{"id":478342,"date":"2023-08-09T09:31:27","date_gmt":"2023-08-09T09:31:27","guid":{"rendered":""},"modified":"2023-09-05T11:16:35","modified_gmt":"2023-09-05T11:16:35","slug":"parquet","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/tr\/wiki\/parquet\/","title":{"rendered":"Parke"},"content":{"rendered":"

Parke, b\u00fcy\u00fck miktarlarda veriyi verimli bir \u015fekilde depolamak ve i\u015flemek i\u00e7in tasarlanm\u0131\u015f s\u00fctunlu bir depolama dosyas\u0131 format\u0131d\u0131r. 2013 y\u0131l\u0131nda Cloudera ve Twitter taraf\u0131ndan a\u00e7\u0131k kaynakl\u0131 bir proje olarak geli\u015ftirildi. Parquet'in temel amac\u0131, b\u00fcy\u00fck veri analiti\u011fi i\u00e7in veri depolama ve i\u015flemeyi optimize ederek onu veri ambar\u0131, veri g\u00f6lleri ve Apache'deki kullan\u0131m durumlar\u0131 i\u00e7in ideal bir format haline getirmektir. Hadoop ekosistemleri.<\/p>\n

Parkenin K\u00f6keni ve \u0130lk S\u00f6z\u00fc<\/h2>\n

Parke'nin k\u00f6kenleri, b\u00fcy\u00fck verilerin verimli bir \u015fekilde depolanmas\u0131 ve i\u015flenmesi ihtiyac\u0131na kadar uzanabilir. B\u00fcy\u00fck veri teknolojilerinin y\u00fckseli\u015fiyle birlikte geleneksel depolama formatlar\u0131, b\u00fcy\u00fck veri k\u00fcmelerinin i\u015flenmesinde zorluklarla kar\u015f\u0131la\u015ft\u0131. Parquet'in geli\u015fimi, s\u00fctunlu bir depolama yakla\u015f\u0131m\u0131 sunarak bu sorunlar\u0131 \u00e7\u00f6zmeyi ama\u00e7l\u0131yordu.<\/p>\n

Parquet'ten ilk s\u00f6z, Twitter m\u00fchendisleri taraf\u0131ndan 2013 y\u0131l\u0131nda \u0130\u015fletim Sistemleri \u0130lkeleri Sempozyumu'nda (SOSP) sunulan bir ara\u015ft\u0131rma makalesinde bulunabilir. Bu makalede, Parquet format\u0131n\u0131 tan\u0131tt\u0131lar ve daha iyi s\u0131k\u0131\u015ft\u0131rma, geli\u015fmi\u015f sorgulama gibi faydalar\u0131n\u0131 vurgulad\u0131lar. performans ve karma\u015f\u0131k veri t\u00fcrleri i\u00e7in destek.<\/p>\n

Parke Hakk\u0131nda Detayl\u0131 Bilgi: Konuyu Geni\u015fletmek<\/h2>\n

Parke, verilerin sat\u0131rlar yerine s\u00fctunlar halinde depoland\u0131\u011f\u0131 ve d\u00fczenlendi\u011fi s\u00fctunlu bir depolama yakla\u015f\u0131m\u0131n\u0131 izler. Bu tasar\u0131m, \u00e7e\u015fitli performans optimizasyonlar\u0131na olanak tan\u0131r ve \u00f6zellikle analitik i\u015f y\u00fckleri i\u00e7in avantajl\u0131d\u0131r. Parkenin baz\u0131 temel \u00f6zellikleri \u015funlard\u0131r:<\/p>\n

    \n
  1. \n

    S\u00fctunlu Depolama:<\/strong> Parquet her s\u00fctunu ayr\u0131 ayr\u0131 depolayarak daha iyi s\u0131k\u0131\u015ft\u0131rmaya ve sorgu y\u00fcr\u00fctme s\u0131ras\u0131nda yaln\u0131zca gerekli s\u00fctunlar\u0131n okunabilmesine olanak tan\u0131r.<\/p>\n<\/li>\n

  2. \n

    S\u0131k\u0131\u015ft\u0131rma Teknikleri:<\/strong> Parquet, depolama alan\u0131n\u0131 azaltmak ve veri okuma performans\u0131n\u0131 art\u0131rmak i\u00e7in Snappy, Gzip ve Zstandard gibi \u00e7e\u015fitli s\u0131k\u0131\u015ft\u0131rma algoritmalar\u0131n\u0131 kullan\u0131r.<\/p>\n<\/li>\n

  3. \n

    Veri T\u00fcr\u00fc Deste\u011fi:<\/strong> \u0130lkel t\u00fcrler (\u00f6rne\u011fin, tamsay\u0131, dize, boolean) ve karma\u015f\u0131k t\u00fcrler (\u00f6rne\u011fin, diziler, haritalar, yap\u0131lar) dahil olmak \u00fczere \u00e7e\u015fitli veri t\u00fcrleri i\u00e7in kapsaml\u0131 destek sunar.<\/p>\n<\/li>\n

  4. \n

    \u015eema Geli\u015fimi:<\/strong> Parquet, \u015fema geli\u015fimini destekleyerek kullan\u0131c\u0131lar\u0131n mevcut verilerle uyumlulu\u011fu bozmadan zaman i\u00e7inde s\u00fctun eklemesine, kald\u0131rmas\u0131na veya de\u011fi\u015ftirmesine olanak tan\u0131r.<\/p>\n<\/li>\n

  5. \n

    A\u015fa\u011f\u0131ya A\u00e7\u0131lan Y\u00fcklem:<\/strong> Bu \u00f6zellik, sorgu tahminlerini depolama katman\u0131na iter ve sorgu y\u00fcr\u00fctme s\u0131ras\u0131nda okunmas\u0131 gereken veri miktar\u0131n\u0131 azalt\u0131r.<\/p>\n<\/li>\n

  6. \n

    Paralel \u0130\u015fleme:<\/strong> Parke dosyalar\u0131 daha k\u00fc\u00e7\u00fck s\u0131ra gruplar\u0131na b\u00f6l\u00fcnerek Hadoop gibi da\u011f\u0131t\u0131lm\u0131\u015f ortamlarda paralel i\u015flemeye olanak sa\u011flan\u0131r.<\/p>\n<\/li>\n

  7. \n

    Platformlar Aras\u0131 Uyumluluk:<\/strong> Parquet, platformdan ba\u011f\u0131ms\u0131z olacak \u015fekilde tasarlanm\u0131\u015ft\u0131r ve farkl\u0131 sistemler aras\u0131nda kesintisiz veri al\u0131\u015fveri\u015fine olanak tan\u0131r.<\/p>\n<\/li>\n<\/ol>\n

    Parkenin \u0130\u00e7 Yap\u0131s\u0131: Parke Nas\u0131l \u00c7al\u0131\u015f\u0131r?<\/h2>\n

    Parke dosyalar\u0131, verimli depolama ve i\u015fleme yeteneklerine katk\u0131da bulunan \u00e7e\u015fitli bile\u015fenlerden olu\u015fur:<\/p>\n

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    1. \n

      Dosya Meta Verileri:<\/strong> Dosyan\u0131n \u015femas\u0131, kullan\u0131lan s\u0131k\u0131\u015ft\u0131rma algoritmalar\u0131 ve di\u011fer \u00f6zellikler hakk\u0131nda bilgi i\u00e7erir.<\/p>\n<\/li>\n

    2. \n

      Sat\u0131r Gruplar\u0131:<\/strong> Her Parke dosyas\u0131, ayr\u0131ca s\u00fctunlara b\u00f6l\u00fcnen sat\u0131r gruplar\u0131na b\u00f6l\u00fcnm\u00fc\u015ft\u00fcr. Sat\u0131r gruplar\u0131 paralel i\u015fleme ve veri s\u0131k\u0131\u015ft\u0131rmaya yard\u0131mc\u0131 olur.<\/p>\n<\/li>\n

    3. \n

      S\u00fctun Meta Verileri:<\/strong> Parquet, her s\u00fctun i\u00e7in veri t\u00fcr\u00fc, s\u0131k\u0131\u015ft\u0131rma codec'i ve kodlama bilgileri gibi meta verileri saklar.<\/p>\n<\/li>\n

    4. \n

      Veri Sayfalar\u0131:<\/strong> Veri sayfalar\u0131 ger\u00e7ek s\u00fctunlu verileri depolar ve depolama verimlili\u011fini en \u00fcst d\u00fczeye \u00e7\u0131karmak i\u00e7in ayr\u0131 ayr\u0131 s\u0131k\u0131\u015ft\u0131r\u0131l\u0131r.<\/p>\n<\/li>\n

    5. \n

      S\u00f6zl\u00fck Sayfalar\u0131 (\u0130ste\u011fe Ba\u011fl\u0131):<\/strong> Tekrarlanan de\u011ferlere sahip s\u00fctunlar i\u00e7in Parquet, benzersiz de\u011ferleri depolamak ve bunlara veri sayfalar\u0131nda referans vermek i\u00e7in s\u00f6zl\u00fck kodlamas\u0131n\u0131 kullan\u0131r.<\/p>\n<\/li>\n

    6. \n

      \u0130statistik:<\/strong> Parquet ayr\u0131ca sorgu optimizasyonu i\u00e7in kullan\u0131labilecek minimum ve maksimum de\u011ferler gibi her s\u00fctuna ili\u015fkin istatistikleri de depolayabilir.<\/p>\n<\/li>\n<\/ol>\n

      Parkenin Temel \u00d6zelliklerinin Analizi<\/h2>\n

      Parquet'in temel \u00f6zellikleri, b\u00fcy\u00fck veri i\u015flemede yayg\u0131n olarak benimsenmesine ve pop\u00fclerli\u011fine katk\u0131da bulunmaktad\u0131r. Bu \u00f6zelliklerden baz\u0131lar\u0131n\u0131 analiz edelim:<\/p>\n

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      1. \n

        Verimli S\u0131k\u0131\u015ft\u0131rma:<\/strong> Parquet'in s\u00fctunlu depolama ve s\u0131k\u0131\u015ft\u0131rma teknikleri dosya boyutlar\u0131n\u0131n k\u00fc\u00e7\u00fclmesine, depolama maliyetlerinin azalmas\u0131na ve veri aktar\u0131m h\u0131zlar\u0131n\u0131n artmas\u0131na neden olur.<\/p>\n<\/li>\n

      2. \n

        Verim iyile\u015ftirmesi:<\/strong> Parquet, sorgular s\u0131ras\u0131nda yaln\u0131zca gerekli s\u00fctunlar\u0131 okuyarak G\/\u00c7 i\u015flemlerini en aza indirerek sorgu i\u015flemenin daha h\u0131zl\u0131 olmas\u0131n\u0131 sa\u011flar.<\/p>\n<\/li>\n

      3. \n

        \u015eema Esnekli\u011fi:<\/strong> \u015eema geli\u015ftirme deste\u011fi, mevcut verilerden \u00f6d\u00fcn vermeden \u00e7evik veri \u015femas\u0131 de\u011fi\u015fikliklerine olanak tan\u0131r.<\/p>\n<\/li>\n

      4. \n

        Diller Aras\u0131 Destek:<\/strong> Parke dosyalar\u0131 Java, Python, C++ ve daha fazlas\u0131 dahil olmak \u00fczere \u00e7e\u015fitli programlama dilleri taraf\u0131ndan kullan\u0131labilir ve bu da onu \u00e7e\u015fitli veri i\u015fleme i\u015f ak\u0131\u015flar\u0131 i\u00e7in \u00e7ok y\u00f6nl\u00fc bir format haline getirir.<\/p>\n<\/li>\n

      5. \n

        Veri T\u00fcr\u00fc Zenginli\u011fi:<\/strong> Farkl\u0131 veri t\u00fcrlerine y\u00f6nelik kapsaml\u0131 destek, b\u00fcy\u00fck veri analiti\u011finde yayg\u0131n olan karma\u015f\u0131k veri yap\u0131lar\u0131na uyum sa\u011flayarak geni\u015f bir kullan\u0131m senaryosu yelpazesine hitap eder.<\/p>\n<\/li>\n

      6. \n

        Birlikte \u00e7al\u0131\u015fabilirlik:<\/strong> \u0130yi tan\u0131mlanm\u0131\u015f spesifikasyonlara sahip a\u00e7\u0131k kaynakl\u0131 bir proje olan Parquet, farkl\u0131 ara\u00e7 ve sistemler aras\u0131nda birlikte \u00e7al\u0131\u015fabilirli\u011fi te\u015fvik eder.<\/p>\n<\/li>\n<\/ol>\n

        Parke \u00c7e\u015fitleri ve \u00d6zellikleri<\/h2>\n

        Parke iki ana versiyonda gelir: Parke-1.0<\/strong> Ve Parke-2.0<\/strong>. \u0130kincisi ayn\u0131 zamanda \u015fu \u015fekilde de bilinir: Apache Ok Parke<\/strong> ve Arrow veri format\u0131n\u0131 temel al\u0131r. Her iki s\u00fcr\u00fcm de ayn\u0131 temel kavramlar\u0131 ve avantajlar\u0131 payla\u015f\u0131yor ancak uyumluluk ve \u00f6zellik setleri a\u00e7\u0131s\u0131ndan farkl\u0131l\u0131k g\u00f6steriyor. A\u015fa\u011f\u0131da iki versiyonun kar\u015f\u0131la\u015ft\u0131rmas\u0131 verilmi\u015ftir:<\/p>\n\n\n\n\n\n\n\n\n\n
        \u00d6zellik<\/th>\nParke-1.0<\/th>\nParke-2.0 (Apache Ok Parke)<\/th>\n<\/tr>\n<\/thead>\n
        \u015eema Geli\u015fimi<\/td>\nDestekleniyor<\/td>\nDestekleniyor<\/td>\n<\/tr>\n
        S\u00fctun S\u0131k\u0131\u015ft\u0131rmas\u0131<\/td>\nDesteklenen (Gzip, Snappy, vb.)<\/td>\nDesteklenir (Gzip, Snappy, LZ4, Zstd)<\/td>\n<\/tr>\n
        S\u00f6zl\u00fck Kodlamas\u0131<\/td>\nDestekleniyor<\/td>\nDestekleniyor<\/td>\n<\/tr>\n
        \u0130\u00e7 \u0130\u00e7e Veri Deste\u011fi<\/td>\nKarma\u015f\u0131k t\u00fcrler i\u00e7in s\u0131n\u0131rl\u0131 destek<\/td>\nKarma\u015f\u0131k t\u00fcrler i\u00e7in tam destek<\/td>\n<\/tr>\n
        Uyumluluk<\/td>\n\u00c7o\u011fu ara\u00e7la uyumlu<\/td>\nArrow arac\u0131l\u0131\u011f\u0131yla geli\u015ftirilmi\u015f uyumluluk<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        Parke Kullan\u0131m Yollar\u0131, Sorunlar\u0131 ve \u00c7\u00f6z\u00fcmleri<\/h2>\n

        Parke Kullan\u0131m Yollar\u0131<\/h3>\n

        Parke, a\u015fa\u011f\u0131dakiler gibi \u00e7e\u015fitli veri yo\u011funluklu senaryolarda uygulama bulur:<\/p>\n

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        1. \n

          Veri depolama:<\/strong> Parquet, h\u0131zl\u0131 sorgulama performans\u0131 ve verimli depolamas\u0131 nedeniyle veri ambar\u0131 i\u00e7in yayg\u0131n olarak kullan\u0131lmaktad\u0131r.<\/p>\n<\/li>\n

        2. \n

          B\u00fcy\u00fck Veri \u0130\u015fleme:<\/strong> Hadoop ve di\u011fer b\u00fcy\u00fck veri i\u015fleme \u00e7er\u00e7evelerinde Parquet dosyalar\u0131, paralel i\u015fleme yetenekleri nedeniyle tercih edilen bir se\u00e7imdir.<\/p>\n<\/li>\n

        3. \n

          Veri G\u00f6lleri:<\/strong> Parke, \u00e7e\u015fitli veri t\u00fcrlerini veri g\u00f6llerinde depolamak i\u00e7in pop\u00fcler bir formatt\u0131r ve analiz etmeyi ve i\u00e7g\u00f6r\u00fc \u00e7\u0131karmay\u0131 kolayla\u015ft\u0131r\u0131r.<\/p>\n<\/li>\n

        4. \n

          Veri Ak\u0131\u015f\u0131:<\/strong> \u015eema geli\u015fimini desteklemesiyle Parquet, geli\u015fen veri ak\u0131\u015flar\u0131n\u0131 y\u00f6netmeye uygundur.<\/p>\n<\/li>\n<\/ol>\n

          Sorunlar ve \u00c7\u00f6z\u00fcmler<\/h3>\n
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          1. \n

            Uyumluluk Sorunlar\u0131:<\/strong> Baz\u0131 eski ara\u00e7lar\u0131n Parquet-2.0 deste\u011fi s\u0131n\u0131rl\u0131 olabilir. \u00c7\u00f6z\u00fcm, Parquet-1.0'\u0131 kullanmak veya ara\u00e7lar\u0131 en son s\u00fcr\u00fcm\u00fc destekleyecek \u015fekilde g\u00fcncellemektir.<\/p>\n<\/li>\n

          2. \n

            \u015eema Tasar\u0131m\u0131 Karma\u015f\u0131kl\u0131\u011f\u0131:<\/strong> Esnek bir \u015fema tasarlamak dikkatli d\u00fc\u015f\u00fcnmeyi gerektirir. Veri kaynaklar\u0131 genelinde birle\u015fik bir \u015fema kullanmak, veri entegrasyonunu basitle\u015ftirebilir.<\/p>\n<\/li>\n

          3. \n

            Veri Kalitesi Sorunlar\u0131:<\/strong> Yanl\u0131\u015f veri t\u00fcrleri veya \u015fema de\u011fi\u015fiklikleri veri kalitesi sorunlar\u0131na yol a\u00e7abilir. Veri do\u011frulama ve \u015fema geli\u015ftirme uygulamalar\u0131 bu sorunlar\u0131 azaltabilir.<\/p>\n<\/li>\n

          4. \n

            So\u011fuk Ba\u015flatma Ek Y\u00fck\u00fc:<\/strong> Bir Parquet dosyas\u0131n\u0131n ilk birka\u00e7 sat\u0131r\u0131n\u0131n okunmas\u0131, meta veri ayr\u0131\u015ft\u0131rma nedeniyle daha yava\u015f olabilir. \u00d6n \u00f6nbelle\u011fe alma veya optimize edilmi\u015f bir dosya yap\u0131s\u0131 kullanmak bu y\u00fck\u00fc hafifletebilir.<\/p>\n<\/li>\n<\/ol>\n

            Ana \u00d6zellikler ve Di\u011fer Kar\u015f\u0131la\u015ft\u0131rmalar<\/h2>\n\n\n\n\n\n\n\n\n\n\n\n\n
            karakteristik<\/th>\nTan\u0131m<\/th>\n<\/tr>\n<\/thead>\n
            Depolama Format\u0131<\/td>\nS\u00fctunlu<\/td>\n<\/tr>\n
            S\u0131k\u0131\u015ft\u0131rma Se\u00e7enekleri<\/td>\nGzip, Snappy, LZ4, Zstandard<\/td>\n<\/tr>\n
            Platform Ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131<\/td>\nEvet<\/td>\n<\/tr>\n
            Veri T\u00fcr\u00fc Deste\u011fi<\/td>\n\u0130lkel ve karma\u015f\u0131k veri t\u00fcrleri i\u00e7in kapsaml\u0131 destek<\/td>\n<\/tr>\n
            \u015eema Geli\u015fimi<\/td>\nDestekleniyor<\/td>\n<\/tr>\n
            A\u015fa\u011f\u0131 A\u00e7\u0131lan Y\u00fcklemi<\/td>\nDestekleniyor<\/td>\n<\/tr>\n
            Paralel \u0130\u015fleme<\/td>\nSat\u0131r gruplar\u0131 arac\u0131l\u0131\u011f\u0131yla etkinle\u015ftirildi<\/td>\n<\/tr>\n
            Birlikte \u00e7al\u0131\u015fabilirlik<\/td>\nApache Hadoop, Apache Spark ve Apache Drill gibi \u00e7e\u015fitli b\u00fcy\u00fck veri \u00e7er\u00e7eveleriyle \u00e7al\u0131\u015f\u0131r<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

            Parkeye \u0130li\u015fkin Gelece\u011fin Perspektifleri ve Teknolojileri<\/h2>\n

            Yeteneklerini ve entegrasyonlar\u0131n\u0131 geli\u015ftirmeye y\u00f6nelik devam eden \u00e7abalarla birlikte Parke'nin gelece\u011fi umut verici g\u00f6r\u00fcn\u00fcyor. Baz\u0131 temel geli\u015ftirme ve benimseme alanlar\u0131 \u015funlard\u0131r:<\/p>\n

              \n
            1. \n

              Optimize Edilmi\u015f Sorgu Motorlar\u0131:<\/strong> Apache Arrow, Apache Drill ve Presto gibi sorgu motorlar\u0131ndaki s\u00fcrekli geli\u015fmeler, Parquet'in sorgu performans\u0131n\u0131 daha da art\u0131racakt\u0131r.<\/p>\n<\/li>\n

            2. \n

              Ak\u0131\u015f Deste\u011fi:<\/strong> Parquet'in Apache Kafka ve Apache Flink gibi yeni geli\u015fen teknolojilerle ger\u00e7ek zamanl\u0131 veri ak\u0131\u015f\u0131 ve analizde \u00f6nemli bir rol oynamas\u0131 bekleniyor.<\/p>\n<\/li>\n

            3. \n

              Bulut Veri G\u00f6lleri:<\/strong> Amazon S3 ve Azure Data Lake Storage gibi platformlar\u0131n kolayla\u015ft\u0131rd\u0131\u011f\u0131 bulut veri g\u00f6llerinin y\u00fckseli\u015fi, maliyet etkinli\u011fi ve \u00f6l\u00e7eklenebilir performans\u0131 nedeniyle Parquet'in benimsenmesini art\u0131racak.<\/p>\n<\/li>\n

            4. \n

              Yapay Zeka ve ML Entegrasyonu:<\/strong> Parquet, b\u00fcy\u00fck veri k\u00fcmelerini verimli bir \u015fekilde saklad\u0131\u011f\u0131ndan, makine \u00f6\u011frenimi ve yapay zeka projelerinde veri haz\u0131rlama ve e\u011fitim s\u00fcre\u00e7lerinin ayr\u0131lmaz bir par\u00e7as\u0131 olmaya devam edecek.<\/p>\n<\/li>\n<\/ol>\n

              Proxy Sunucular Nas\u0131l Kullan\u0131labilir veya Parke ile \u0130li\u015fkilendirilebilir?<\/h2>\n

              Proxy sunucular Parquet'ten \u00e7e\u015fitli \u015fekillerde yararlanabilir:<\/p>\n

                \n
              1. \n

                \u00d6nbelle\u011fe Alma ve Veri S\u0131k\u0131\u015ft\u0131rma:<\/strong> Proxy sunucular\u0131, s\u0131k eri\u015filen verileri verimli bir \u015fekilde \u00f6nbelle\u011fe almak i\u00e7in Parquet'i kullanabilir ve b\u00f6ylece sonraki isteklere yan\u0131t verme s\u00fcresi k\u0131salt\u0131labilir.<\/p>\n<\/li>\n

              2. \n

                G\u00fcnl\u00fck \u0130\u015fleme ve Analitik:<\/strong> Parquet format\u0131nda toplanan proxy sunucu g\u00fcnl\u00fckleri, b\u00fcy\u00fck veri i\u015fleme ara\u00e7lar\u0131 kullan\u0131larak analiz edilebilir ve bu da a\u011f optimizasyonu ve g\u00fcvenli\u011fi i\u00e7in de\u011ferli bilgiler sa\u011flar.<\/p>\n<\/li>\n

              3. \n

                Veri De\u011fi\u015fimi ve Entegrasyon:<\/strong> \u00c7e\u015fitli kaynaklardan gelen verileri i\u015fleyen proxy sunucular, verileri Parquet format\u0131nda d\u00f6n\u00fc\u015ft\u00fcr\u00fcp depolayabilir, b\u00f6ylece b\u00fcy\u00fck veri platformlar\u0131 ve analiz sistemleriyle kusursuz entegrasyon sa\u011flan\u0131r.<\/p>\n<\/li>\n

              4. \n

                Kaynak Optimizasyonu:<\/strong> Proxy sunucular, Parquet'in s\u00fctunlu depolama ve y\u00fcklem a\u015fa\u011f\u0131 itme \u00f6zelliklerinden yararlanarak kaynak kullan\u0131m\u0131n\u0131 optimize edebilir ve genel performans\u0131 iyile\u015ftirebilir.<\/p>\n<\/li>\n<\/ol>\n

                \u0130lgili Ba\u011flant\u0131lar<\/h2>\n

                Parke hakk\u0131nda daha fazla bilgi i\u00e7in a\u015fa\u011f\u0131daki kaynaklara ba\u015fvurabilirsiniz:<\/p>\n

                  \n
                1. Apache Parke Resmi Web Sitesi<\/a><\/li>\n
                2. Parke Format\u0131 \u015eartnamesi<\/a><\/li>\n
                3. Parke \u00dczerine Cloudera M\u00fchendislik Blogu<\/a><\/li>\n
                4. Apache Arrow Resmi Web Sitesi<\/a> (Parke-2.0 hakk\u0131nda bilgi i\u00e7in)<\/li>\n<\/ol>","protected":false},"featured_media":0,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-478342","wiki","type-wiki","status-publish","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Parquet: A Comprehensive Guide<\/mark>","faq_items":[{"question":"What is Parquet?","answer":"

                  Parquet is a columnar storage file format designed for efficient storage and processing of large datasets. It is particularly well-suited for big data analytics, data warehousing, and Apache Hadoop environments.<\/p>"},{"question":"How did Parquet originate, and when was it first mentioned?","answer":"

                  Parquet was developed as an open-source project by Cloudera and Twitter in 2013. It was first mentioned in a research paper presented by Twitter engineers at the Symposium on Operating Systems Principles (SOSP) in the same year.<\/p>"},{"question":"What are the key features of Parquet?","answer":"

                  Parquet offers several key features, including columnar storage, efficient compression techniques, support for various data types (primitive and complex), schema evolution, predicate pushdown, and parallel processing.<\/p>"},{"question":"How does Parquet work internally?","answer":"

                  Internally, Parquet files consist of file metadata, row groups, column metadata, data pages, and optional dictionary pages. This design allows for optimized storage, fast query processing, and support for various data types.<\/p>"},{"question":"What are the different types of Parquet versions, and how do they differ?","answer":"

                  Parquet comes in two main versions: Parquet-1.0 and Parquet-2.0 (Apache Arrow Parquet). While both versions share core concepts, Parquet-2.0 offers improved compatibility with Arrow-based systems and additional compression options.<\/p>"},{"question":"In what ways can Parquet be used, and what problems does it solve?","answer":"

                  Parquet finds applications in data warehousing, big data processing, data lakes, and handling streaming data. It solves challenges related to efficient storage, fast query performance, schema evolution, and cross-platform compatibility.<\/p>"},{"question":"What are the main characteristics of Parquet compared to other storage formats?","answer":"

                  Compared to other formats, Parquet stands out for its columnar storage, efficient compression options, extensive data type support, schema evolution capabilities, and the ability to enable predicate pushdown for query optimization.<\/p>"},{"question":"What are the perspectives and future technologies related to Parquet?","answer":"

                  The future of Parquet is promising, with ongoing improvements in query engines, support for real-time data streaming, and its growing role in cloud data lakes and AI\/ML integration.<\/p>"},{"question":"How can proxy servers benefit from Parquet?","answer":"

                  Proxy servers can utilize Parquet for caching, data compression, log processing, and seamless data integration. Parquet's resource optimization features can improve overall proxy server performance.<\/p>"},{"question":"Where can I find more information about Parquet?","answer":"

                  For more information about Parquet, you can visit the Apache Parquet Official Website<\/a> or refer to the Parquet Format Specification on GitHub<\/a>. Additionally, you can explore Cloudera's Engineering Blog for insightful articles on Parquet. For information on Parquet-2.0, you can visit the Apache Arrow Official Website<\/a>.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/wiki\/478342","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\/478342\/revisions"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/tr\/wp-json\/wp\/v2\/media?parent=478342"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}