYYY-MM-DD HH:MM:SS' format\u0131\ndate_str_1 = \"2023-05-12 14:30:00\"\ndt_obj_1 = datetime.strptime(date_str_1, \"%Y-%m-%d %H:%M:%S\")\nprint(f\"Format 1: {dt_obj_1}\")\n\n# \u00d6rnek 2: 'DD.MM.YYYY' format\u0131\ndate_str_2 = \"12.05.2023\"\ndt_obj_2 = datetime.strptime(date_str_2, \"%d.%m.%Y\")\nprint(f\"Format 2: {dt_obj_2}\")\n\n# \u00d6rnek 3: 'May 12, 2023 2:30 PM' format\u0131\ndate_str_3 = \"May 12, 2023 2:30 PM\"\ndt_obj_3 = datetime.strptime(date_str_3, \"%b %d, %Y %I:%M %p\")\nprint(f\"Format 3: {dt_obj_3}\")\n\n# \u00d6rnek 4: ISO 8601 format\u0131 (genellikle API'lerden gelir)\ndate_str_4 = \"2023-05-12T14:30:00Z\" # 'Z' UTC oldu\u011funu belirtir\n# 'Z' i\u00e7in 'utcfromtimestamp' veya dateutil.parser daha uygun olabilir\n# Ya da strptime'\u0131 'Z'siz k\u0131sm\u0131 i\u00e7in kullan\u0131p manuel olarak UTC'yi ekleyebiliriz.\ndt_obj_4 = datetime.strptime(date_str_4.replace('Z', ''), \"%Y-%m-%dT%H:%M:%S\")\nprint(f\"Format 4 (ISO): {dt_obj_4}\")\n<\/pre>\n<p><\/code><\/p>\n<p>Farkl\u0131 format kodlar\u0131 i\u00e7in <code>strftime<\/code> ve <code>strptime<\/code> davran\u0131\u015f belgelerini incelemek faydal\u0131 olacakt\u0131r. E\u011fer \u00e7ok say\u0131da farkl\u0131 formatla kar\u015f\u0131la\u015f\u0131yorsan\u0131z ve bunlar\u0131 tek tek y\u00f6netmek istemiyorsan\u0131z, <code>dateutil.parser.parse<\/code> gibi k\u00fct\u00fcphaneler daha esnek bir \u00e7\u00f6z\u00fcm sunabilir. Bu k\u00fct\u00fcphane, \u00e7o\u011fu yayg\u0131n format\u0131 otomatik olarak alg\u0131layabilir.<\/p>\n<pre><code class=\"language-python\">\nfrom dateutil.parser import parse\n\ndate_str_flexible_1 = \"2023-05-12 14:30\"\ndate_str_flexible_2 = \"12th May, 2023\"\ndate_str_flexible_3 = \"Yesterday at 3 PM\" # Bu t\u00fcr g\u00f6receli ifadeler i\u00e7in ek mant\u0131k gerekir.\n\nprint(f\"Flexible Parse 1: {parse(date_str_flexible_1)}\")\nprint(f\"Flexible Parse 2: {parse(date_str_flexible_2)}\")\n\n# G\u00f6receli ifadeler i\u00e7in genellikle \u00f6zel bir yorumlama veya \"now\" parametresi gerekir.\n# \u00d6rne\u011fin, \"Yesterday\" i\u00e7in <code>dateutil.relativedelta<\/code> kullan\u0131labilir veya scraper'\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 an g\u00f6z \u00f6n\u00fcnde bulundurulur.\n# Basit\u00e7e, <code>parse<\/code> fonksiyonu \"Yesterday\" gibi ifadeleri belirli bir referans tarihi olmadan do\u011frudan datetime objesine d\u00f6n\u00fc\u015ft\u00fcrmekte zorlanabilir.\n<\/pre>\n<p><\/code><\/p>\n<h3>Zaman Dilimi Y\u00f6netimi ve UTC D\u00f6n\u00fc\u015f\u00fcm\u00fc<\/h3>\n<p>Veri b\u00fct\u00fcnl\u00fc\u011f\u00fc i\u00e7in en iyi uygulama, t\u00fcm tarih-saat verilerini UTC'ye (Coordinated Universal Time) d\u00f6n\u00fc\u015ft\u00fcrerek depolamakt\u0131r. Python 3.9 ve sonras\u0131 i\u00e7in yerle\u015fik <code>zoneinfo<\/code> mod\u00fcl\u00fc veya eski s\u00fcr\u00fcmler i\u00e7in \u00fc\u00e7\u00fcnc\u00fc taraf <code>pytz<\/code> k\u00fct\u00fcphanesi bu konuda yard\u0131mc\u0131 olur.<\/p>\n<pre><code class=\"language-python\">\nfrom datetime import datetime, timezone\nimport zoneinfo # Python 3.9+\n\n# E\u011fer sistemde zoneinfo yoksa veya Python 3.9 \u00f6ncesiyse:\n# pip install pytz\n# import pytz\n\n# \u015eu anki UTC zaman\u0131\nnow_utc = datetime.now(timezone.utc)\nprint(f\"\u015eu anki UTC zaman\u0131: {now_utc}\")\n\n# Belirli bir zaman dilimine sahip bir datetime objesi olu\u015fturma\ntry:\n    istanbul_tz = zoneinfo.ZoneInfo(\"Europe\/Istanbul\")\n    london_tz = zoneinfo.ZoneInfo(\"Europe\/London\")\nexcept zoneinfo.ZoneInfoNotFoundError:\n    print(\"ZoneInfo bulunamad\u0131. pytz kullan\u0131l\u0131yor.\")\n    # import pytz\n    # istanbul_tz = pytz.timezone(\"Europe\/Istanbul\")\n    # london_tz = pytz.timezone(\"Europe\/London\")\n\n# Bir string'i \u0130stanbul zaman diliminde varsayarak i\u015fleme\ndate_str_istanbul = \"2023-05-12 14:30:00\"\ndt_istanbul_naive = datetime.strptime(date_str_istanbul, \"%Y-%m-%d %H:%M:%S\")\ndt_istanbul_aware = dt_istanbul_naive.replace(tzinfo=istanbul_tz)\n# Veya daha do\u011fru bir yol:\n# dt_istanbul_aware = istanbul_tz.localize(dt_istanbul_naive) # pytz ile\n\nprint(f\"\u0130stanbul'da belirlenen zaman: {dt_istanbul_aware}\")\n\n# \u0130stanbul zaman\u0131n\u0131 UTC'ye d\u00f6n\u00fc\u015ft\u00fcrme\ndt_istanbul_to_utc = dt_istanbul_aware.astimezone(timezone.utc)\nprint(f\"\u0130stanbul zaman\u0131n\u0131n UTC kar\u015f\u0131l\u0131\u011f\u0131: {dt_istanbul_to_utc}\")\n\n# London zaman\u0131n\u0131 UTC'ye d\u00f6n\u00fc\u015ft\u00fcrme (kar\u015f\u0131la\u015ft\u0131rma i\u00e7in)\ndate_str_london = \"2023-05-12 12:30:00\" # \u0130stanbul 14:30'a denk gelmesi i\u00e7in\ndt_london_naive = datetime.strptime(date_str_london, \"%Y-%m-%d %H:%M:%S\")\ndt_london_aware = dt_london_naive.replace(tzinfo=london_tz)\ndt_london_to_utc = dt_london_aware.astimezone(timezone.utc)\nprint(f\"London zaman\u0131n\u0131n UTC kar\u015f\u0131l\u0131\u011f\u0131: {dt_london_to_utc}\")\n\n# G\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere, her iki farkl\u0131 yerel saat de UTC'ye \u00e7evrildi\u011finde ayn\u0131 oluyor.\n# Bu da PostgreSQL'e TIMESTAMPTZ olarak kaydedilecek do\u011fru format\u0131 sa\u011fl\u0131yor.\n<\/pre>\n<p><\/code><\/p>\n<p>Bu s\u00fcre\u00e7, web scraper'\u0131n\u0131z\u0131n farkl\u0131 kaynaklardan toplad\u0131\u011f\u0131 tarih verilerini tek bir, evrensel ve tutarl\u0131 formatta birle\u015ftirmenizi sa\u011flar. Bu, daha sonra veriler \u00fczerinde yapaca\u011f\u0131n\u0131z her t\u00fcrl\u00fc analiz veya raporlama i\u00e7in temel bir ad\u0131md\u0131r.<\/p>\n<h3>Asyncio Ortam\u0131nda <code>datetime<\/code> Kullan\u0131m\u0131: Performans \u0130pu\u00e7lar\u0131 Nelerdir?<\/h3>\n<p>Asyncio, Python'da e\u015fzamanl\u0131 (concurrent) programlama i\u00e7in tasarlanm\u0131\u015f bir yap\u0131d\u0131r ve I\/O yo\u011fun (disk, a\u011f) i\u015flemlerde blokaj\u0131 \u00f6nleyerek performans\u0131 art\u0131r\u0131r. <code>datetime<\/code> mod\u00fcl\u00fc ile yap\u0131lan i\u015flemler genellikle CPU yo\u011fun (CPU-bound) ve \u00e7ok h\u0131zl\u0131d\u0131r. Bu t\u00fcr k\u00fc\u00e7\u00fck, anl\u0131k hesaplamalar event loop'u bloklamaz ve asyncio ortam\u0131nda do\u011frudan kullan\u0131labilir.<\/p>\n<p>Ancak, baz\u0131 durumlarda (\u00f6rne\u011fin, \u00e7ok b\u00fcy\u00fck bir veri setindeki her \u00f6\u011fe i\u00e7in karma\u015f\u0131k tarih hesaplamalar\u0131 yap\u0131l\u0131yorsa), bu i\u015flemlerin toplam s\u00fcresi dikkate de\u011fer hale gelebilir. Bu t\u00fcr senaryolarda:<\/p>\n<ul>\n<li><code>asyncio.to_thread()<\/code> kullan\u0131larak CPU yo\u011fun i\u015flemlerin ayr\u0131 bir i\u015f par\u00e7ac\u0131\u011f\u0131na ta\u015f\u0131nmas\u0131 d\u00fc\u015f\u00fcn\u00fclebilir. Bu, ana event loop'un serbest kalmas\u0131n\u0131 sa\u011flar. Ancak <code>datetime<\/code> d\u00f6n\u00fc\u015f\u00fcmleri genellikle o kadar h\u0131zl\u0131d\u0131r ki bu, \u00e7o\u011fu zaman gereksiz bir ek y\u00fck getirir.<\/li>\n<li>Veri d\u00f6n\u00fc\u015f\u00fcmlerini m\u00fcmk\u00fcn oldu\u011funca erken bir a\u015famada ve veritaban\u0131na kaydetmeden \u00f6nce tek seferde yapmak, genel veri ak\u0131\u015f\u0131n\u0131z\u0131 optimize eder.<\/li>\n<li>Karma\u015f\u0131k tarih\/saat i\u015fleme mant\u0131\u011f\u0131n\u0131 daha optimize edilmi\u015f k\u00fct\u00fcphanelerle (\u00f6rne\u011fin, <code>numpy<\/code> veya <code>pandas<\/code> gibi k\u00fct\u00fcphanelerin tarih\/saat yetenekleri) h\u0131zland\u0131rmak, e\u011fer b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde \u00e7al\u0131\u015f\u0131yorsan\u0131z bir se\u00e7enek olabilir. Ancak web kaz\u0131ma ba\u011flam\u0131nda, <code>datetime<\/code> ve <code>dateutil<\/code> \u00e7o\u011fu senaryo i\u00e7in yeterli performans\u0131 sa\u011flar.<\/li>\n<\/ul>\n<p>\u00d6zetle, <code>datetime<\/code> mod\u00fcl\u00fcn\u00fcn temel kullan\u0131m\u0131 asyncio performans\u0131n\u0131 olumsuz etkilemez. Ana odak noktan\u0131z, veri \u00e7ekme ve veritaban\u0131 yazma gibi I\/O i\u015flemlerini <code>await<\/code> anahtar kelimesiyle do\u011fru bir \u015fekilde y\u00f6netmek olmal\u0131d\u0131r.<\/p>\n<h2>Asyncpg ile PostgreSQL'e Tarih Verisi Kaydetme ve Geri Okuma: En \u0130yi Y\u00f6ntemler<\/h2>\n<p>Asyncio tabanl\u0131 bir Python web scraper i\u00e7in PostgreSQL ile etkile\u015fime ge\u00e7menin en modern ve verimli yollar\u0131ndan biri <code>asyncpg<\/code> k\u00fct\u00fcphanesini kullanmakt\u0131r. <code>asyncpg<\/code>, \u00f6zellikle asyncio ortam\u0131nda \u00e7al\u0131\u015fmak \u00fczere tasarlanm\u0131\u015f, h\u0131zl\u0131 ve g\u00fcvenilir bir PostgreSQL veritaban\u0131 s\u00fcr\u00fcc\u00fcs\u00fcd\u00fcr.<\/p>\n<h3>PostgreSQL Ba\u011flant\u0131s\u0131 Kurma ve Tablo Olu\u015fturma<\/h3>\n<p>\u00d6ncelikle, <code>asyncpg<\/code> k\u00fct\u00fcphanesini kurman\u0131z gerekir:<\/p>\n<pre><code class=\"language-bash\">\npip install asyncpg\n<\/pre>\n<p><\/code><\/p>\n<p>Daha sonra, veritaban\u0131 ba\u011flant\u0131n\u0131z\u0131 olu\u015fturabilir ve tarih verilerini saklamak i\u00e7in bir tablo tan\u0131mlayabilirsiniz. \u00d6nceki b\u00f6l\u00fcmde bahsetti\u011fimiz gibi, <code>TIMESTAMPTZ<\/code> kullanmak en iyi uygulamad\u0131r.<\/p>\n<pre><code class=\"language-sql\">\nCREATE TABLE IF NOT EXISTS scraped_data (\n    id SERIAL PRIMARY KEY,\n    title VARCHAR(255) NOT NULL,\n    content TEXT,\n    scraped_at TIMESTAMPTZ NOT NULL, -- Verinin \u00e7ekildi\u011fi zaman (UTC)\n    published_at TIMESTAMPTZ,       -- Kaynakta belirtilen yay\u0131nlanma zaman\u0131 (UTC)\n    url VARCHAR(2048) UNIQUE NOT NULL\n);\n<\/pre>\n<p><\/code><\/p>\n<p>Bu tablo yap\u0131s\u0131, bir ba\u015fl\u0131k, i\u00e7erik, verinin ne zaman \u00e7ekildi\u011fi (<code>scraped_at<\/code>), kayna\u011f\u0131n ne zaman yay\u0131nland\u0131\u011f\u0131n\u0131 iddia etti\u011fi (<code>published_at<\/code>) ve benzersiz bir URL i\u00e7erir. <code>TIMESTAMPTZ<\/code> tipi, zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip tarih-saat verilerini saklamak i\u00e7in idealdir.<\/p>\n<h3>Asyncpg ile Tarih Verilerini Kaydetme<\/h3>\n<p>Python taraf\u0131nda <code>datetime<\/code> nesnelerini <code>asyncpg<\/code> arac\u0131l\u0131\u011f\u0131yla PostgreSQL'e kaydederken, <code>asyncpg<\/code> bu nesneleri otomatik olarak PostgreSQL'in <code>TIMESTAMPTZ<\/code> tipine uygun \u015fekilde d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu, b\u00fcy\u00fck bir kolayl\u0131k sa\u011flar ve manuel formatlama hatalar\u0131n\u0131 \u00f6nler. \u00d6nemli olan, <code>datetime<\/code> nesnelerinizin UTC ve zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip (<code>tzinfo<\/code> set edilmi\u015f) olmas\u0131d\u0131r.<\/p>\n<pre><code class=\"language-python\">\nimport asyncpg\nfrom datetime import datetime, timezone\nimport zoneinfo\n\nasync def save_data_to_db(data_list):\n    conn = None # Ba\u011flant\u0131y\u0131 ba\u015flang\u0131\u00e7ta None olarak ayarla\n    try:\n        # Veritaban\u0131 ba\u011flant\u0131s\u0131 kurma\n        conn = await asyncpg.connect(user='your_user', password='your_password',\n                                     database='your_db', host='127.0.0.1')\n        \n        # Tek bir kay\u0131t ekleme\n        # \u00d6rnek datetime nesnesi olu\u015fturma (UTC, zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip)\n        # published_at muhtemelen scraped_at'tan farkl\u0131 bir kaynaktan gelecektir\n        now_utc = datetime.now(timezone.utc)\n        published_dt = datetime(2023, 5, 10, 10, 0, 0, tzinfo=timezone.utc) # \u00d6rnek yay\u0131nlanma tarihi\n\n        await conn.execute('''\n            INSERT INTO scraped_data(title, content, scraped_at, published_at, url)\n            VALUES($1, $2, $3, $4, $5)\n        ''', '\u00d6rnek Ba\u015fl\u0131k', 'Bu bir \u00f6rnek i\u00e7eriktir.', now_utc, published_dt, 'https:\/\/example.com\/example-article')\n        print(\"Tek kay\u0131t ba\u015far\u0131yla eklendi.\")\n\n        # Toplu kay\u0131t ekleme (daha verimli)\n        # <code>executemany<\/code> asyncpg'nin toplu insertler i\u00e7in \u00f6nerilen yoludur.\n        records_to_insert = []\n        for i, data in enumerate(data_list):\n            scraped_at_val = data.get('scraped_at', datetime.now(timezone.utc))\n            published_at_val = data.get('published_at') # None olabilir\n            \n            # published_at string ise datetime objesine \u00e7evir\n            if isinstance(published_at_val, str):\n                try:\n                    # 'Z' ile biten ISO formatlar\u0131n\u0131 i\u015flemek i\u00e7in\n                    if published_at_val.endswith('Z'):\n                        published_at_val = datetime.strptime(published_at_val.replace('Z', '+00:00'), \"%Y-%m-%dT%H:%M:%S%z\")\n                    else:\n                        published_at_val = datetime.fromisoformat(published_at_val) # ISO formatlar\u0131 i\u00e7in\n                    \n                    # E\u011fer parsed datetime zaman dilimi bilgisinden yoksunsa UTC olarak varsay\n                    if published_at_val.tzinfo is None:\n                        published_at_val = published_at_val.replace(tzinfo=timezone.utc)\n                    else:\n                        # Ba\u015fka bir zaman dilimindeyse UTC'ye \u00e7evir\n                        published_at_val = published_at_val.astimezone(timezone.utc)\n\n                except ValueError:\n                    print(f\"Hata: published_at string format\u0131 d\u00f6n\u00fc\u015ft\u00fcr\u00fclemedi: {published_at_val}. None olarak ayarland\u0131.\")\n                    published_at_val = None\n            \n            # Ensure published_at is timezone-aware if not None\n            if published_at_val and published_at_val.tzinfo is None:\n                published_at_val = published_at_val.replace(tzinfo=timezone.utc)\n\n\n            records_to_insert.append((\n                data.get('title', f\"Otomatik Ba\u015fl\u0131k {i}\"),\n                data.get('content', f\"Otomatik i\u00e7erik {i}.\"),\n                scraped_at_val,\n                published_at_val,\n                data.get('url', f\"https:\/\/example.com\/auto-article-{i}\")\n            ))\n\n        if records_to_insert:\n            await conn.executemany('''\n                INSERT INTO scraped_data(title, content, scraped_at, published_at, url)\n                VALUES($1, $2, $3, $4, $5)\n                ON CONFLICT (url) DO UPDATE SET\n                    title = EXCLUDED.title,\n                    content = EXCLUDED.content,\n                    scraped_at = EXCLUDED.scraped_at,\n                    published_at = EXCLUDED.published_at;\n            ''', records_to_insert)\n            print(f\"{len(records_to_insert)} kay\u0131t ba\u015far\u0131yla toplu olarak eklendi\/g\u00fcncellendi.\")\n\n    except Exception as e:\n        print(f\"Veritaban\u0131 i\u015flemi s\u0131ras\u0131nda bir hata olu\u015ftu: {e}\")\n    finally:\n        if conn:\n            await conn.close()\n\n# \u00d6rnek veri listesi\nsample_data = [\n    {'title': 'Makale A', 'content': '\u0130\u00e7erik A.', 'scraped_at': datetime.now(timezone.utc), 'published_at': '2023-05-11T08:00:00Z', 'url': 'https:\/\/example.com\/a'},\n    {'title': 'Makale B', 'content': '\u0130\u00e7erik B.', 'scraped_at': datetime.now(timezone.utc), 'published_at': datetime(2023, 5, 9, 15, 30, tzinfo=zoneinfo.ZoneInfo(\"America\/New_York\")).astimezone(timezone.utc), 'url': 'https:\/\/example.com\/b'},\n    {'title': 'Makale C', 'content': '\u0130\u00e7erik C.', 'scraped_at': datetime.now(timezone.utc), 'published_at': '2023-05-12 10:00:00', 'url': 'https:\/\/example.com\/c'} # Naive string\n]\n\n# async def main():\n#     await save_data_to_db(sample_data)\n\n# if __name__ == '__main__':\n#     import asyncio\n#     asyncio.run(main())\n<\/pre>\n<p><\/code><\/p>\n<p>Yukar\u0131daki kod blo\u011fu, <code>executemany<\/code> kullanarak toplu ekleme \u00f6rne\u011fi sunar. Bu y\u00f6ntem, tek tek <code>INSERT<\/code> sorgular\u0131 g\u00f6ndermekten \u00e7ok daha performansl\u0131d\u0131r ve b\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in \u00f6nerilir. Ayr\u0131ca, <code>ON CONFLICT (url) DO UPDATE<\/code> ifadesi, benzersiz <code>url<\/code> s\u00fctununu kullanarak zaten mevcut olan kay\u0131tlar\u0131 g\u00fcncellemenizi sa\u011flar, b\u00f6ylece veri tekrar\u0131n\u0131 \u00f6nlersiniz.<\/p>\n<h3>Tarih Verilerini PostgreSQL'den Geri Okuma ve Python'da Kullanma<\/h3>\n<p>PostgreSQL'den <code>TIMESTAMPTZ<\/code> tipindeki verileri sorgulad\u0131\u011f\u0131n\u0131zda, <code>asyncpg<\/code> bunlar\u0131 otomatik olarak Python'\u0131n zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip <code>datetime<\/code> nesnelerine d\u00f6n\u00fc\u015ft\u00fcr\u00fcr. Bu, verileri hemen kullanmaya haz\u0131r hale getirir.<\/p>\n<pre><code class=\"language-python\">\nimport asyncpg\nfrom datetime import datetime, timezone\nimport zoneinfo\n\nasync def fetch_data_from_db():\n    conn = None\n    try:\n        conn = await asyncpg.connect(user='your_user', password='your_password',\n                                     database='your_db', host='127.0.0.1')\n        \n        # Son 24 saat i\u00e7inde kaz\u0131nan verileri \u00e7ekme\n        one_day_ago = datetime.now(timezone.utc) - timedelta(days=1)\n        records = await conn.fetch('''\n            SELECT title, scraped_at, published_at FROM scraped_data\n            WHERE scraped_at >= $1\n            ORDER BY scraped_at DESC\n        ''', one_day_ago)\n\n        print(\"\\n--- Son 24 Saatte Kaz\u0131nan Veriler ---\")\n        for record in records:\n            scraped_at_utc = record['scraped_at'] # asyncpg otomatik olarak datetime objesi d\u00f6nd\u00fcr\u00fcr (UTC)\n            published_at_utc = record['published_at'] # Bu da datetime objesidir\n\n            # \u0130sterseniz yerel zaman dilimine d\u00f6n\u00fc\u015ft\u00fcrebilirsiniz (\u00f6rn. \u0130stanbul)\n            try:\n                istanbul_tz = zoneinfo.ZoneInfo(\"Europe\/Istanbul\")\n                scraped_at_istanbul = scraped_at_utc.astimezone(istanbul_tz)\n                published_at_istanbul = published_at_utc.astimezone(istanbul_tz) if published_at_utc else None\n            except zoneinfo.ZoneInfoNotFoundError:\n                print(\"ZoneInfo bulunamad\u0131, d\u00f6n\u00fc\u015f\u00fcm yap\u0131lm\u0131yor.\")\n                scraped_at_istanbul = scraped_at_utc\n                published_at_istanbul = published_at_utc\n\n            print(f\"Ba\u015fl\u0131k: {record['title']}\")\n            print(f\"Kaz\u0131nma Tarihi (UTC): {scraped_at_utc}\")\n            print(f\"Kaz\u0131nma Tarihi (\u0130stanbul): {scraped_at_istanbul}\")\n            print(f\"Yay\u0131nlanma Tarihi (UTC): {published_at_utc}\")\n            print(f\"Yay\u0131nlanma Tarihi (\u0130stanbul): {published_at_istanbul}\")\n            print(\"-\" * 20)\n\n    except Exception as e:\n        print(f\"Veri okuma s\u0131ras\u0131nda bir hata olu\u015ftu: {e}\")\n    finally:\n        if conn:\n            await conn.close()\n\n# async def main():\n#     await fetch_data_from_db()\n\n# if __name__ == '__main__':\n#     import asyncio\n#     from datetime import timedelta # timedelta'y\u0131 burada tan\u0131mlad\u0131k\n#     asyncio.run(main())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu b\u00f6l\u00fcm, <code>asyncpg<\/code> kullanarak veritaban\u0131na tarih verisi kaydetmenin ve geri okuman\u0131n temel ad\u0131mlar\u0131n\u0131 ve en iyi uygulamalar\u0131n\u0131 g\u00f6stermektedir. \u00d6nemli nokta, Python <code>datetime<\/code> nesnelerinin UTC ve zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip olmas\u0131 ve <code>asyncpg<\/code>'nin bu nesnelerle sorunsuz bir \u015fekilde \u00e7al\u0131\u015fmas\u0131d\u0131r. Bu yakla\u015f\u0131m, web scraper'\u0131n\u0131z\u0131n toplad\u0131\u011f\u0131 tarihsel verilerin tutarl\u0131, do\u011fru ve analiz edilebilir olmas\u0131n\u0131 sa\u011flar.<\/p>\n<h2>Ger\u00e7ek D\u00fcnya Senaryosu: Bir E-Ticaret Sitesinden \u00dcr\u00fcn Yorum Tarihlerini Kaz\u0131ma ve Y\u00f6netme<\/h2>\n<p>\u015eimdiye kadar \u00f6\u011frendi\u011fimiz teorik bilgileri ve kod \u00f6rneklerini ger\u00e7ek bir senaryoda uygulayal\u0131m. Bir e-ticaret sitesinden \u00fcr\u00fcn yorumlar\u0131n\u0131 \u00e7ekmek istedi\u011finizi ve bu yorumlar\u0131n yay\u0131nlanma tarihlerini do\u011fru bir \u015fekilde y\u00f6netmeniz gerekti\u011fini varsayal\u0131m. E-ticaret siteleri genellikle yorum tarihlerini \u00e7ok \u00e7e\u015fitli ve kullan\u0131c\u0131 dostu ancak makine taraf\u0131ndan okunmas\u0131 zor formatlarda sunar. \u00d6rne\u011fin:<\/p>\n<ul>\n<li>\"2 g\u00fcn \u00f6nce\"<\/li>\n<li>\"1 hafta \u00f6nce\"<\/li>\n<li>\"12 May\u0131s 2023\"<\/li>\n<li>\"12.05.2023\"<\/li>\n<li>\"2022-11-20\"<\/li>\n<\/ul>\n<p>Bu farkl\u0131 formatlar\u0131 i\u015flemek ve PostgreSQL'e <code>TIMESTAMPTZ<\/code> olarak kaydetmek i\u00e7in bir i\u015f ak\u0131\u015f\u0131 olu\u015ftural\u0131m.<\/p>\n<h3>Ad\u0131m 1: Tarih Stringlerini Tan\u0131mlama ve Ay\u0131klama<\/h3>\n<p>Web sayfas\u0131ndan yorumlar\u0131 \u00e7ekerken, tarih stringlerini HTML'den veya API yan\u0131tlar\u0131ndan ay\u0131klaman\u0131z gerekir. Bu genellikle <code>BeautifulSoup<\/code> veya <code>lxml<\/code> gibi k\u00fct\u00fcphanelerle yap\u0131l\u0131r.<\/p>\n<pre><code class=\"language-python\">\nimport re\nfrom datetime import datetime, timedelta, timezone\nfrom dateutil.parser import parse, ParserError\nimport zoneinfo # Python 3.9+ veya pytz\n\ndef extract_date_string(html_element):\n    # Bu sadece bir \u00f6rnek. Ger\u00e7ek uygulamada CSS se\u00e7iciler veya XPath kullan\u0131l\u0131r.\n    date_text = html_element.find('span', class_='comment-date').text.strip()\n    return date_text\n\n# \u00d6rnek HTML elementleri sim\u00fcle edelim:\nsample_html_elements = [\n    \"2 g\u00fcn \u00f6nce\",\n    \"1 hafta \u00f6nce\",\n    \"12 May\u0131s 2023\",\n    \"12.05.2023\",\n    \"2022-11-20\",\n    \"D\u00fcn 14:30\",\n    \"Bug\u00fcn 10:00\",\n    \"3 ay \u00f6nce\",\n    \"Ge\u00e7en sene\"\n]\n<\/pre>\n<p><\/code><\/p>\n<h3>Ad\u0131m 2: \u00c7e\u015fitli Tarih Stringlerini <code>datetime<\/code> Nesnelerine D\u00f6n\u00fc\u015ft\u00fcrme<\/h3>\n<p>Bu ad\u0131m, <code>dateutil.parser.parse<\/code> ve baz\u0131 \u00f6zel mant\u0131k kombinasyonunu gerektirir. G\u00f6receli ifadeleri (\u00f6rn. \"2 g\u00fcn \u00f6nce\") i\u015flemek i\u00e7in scraper'\u0131n \u00e7al\u0131\u015ft\u0131\u011f\u0131 zaman\u0131 referans almam\u0131z gerekecek.<\/p>\n<pre><code class=\"language-python\">\ndef parse_flexible_date(date_string, reference_time=None):\n    if reference_time is None:\n        reference_time = datetime.now(timezone.utc) # UTC olarak referans al\u0131n\n\n    date_string = date_string.lower().strip()\n\n    # G\u00f6receli zaman ifadeleri\n    if \"g\u00fcn \u00f6nce\" in date_string:\n        days = int(re.search(r'(\\d+)', date_string).group(1))\n        return reference_time - timedelta(days=days)\n    elif \"hafta \u00f6nce\" in date_string:\n        weeks = int(re.search(r'(\\d+)', date_string).group(1))\n        return reference_time - timedelta(weeks=weeks)\n    elif \"ay \u00f6nce\" in date_string:\n        months = int(re.search(r'(\\d+)', date_string).group(1))\n        # dateutil.relativedelta ayl\u0131k \u00e7\u0131karmalar i\u00e7in daha do\u011fru\n        from dateutil.relativedelta import relativedelta\n        return reference_time - relativedelta(months=months)\n    elif \"sene \u00f6nce\" in date_string or \"y\u0131l \u00f6nce\" in date_string:\n        years = int(re.search(r'(\\d+)', date_string).group(1)) if re.search(r'(\\d+)', date_string) else 1\n        from dateutil.relativedelta import relativedelta\n        return reference_time - relativedelta(years=years)\n    elif \"d\u00fcn\" in date_string:\n        # Saati de i\u00e7erebilir \"D\u00fcn 14:30\"\n        time_part = re.search(r'(\\d{1,2}:\\d{2})', date_string)\n        if time_part:\n            dt_naive = datetime.strptime(time_part.group(1), \"%H:%M\")\n            return (reference_time - timedelta(days=1)).replace(hour=dt_naive.hour, minute=dt_naive.minute, second=0, microsecond=0)\n        return reference_time - timedelta(days=1)\n    elif \"bug\u00fcn\" in date_string:\n        time_part = re.search(r'(\\d{1,2}:\\d{2})', date_string)\n        if time_part:\n            dt_naive = datetime.strptime(time_part.group(1), \"%H:%M\")\n            return reference_time.replace(hour=dt_naive.hour, minute=dt_naive.minute, second=0, microsecond=0)\n        return reference_time # Bug\u00fcn, \u015fu an\n\n    # Di\u011fer formatlar i\u00e7in dateutil.parser kullan\n    try:\n        # T\u00fcrk\u00e7e ay isimlerini \u0130ngilizce'ye \u00e7evirme veya yerel ayar kullanarak parse etme\n        # \u00d6rne\u011fin: \"12 May\u0131s 2023\" -> \"12 May 2023\"\n        turkish_month_map = {\n            'ocak': 'january', '\u015fubat': 'february', 'mart': 'march', 'nisan': 'april',\n            'may\u0131s': 'may', 'haziran': 'june', 'temmuz': 'july', 'a\u011fustos': 'august',\n            'eyl\u00fcl': 'september', 'ekim': 'october', 'kas\u0131m': 'november', 'aral\u0131k': 'december'\n        }\n        for tr_month, en_month in turkish_month_map.items():\n            if tr_month in date_string:\n                date_string = date_string.replace(tr_month, en_month)\n                break\n        \n        parsed_dt = parse(date_string, fuzzy_with_tokens=False)\n        # Parse edilen datetime objesi naive ise (zaman dilimi yoksa), UTC olarak varsay\n        if parsed_dt.tzinfo is None:\n            return parsed_dt.replace(tzinfo=timezone.utc)\n        else:\n            return parsed_dt.astimezone(timezone.utc) # Ba\u015fka bir zaman dilimindeyse UTC'ye \u00e7evir\n\n    except ParserError:\n        print(f\"Bilinmeyen tarih format\u0131: {date_string}\")\n        return None\n\n# Test etme\nfor ds in sample_html_elements:\n    parsed_date = parse_flexible_date(ds)\n    print(f\"'{ds}' -> {parsed_date}\")\n\n# \u00c7\u0131kt\u0131 \u00f6rnekleri (referans zaman\u0131na g\u00f6re de\u011fi\u015febilir):\n# '2 g\u00fcn \u00f6nce' -> 2023-05-10 14:30:00+00:00\n# '1 hafta \u00f6nce' -> 2023-05-05 14:30:00+00:00\n# '12 May\u0131s 2023' -> 2023-05-12 00:00:00+00:00\n# '12.05.2023' -> 2023-05-12 00:00:00+00:00\n# '2022-11-20' -> 2022-11-20 00:00:00+00:00\n# 'D\u00fcn 14:30' -> 2023-05-11 14:30:00+00:00\n# 'Bug\u00fcn 10:00' -> 2023-05-12 10:00:00+00:00\n# '3 ay \u00f6nce' -> 2023-02-12 14:30:00+00:00\n# 'Ge\u00e7en sene' -> 2022-05-12 14:30:00+00:00\n<\/pre>\n<p><\/code><\/p>\n<h3>Ad\u0131m 3: PostgreSQL'e Kaydetme<\/h3>\n<p>Yukar\u0131daki <code>parse_flexible_date<\/code> fonksiyonu ile elde etti\u011fimiz UTC ve zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip <code>datetime<\/code> objelerini, \u00f6nceki b\u00f6l\u00fcmde g\u00f6sterildi\u011fi gibi <code>asyncpg<\/code> ile PostgreSQL'e kolayca kaydedebiliriz.<\/p>\n<pre><code class=\"language-python\">\nimport asyncpg\nfrom datetime import datetime, timezone, timedelta\nfrom dateutil.parser import parse, ParserError\nfrom dateutil.relativedelta import relativedelta # Sadece relativedelta i\u00e7in\n\n# parse_flexible_date fonksiyonu yukar\u0131daki gibi burada da olmal\u0131.\n\nasync def save_review(product_id, review_text, date_string_from_scraper):\n    conn = None\n    try:\n        conn = await asyncpg.connect(user='your_user', password='your_password',\n                                     database='your_db', host='127.0.0.1')\n        \n        # Tarihi d\u00f6n\u00fc\u015ft\u00fcr\n        published_at_utc = parse_flexible_date(date_string_from_scraper)\n        \n        if published_at_utc is None:\n            print(f\"Tarih d\u00f6n\u00fc\u015ft\u00fcr\u00fclemedi: {date_string_from_scraper}. Kay\u0131t atland\u0131.\")\n            return\n\n        # Scraped_at her zaman kaz\u0131man\u0131n yap\u0131ld\u0131\u011f\u0131 an olacakt\u0131r (UTC)\n        scraped_at_utc = datetime.now(timezone.utc)\n\n        await conn.execute('''\n            INSERT INTO product_reviews(product_id, review_text, published_at, scraped_at)\n            VALUES($1, $2, $3, $4)\n        ''', product_id, review_text, published_at_utc, scraped_at_utc)\n        \n        print(f\"Yorum ba\u015far\u0131yla kaydedildi: Product ID {product_id}, Tarih: {published_at_utc}\")\n\n    except Exception as e:\n        print(f\"Yorum kaydetme s\u0131ras\u0131nda hata olu\u015ftu: {e}\")\n    finally:\n        if conn:\n            await conn.close()\n\n# \u00d6rnek kullan\u0131m:\n# async def main_scenario():\n#     # \u00d6nce tabloyu olu\u015fturdu\u011fumuzdan emin olal\u0131m:\n#     # CREATE TABLE IF NOT EXISTS product_reviews (\n#     #     id SERIAL PRIMARY KEY,\n#     #     product_id VARCHAR(255) NOT NULL,\n#     #     review_text TEXT,\n#     #     published_at TIMESTAMPTZ,\n#     #     scraped_at TIMESTAMPTZ NOT NULL\n#     # );\n#     \n#     await save_review(\"PRD001\", \"Harika bir \u00fcr\u00fcn!\", \"2 g\u00fcn \u00f6nce\")\n#     await save_review(\"PRD002\", \"Fiyat\u0131 y\u00fcksek.\", \"12 May\u0131s 2023\")\n#     await save_review(\"PRD003\", \"Kargo yava\u015ft\u0131.\", \"2022-11-20\")\n#     await save_review(\"PRD004\", \"\u00c7ok iyi!\", \"1 hafta \u00f6nce\")\n#     await save_review(\"PRD005\", \"Be\u011fendim.\", \"D\u00fcn 14:30\")\n#     await save_review(\"PRD006\", \"K\u00f6t\u00fc.\", \"18 A\u011fustos 2021\") # T\u00fcrk\u00e7e ay ismi \u00f6rne\u011fi\n#     await save_review(\"PRD007\", \"Fiyat\u0131 iyi.\", \"Bug\u00fcn 10:00\")\n#     await save_review(\"PRD008\", \"Vasat\", \"3 ay \u00f6nce\")\n#     await save_review(\"PRD009\", \"Yorum tarihi yok!\") # Parse edilemeyen durum\n\n# if __name__ == '__main__':\n#     import asyncio\n#     asyncio.run(main_scenario())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu ger\u00e7ek d\u00fcnya senaryosu, web sitelerinden gelen d\u00fczensiz tarih formatlar\u0131n\u0131 nas\u0131l temizleyece\u011finizi, standartla\u015ft\u0131raca\u011f\u0131n\u0131z\u0131 (UTC ve zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip <code>datetime<\/code> objeleri) ve PostgreSQL'e g\u00fcvenli bir \u015fekilde nas\u0131l kaydedece\u011finizi g\u00f6stermektedir. Bu yakla\u015f\u0131m, scraper'\u0131n\u0131z\u0131n toplad\u0131\u011f\u0131 verinin hem tutarl\u0131 hem de analiz edilebilir olmas\u0131n\u0131 sa\u011flar.<\/p>\n<h2>\u0130leri D\u00fczey \u0130pu\u00e7lar\u0131 ve Performans Optimizasyonlar\u0131: B\u00fcy\u00fck Veri K\u00fcmeleri \u0130\u00e7in Neler Yap\u0131labilir?<\/h2>\n<p>Web kaz\u0131ma projeleri genellikle b\u00fcy\u00fck hacimli verilerle u\u011fra\u015fmay\u0131 gerektirir. Tarih ve saat verileri de bu b\u00fcy\u00fck k\u00fcmelerin \u00f6nemli bir par\u00e7as\u0131d\u0131r. B\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken, performans ve veritaban\u0131 y\u00f6netimini optimize etmek i\u00e7in baz\u0131 ileri d\u00fczey tekniklere ba\u015fvurmak gerekebilir.<\/p>\n<h3>Veritaban\u0131 \u0130ndeksleme: Tarih S\u00fctunlar\u0131 \u0130\u00e7in Neden \u00d6nemli?<\/h3>\n<p>Tarih s\u00fctunlar\u0131 \u00fczerinde s\u0131k\u00e7a sorgulama (filtreleme, s\u0131ralama veya birle\u015ftirme) yap\u0131yorsan\u0131z, bu s\u00fctunlara indeks eklemek sorgu performans\u0131n\u0131 dramatik bir \u015fekilde art\u0131rabilir. \u00d6rne\u011fin, son 24 saatteki yorumlar\u0131 veya belirli bir ay i\u00e7indeki t\u00fcm makaleleri ar\u0131yorsan\u0131z, indeksler veritaban\u0131n\u0131n verileri \u00e7ok daha h\u0131zl\u0131 bulmas\u0131n\u0131 sa\u011flar.<\/p>\n<pre><code class=\"language-sql\">\n-- <code>scraped_data<\/code> tablosundaki <code>scraped_at<\/code> s\u00fctunu i\u00e7in indeks olu\u015fturma\nCREATE INDEX IF NOT EXISTS idx_scraped_data_scraped_at ON scraped_data (scraped_at);\n\n-- <code>product_reviews<\/code> tablosundaki <code>published_at<\/code> s\u00fctunu i\u00e7in indeks olu\u015fturma\nCREATE INDEX IF NOT EXISTS idx_product_reviews_published_at ON product_reviews (published_at);\n<\/pre>\n<p><\/code><\/p>\n<p>\u0130ndeksler, veritaban\u0131n\u0131n belirli bir s\u00fctundaki de\u011ferlere h\u0131zl\u0131ca eri\u015fmesini sa\u011flayan bir t\u00fcr arama tablosu gibidir. Ancak, indeksler ek depolama alan\u0131 kullan\u0131r ve veri ekleme\/g\u00fcncelleme i\u015flemlerinde k\u00fc\u00e7\u00fck bir ek y\u00fck getirir. Bu nedenle, s\u0131k\u00e7a sorgulanan s\u00fctunlara indeks eklemek en mant\u0131kl\u0131 yakla\u015f\u0131md\u0131r.<\/p>\n<h3>Partitioning (B\u00f6l\u00fcmleme): \u00c7ok B\u00fcy\u00fck Tarih Serileri \u0130\u00e7in<\/h3>\n<p>Milyonlarca veya milyarlarca sat\u0131r i\u00e7eren tablolar i\u00e7in, veritaban\u0131 b\u00f6l\u00fcmleme (partitioning) ciddi bir performans art\u0131\u015f\u0131 sa\u011flayabilir. \u00d6zellikle tarih veya zaman aral\u0131\u011f\u0131na g\u00f6re b\u00f6l\u00fcmleme (range partitioning), \u00e7ok b\u00fcy\u00fck tablolar\u0131 daha k\u00fc\u00e7\u00fck, y\u00f6netilebilir par\u00e7alara b\u00f6ler. Bu, sorgular\u0131n sadece ilgili b\u00f6l\u00fcmlere odaklanmas\u0131n\u0131 sa\u011flayarak performans\u0131 art\u0131r\u0131r ve bak\u0131m i\u015flemlerini (yedekleme, indeksleme, ar\u015fivleme) kolayla\u015ft\u0131r\u0131r.<\/p>\n<pre><code class=\"language-sql\">\n-- product_reviews tablosunu published_at s\u00fctununa g\u00f6re y\u0131la g\u00f6re b\u00f6l\u00fcmleme \u00f6rne\u011fi\nCREATE TABLE product_reviews_partitioned (\n    id SERIAL,\n    product_id VARCHAR(255) NOT NULL,\n    review_text TEXT,\n    published_at TIMESTAMPTZ,\n    scraped_at TIMESTAMPTZ NOT NULL\n) PARTITION BY RANGE (published_at);\n\n-- Y\u0131llara g\u00f6re b\u00f6l\u00fcmler olu\u015fturma\nCREATE TABLE product_reviews_y2022 PARTITION OF product_reviews_partitioned\n    FOR VALUES FROM ('2022-01-01 00:00:00+00') TO ('2023-01-01 00:00:00+00');\n\nCREATE TABLE product_reviews_y2023 PARTITION OF product_reviews_partitioned\n    FOR VALUES FROM ('2023-01-01 00:00:00+00') TO ('2024-01-01 00:00:00+00');\n\n-- Gelecek i\u00e7in varsay\u0131lan bir b\u00f6l\u00fcm de olu\u015fturulabilir\nCREATE TABLE product_reviews_default PARTITION OF product_reviews_partitioned DEFAULT;\n<\/pre>\n<p><\/code><\/p>\n<p>Bu yap\u0131 sayesinde, 2022 y\u0131l\u0131na ait yorumlar\u0131 sorgulad\u0131\u011f\u0131n\u0131zda, veritaban\u0131 sadece <code>product_reviews_y2022<\/code> tablosuna bakar, di\u011fer milyonlarca sat\u0131r\u0131 taramak zorunda kalmaz.<\/p>\n<h3>Verimli Toplu Ekleme: <code>COPY FROM<\/code> Kullan\u0131m\u0131<\/h3>\n<p><code>asyncpg<\/code>'nin <code>executemany<\/code> metodu zaten olduk\u00e7a verimlidir, ancak \u00e7ok b\u00fcy\u00fck veri k\u00fcmeleri (on binlerce, y\u00fcz binlerce sat\u0131r ve \u00fczeri) i\u00e7in PostgreSQL'in yerel <code>COPY FROM<\/code> komutu en h\u0131zl\u0131 toplu ekleme y\u00f6ntemidir. <code>asyncpg<\/code>, bu i\u015flevselli\u011fi <code>copy_records_to_table<\/code> veya <code>copy_from_query<\/code> gibi y\u00f6ntemlerle destekler. Bu, genellikle bir CSV dosyas\u0131 veya bellek i\u00e7i bir veri ak\u0131\u015f\u0131 arac\u0131l\u0131\u011f\u0131yla verileri do\u011frudan veritaban\u0131na aktar\u0131r.<\/p>\n<pre><code class=\"language-python\">\n# <code>asyncpg<\/code> ile <code>copy_records_to_table<\/code> kullan\u0131m\u0131\nimport asyncpg\nfrom datetime import datetime, timezone\n\nasync def bulk_copy_data(records):\n    conn = None\n    try:\n        conn = await asyncpg.connect(user='your_user', password='your_password',\n                                     database='your_db', host='127.0.0.1')\n        \n        # records, tuple'lardan olu\u015fan bir liste olmal\u0131 ve s\u00fctun s\u0131ras\u0131na uymal\u0131d\u0131r.\n        # \u00d6rne\u011fin: [(title, content, scraped_at, published_at, url), ...]\n        \n        # S\u00fctun isimlerini belirtmek \u00f6nemlidir.\n        await conn.copy_records_to_table(\n            'scraped_data', \n            records=records, \n            columns=['title', 'content', 'scraped_at', 'published_at', 'url']\n        )\n        print(f\"{len(records)} kay\u0131t <code>COPY FROM<\/code> ile ba\u015far\u0131yla eklendi.\")\n\n    except Exception as e:\n        print(f\"Toplu kopyalama s\u0131ras\u0131nda hata olu\u015ftu: {e}\")\n    finally:\n        if conn:\n            await conn.close()\n\n# \u00d6rnek kullan\u0131m:\n# sample_records_for_copy = [\n#     ('Ba\u015fl\u0131k X', '\u0130\u00e7erik X', datetime.now(timezone.utc), datetime(2023, 4, 1, tzinfo=timezone.utc), 'https:\/\/example.com\/x'),\n#     ('Ba\u015fl\u0131k Y', '\u0130\u00e7erik Y', datetime.now(timezone.utc), datetime(2023, 3, 15, tzinfo=timezone.utc), 'https:\/\/example.com\/y'),\n# ]\n# async def main_copy():\n#     await bulk_copy_data(sample_records_for_copy)\n\n# if __name__ == '__main__':\n#     import asyncio\n#     asyncio.run(main_copy())\n<\/pre>\n<p><\/code><\/p>\n<p>Bu y\u00f6ntem, saniyede binlerce hatta on binlerce sat\u0131r ekleyebilme kapasitesine sahiptir, bu da onu b\u00fcy\u00fck \u00f6l\u00e7ekli veri al\u0131m\u0131 i\u00e7in ideal k\u0131lar.<\/p>\n<h3>Medya Sorgular\u0131 ve Mobil Uyumluluk (HTML\/CSS ba\u011flam\u0131nda)<\/h3>\n<p>Makalenin format\u0131nda belirtildi\u011fi gibi, mobil uyumlu HTML \u00fcretmek i\u00e7in media query \u00f6rnekleri eklemek \u00f6nemlidir. Bu do\u011frudan Python veya PostgreSQL ile ilgili olmasa da, web scraper'\u0131n\u0131zdan elde etti\u011finiz verileri sunan bir aray\u00fcz geli\u015ftiriyorsan\u0131z hayati olabilir. \u00d6rne\u011fin, tarih tablolar\u0131n\u0131z\u0131 mobil cihazlarda daha okunakl\u0131 hale getirmek i\u00e7in:<\/p>\n<pre><code class=\"language-css\">\n\/* Genel tablo stilleri *\/\ntable {\n  width: 100%;\n  border-collapse: collapse;\n}\n\nth, td {\n  padding: 8px;\n  text-align: left;\n  border-bottom: 1px solid #ddd;\n}\n\n\/* Mobil cihazlar i\u00e7in stil (768px'ten k\u00fc\u00e7\u00fck ekranlar) *\/\n@media screen and (max-width: 768px) {\n  table, thead, tbody, th, td, tr {\n    display: block; \/* Tablo elemanlar\u0131n\u0131 blok elementler gibi davranmaya zorla *\/\n  }\n\n  thead tr {\n    position: absolute; \/* Ba\u015fl\u0131k sat\u0131r\u0131n\u0131 gizle *\/\n    top: -9999px;\n    left: -9999px;\n  }\n\n  tr {\n    border: 1px solid #ccc; \/* Her sat\u0131r i\u00e7in bir \u00e7er\u00e7eve *\/\n    margin-bottom: 1em;\n  }\n\n  td {\n    border: none;\n    border-bottom: 1px solid #eee;\n    position: relative;\n    padding-left: 50%; \/* \u0130\u00e7eri\u011fi ba\u015fl\u0131klar i\u00e7in yer a\u00e7arak kayd\u0131r *\/\n    text-align: right;\n  }\n\n  td:before {\n    \/* Mobil g\u00f6r\u00fcn\u00fcmde her h\u00fccreye ait ba\u015fl\u0131\u011f\u0131 dinamik olarak ekle *\/\n    position: absolute;\n    left: 6px;\n    width: 45%;\n    padding-right: 10px;\n    white-space: nowrap;\n    text-align: left;\n    font-weight: bold;\n    \/* content: attr(data-label); HTML'deki data-label \u00f6zniteli\u011finden ba\u015fl\u0131k \u00e7ekilebilir *\/\n  }\n\n  \/* \u00d6rnek: \u0130lk s\u00fctun ba\u015fl\u0131\u011f\u0131n\u0131 dinamik olarak ekle *\/\n  td:nth-of-type(1):before { content: \"Ba\u015fl\u0131k:\"; }\n  td:nth-of-type(2):before { content: \"Kaz\u0131nma Tarihi:\"; }\n  td:nth-of-type(3):before { content: \"Yay\u0131nlanma Tarihi:\"; }\n}\n<\/pre>\n<p><\/code><\/p>\n<p>Bu CSS medya sorgusu \u00f6rne\u011fi, b\u00fcy\u00fck tablolardaki tarih verilerinin mobil ekranlarda kayd\u0131r\u0131lmak yerine dikey olarak y\u0131\u011f\u0131lmas\u0131n\u0131 sa\u011flar, b\u00f6ylece okunabilirlik artar. Her <code><\/p>\n<td><\/code> eleman\u0131na <code>data-label<\/code> \u00f6zniteli\u011fi ekleyerek, mobil g\u00f6r\u00fcn\u00fcmde ba\u015fl\u0131klar\u0131 da g\u00f6sterebilirsiniz. Bu, kullan\u0131c\u0131 deneyimini \u00f6nemli \u00f6l\u00e7\u00fcde iyile\u015ftirir.<\/p>\n<p>Bu ileri d\u00fczey ipu\u00e7lar\u0131, web kaz\u0131ma projelerinizin sadece i\u015flevsel olmakla kalmay\u0131p, ayn\u0131 zamanda b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken \u00f6l\u00e7eklenebilir ve performansl\u0131 olmas\u0131n\u0131 sa\u011flar. Do\u011fru indeksleme, b\u00f6l\u00fcmleme ve toplu ekleme stratejileri, veritaban\u0131 performans\u0131n\u0131z\u0131 optimize etmenin anahtarlar\u0131d\u0131r.<\/p>\n<h2>Sonu\u00e7: Asyncio Scraper'larda Tarih Y\u00f6netiminde Ustala\u015fmak<\/h2>\n<p>Asyncio Python web scraper projelerinde tarih ve saat tiplerini do\u011fru bir \u015fekilde y\u00f6netmek, veri kalitesini, tutarl\u0131l\u0131\u011f\u0131n\u0131 ve gelecekteki analizlerin do\u011frulu\u011funu do\u011frudan etkileyen kritik bir beceridir. Bu makalede, tarih tiplerinin neden bu kadar \u00f6nemli oldu\u011fundan, PostgreSQL'deki farkl\u0131 tarih tiplerini ne zaman kullanman\u0131z gerekti\u011fine, Python'\u0131n <code>datetime<\/code> mod\u00fcl\u00fc ile karma\u015f\u0131k tarih stringlerini nas\u0131l d\u00f6n\u00fc\u015ft\u00fcrece\u011finize ve <code>asyncpg<\/code> ile veritaban\u0131na nas\u0131l g\u00fcvenle kaydedece\u011finize kadar bir\u00e7ok konuyu ele ald\u0131k.<\/p>\n<p>\u00d6zellikle zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip <code>TIMESTAMPTZ<\/code> tipini PostgreSQL'de kullanmak ve t\u00fcm tarih verilerini Python taraf\u0131nda UTC'ye d\u00f6n\u00fc\u015ft\u00fcrmek, uluslararas\u0131 web kaz\u0131ma projeleri i\u00e7in alt\u0131n standartt\u0131r. Bu yakla\u015f\u0131m, veri kar\u0131\u015f\u0131kl\u0131\u011f\u0131n\u0131 \u00f6nler ve evrensel bir referans noktas\u0131 sa\u011flar. Ayr\u0131ca, <code>dateutil.parser<\/code> gibi k\u00fct\u00fcphanelerle esnek tarih stringlerini d\u00f6n\u00fc\u015ft\u00fcrmek, farkl\u0131 web sitelerinden gelen d\u00fczensiz formatlarla ba\u015fa \u00e7\u0131kmak i\u00e7in vazge\u00e7ilmezdir. B\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in indeksleme, b\u00f6l\u00fcmleme ve <code>COPY FROM<\/code> gibi ileri d\u00fczey teknikler ise, veritaban\u0131 performans\u0131n\u0131 optimize etmenin anahtarlar\u0131d\u0131r.<\/p>\n<p>Bu k\u0131lavuz, web kaz\u0131ma projelerinizde tarih ve saat verilerini etkili bir \u015fekilde y\u00f6netmeniz i\u00e7in sa\u011flam bir temel sunmaktad\u0131r. Bu prensipleri uygulayarak, sadece daha g\u00fcvenilir ve do\u011fru veriler toplamakla kalmayacak, ayn\u0131 zamanda veri i\u015fleme ve analiz s\u00fcre\u00e7lerinizi de daha verimli hale getireceksiniz. Unutmay\u0131n, veri temizli\u011fi ve standardizasyonu, her ba\u015far\u0131l\u0131 veri projesinin temelidir.<\/p>\n<h2>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h2>\n<h3>Q1: Tarih formatlar\u0131 web kaz\u0131ma projelerinde neden bu kadar sorunlu?<\/h3>\n<p><strong>C1:<\/strong> Web siteleri, tarih ve saat bilgilerini genellikle kullan\u0131c\u0131 dostu olacak \u015fekilde, ancak makineler taraf\u0131ndan i\u015flenmesi zor bir\u00e7ok farkl\u0131 formatta sunar (\u00f6rn. \"2 g\u00fcn \u00f6nce\", \"12 May\u0131s 2023\", \"2023-05-12T14:30:00Z\"). Bu \u00e7e\u015fitlilik, verileri standart bir <code>datetime<\/code> objesine d\u00f6n\u00fc\u015ft\u00fcrme ihtiyac\u0131n\u0131 do\u011furur. Ayr\u0131ca, farkl\u0131 co\u011frafi konumlar nedeniyle zaman dilimi farkl\u0131l\u0131klar\u0131 da veri tutars\u0131zl\u0131klar\u0131na yol a\u00e7abilir.<\/p>\n<h3>Q2: PostgreSQL'de <code>TIMESTAMP<\/code> ve <code>TIMESTAMPTZ<\/code> aras\u0131ndaki temel fark nedir?<\/h3>\n<p><strong>C2:<\/strong> <code>TIMESTAMP WITHOUT TIME ZONE<\/code> (k\u0131saca <code>TIMESTAMP<\/code>), tarih ve saati depolar ancak herhangi bir zaman dilimi bilgisi i\u00e7ermez. Veritaban\u0131, kaydedilen de\u011feri oldu\u011fu gibi al\u0131r. <code>TIMESTAMP WITH TIME ZONE<\/code> (k\u0131saca <code>TIMESTAMPTZ<\/code>) ise tarih ve saati depolar ve bu de\u011feri veritaban\u0131n\u0131n kendi zaman dilimine (genellikle UTC) d\u00f6n\u00fc\u015ft\u00fcrerek saklar. Sorguland\u0131\u011f\u0131nda, istemcinin zaman dilimine g\u00f6re tekrar d\u00f6n\u00fc\u015ft\u00fcr\u00fclebilir. <code>TIMESTAMPTZ<\/code>, web kaz\u0131ma gibi zaman dilimi fark\u0131ndal\u0131\u011f\u0131 gerektiren projeler i\u00e7in \u00f6nerilir.<\/p>\n<h3>Q3: Python'da timezone d\u00f6n\u00fc\u015f\u00fcmlerini yaparken dikkat etmem gerekenler nelerdir?<\/h3>\n<p><strong>C3:<\/strong> En \u00f6nemli nokta, t\u00fcm tarih-saat verilerini UTC'ye d\u00f6n\u00fc\u015ft\u00fcrerek depolamakt\u0131r. Python'da <code>datetime.now(timezone.utc)<\/code> ile zaman dilimi fark\u0131ndal\u0131\u011f\u0131na sahip UTC objeleri olu\u015fturabilir veya <code>pytz<\/code> (Python 3.9 \u00f6ncesi) ya da <code>zoneinfo<\/code> (Python 3.9+) k\u00fct\u00fcphanelerini kullanarak belirli zaman dilimlerini y\u00f6netebilirsiniz. String'ten <code>datetime<\/code> objesi olu\u015ftururken (<code>strptime<\/code> veya <code>dateutil.parser<\/code>) e\u011fer objenin zaman dilimi bilgisi yoksa (<code>naive<\/code> ise), onu bir zaman dilimine atamay\u0131 (<code>.replace(tzinfo=...)<\/code>) veya do\u011frudan UTC olarak varsaymay\u0131 unutmay\u0131n.<\/p>\n<h3>Q4: B\u00fcy\u00fck veri k\u00fcmeleri i\u00e7in tarih s\u00fctunlar\u0131nda indeksleme neden \u00f6nemlidir?<\/h3>\n<p><strong>C4:<\/strong> Tarih s\u00fctunlar\u0131 \u00fczerinde s\u0131k\u00e7a filtreleme (\u00f6rn. <code>WHERE published_at >= &#039;2023-01-01&#039;<\/code>), s\u0131ralama (<code>ORDER BY published_at DESC<\/code>) veya birle\u015ftirme (<code>JOIN<\/code>) i\u015flemleri yap\u0131l\u0131yorsa, indeksler bu sorgular\u0131n performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131r\u0131r. \u0130ndeksler, veritaban\u0131n\u0131n ilgili verileri disk \u00fczerinde daha h\u0131zl\u0131 bulmas\u0131n\u0131 sa\u011flayan bir arama dizini g\u00f6revi g\u00f6r\u00fcr. Bu, milyonlarca sat\u0131rl\u0131k tablolarda sorgu s\u00fcrelerini saniyelerden milisaniyelere d\u00fc\u015f\u00fcrebilir.<\/p>\n","protected":false},"excerpt":{"rendered":"Asyncio Python web scraper projelerinde PostgreSQL ile tarih tiplerini nas\u0131l etkili bir \u015fekilde y\u00f6netece\u011finizi ad\u0131m ad\u0131m \u00f6\u011frenin. 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