{"id":6115,"date":"2023-05-16T16:27:33","date_gmt":"2023-05-16T16:27:33","guid":{"rendered":"https:\/\/bentego.com\/hadoop-nedir\/"},"modified":"2025-10-20T16:09:19","modified_gmt":"2025-10-20T16:09:19","slug":"hadoop-nedir","status":"publish","type":"post","link":"https:\/\/bentego.com\/tr\/hadoop-nedir\/","title":{"rendered":"Hadoop Nedir?"},"content":{"rendered":"

Hadoop Nedir?<\/strong><\/h3>\n

RDBMS(Relational Database Management System) yani ili\u015fkisel veri taban\u0131 y\u00f6netim sistemlerinden farkl\u0131 olarak verileri tek bir bilgisayarda tutmay\u0131p gelen verileri -her birinin kendine ait i\u015flemcisi ve rami- olan Node\u2019lerde(k\u00fcme) HDFS dosya sistemi ile denormalize bir \u015fekilde veriyi saklayan ve i\u015flenmesine olanak sa\u011flayan a\u00e7\u0131k kaynak kodlu k\u00fct\u00fcphanedir.<\/p>\n

HDFS(Hadoop Distributed File System) nedir?<\/h3>\n

S\u0131radan sunucular\u0131n disklerini bir araya getirerek b\u00fcy\u00fck ve sanal bir disk olu\u015fturan dosya sistemidir. Bu sayede \u00e7ok b\u00fcy\u00fck boyuttaki dosyalar\u0131 sistemde saklayabilir ve i\u015flenmesine olanak sa\u011flar. Bu dosyalar bloklar halinde (varsay\u0131lan 64MB) birden fazla ve farkl\u0131 sunucu
\n\u00fczerine (varsay\u0131lan 3 kopya) da\u011f\u0131t\u0131larak yedeklenir. Bu sayede veri kayb\u0131 \u00f6nlenmi\u015f olur. Ayr\u0131ca HDFS \u00e7ok b\u00fcy\u00fck boyutlu dosyalar \u00fczerinde okuma i\u015flemi imkan\u0131 sa\u011flar, ancak rastlant\u0131sal eri\u015fim (random access) \u00f6zelli\u011fi bulunmaz. HDFS, NameNode ve DataNode s\u00fcre\u00e7lerinden (process) olu\u015fmaktad\u0131r.
\nNameNode<\/strong><\/em> ana (master) s\u00fcre\u00e7 olarak bloklar\u0131n sunucular \u00fczerindeki da\u011f\u0131l\u0131m\u0131ndan, yarat\u0131lmas\u0131ndan, silinmesinden, bir blokta sorun meydana geldi\u011finde yeniden olu\u015fturulmas\u0131ndan ve her t\u00fcrl\u00fc dosya eri\u015fiminden sorumludur. K\u0131sacas\u0131 HDFS \u00fczerindeki t\u00fcm dosyalar hakk\u0131ndaki bilgiler (metadata) NameNode taraf\u0131ndan saklan\u0131r ve y\u00f6netilir.
\nDataNode<\/strong><\/em> ise i\u015flevi bloklar\u0131 saklamak olan i\u015f\u00e7i (slave) s\u00fcre\u00e7tir. Her DataNode kendi yerel diskindeki veriden sorumludur. Ayr\u0131ca di\u011fer DataNode\u2019lardaki verilerin yedeklerini de bar\u0131nd\u0131r\u0131r. DataNode\u2019lar k\u00fcme i\u00e7erisinde birden fazla olabilir.<\/p>\n

 <\/p>\n

\"\"<\/h3>\n

 <\/p>\n

Hadoop\u2019un da\u011f\u0131t\u0131k veri i\u015fleme modeli: Map-Reduce<\/h3>\n

T\u00fcm verileri merkeze toplamad\u0131\u011f\u0131m\u0131z i\u00e7in b\u00fct\u00fcn i\u015flemler ayr\u0131 ayr\u0131 Node\u2019lerde yap\u0131l\u0131r. Bu i\u015flemler bittikten sonra her Node\u2019den d\u00f6nen cevap al\u0131n\u0131r ve toplam sonu\u00e7 olu\u015fturulur. Bu i\u015flemler b\u00fct\u00fcne Map-Reduce denir.<\/p>\n

Map-Reduce Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h3>\n

Yaz\u0131l\u0131\u015f\u0131 \u00e7e\u015fitli d\u00f6k\u00fcmanlarda birle\u015fik gibi g\u00f6r\u00fcnsede Map-Reduce asl\u0131nda Map (Mapping) ve Reduce (Reducing) iki ayr\u0131 i\u015flem yapan, bir arada \u00e7al\u0131\u015farak da\u011f\u0131t\u0131k dosya sistemlerinde veri analizinin olduk\u00e7a kolayla\u015ft\u0131r\u0131lmas\u0131n\u0131 sa\u011flayan methodlard\u0131r. Map, bir listedeki her bir elemana tek tek bir kural\u0131 uygular. Reduce, Map ile elde edilen sonu\u00e7lar\u0131 belli bir sistematik ile analiz ederek birle\u015ftirir. Yapt\u0131\u011f\u0131 analiz sonunda da bir de\u011fer d\u00f6nd\u00fcr\u00fcr.<\/p>\n

    \n
  1. Input:<\/strong><\/em> Veri giri\u015flerinin yap\u0131ld\u0131\u011f\u0131 ad\u0131md\u0131r.<\/li>\n
  2. Splitting:<\/strong><\/em> Gelen veriler bu a\u015famada i\u015flemesi daha kolay olabilmesi i\u00e7in par\u00e7alara b\u00f6l\u00fcn\u00fcr.<\/li>\n
  3. Mapping:<\/strong><\/em> Veriler bu a\u015famada ilgili d\u00fc\u011f\u00fcmlere de da\u011f\u0131t\u0131l\u0131r ve ka\u00e7 tane yede\u011fi olaca\u011f\u0131 bu ad\u0131mda belirtilir. Ve daha sonras\u0131nda her ilgili d\u00fc\u011f\u00fcmde i\u015flenir.<\/li>\n
  4. Shuffling:<\/strong><\/em> Her d\u00fc\u011f\u00fcmde verilerin sayma i\u015flemi yap\u0131l\u0131r. \u00d6rne\u011fin bir text dok\u00fcman\u0131n\u0131 input olarak verdiysek ve sonu\u00e7 olarak hangi kelimenin ka\u00e7 defa ge\u00e7ti\u011fini ar\u0131yorsak bu a\u015famada kelime say\u0131lar\u0131 Node\u2019lerde belirlenir.<\/li>\n
  5. Reducing:<\/strong><\/em> Her Node\u2019den gelen sonu\u00e7 bu a\u015famada toplan\u0131r.<\/li>\n
  6. Final Result:<\/strong><\/em> Sonu\u00e7lar art\u0131k elimizdedir. Bunun raporlamas\u0131n\u0131 yapabiliriz.
    \n

    \"\"<\/h3>\n<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Hadoop Nedir? RDBMS(Relational Database Management System) yani ili\u015fkisel veri taban\u0131 y\u00f6netim sistemlerinden farkl\u0131 olarak verileri tek bir bilgisayarda tutmay\u0131p gelen verileri -her […]<\/p>\n","protected":false},"author":2,"featured_media":5927,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[87],"tags":[110,111,112,113],"class_list":["post-6115","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-apache-hadoop-tr","tag-bigdata-tr","tag-hadoop-tr","tag-hdfs-tr"],"yoast_head":"\nHadoop Nedir? - Bentego<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bentego.com\/tr\/hadoop-nedir\/\" \/>\n<meta property=\"og:locale\" content=\"tr_TR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Hadoop Nedir? - Bentego\" \/>\n<meta property=\"og:description\" content=\"Hadoop Nedir? 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