{"id":477961,"date":"2023-08-09T09:23:08","date_gmt":"2023-08-09T09:23:08","guid":{"rendered":""},"modified":"2023-09-05T11:15:45","modified_gmt":"2023-09-05T11:15:45","slug":"mapreduce","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/mapreduce\/","title":{"rendered":"Harita indirgeme"},"content":{"rendered":"<p>MapReduce, b\u00fcy\u00fck \u00f6l\u00e7ekli veri k\u00fcmelerini da\u011f\u0131t\u0131lm\u0131\u015f bir bilgi i\u015flem ortam\u0131nda i\u015flemek i\u00e7in tasarlanm\u0131\u015f bir programlama modeli ve hesaplama \u00e7er\u00e7evesidir. \u0130\u015f y\u00fck\u00fcn\u00fc bir bilgisayar k\u00fcmesinde paralel olarak y\u00fcr\u00fct\u00fclebilecek daha k\u00fc\u00e7\u00fck g\u00f6revlere b\u00f6lerek b\u00fcy\u00fck miktarlardaki verilerin verimli bir \u015fekilde i\u015flenmesine olanak tan\u0131r. MapReduce, b\u00fcy\u00fck veri d\u00fcnyas\u0131nda, i\u015fletmelerin ve kurulu\u015flar\u0131n b\u00fcy\u00fck miktarda bilgiden de\u011ferli i\u00e7g\u00f6r\u00fcler elde etmesini sa\u011flayan temel bir ara\u00e7 haline geldi.<\/p>\n<h2>MapReduce&#039;un k\u00f6keninin tarihi ve ilk s\u00f6z\u00fc<\/h2>\n<p>MapReduce kavram\u0131, Google&#039;dan Jeffrey Dean ve Sanjay Ghemawat taraf\u0131ndan 2004 y\u0131l\u0131nda yay\u0131nlanan &quot;MapReduce: B\u00fcy\u00fck K\u00fcmelerde Basitle\u015ftirilmi\u015f Veri \u0130\u015fleme&quot; ba\u015fl\u0131kl\u0131 ufuk a\u00e7\u0131c\u0131 makalelerinde tan\u0131t\u0131ld\u0131. Makalede, b\u00fcy\u00fck \u00f6l\u00e7ekli veri i\u015fleme g\u00f6revlerini verimli ve g\u00fcvenilir bir \u015fekilde ele almak i\u00e7in g\u00fc\u00e7l\u00fc bir yakla\u015f\u0131m\u0131n ana hatlar\u0131 \u00e7izildi. . Google, web belgelerini dizine eklemek ve i\u015flemek i\u00e7in MapReduce&#039;u kullanarak daha h\u0131zl\u0131 ve daha etkili arama sonu\u00e7lar\u0131 elde etti.<\/p>\n<h2>MapReduce hakk\u0131nda detayl\u0131 bilgi<\/h2>\n<p>MapReduce basit, iki ad\u0131ml\u0131 bir s\u00fcreci takip eder: harita a\u015famas\u0131 ve azaltma a\u015famas\u0131. Haritalama a\u015famas\u0131nda, giri\u015f verileri daha k\u00fc\u00e7\u00fck par\u00e7alara b\u00f6l\u00fcn\u00fcr ve k\u00fcmedeki birden fazla d\u00fc\u011f\u00fcm taraf\u0131ndan paralel olarak i\u015flenir. Her d\u00fc\u011f\u00fcm, ara \u00e7\u0131kt\u0131 olarak anahtar-de\u011fer \u00e7iftleri \u00fcreten bir e\u015fleme i\u015flevi ger\u00e7ekle\u015ftirir. Azaltma a\u015famas\u0131nda bu ara sonu\u00e7lar anahtarlar\u0131na g\u00f6re konsolide edilir ve nihai \u00e7\u0131kt\u0131 elde edilir.<\/p>\n<p>MapReduce&#039;un g\u00fczelli\u011fi hata tolerans\u0131 ve \u00f6l\u00e7eklenebilirli\u011finde yatmaktad\u0131r. Veriler d\u00fc\u011f\u00fcmler aras\u0131nda \u00e7o\u011falt\u0131ld\u0131\u011f\u0131 i\u00e7in donan\u0131m ar\u0131zalar\u0131n\u0131 sorunsuz bir \u015fekilde ele alabilir ve d\u00fc\u011f\u00fcm ar\u0131zalar\u0131 durumunda bile veri kullan\u0131labilirli\u011fini garanti eder.<\/p>\n<h2>MapReduce&#039;un i\u00e7 yap\u0131s\u0131: MapReduce nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h2>\n<p>MapReduce&#039;un dahili i\u015fleyi\u015fini daha iyi anlamak i\u00e7in s\u00fcreci ad\u0131m ad\u0131m inceleyelim:<\/p>\n<ol>\n<li>\n<p>Giri\u015f B\u00f6lme: Giri\u015f verileri, giri\u015f b\u00f6lmeleri ad\u0131 verilen daha k\u00fc\u00e7\u00fck y\u00f6netilebilir par\u00e7alara b\u00f6l\u00fcn\u00fcr. Her giri\u015f b\u00f6l\u00fcm\u00fc paralel i\u015fleme i\u00e7in bir e\u015fleyiciye atan\u0131r.<\/p>\n<\/li>\n<li>\n<p>E\u015fleme: E\u015fle\u015ftirici, girdi b\u00f6l\u00fcnmesini i\u015fler ve ara \u00e7\u0131kt\u0131 olarak anahtar-de\u011fer \u00e7iftleri \u00fcretir. Veri d\u00f6n\u00fc\u015ft\u00fcrme ve filtrelemenin ger\u00e7ekle\u015fti\u011fi yer buras\u0131d\u0131r.<\/p>\n<\/li>\n<li>\n<p>Kar\u0131\u015ft\u0131r ve S\u0131rala: Ara anahtar\/de\u011fer \u00e7iftleri, anahtarlar\u0131na g\u00f6re grupland\u0131r\u0131l\u0131r ve s\u0131ralan\u0131r; b\u00f6ylece ayn\u0131 anahtara sahip t\u00fcm de\u011ferlerin ayn\u0131 azalt\u0131c\u0131da bulunmas\u0131 sa\u011flan\u0131r.<\/p>\n<\/li>\n<li>\n<p>Azaltma: Her indirgeyici, ara anahtar\/de\u011fer \u00e7iftlerinin bir alt k\u00fcmesini al\u0131r ve verileri ayn\u0131 anahtarla birle\u015ftirmek ve toplamak i\u00e7in bir azaltma i\u015flevi ger\u00e7ekle\u015ftirir.<\/p>\n<\/li>\n<li>\n<p>Nihai \u00c7\u0131kt\u0131: Red\u00fckt\u00f6rler, daha fazla analiz i\u00e7in saklanabilen veya kullan\u0131labilen nihai \u00e7\u0131kt\u0131y\u0131 \u00fcretir.<\/p>\n<\/li>\n<\/ol>\n<h2>MapReduce&#039;un temel \u00f6zelliklerinin analizi<\/h2>\n<p>MapReduce, onu b\u00fcy\u00fck \u00f6l\u00e7ekli veri i\u015fleme i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7 haline getiren \u00e7e\u015fitli temel \u00f6zelliklere sahiptir:<\/p>\n<ul>\n<li>\n<p>\u00d6l\u00e7eklenebilirlik: MapReduce, da\u011f\u0131t\u0131lm\u0131\u015f bir makine k\u00fcmesinin hesaplama g\u00fcc\u00fcnden yararlanarak b\u00fcy\u00fck veri k\u00fcmelerini verimli bir \u015fekilde i\u015fleyebilir.<\/p>\n<\/li>\n<li>\n<p>Hata Tolerans\u0131: Verileri kopyalayarak ve ba\u015far\u0131s\u0131z g\u00f6revleri di\u011fer kullan\u0131labilir d\u00fc\u011f\u00fcmlerde yeniden \u00e7al\u0131\u015ft\u0131rarak d\u00fc\u011f\u00fcm hatalar\u0131n\u0131 ve veri kayb\u0131n\u0131 i\u015fleyebilir.<\/p>\n<\/li>\n<li>\n<p>Esneklik: MapReduce, \u00e7e\u015fitli veri i\u015fleme g\u00f6revlerine uygulanabildi\u011fi ve belirli gereksinimlere uyacak \u015fekilde \u00f6zelle\u015ftirilebildi\u011fi i\u00e7in \u00e7ok y\u00f6nl\u00fc bir \u00e7er\u00e7evedir.<\/p>\n<\/li>\n<li>\n<p>Basitle\u015ftirilmi\u015f Programlama Modeli: Geli\u015ftiriciler, d\u00fc\u015f\u00fck d\u00fczeyli paralelle\u015ftirme ve da\u011f\u0131t\u0131m karma\u015f\u0131kl\u0131klar\u0131 konusunda endi\u015felenmeden haritaya odaklanabilir ve i\u015flevleri azaltabilir.<\/p>\n<\/li>\n<\/ul>\n<h2>MapReduce T\u00fcrleri<\/h2>\n<p>MapReduce uygulamalar\u0131, temel sisteme ba\u011fl\u0131 olarak de\u011fi\u015fiklik g\u00f6sterebilir. MapReduce&#039;un baz\u0131 pop\u00fcler t\u00fcrleri \u015funlard\u0131r:<\/p>\n<table>\n<thead>\n<tr>\n<th>Tip<\/th>\n<th>Tan\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Hadoop Haritas\u0131Azalt<\/td>\n<td>Apache Hadoop ekosisteminin bir par\u00e7as\u0131 olan orijinal ve en bilinen uygulama.<\/td>\n<\/tr>\n<tr>\n<td>Google Bulut<\/td>\n<td>Google Cloud, Google Cloud Dataflow&#039;un bir par\u00e7as\u0131 olarak kendi MapReduce hizmetini sunar.<\/td>\n<\/tr>\n<tr>\n<td>Apache K\u0131v\u0131lc\u0131m\u0131<\/td>\n<td>Hadoop MapReduce&#039;a alternatif olan Apache Spark, daha h\u0131zl\u0131 veri i\u015fleme yetenekleri sa\u011flar.<\/td>\n<\/tr>\n<tr>\n<td>Microsoft HDInsight<\/td>\n<td>MapReduce i\u015fleme deste\u011fini i\u00e7eren Microsoft&#039;un bulut tabanl\u0131 Hadoop hizmeti.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>MapReduce&#039;\u0131 kullanma yollar\u0131, kullan\u0131mla ilgili sorunlar ve \u00e7\u00f6z\u00fcmleri<\/h2>\n<p>MapReduce, a\u015fa\u011f\u0131dakiler de dahil olmak \u00fczere \u00e7e\u015fitli alanlardaki uygulamalar\u0131 bulur:<\/p>\n<ol>\n<li>\n<p><strong>Veri analizi<\/strong>: G\u00fcnl\u00fck i\u015fleme, duyarl\u0131l\u0131k analizi ve m\u00fc\u015fteri davran\u0131\u015f\u0131 analizi gibi b\u00fcy\u00fck veri k\u00fcmeleri \u00fczerinde karma\u015f\u0131k veri analizi g\u00f6revlerinin ger\u00e7ekle\u015ftirilmesi.<\/p>\n<\/li>\n<li>\n<p><strong>Arama motorlar\u0131<\/strong>: B\u00fcy\u00fck web belgelerinden ilgili sonu\u00e7lar\u0131 verimli bir \u015fekilde dizine eklemesi ve almas\u0131 i\u00e7in arama motorlar\u0131na g\u00fc\u00e7 verilmesi.<\/p>\n<\/li>\n<li>\n<p><strong>Makine \u00f6\u011frenme<\/strong>: B\u00fcy\u00fck \u00f6l\u00e7ekli makine \u00f6\u011frenimi modellerini e\u011fitmek ve i\u015flemek i\u00e7in MapReduce&#039;u kullanma.<\/p>\n<\/li>\n<li>\n<p><strong>\u00d6neri Sistemleri<\/strong>: Kullan\u0131c\u0131 tercihlerine g\u00f6re ki\u015fiselle\u015ftirilmi\u015f \u00f6neri sistemleri olu\u015fturmak.<\/p>\n<\/li>\n<\/ol>\n<p>MapReduce bir\u00e7ok avantaj sunsa da, zorluklar\u0131 da var:<\/p>\n<ul>\n<li>\n<p><strong>Veri \u00c7arp\u0131kl\u0131\u011f\u0131<\/strong>: Red\u00fckt\u00f6rler aras\u0131ndaki dengesiz veri da\u011f\u0131t\u0131m\u0131 performans sorunlar\u0131na neden olabilir. Veri b\u00f6l\u00fcmleme ve birle\u015ftiriciler gibi teknikler bu sorunun hafifletilmesine yard\u0131mc\u0131 olabilir.<\/p>\n<\/li>\n<li>\n<p><strong>\u0130\u015f Planlama<\/strong>: K\u00fcme kaynaklar\u0131n\u0131 en iyi \u015fekilde kullanmak i\u00e7in i\u015fleri verimli bir \u015fekilde planlamak performans a\u00e7\u0131s\u0131ndan \u00e7ok \u00f6nemlidir.<\/p>\n<\/li>\n<li>\n<p><strong>Disk G\/\u00c7<\/strong>: Y\u00fcksek disk G\/\u00c7&#039;si darbo\u011faz haline gelebilir. \u00d6nbelle\u011fe alma, s\u0131k\u0131\u015ft\u0131rma ve daha h\u0131zl\u0131 depolama kullanmak bu sorunu \u00e7\u00f6zebilir.<\/p>\n<\/li>\n<\/ul>\n<h2>Ana \u00f6zellikler ve benzer terimlerle di\u011fer kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n<table>\n<thead>\n<tr>\n<th>karakteristik<\/th>\n<th>Harita indirgeme<\/th>\n<th>Hadoop<\/th>\n<th>K\u0131v\u0131lc\u0131m<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Veri \u0130\u015fleme Modeli<\/td>\n<td>Toplu i\u015fleme<\/td>\n<td>Toplu i\u015fleme<\/td>\n<td>Bellek i\u00e7i i\u015fleme<\/td>\n<\/tr>\n<tr>\n<td>Veri depolama<\/td>\n<td>HDFS (Hadoop Da\u011f\u0131t\u0131lm\u0131\u015f Dosya Sistemi)<\/td>\n<td>HDFS (Hadoop Da\u011f\u0131t\u0131lm\u0131\u015f Dosya Sistemi)<\/td>\n<td>HDFS ve di\u011fer depolama<\/td>\n<\/tr>\n<tr>\n<td>Hata Tolerans\u0131<\/td>\n<td>Evet<\/td>\n<td>Evet<\/td>\n<td>Evet<\/td>\n<\/tr>\n<tr>\n<td>\u0130\u015fleme h\u0131z\u0131<\/td>\n<td>Il\u0131man<\/td>\n<td>Il\u0131man<\/td>\n<td>Y\u00fcksek<\/td>\n<\/tr>\n<tr>\n<td>Kullan\u0131m kolayl\u0131\u011f\u0131<\/td>\n<td>Il\u0131man<\/td>\n<td>Il\u0131man<\/td>\n<td>Kolay<\/td>\n<\/tr>\n<tr>\n<td>Kullan\u0131m \u00d6rne\u011fi<\/td>\n<td>B\u00fcy\u00fck \u00f6l\u00e7ekli toplu i\u015fleme<\/td>\n<td>B\u00fcy\u00fck \u00f6l\u00e7ekli veri i\u015fleme<\/td>\n<td>Ger\u00e7ek zamanl\u0131 veri analizi<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>MapReduce ile ilgili gelece\u011fin perspektifleri ve teknolojileri<\/h2>\n<p>B\u00fcy\u00fck veri alan\u0131 geli\u015ftik\u00e7e, belirli kullan\u0131m durumlar\u0131 i\u00e7in MapReduce&#039;u tamamlayacak veya onun yerini alacak yeni teknolojiler ortaya \u00e7\u0131k\u0131yor. Baz\u0131 dikkate de\u011fer trendler ve teknolojiler \u015funlar\u0131 i\u00e7erir:<\/p>\n<ol>\n<li>\n<p><strong>Apache Flink&#039;i<\/strong>: Flink, d\u00fc\u015f\u00fck gecikmeli ve y\u00fcksek verimli veri i\u015fleme olana\u011f\u0131 sunan, ger\u00e7ek zamanl\u0131 veri analizine uygun hale getiren a\u00e7\u0131k kaynakl\u0131 bir ak\u0131\u015f i\u015fleme \u00e7er\u00e7evesidir.<\/p>\n<\/li>\n<li>\n<p><strong>Apa\u00e7i I\u015f\u0131n\u0131<\/strong>: Apache Beam, hem toplu hem de ak\u0131\u015f i\u015fleme i\u00e7in birle\u015fik bir programlama modeli sa\u011flayarak farkl\u0131 y\u00fcr\u00fctme motorlar\u0131 aras\u0131nda esneklik ve ta\u015f\u0131nabilirlik sunar.<\/p>\n<\/li>\n<li>\n<p><strong>Sunucusuz Bilgi \u0130\u015flem<\/strong>: AWS Lambda ve Google Cloud Functions gibi sunucusuz mimariler, altyap\u0131y\u0131 a\u00e7\u0131k\u00e7a y\u00f6netmeye gerek kalmadan verileri i\u015flemek i\u00e7in uygun maliyetli ve \u00f6l\u00e7eklenebilir bir yol sa\u011flar.<\/p>\n<\/li>\n<\/ol>\n<h2>Proxy sunucular nas\u0131l kullan\u0131labilir veya MapReduce ile ili\u015fkilendirilebilir?<\/h2>\n<p>Proxy sunucular, \u00f6zellikle b\u00fcy\u00fck \u00f6l\u00e7ekli uygulamalarda internet trafi\u011finin y\u00f6netilmesinde ve optimize edilmesinde \u00e7ok \u00f6nemli bir rol oynamaktad\u0131r. MapReduce ba\u011flam\u0131nda proxy sunucular \u00e7e\u015fitli \u015fekillerde kullan\u0131labilir:<\/p>\n<ol>\n<li>\n<p><strong>Y\u00fck dengeleme<\/strong>: Proxy sunucular\u0131, gelen MapReduce i\u015f isteklerini bir sunucu k\u00fcmesine da\u011f\u0131tarak bilgi i\u015flem kaynaklar\u0131n\u0131n verimli kullan\u0131m\u0131n\u0131 sa\u011flar.<\/p>\n<\/li>\n<li>\n<p><strong>\u00d6nbelle\u011fe almak<\/strong>: Proxy sunucular\u0131 ara MapReduce sonu\u00e7lar\u0131n\u0131 \u00f6nbelle\u011fe alabilir, gereksiz hesaplamalar\u0131 azalt\u0131r ve genel i\u015flem h\u0131z\u0131n\u0131 art\u0131r\u0131r.<\/p>\n<\/li>\n<li>\n<p><strong>G\u00fcvenlik<\/strong>: Proxy sunucular\u0131, yetkisiz eri\u015fimi ve olas\u0131 sald\u0131r\u0131lar\u0131 \u00f6nlemek i\u00e7in d\u00fc\u011f\u00fcmler aras\u0131ndaki veri trafi\u011fini filtreleyerek ve izleyerek bir g\u00fcvenlik katman\u0131 g\u00f6revi g\u00f6rebilir.<\/p>\n<\/li>\n<\/ol>\n<h2>\u0130lgili Ba\u011flant\u0131lar<\/h2>\n<p>MapReduce hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklar\u0131 inceleyebilirsiniz:<\/p>\n<ol>\n<li><a href=\"https:\/\/research.google\/pubs\/pub62\/\" target=\"_new\" rel=\"noopener nofollow\">MapReduce: B\u00fcy\u00fck K\u00fcmelerde Basitle\u015ftirilmi\u015f Veri \u0130\u015fleme<\/a><\/li>\n<li><a href=\"https:\/\/hadoop.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">Apache Hadoop<\/a><\/li>\n<li><a href=\"https:\/\/spark.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">Apache K\u0131v\u0131lc\u0131m\u0131<\/a><\/li>\n<li><a href=\"https:\/\/flink.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">Apache Flink&#039;i<\/a><\/li>\n<li><a href=\"https:\/\/beam.apache.org\/\" target=\"_new\" rel=\"noopener nofollow\">Apa\u00e7i I\u015f\u0131n\u0131<\/a><\/li>\n<\/ol>\n<p>Sonu\u00e7 olarak MapReduce, b\u00fcy\u00fck \u00f6l\u00e7ekli verileri i\u015fleme ve analiz etme y\u00f6ntemimizde devrim yaratarak i\u015fletmelerin devasa veri k\u00fcmelerinden de\u011ferli bilgiler elde etmesini sa\u011flad\u0131. Hata tolerans\u0131, \u00f6l\u00e7eklenebilirli\u011fi ve esnekli\u011fiyle MapReduce, b\u00fcy\u00fck veri \u00e7a\u011f\u0131nda g\u00fc\u00e7l\u00fc bir ara\u00e7 olmaya devam ediyor. Veri i\u015fleme ortam\u0131 geli\u015ftik\u00e7e, veriye dayal\u0131 \u00e7\u00f6z\u00fcmlerin t\u00fcm potansiyelinden yararlanmak i\u00e7in yeni geli\u015fen teknolojilerle g\u00fcncel kalmak \u00f6nemlidir.<\/p>","protected":false},"featured_media":468863,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477961","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>MapReduce: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is MapReduce and how does it work?","answer":"<p>MapReduce is a programming model and computational framework used for processing large-scale data sets in a distributed computing environment. It divides the data processing task into two steps: the map phase and the reduce phase. In the map phase, the input data is processed in parallel by multiple nodes, generating key-value pairs as intermediate output. The reduce phase then consolidates and aggregates the intermediate results based on their keys to produce the final output.<\/p>"},{"question":"How did MapReduce originate?","answer":"<p>The concept of MapReduce was introduced by Jeffrey Dean and Sanjay Ghemawat at Google in their 2004 paper titled \"MapReduce: Simplified Data Processing on Large Clusters.\" It was initially utilized by Google to index and process web documents for more efficient search results.<\/p>"},{"question":"What are the key features of MapReduce?","answer":"<p>MapReduce offers several essential features, including scalability to handle massive datasets, fault tolerance to handle node failures, flexibility for various data processing tasks, and a simplified programming model for developers.<\/p>"},{"question":"What are the different types of MapReduce implementations?","answer":"<p>Some popular types of MapReduce implementations are Hadoop MapReduce, Google Cloud Dataflow, Apache Spark, and Microsoft HDInsight.<\/p>"},{"question":"How is MapReduce used in practice?","answer":"<p>MapReduce finds applications in various domains, such as data analysis, search engines, machine learning, and recommendation systems. It allows businesses to process and analyze large-scale data efficiently.<\/p>"},{"question":"What challenges are associated with using MapReduce?","answer":"<p>Common challenges with MapReduce include data skew, efficient job scheduling, and disk I\/O bottlenecks. Proper techniques like data partitioning and combiners can address these issues.<\/p>"},{"question":"What are the future perspectives and technologies related to MapReduce?","answer":"<p>As big data technology evolves, new technologies like Apache Flink, Apache Beam, and serverless computing are emerging to complement or replace MapReduce for specific use cases.<\/p>"},{"question":"How can proxy servers enhance MapReduce performance?","answer":"<p>Proxy servers can play a vital role in managing and optimizing MapReduce jobs by providing load balancing, caching intermediate results, and adding an extra layer of security for data traffic between nodes.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477961","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/477961\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media\/468863"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=477961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}