{"id":14099,"date":"2023-02-16T13:00:28","date_gmt":"2023-02-16T21:00:28","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=14099"},"modified":"2025-06-13T23:11:07","modified_gmt":"2025-06-14T06:11:07","slug":"high-volume-data-management-using-couchbase-magma-a-real-life-case-study","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/high-volume-data-management-using-couchbase-magma-a-real-life-case-study\/","title":{"rendered":"Gerenciamento de dados de alto volume usando o Couchbase Magma - um estudo de caso real"},"content":{"rendered":"

O gerenciamento de grandes volumes de dados de alta velocidade traz um conjunto exclusivo de desafios. Desde a capacidade do banco de dados de ingerir os dados at\u00e9 o custo total de propriedade (TCO) da solu\u00e7\u00e3o, considerando a expans\u00e3o dos volumes de dados. O novo Mecanismo de armazenamento de magma<\/a> oferece um argumento convincente para armazenar esses dados a custos ideais e, ao mesmo tempo, oferecer alto desempenho para cargas de trabalho anal\u00edticas. Neste blog, descrevo uma implementa\u00e7\u00e3o real do Magma e os benef\u00edcios que ele est\u00e1 proporcionando em um cliente.<\/em><\/p>\n

A Infosys tem dado suporte a um grande cliente do setor de turismo em colabora\u00e7\u00e3o com o Couchbase. O cliente usa o Couchbase como a tecnologia principal para dar suporte a casos de uso l\u00edderes do setor que proporcionam o pr\u00f3ximo n\u00edvel de experi\u00eancia aos seus h\u00f3spedes. Isso inclui dispositivos baseados em IoT que transmitem informa\u00e7\u00f5es de v\u00e1rios sites administrados pelo cliente. Esses dados s\u00e3o variados, com mais de 20 tipos diferentes de pontos de dados, e s\u00e3o de alta velocidade, com dados sendo gerados a uma taxa de milhares por segundo. Esses dados s\u00e3o transmitidos para clusters locais do Couchbase e processados antes de serem enviados para um cluster de an\u00e1lise baseado em nuvem.<\/p>\n

A solu\u00e7\u00e3o resistiu ao teste do tempo e tem fornecido insights valiosos para a empresa sobre as prefer\u00eancias e o comportamento dos h\u00f3spedes, al\u00e9m de atender a importantes necessidades regulat\u00f3rias. Devido ao sucesso do programa, o n\u00famero de locais e h\u00f3spedes teve um aumento de 4x no passado recente, o que resultou em um grande aumento no volume de dados. Os clusters do Couchbase absorveram essa enxurrada de dados com facilidade, e o cluster do Analytics atendeu com efici\u00eancia \u00e0s necessidades crescentes da empresa de obter insights sobre os dados.<\/p>\n

\u00c0 medida que os volumes de dados cresceram, tivemos que aumentar o tamanho do cluster para acompanhar o ritmo, o que aumentou o custo de execu\u00e7\u00e3o do cluster. Considerando o futuro do n\u00famero de implementa\u00e7\u00f5es, nosso cliente estava procurando otimizar o TCO da execu\u00e7\u00e3o do cluster de an\u00e1lise.<\/p>\n

O lan\u00e7amento do mecanismo de armazenamento Couchbase Magma veio no momento perfeito para n\u00f3s, e fomos um dos primeiros a adotar a nova tecnologia. Criamos um cluster de arquivamento para coletar dados do cluster principal baseado no Couchstore (o Couchstore \u00e9 o mecanismo original de armazenamento profundo) e implementamos um programa de 6 semanas de TTL<\/a> no cluster principal. O cluster principal tinha 14 n\u00f3s de dados, mas conseguimos reduzir o tamanho do cluster de arquivamento que executava o Magma para apenas 4 n\u00f3s de dados. Isso representa uma redu\u00e7\u00e3o de 3,5 vezes na capacidade necess\u00e1ria de hardware e armazenamento. Isso resultou em uma economia anual de cerca de $800K! Poder\u00edamos ter aumentado ainda mais, mas deixamos espa\u00e7o suficiente para acomodar o crescimento futuro nos pr\u00f3ximos 12 a 14 meses.<\/p>\n

Foram dois os principais recursos do Magma que nos permitiram fazer essa redu\u00e7\u00e3o dr\u00e1stica:<\/p>\n

    \n
  1. \n
      \n
    1. O tamanho dos discos foi aumentado para 10 TB por n\u00f3 (m\u00e1ximo recomendado no Magma) em compara\u00e7\u00e3o com o limite de 1,5 TB por n\u00f3<\/li>\n
    2. Reduzir a taxa de resid\u00eancia para 5% para os buckets (o m\u00ednimo recomendado \u00e9 1%) em compara\u00e7\u00e3o com 40% para o Couchstore<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Outro benef\u00edcio interessante que pudemos observar \u00e9 a economia no uso do disco devido \u00e0 compacta\u00e7\u00e3o de blocos. Observamos uma compacta\u00e7\u00e3o quase 50% melhor no mecanismo Magma em compara\u00e7\u00e3o com o Couchstore, com o uso de disco por documento passando de 2,28 KB\/doc para 1,47 KB\/doc em disco para dados que s\u00e3o razoavelmente uniformes.<\/p>\n

      \"Key

      Principais benef\u00edcios da Magma<\/p><\/div>\n

      Como pr\u00f3ximos passos, pretendemos usar a vers\u00e3o 7.1.2 do Couchbase, que tem suporte para Analytics e Eventing no Magma, e melhorar a segrega\u00e7\u00e3o de dados no cluster do Magma. Poderemos dividir os dados por m\u00eas e ano para permitir a localiza\u00e7\u00e3o eficiente de dados espec\u00edficos necess\u00e1rios para a pesquisa hist\u00f3rica.<\/p>\n

      Se voc\u00ea estiver interessado no funcionamento interno do Magma, recomendo que leia o artigo apresentado no VLDB 2022 aqui<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

      Managing large volumes of high velocity data brings a unique set of challenges. From the ability of the database to ingest the data to the TCO of the solution given expanding data volumes. Couchbase\u2019s new Magma storage engine provides a […]<\/p>","protected":false},"author":84181,"featured_media":14103,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2294,1816,6319],"tags":[9454,9632,9597],"ppma_author":[9781],"class_list":["post-14099","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-couchbase-server","category-customers","tag-cloud-database","tag-database-tco","tag-magma"],"acf":[],"yoast_head":"\nHigh-volume Data Management Using Couchbase Magma - A Real Life Case Study - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"In this blog I describe a real-life implementation of Magma and the benefits it is providing at a client site.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/pt\/high-volume-data-management-using-couchbase-magma-a-real-life-case-study\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"High-volume Data Management Using Couchbase Magma - 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