{"id":8813,"date":"2020-06-23T07:00:22","date_gmt":"2020-06-23T14:00:22","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=8813"},"modified":"2025-06-13T18:44:13","modified_gmt":"2025-06-14T01:44:13","slug":"analyze-this-mongodb-couchbase-analytics","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/es\/analyze-this-mongodb-couchbase-analytics\/","title":{"rendered":"Analiza esto: MongoDB & Couchbase Analytics."},"content":{"rendered":"

El objetivo de la inform\u00e1tica es el conocimiento, no los n\u00fameros. - <\/span>Richard Hamming<\/span><\/a><\/p><\/blockquote>\n

La espiral de dirigir el negocio, analizar qu\u00e9 cambiar y a qu\u00e9 cambiar, y luego cambiar el negocio es eterna. Si haces el an\u00e1lisis correcto, tu espiral aumentar\u00e1. Si no, caer\u00e1s en espiral.<\/span><\/p>\n

Couchbase<\/span><\/a>Couchbase, al igual que otros pioneros de los sistemas NoSQL, se cre\u00f3 para dar respuesta a los requisitos extremos de escala, rendimiento y disponibilidad del mundo web 2.0. De la simple clave-valor, Couchbase ha evolucionado para manejar <\/span>consulta<\/span><\/a>, <\/span>busque en<\/span><\/a> y <\/span>an\u00e1lisis<\/span><\/a> - a escala. Cada uno de ellos son motores creados espec\u00edficamente e integrados a trav\u00e9s de la plataforma de Couchbase <\/span>multidimensional<\/span><\/a> arquitectura. Tanto el servicio de consulta como el de an\u00e1lisis hablan N1QL. \u00bfPor qu\u00e9 construir dos motores distintos que hablan el mismo idioma? Porque...<\/span><\/p>\n

Talla \u00fanica: Una idea cuyo tiempo ha llegado y se ha ido. - <\/span>Michael Stonebraker<\/span><\/a><\/p><\/blockquote>\n

El motor de consulta se cre\u00f3 para la carga de trabajo operativa y el motor de an\u00e1lisis para la carga de trabajo anal\u00edtica. Hemos <\/span>en comparaci\u00f3n con<\/span><\/a> los dos motores y dado el <\/span>orientaci\u00f3n<\/span><\/a>. MongoDB ha seguido un camino similar, pasando de ser una base de datos en cl\u00faster que gestionaba cargas de trabajo sencillas a cargas de trabajo complejas para an\u00e1lisis y consultas en lagos de datos.\u00a0<\/span><\/p>\n

El a\u00f1o pasado, MongoDB anunci\u00f3 nodos anal\u00edticos en sus cl\u00fasteres para el procesamiento anal\u00edtico. En este blog, comparamos y contrastamos los dos motores para el caso de uso anal\u00edtico.<\/span><\/p>\n

Couchbase: Arquitectura de alto nivel<\/strong><\/h5>\n

\"\"<\/a><\/p>\n

Dentro de Couchbase Analytics: Arquitectura de alto nivel<\/strong>
\n\"\"<\/a><\/h5>\n
Nodos de an\u00e1lisis MongoDB:<\/strong>\"\"<\/a><\/h5>\n

Vamos a comparar y contrastar el soporte anal\u00edtico en los nodos MongoDB Analytic y Couchbase Analytics.<\/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
<\/td>\nNodos anal\u00edticos MongoDB<\/span><\/td>\nAn\u00e1lisis de Couchbase<\/span><\/td>\n<\/tr>\n
Docs<\/span><\/td>\nhttps:\/\/docs.atlas.mongodb.com\/reference\/replica-set-tags\/<\/span><\/a><\/td>\nhttps:\/\/docs.couchbase.com\/server\/6.5\/analytics\/introduction.html<\/span><\/a><\/td>\n<\/tr>\n
Arquitectura<\/span><\/td>\nUtilizar un conjunto de nodos r\u00e9plica secundarios con una copia completa de los datos operativos. El lenguaje de consulta es el mismo (MQL); el procesamiento de las consultas es el mismo que el de la carga de trabajo operativa.<\/span><\/td>\nDistintos nodos de an\u00e1lisis que disponen de un subconjunto de datos operativos definido por el usuario. El lenguaje de consulta es el mismo (N1QL); el procesamiento de consultas est\u00e1 dise\u00f1ado para conjuntos de datos m\u00e1s grandes (v\u00e9ase m\u00e1s adelante).<\/span><\/td>\n<\/tr>\n
Detalles de la arquitectura<\/span><\/td>\nNodos anal\u00edticos mapeados de Atlas<\/span><\/a><\/td>\nCouchbase Analytics: NoETL para el an\u00e1lisis escalable de datos NoSQL<\/span><\/a><\/td>\n<\/tr>\n
Modelo de datos<\/span><\/td>\nBSON<\/span><\/td>\nJSON<\/span><\/td>\n<\/tr>\n
Lenguaje de consulta<\/span><\/td>\nMQL<\/span><\/a> - Lenguaje de consulta de MongoDB<\/span><\/td>\nN1QL<\/span><\/a> - Lenguaje de consulta de forma normal no 1; SQL para JSON<\/span><\/td>\n<\/tr>\n
P\u00e1gina de consulta<\/span><\/td>\nConsulta MongoDB<\/span><\/a><\/td>\nConsulta anal\u00edtica<\/span><\/a><\/td>\n<\/tr>\n
Tratamiento de consultas<\/span><\/td>\nIgual que el procesamiento de consultas operativas, utilizando mongos y mongod para el procesamiento de consultas distribuidas.\u00a0<\/span><\/td>\nMotor de an\u00e1lisis dise\u00f1ado para el procesamiento paralelo masivo (MPP) de los datos. Cada N1QL\u00a0<\/span><\/td>\n<\/tr>\n
Optimizador de consultas<\/span><\/td>\nOptimizador basado en formas; Requiere <\/span>gesti\u00f3n de planes<\/span><\/a>.<\/span><\/td>\nOptimizador basado en reglas. No requiere gesti\u00f3n de planes.\u00a0<\/span><\/td>\n<\/tr>\n
Explique<\/span><\/td>\nTexto y gr\u00e1ficos.<\/span><\/td>\nTexto y gr\u00e1ficos.<\/span><\/td>\n<\/tr>\n
Indexaci\u00f3n<\/span><\/td>\nHay que crear el \u00edndice en el operativo y hacer que se copie.<\/span><\/td>\nS\u00f3lo an\u00e1lisis Indexaci\u00f3n<\/span><\/td>\n<\/tr>\n
Procesamiento paralelo<\/span><\/td>\nCada nodo Mongod ejecuta las operaciones b\u00e1sicas y los mongos las combinan (por ejemplo, el grupo final y la agregaci\u00f3n).\u00a0<\/span><\/td>\nGestionar consultas anal\u00edticas complejas de forma eficaz y ofrecer<\/span><\/p>\n

las propiedades de escalado y aceleraci\u00f3n deseadas, el Servicio de An\u00e1lisis<\/span><\/p>\n

emplea los mismos tipos de MPP de \u00faltima generaci\u00f3n y no comparte nada<\/span><\/p>\n

(procesamiento paralelo masivo) basadas en estrategias de procesamiento de consultas [<\/span>Del documento de VLDB]<\/span><\/a><\/td>\n<\/tr>\n

Indexaci\u00f3n<\/span><\/td>\nIndexaci\u00f3n local\u00a0<\/span><\/td>\nIndexaci\u00f3n local<\/span><\/td>\n<\/tr>\n
Uniones - Lengua<\/span><\/td>\n1TP4B\u00fasqueda<\/span><\/a> admite uniones de igualdad simple entre dos colecciones; s\u00f3lo se permiten campos escalares simples. Las matrices deben desenrollarse antes de unirse.<\/span><\/p>\n
    \n
  1. Una de las dos colecciones NO PUEDE ser fragmentada. Es decir, no se puede unir en colecciones grandes.<\/span><\/li>\n
  2. Necesidad de una etapa de canalizaci\u00f3n separada para las uniones simples sin igualdad. Esto significa que las consultas son ineficientes y consumen muchos recursos;\u00a0<\/span><\/li>\n
  3. Esto es m\u00e1s o menos equivalente a los LEFT OUTER JOINs en SQL. Los usuarios tendr\u00e1n que realizar un procesamiento de canalizaci\u00f3n adicional para obtener el INNER JOIN y otras uniones.<\/span><\/li>\n<\/ol>\n<\/td>\n
Operaciones INNER JOIN, LEFT OUTER JOIN, NEST y UNNEST.<\/span><\/p>\n
    \n
  1. Sintaxis SQL est\u00e1ndar<\/span><\/li>\n
  2. Soporta sharded dataset por defecto.<\/span><\/li>\n
  3. Admite la igualdad y expresiones de uni\u00f3n arbitrariamente complejas.<\/span><\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
Tratamiento de consultas: tama\u00f1o de los datos<\/span><\/td>\nLas etapas intermedias de la canalizaci\u00f3n de aggregate() no pueden ser superiores a <\/span>100 MB<\/span><\/a> en tama\u00f1o. Los escritores\/usuarios de consultas deben utilizar un indicador especial para permitirlo.<\/span><\/td>\nSin limitaciones; Cuando los datos intermedios (por ejemplo, tabla hash, datos de ordenaci\u00f3n) se hacen m\u00e1s grandes, se vuelcan al disco.<\/span><\/td>\n<\/tr>\n
Procesamiento de consultas: Tipo de uni\u00f3n<\/span><\/td>\n(aproximadamente) LEFT OUTER JOIN<\/span><\/td>\nINNER JOIN<\/span><\/p>\n

LEFT OUTER JOIN<\/span><\/td>\n<\/tr>\n

Buscar en<\/span><\/td>\nAdmite la b\u00fasqueda dentro de la consulta. Utiliza la b\u00fasqueda Atlas en la nube y la b\u00fasqueda b\u00e1sica basada en el \u00e1rbol B en las instalaciones.\u00a0<\/span><\/td>\nEl servicio Analytics no tiene una b\u00fasqueda integrada. Tenemos que utilizar el servicio de consulta con FTS para combinar la b\u00fasqueda dentro de una consulta.<\/span><\/td>\n<\/tr>\n
Consultas admitidas<\/span><\/td>\nfind() y aggregate()<\/span><\/td>\nSentencia SELECT (de SQL y SQL++)<\/span><\/td>\n<\/tr>\n
Tipos de JOIN (Idioma)<\/span><\/td>\n$lookup - esto es aproximadamente LEFT OUTER JOIN a trav\u00e9s de\u00a0<\/span><\/td>\nINNER JOIN<\/span><\/p>\n

LEFT OUTER JOIN\u00a0<\/span><\/td>\n<\/tr>\n

Tipos de JOIN (Aplicaci\u00f3n)<\/span><\/td>\n\n
    \n
  1. Unir s\u00f3lo entre una colecci\u00f3n sharded y otra no sharded.<\/span><\/li>\n
  2. S\u00f3lo bucle anidado (NL). (NL es malo para el rendimiento en el manejo de datos de gran tama\u00f1o).<\/span><\/li>\n
  3. Los resultados intermedios est\u00e1n limitados a 100 MB de memoria. El usuario tendr\u00e1 que conocer el tama\u00f1o y utilizar las opciones para permitir el desbordamiento.<\/span><\/li>\n<\/ol>\n<\/td>\n
\n
    \n
  1. Une conjuntos de datos fragmentados; todos los conjuntos de datos est\u00e1n fragmentados por defecto.<\/span><\/li>\n
  2. Admite bucle anidado, broadcast y hash join paralelo<\/span><\/li>\n
  3. Admite tanto Nested Loop join como Hash join.\u00a0<\/span><\/li>\n
  4. Utiliza Hash join por defecto - muy adecuado para el procesamiento de datos a gran escala.\u00a0<\/span><\/li>\n
  5. No hay limitaciones de tama\u00f1o para los datos intermedios.<\/span><\/li>\n<\/ol>\n<\/td>\n<\/tr>\n
Agregaci\u00f3n<\/span><\/td>\nAdmite la agrupaci\u00f3n y agregaci\u00f3n com\u00fan mediante el m\u00e9todo aggregate().<\/span><\/td>\nAdmite la agrupaci\u00f3n y agregaci\u00f3n com\u00fan mediante GROUP BY y las respectivas agregaciones. Consulte a continuaci\u00f3n los agregados con ventanas.\u00a0<\/span><\/td>\n<\/tr>\n
Funciones agregadas en ventana: <\/span>Probablemente, la funci\u00f3n SQL m\u00e1s interesante.<\/span><\/a><\/td>\nNo disponible.<\/span><\/td>\nCompletamente <\/span>compatible<\/span><\/a>.<\/span><\/p>\n

RANGO()<\/span><\/p>\n

PERCENT_RANK()<\/span><\/p>\n

DENSERANK()<\/span><\/p>\n

ROW_NUMBER()<\/span><\/p>\n

CUME_DIST()<\/span><\/p>\n

PRIMER_VALOR()<\/span><\/p>\n

\u00daLTIMO_VALOR()<\/span><\/p>\n

NTH_VALUE()<\/span>
\n<\/span>LAG()<\/span><\/p>\n

LEAD()<\/span><\/p>\n

NTILE()<\/span><\/p>\n

RATIO_TO_REPORT()<\/span><\/td>\n<\/tr>\n

An\u00e1lisis de datos de cl\u00fasteres m\u00faltiples<\/span><\/td>\nTodos los datos analizados proceden de un \u00fanico cl\u00faster MongoDB.<\/span><\/td>\n6.5: Todos los datos analizados proceden de un \u00fanico cl\u00faster Couchbase.<\/span><\/p>\n

6.6: Puede ingerir y analizar los datos de m\u00faltiples clusters Couchbase.<\/span><\/td>\n<\/tr>\n

Datos externos<\/span><\/td>\nAdmite el procesamiento de consultas sobre datos S3. Admite los formatos BSON, CSV, TSV, Avro y Parquet.<\/span><\/td>\n6.6: Soporta datos externos JSON, CSV y TSV en S3<\/span><\/td>\n<\/tr>\n
Fuentes de datos externas<\/span><\/td>\nAdmite fuentes de datos adicionales a trav\u00e9s del controlador JDBC. Integrado con la tuber\u00eda de agregaci\u00f3n a trav\u00e9s de, tienes que esperar para ello, <\/span>Operador $sql<\/span><\/a>.<\/span><\/td>\nNinguna, excepto las mencionadas anteriormente.\u00a0<\/span><\/td>\n<\/tr>\n
Subconsultas<\/span><\/td>\nSubconsultas a trav\u00e9s del canal de agregaci\u00f3n.<\/span><\/td>\nSubconsultas SQL est\u00e1ndar.<\/span><\/td>\n<\/tr>\n
Plan de consulta<\/span><\/td>\n1TP4Explicar<\/span><\/td>\nEXPLICAR<\/span><\/td>\n<\/tr>\n
DataViz<\/span><\/td>\nGr\u00e1ficos MongoDB integrados<\/span><\/td>\nSin DataViz integrado<\/span><\/td>\n<\/tr>\n
Inteligencia empresarial\u00a0<\/span><\/td>\nKnowi<\/span><\/a><\/p>\n

Tableau y otros motores de BI compatibles con ODBC y JDBC.<\/span><\/td>\n

Knowi<\/span><\/a><\/p>\n

Tableau y otros motores de BI compatibles con ODBC y JDBC.<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

 <\/p>\n

Referencias:<\/b><\/p>\n

    \n
  1. Comparaci\u00f3n de dos enfoques basados en SQL para consultar JSON: SQL++ y SQL:2016<\/span><\/a><\/li>\n
  2. De SQL a NoSQL - 7 m\u00e9tricas para comparar lenguajes de consulta<\/span><\/a><\/li>\n
  3. Couchbase Analytics: NoETL para el an\u00e1lisis escalable de datos NoSQL<\/span><\/a><\/li>\n<\/ol>\n

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

    The purpose of computing is insight, not numbers.\u00a0 — Richard Hamming The spiral of running the business, analyzing what to change & what to change to, and then changing the business is an eternal one. Do the right analysis, your […]<\/p>","protected":false},"author":55,"featured_media":8815,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1815,2294,1821,1812],"tags":[1261,1309,1725],"ppma_author":[8929],"class_list":["post-8813","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-best-practices-and-tutorials","category-analytics","category-couchbase-architecture","category-n1ql-query","tag-json","tag-mongodb","tag-nosql-database"],"acf":[],"yoast_head":"\nAnalyze This: MongoDB & Couchbase Analytics. - 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\/analyze-this-mongodb-couchbase-analytics\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Analyze This: MongoDB & Couchbase Analytics.\" \/>\n<meta property=\"og:description\" content=\"The purpose of computing is insight, not numbers.\u00a0 — Richard Hamming The spiral of running the business, analyzing what to change & what to change to, and then changing the business is an eternal one. Do the right analysis, your […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/es\/analyze-this-mongodb-couchbase-analytics\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2020-06-23T14:00:22+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T01:44:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2020\/06\/Screen-Shot-2020-06-21-at-12.26.25-PM.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1136\" \/>\n\t<meta property=\"og:image:height\" content=\"576\" \/>\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=\"5 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/\"},\"author\":{\"name\":\"Keshav Murthy\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/c261644262bf98e146372fe647682636\"},\"headline\":\"Analyze This: MongoDB & Couchbase Analytics.\",\"datePublished\":\"2020-06-23T14:00:22+00:00\",\"dateModified\":\"2025-06-14T01:44:13+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/\"},\"wordCount\":1041,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2020\/06\/Screen-Shot-2020-06-21-at-12.26.25-PM.png\",\"keywords\":[\"JSON\",\"mongodb\",\"NoSQL Database\"],\"articleSection\":[\"Best Practices and Tutorials\",\"Couchbase Analytics\",\"Couchbase Architecture\",\"SQL++ \/ N1QL Query\"],\"inLanguage\":\"es\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/\",\"url\":\"https:\/\/www.couchbase.com\/blog\/analyze-this-mongodb-couchbase-analytics\/\",\"name\":\"Analyze This: MongoDB & Couchbase Analytics. - 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