{"id":4708,"date":"2018-03-03T00:17:56","date_gmt":"2018-03-03T08:17:56","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=4708"},"modified":"2023-11-28T14:18:24","modified_gmt":"2023-11-28T22:18:24","slug":"tuning-replication-stream-optimal-performance","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/es\/tuning-replication-stream-optimal-performance\/","title":{"rendered":"Ajuste del flujo de replicaci\u00f3n para un rendimiento \u00f3ptimo"},"content":{"rendered":"

XDCR<\/a> es un sistema de replicaci\u00f3n de datos as\u00edncrono de alto rendimiento utilizado principalmente para replicar datos entre sus clusters Couchbase desplegados en diferentes centros de datos. Varias empresas de Fortune 500 conf\u00edan en XDCR para sus aplicaciones de misi\u00f3n cr\u00edtica. Alta disponibilidad, recuperaci\u00f3n de desastres y localizaci\u00f3n de datos son las principales aplicaciones en las que XDCR es la soluci\u00f3n a la que acudir.<\/span><\/p>\n

XDCR est\u00e1 dise\u00f1ado para ofrecer un alto rendimiento y un rendimiento resistente para la mayor\u00eda de los casos de uso, pero podr\u00eda haber casos en los que es necesario ajustarlo para un rendimiento \u00f3ptimo. Hemos introducido la configuraci\u00f3n avanzada para permitir este ajuste y personalizaci\u00f3n del rendimiento.<\/span><\/p>\n

El siguiente contenido pretende proporcionar una gu\u00eda gen\u00e9rica sobre el ajuste XDCR a trav\u00e9s de la Configuraci\u00f3n Avanzada que es accesible a trav\u00e9s de la Consola del Administrador de Couchbase y las APIs soportadas. <\/span>
\n<\/span><\/p>\n

Cuando a\u00f1ada una replicaci\u00f3n por primera vez, puede hacer clic en \"Mostrar configuraci\u00f3n avanzada\" para mostrar las opciones como se muestra a continuaci\u00f3n:<\/span><\/p>\n

\"\"<\/p>\n

A continuaci\u00f3n se indica un subconjunto de los ajustes XDCR m\u00e1s relevantes para el ajuste. Estos ajustes se pueden utilizar para mejorar el rendimiento o para conservar el ancho de banda de la red.<\/span><\/p>\n

\"\"<\/p>\n\n\n\n
Configuraci\u00f3n avanzada de XDCR<\/b><\/p>\n
    \n
  • Source Nozzles (SN) : Para establecer el n\u00famero de emisores por nodo; por defecto = 2<\/li>\n
  • Boquillas de destino (TN) : Para establecer el n\u00famero de receptores por nodo; por defecto = 2<\/li>\n
  • Recuento de lotes (BC) - Para establecer el n\u00famero de elementos por lote de replicaci\u00f3n; por defecto = 500<\/li>\n
  • Tama\u00f1o de lote (BS) - Para establecer el tama\u00f1o m\u00e1ximo en Kilobytes para cada lote; por defecto = 2048<\/li>\n
  • Optimistic Replication Threshold (ORT) : establece un umbral en bytes; (los documentos con un tama\u00f1o inferior a este umbral se replican sin realizar una comprobaci\u00f3n get_meta); por defecto = 256<\/li>\n<\/ul>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    Ajuste del rendimiento<\/b><\/p>\n

    Algunos factores que influyen en el rendimiento pueden ser la naturaleza de los datos, la tasa de mutaci\u00f3n de datos, la carga de trabajo en los clusters, la configuraci\u00f3n de la red, etc. En funci\u00f3n de estos par\u00e1metros, la replicaci\u00f3n debe ajustarse para obtener el rendimiento deseado. <\/span><\/p>\n

    A continuaci\u00f3n se indican algunas de las mejores pr\u00e1cticas para mejorar el rendimiento o conservar el ancho de banda:<\/span><\/p>\n

    a.Mejora del rendimiento:<\/b><\/p>\n

      \n
    • Dado el margen de recursos en los clusters de origen y destino, recomendamos un rango superior al predeterminado para Source Nozzle y Target Nozzle<\/span><\/li>\n
    • Si sus datos se componen de documentos de tama\u00f1o superior a 10K, le recomendamos que ajuste el Recuento de lotes y el Tama\u00f1o de lote a un valor superior al predeterminado.<\/span><\/li>\n<\/ul>\n

      b.Conservaci\u00f3n del ancho de banda de la red:<\/b><\/p>\n

        \n
      • Utilice un valor de Umbral de Replicaci\u00f3n Optimista superior al tama\u00f1o medio del documento.<\/span><\/li>\n<\/ul>\n

        Hemos realizado varios experimentos para demostrar el comportamiento, que se muestra a continuaci\u00f3n.<\/span><\/p>\n

        \"\"<\/p>\n

        Configuraci\u00f3n del experimento: El cl\u00faster A y el cl\u00faster B son dos cl\u00fasteres con replicaci\u00f3n unidireccional en curso (<\/b>activo <\/b>los datos se replican del cl\u00faster de origen al cl\u00faster de destino).<\/b><\/p>\n

        Configuraci\u00f3n del entorno de prueba:<\/b><\/p>\n

          \n
        • Dos cl\u00fasteres de Couchbase ejecut\u00e1ndose en AWS<\/span><\/li>\n
        • Grupo de origen: regi\u00f3n West1, grupo de destino: regi\u00f3n East1 <\/span><\/li>\n
        • Latencia media de la red entre los clusters en el momento de la prueba : 72ms<\/span><\/li>\n
        • Tama\u00f1o del cl\u00faster : 5 nodos ejecut\u00e1ndose en instancias dedicadas m4.4xlarge<\/span><\/li>\n
        • Configuraci\u00f3n de Couchbase Bucket :<\/span> Tipo de cubo - Couchbase; Cuota de memoria del cubo por nodo = 60 GB; (configuraci\u00f3n predeterminada para r\u00e9plicas, resoluci\u00f3n de conflictos y m\u00e9todo de expulsi\u00f3n)<\/span><\/li>\n
        • Couchbase Server Enterprise R5.0 ejecut\u00e1ndose en Amazon Linux<\/span><\/li>\n
        • Replicaci\u00f3n XDCR unidireccional creada yendo del bucket de origen al bucket de destino<\/span><\/li>\n<\/ul>\n

          Test1:<\/b> Establecer una base de referencia<\/b><\/p>\n

          Configuraci\u00f3n : Valores por defecto para todos los ajustes avanzados <\/span><\/p>\n

          Prueba2<\/b>: <\/span>Demostrar el aumento del rendimiento mediante la paralelizaci\u00f3n<\/b><\/p>\n

          Configuraci\u00f3n : Source Nozzle = 8, Target Nozzle = 8 (ambos 4x), average doc size=1KB<\/span><\/p>\n

          \"\"<\/p>\n

          Prueba3<\/b>: <\/span>Demuestre un mayor rendimiento con cargas \u00fatiles de red m\u00e1s grandes<\/b><\/p>\n

          Configuraci\u00f3n: Recuento de lotes = 4000, Tama\u00f1o de lote = 8192, tama\u00f1o medio del documento = 20 KB.<\/span><\/p>\n

          \"\"<\/p>\n

          Test4:<\/b> Demostrar una menor utilizaci\u00f3n del ancho de banda<\/strong><\/p>\n

          Configuraci\u00f3n : Umbral de replicaci\u00f3n optimista > tama\u00f1o medio del documento<\/span><\/p>\n

          \"\"<\/p>\n

          Nota :<\/i><\/b><\/p>\n

            \n
          1. Los resultados de las pruebas que se muestran en los gr\u00e1ficos no deben considerarse absolutos, ya que podr\u00eda observarse una variaci\u00f3n de 3-5% en las repeticiones de estas pruebas en una configuraci\u00f3n id\u00e9ntica debido a los caprichos de los entornos de nube de AWS.<\/span><\/li>\n
          2. La utilizaci\u00f3n media de la CPU fue igual o inferior a 40% en todas las pruebas.<\/span><\/li>\n<\/ol>\n

            Aunque queremos que experimentes y ajustes la replicaci\u00f3n a tus necesidades de rendimiento deseadas, te aconsejamos que consultes con Couchbase antes de cambiar la configuraci\u00f3n de XDCR para casos de uso que sean muy especializados o muy cr\u00edticos para tu negocio.<\/span><\/p>\n

            Nirvair Singh es el ingeniero de soluciones que ha llevado a cabo este experimento. No dude en ponerse en contacto conmigo o con Nirvair para cualquier aclaraci\u00f3n u orientaci\u00f3n adicional. <\/span><\/p>\n

            Como siempre, estamos deseando conocer sus experimentos y experiencias.<\/span><\/p>\n

             <\/p>","protected":false},"excerpt":{"rendered":"

            XDCR is a high performance asynchronous data replication system used primarily to replicate data between your Couchbase clusters deployed in different data centers. Several Fortune 500 enterprises rely on XDCR for their mission-critical applications. High availability, disaster recovery and data […]<\/p>","protected":false},"author":6588,"featured_media":13873,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1815,1821,1816],"tags":[],"ppma_author":[9064],"class_list":["post-4708","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-best-practices-and-tutorials","category-couchbase-architecture","category-couchbase-server"],"acf":[],"yoast_head":"\nTuning your replication stream for most optimal performance<\/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:\/\/www.couchbase.com\/blog\/es\/tuning-replication-stream-optimal-performance\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Tuning your replication stream for most optimal performance\" \/>\n<meta property=\"og:description\" content=\"XDCR is a high performance asynchronous data replication system used primarily to replicate data between your Couchbase clusters deployed in different data centers. 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