{"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\/es\/high-volume-data-management-using-couchbase-magma-a-real-life-case-study\/","title":{"rendered":"Gesti\u00f3n de grandes vol\u00famenes de datos con Couchbase Magma: un caso real"},"content":{"rendered":"

La gesti\u00f3n de grandes vol\u00famenes de datos a alta velocidad plantea una serie de retos \u00fanicos. Desde la capacidad de la base de datos para ingerir los datos hasta el coste total de propiedad de la soluci\u00f3n, dados los crecientes vol\u00famenes de datos. El nuevo Motor de almacenamiento Magma<\/a> ofrece un argumento convincente para almacenar estos datos a un coste \u00f3ptimo, al tiempo que proporciona un alto rendimiento para las cargas de trabajo anal\u00edticas. En este blog, describo una implementaci\u00f3n real de Magma y las ventajas que est\u00e1 proporcionando en las instalaciones de un cliente.<\/em><\/p>\n

Infosys ha estado apoyando a un gran cliente de la industria del turismo en colaboraci\u00f3n con Couchbase. El cliente utiliza Couchbase como tecnolog\u00eda central para dar soporte a casos de uso l\u00edderes en la industria que proporcionan el siguiente nivel de experiencia a sus hu\u00e9spedes. Esto incluye dispositivos basados en IoT que transmiten informaci\u00f3n desde m\u00faltiples sitios gestionados por el cliente. Estos datos son variados, con m\u00e1s de 20 tipos diferentes de puntos de datos y son de alta velocidad, con datos que se generan a un ritmo de miles por segundo. Estos datos se transmiten a cl\u00fasteres locales de Couchbase y se procesan antes de enviarse a un cl\u00faster de an\u00e1lisis basado en la nube.<\/p>\n

La soluci\u00f3n ha superado la prueba del tiempo y ha proporcionado informaci\u00f3n valiosa a la empresa sobre las preferencias y el comportamiento de los clientes, adem\u00e1s de satisfacer importantes necesidades normativas. Debido al \u00e9xito del programa, el n\u00famero de establecimientos y hu\u00e9spedes ha aumentado 4 veces en los \u00faltimos tiempos, lo que ha provocado un enorme incremento del volumen de datos. Los cl\u00fasteres de Couchbase han absorbido esta avalancha de datos con facilidad, y el cl\u00faster Analytics ha atendido eficazmente las crecientes necesidades de la empresa de obtener informaci\u00f3n sobre los datos.<\/p>\n

A medida que los vol\u00famenes de datos han ido creciendo, hemos tenido que aumentar el tama\u00f1o del cl\u00faster para mantener el ritmo, y esto ha incrementado el coste de funcionamiento del cl\u00faster. Dado el futuro del n\u00famero de implantaciones, nuestro cliente buscaba optimizar el coste total de propiedad del cl\u00faster de an\u00e1lisis.<\/p>\n

El lanzamiento del motor de almacenamiento Couchbase Magma lleg\u00f3 en el momento perfecto para nosotros, y fuimos uno de los primeros en adoptar la nueva tecnolog\u00eda. Creamos un cl\u00faster de archivo para tomar los datos del cl\u00faster principal basado en Couchstore (Couchstore es el motor original de almacenamiento profundo) e implementamos un programa de 6 semanas de duraci\u00f3n. TTL<\/a> en el cl\u00faster principal. El cl\u00faster principal ten\u00eda 14 nodos de datos, pero pudimos reducir el tama\u00f1o del cl\u00faster de archivo que ejecuta Magma a s\u00f3lo 4 nodos de datos. Esto supone una reducci\u00f3n de 3,5 veces en la capacidad de hardware y almacenamiento necesaria. Esto supuso un ahorro anual de unos $800K. Podr\u00edamos haberlo ampliado a\u00fan m\u00e1s, pero hemos dejado margen suficiente para acomodar el crecimiento futuro durante los pr\u00f3ximos 12 a 14 meses.<\/p>\n

Dos caracter\u00edsticas clave de Magma nos permitieron realizar esta dr\u00e1stica reducci\u00f3n:<\/p>\n

    \n
  1. \n
      \n
    1. Se ha aumentado el tama\u00f1o de los discos a 10 TB por nodo (m\u00e1ximo recomendado en Magma) frente al l\u00edmite de 1,5 TB por nodo.<\/li>\n
    2. Reducir el ratio de residencia a 5% para los cubos (el m\u00ednimo recomendado es 1%) frente a los 40% de Couchstore.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      Otro beneficio interesante que pudimos observar es el ahorro en el uso del disco debido a la compresi\u00f3n por bloques. Vimos una compresi\u00f3n casi 50% mejor en el motor Magma en comparaci\u00f3n con Couchstore, con el uso de disco por documento pasando de 2,28 KB\/doc a 1,47 KB\/doc en disco para datos que son bastante uniformes.<\/p>\n

      \"Key

      Principales ventajas de Magma<\/p><\/div>\n

      Como pr\u00f3ximos pasos tenemos la intenci\u00f3n de tomar la versi\u00f3n 7.1.2 de Couchbase que tiene soporte para Analytics y Eventing en Magma y mejorar la segregaci\u00f3n de datos en el cl\u00faster Magma. Podremos dividir los datos por meses y a\u00f1os para localizar de forma eficiente los datos espec\u00edficos necesarios para la investigaci\u00f3n hist\u00f3rica.<\/p>\n

      Si est\u00e1s interesado en el funcionamiento interno de Magma, te recomiendo que leas la ponencia presentada en VLDB 2022. aqu\u00ed<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

      La gesti\u00f3n de grandes vol\u00famenes de datos a alta velocidad plantea una serie de retos \u00fanicos. Desde la capacidad de la base de datos para ingerir los datos hasta el coste total de propiedad de la soluci\u00f3n dados los crecientes vol\u00famenes de datos. El nuevo motor de almacenamiento Magma de Couchbase proporciona una [...]<\/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\/es\/high-volume-data-management-using-couchbase-magma-a-real-life-case-study\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"High-volume Data Management Using Couchbase Magma - 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