{"id":6322,"date":"2019-01-24T12:04:52","date_gmt":"2019-01-24T20:04:52","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=6322"},"modified":"2025-06-13T20:19:53","modified_gmt":"2025-06-14T03:19:53","slug":"n1ql-to-query-or-to-analyze","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/es\/n1ql-to-query-or-to-analyze\/","title":{"rendered":"N1QL: \u00bfConsultar o analizar?"},"content":{"rendered":"

Nota: Esta nota de recomendaci\u00f3n se ha redactado en colaboraci\u00f3n con Till Westmann y Mike Carey, que dirigen el departamento de I+D de an\u00e1lisis de Couchbase.<\/p>\n

[La segunda parte de este art\u00edculo de Even Pease est\u00e1 en: https:\/\/www.couchbase.com\/blog\/part-2-n1ql-to-query-or-to-analyze\/<\/a>]<\/p>\n

Tanto el servicio de consultas como el de an\u00e1lisis de Couchbase soportan N1QL (SQL para JSON). Una pregunta habitual es: \"\u00bfQu\u00e9 servicio debe ejecutar mi consulta?\".\u00a0<\/span>La respuesta r\u00e1pida es: \"Depende de su carga de trabajo<\/a>\". Este blog explica esta respuesta.<\/span><\/p>\n

Una transacci\u00f3n es cuando compras caf\u00e9 y pastel en el supermercado.\u00a0<\/span>El an\u00e1lisis es cuando la tienda de comestibles examina todos los datos de ventas para ver qu\u00e9 d\u00eda de la semana, qu\u00e9 mes del a\u00f1o se vende m\u00e1s caf\u00e9 y\/o pasteles, y as\u00ed poder planificar el inventario, las ventas y los precios.<\/span><\/p>\n

Todas las empresas hacen estas tres cosas en un ciclo o espiral [El Objetivo]. <\/span><\/p>\n

    \n
  1. Ejecutar el proceso empresarial para suministrar productos o servicios a los clientes.<\/span><\/li>\n
  2. Analizar la empresa para determinar qu\u00e9 hay que cambiar y a qu\u00e9.<\/span><\/li>\n
  3. Haz que se produzca el cambio.<\/span><\/li>\n<\/ol>\n

    En la empresa actual, cada paso requiere aplicaciones que lo realicen.<\/span><\/p>\n

    \"\"<\/p>\n

    El Servicio de Consultas es utilizado por las aplicaciones necesarias para el funcionamiento de la empresa; est\u00e1 dise\u00f1ado para un gran n\u00famero de consultas concurrentes, cada una de las cuales realiza una peque\u00f1a cantidad de trabajo. En el mundo de los RDBMS, esta carga de trabajo se denomina carga de trabajo OLTP. <\/span><\/p>\n

    Las aplicaciones o herramientas utilizadas para el an\u00e1lisis tienen diferentes caracter\u00edsticas de carga de trabajo. \u00c9stas suelen utilizar el Servicio de An\u00e1lisis; est\u00e1 dise\u00f1ado para un menor n\u00famero de consultas concurrentes que analizan un mayor n\u00famero de documentos. En el mundo de los RDBMS, esta carga de trabajo se denomina carga de trabajo OLAP. \u00a0<\/span><\/p>\n

    Profundicemos en los detalles de estos dos servicios.<\/span><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Servicio de consulta<\/b><\/span><\/td>\nServicio de an\u00e1lisis<\/b><\/span><\/td>\n<\/tr>\n
    Tutoriales<\/strong><\/span><\/td>\n<\/tr>\n
    https:\/\/query-tutorial.couchbase.com\/tutorial\/#1<\/span><\/a><\/td>\nhttps:\/\/sqlplusplus-tutorial.couchbase.com\/tutorial\/#1<\/span><\/a><\/td>\n<\/tr>\n
    Comparaci\u00f3n a alto nivel<\/strong><\/span><\/td>\n<\/tr>\n
    Se utiliza para la manipulaci\u00f3n de datos dentro de la l\u00f3gica de la aplicaci\u00f3n<\/span><\/td>\nSe utiliza para informes, an\u00e1lisis (hist\u00f3ricos, interactivos), cuadros de mando<\/span><\/td>\n<\/tr>\n
    Consultas breves<\/span><\/p>\n

    \u00a0- SQL relativamente sencillo<\/span><\/p>\n

    \u00a0- Normalmente se trata de peque\u00f1as cantidades de datos<\/span><\/td>\n

    		Operaciones m\u00e1s largas<\/span><\/p>\n

    \u00a0- SQL complejo con an\u00e1lisis<\/span><\/p>\n

    \u00a0- Suelen implicar grandes cantidades de datos<\/span><\/td>\n<\/tr>\n

    Ejecutar SELECT, INSERT, UPDATE, DELETE, MERGE para aplicaciones operativas<\/span><\/td>\nEjecutar SELECT para el an\u00e1lisis<\/span><\/td>\n<\/tr>\n
    Actualizaciones aleatorias<\/span><\/p>\n

    \u00a0- Actualizar pocos documentos por consulta<\/span><\/td>\n

    		No hay actualizaciones<\/span><\/p>\n

    \u00a0- Cambios introducidos desde el Servicio de Datos<\/span><\/td>\n<\/tr>\n

    Latencia de un milisegundo a un segundo,<\/span><\/p>\n

    alto rendimiento (10-1000 qps), el objetivo de rendimiento es consultas\/segundo<\/span><\/td>\n

    		Tiempo de respuesta de segundos a minutos,<\/span><\/p>\n

    <1-10 consultas por segundo,<\/span><\/p>\n

    el objetivo de rendimiento es segundos\/consulta<\/span><\/td>\n<\/tr>\n

    Gran n\u00famero de \u00edndices<\/span><\/td>\nMenos \u00edndices<\/span><\/td>\n<\/tr>\n
    Consultas escritas por el desarrollador; las consultas generadas son bien conocidas<\/span><\/td>\nConsultas ad hoc; informes complejos, cuadros de mando, carga de trabajo de BI<\/span><\/td>\n<\/tr>\n
    Las consultas se ejecutan en un \u00fanico nodo de consulta utilizando la infraestructura de datos e \u00edndices distribuidos.<\/span><\/td>\nLas consultas se ejecutan en todos los nodos de an\u00e1lisis utilizando su infraestructura distribuida de c\u00e1lculo, \u00edndices y datos.<\/span><\/td>\n<\/tr>\n
    Comparaci\u00f3n t\u00e9cnica: Arquitectura<\/strong><\/span><\/td>\n<\/tr>\n
    Las consultas se ejecutan en modo SMP, el rendimiento es escalable a\u00f1adiendo nuevos nodos de consulta.<\/span><\/td>\nLas consultas se ejecutan en modo MPP, pueden manejar datos m\u00e1s grandes o reducir los tiempos de ejecuci\u00f3n de las consultas a\u00f1adiendo nuevos nodos de an\u00e1lisis.<\/span><\/td>\n<\/tr>\n
    Comparaci\u00f3n t\u00e9cnica: Optimizer<\/strong><\/span><\/td>\n<\/tr>\n
    Uni\u00f3n de bucles anidados por defecto<\/span><\/p>\n

    Hash join mediante sugerencia de consulta<\/span><\/td>\n

    		Hash join paralelo por defecto,<\/span><\/p>\n

    Uni\u00f3n de bucles anidados (\u00edndice) o uni\u00f3n de difusi\u00f3n mediante sugerencia de consulta<\/span><\/td>\n<\/tr>\n

    Comparaci\u00f3n t\u00e9cnica: \u00cdndices<\/strong><\/span><\/td>\n<\/tr>\n
    \u00cdndices secundarios globales<\/span><\/td>\n\u00cdndices secundarios locales (ubicados junto a las particiones de datos)<\/span><\/td>\n<\/tr>\n
    Utiliza \u00edndices optimizados para memoria; \u00edndices secundarios est\u00e1ndar (Plasma)<\/span><\/td>\n\u00cdndices secundarios basados en \u00e1rboles combinados con estructura logar\u00edtmica (LSM)<\/span><\/td>\n<\/tr>\n
    Soporte para escaneos de \u00edndices cubiertos y no cubiertos<\/span><\/td>\nExploraciones de \u00edndices no cubiertas<\/span><\/td>\n<\/tr>\n
    Puede utilizar el \u00edndice de texto completo para las consultas (6.5)<\/span><\/td>\n<\/td>\n<\/tr>\n
    Comparaci\u00f3n t\u00e9cnica: Ejecuci\u00f3n<\/strong><\/span><\/td>\n<\/tr>\n
    La mayor\u00eda de las consultas se preparan una vez y se ejecutan muchas veces<\/span><\/td>\nConsultas ad hoc y exploratorias<\/span><\/td>\n<\/tr>\n
    Los SDK utilizan el modelo preparar-ejecutar basado en una bandera ad hoc<\/span><\/td>\nLos SDK ofrecen consultas ad hoc y parametrizadas<\/span><\/td>\n<\/tr>\n
    La mayor\u00eda de las operaciones se realizan en memoria; s\u00f3lo cuando una exploraci\u00f3n de \u00edndices devuelve datos de gran tama\u00f1o se escriben en el disco de relleno<\/span><\/td>\nOperaciones de memoria limitada en datos de gran tama\u00f1o (superior a la memoria del cl\u00faster) con un desbordamiento gradual seg\u00fan sea necesario.<\/span><\/td>\n<\/tr>\n
    Paralelismo de consultas en un \u00fanico nodo<\/span><\/td>\nOperadores de uni\u00f3n, ordenaci\u00f3n, agregaci\u00f3n y agregaci\u00f3n agrupada multinodo en paralelo con partici\u00f3n<\/span><\/td>\n<\/tr>\n
    Espera un \u00fanico dispositivo de almacenamiento (posiblemente RAID)<\/span><\/td>\nUso sin RAID de varios dispositivos de almacenamiento<\/span><\/td>\n<\/tr>\n
    Aislamiento del rendimiento mediante \u00edndices de cobertura que admiten operaciones de escaneado, agrupaci\u00f3n y agregaci\u00f3n en nodos Index Service.<\/span><\/td>\nAislamiento del rendimiento para todas las consultas mediante la sombra de los datos en los nodos del Servicio de An\u00e1lisis.<\/span><\/td>\n<\/tr>\n
    Documentaci\u00f3n t\u00e9cnica<\/span><\/strong><\/td>\n<\/tr>\n
    https:\/\/docs.couchbase.com\/server\/6.0\/n1ql\/n1ql-language-reference\/index.html<\/span><\/a><\/td>\nhttps:\/\/docs.couchbase.com\/server\/6.0\/analytics\/introduction.html<\/span><\/a><\/td>\n<\/tr>\n
    Informaci\u00f3n t\u00e9cnica<\/strong><\/span><\/td>\n<\/tr>\n
    https:\/\/www.couchbase.com\/products\/n1ql<\/span><\/a><\/td>\nhttps:\/\/www.couchbase.com\/sqlplusplus<\/span><\/a><\/td>\n<\/tr>\n
    Libros t\u00e9cnicos<\/strong><\/span><\/td>\n<\/tr>\n
    https:\/\/www.couchbase.com\/blog\/a-guide-to-n1ql-features-in-couchbase-5-5-special-edition\/<\/span><\/a><\/td>\nhttps:\/\/resources.couchbase.com\/sql_tutorial<\/span><\/a><\/p>\n

    https:\/\/www.amazon.com\/SQL-Users-Tutorial-Don-Chamberlin\/dp\/0692184503\/<\/span><\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

     <\/p>\n

      \n
    1. [El OBJETIVO] El Objetivo: El proceso de mejora continua.\u00a0<\/span>https:\/\/www.amazon.com\/Goal-Process-Ongoing-Improvement\/dp\/0884271951\/ref=sr_1_1?keywords=the+goal&qid=1547969233&sr=8-1<\/a><\/li>\n
    2. Sistemas de compromiso para los puestos de trabajo de los clientes: https:\/\/marketing.cioreview.com\/cxoinsight\/systems-of-engagement-for-customer-jobs-nid-24677-cid-51.html<\/a><\/li>\n
    3. Parte 2 de este art\u00edculo de Even Pease: https:\/\/www.couchbase.com\/blog\/part-2-n1ql-to-query-or-to-analyze\/<\/a><\/li>\n<\/ol>","protected":false},"excerpt":{"rendered":"

      Note: This recommendation note was written in collaboration with Till Westmann and Mike Carey, who lead the Couchbase analytics R&D. [Part 2 of this article by Even Pease is at: https:\/\/www.couchbase.com\/blog\/part-2-n1ql-to-query-or-to-analyze\/] Couchbase’s Query and Analytics Services both support N1QL (SQL […]<\/p>","protected":false},"author":55,"featured_media":5959,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1815,2294,1816,9417,1812],"tags":[1261,1725,2297],"ppma_author":[8929],"class_list":["post-6322","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-best-practices-and-tutorials","category-analytics","category-couchbase-server","category-performance","category-n1ql-query","tag-json","tag-nosql-database","tag-workload"],"acf":[],"yoast_head":"\nN1QL: To Query or To Analyze? - The Couchbase Blog<\/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\/n1ql-to-query-or-to-analyze\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"N1QL: To Query or To Analyze?\" \/>\n<meta property=\"og:description\" content=\"Note: This recommendation note was written in collaboration with Till Westmann and Mike Carey, who lead the Couchbase analytics R&D. [Part 2 of this article by Even Pease is at: https:\/\/www.couchbase.com\/blog\/part-2-n1ql-to-query-or-to-analyze\/] Couchbase’s Query and Analytics Services both support N1QL (SQL […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/es\/n1ql-to-query-or-to-analyze\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2019-01-24T20:04:52+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T03:19:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2018\/11\/Screen-Shot-2018-11-14-at-3.46.47-PM.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1212\" \/>\n\t<meta property=\"og:image:height\" content=\"748\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Keshav Murthy\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@rkeshavmurthy\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Keshav Murthy\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/\"},\"author\":{\"name\":\"Keshav Murthy\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/c261644262bf98e146372fe647682636\"},\"headline\":\"N1QL: To Query or To Analyze?\",\"datePublished\":\"2019-01-24T20:04:52+00:00\",\"dateModified\":\"2025-06-14T03:19:53+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/\"},\"wordCount\":794,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2018\/11\/Screen-Shot-2018-11-14-at-3.46.47-PM.png\",\"keywords\":[\"JSON\",\"NoSQL Database\",\"workload\"],\"articleSection\":[\"Best Practices and Tutorials\",\"Couchbase Analytics\",\"Couchbase Server\",\"High Performance\",\"SQL++ \/ N1QL Query\"],\"inLanguage\":\"es\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/\",\"url\":\"https:\/\/www.couchbase.com\/blog\/n1ql-to-query-or-to-analyze\/\",\"name\":\"N1QL: To Query or To Analyze? 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