{"id":7849,"date":"2019-11-17T19:20:31","date_gmt":"2019-11-18T03:20:31","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=7849"},"modified":"2025-06-13T20:19:31","modified_gmt":"2025-06-14T03:19:31","slug":"utilizing-multiple-disks-to-speed-up-couchbase-analytics-queries","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/es\/utilizing-multiple-disks-to-speed-up-couchbase-analytics-queries\/","title":{"rendered":"Uso de varios discos para acelerar las consultas de Couchbase Analytics"},"content":{"rendered":"

Las consultas anal\u00edticas ad hoc t\u00edpicas tienen que procesar muchos m\u00e1s datos de los que caben en la memoria. Consecuentemente, esas consultas tienden a estar limitadas por E\/S. Cuando el servicio Analytics fue introducido en Couchbase 6.0, permiti\u00f3 a los usuarios especificar m\u00faltiples \"Analytics Disk Paths\" durante la inicializaci\u00f3n del nodo. En este art\u00edculo, realizamos un par de experimentos en diferentes instancias en la nube para mostrar c\u00f3mo configurar correctamente m\u00faltiples \"Analytics Disk Paths\" y c\u00f3mo esta caracter\u00edstica puede ser utilizada para acelerar las consultas de Analytics.<\/span><\/p>\n

\"Figure
\nFigura 1: Especificaci\u00f3n de las rutas de disco de Analytics durante la inicializaci\u00f3n del nodo<\/p>\n

Durante la inicializaci\u00f3n del nodo, se puede utilizar cualquier ruta \u00fanica del sistema de archivos como \"Ruta de disco de an\u00e1lisis\", independientemente del dispositivo de almacenamiento f\u00edsico real en el que resida esta ruta. Se pueden utilizar m\u00faltiples rutas que residan en el mismo dispositivo. Los datos del servicio Analytics se dividen en todas las \"rutas de disco Analytics\" especificadas en todos los nodos que tienen el servicio Analytics. Por ejemplo, si un cluster tiene dos nodos con el servicio Analytics y uno de los nodos tiene especificadas 4 \"Rutas de Disco Analytics\", y el otro nodo tiene 8 \"Rutas de Disco Analytics\", cada conjunto de datos creado en Analytics tendr\u00e1 un total de 12 particiones (particiones de datos).<\/span><\/p>\n

Durante la ejecuci\u00f3n de la consulta, el motor de consulta MPP de Analytics intenta leer y procesar simult\u00e1neamente los datos de todas las particiones de datos. Por ello, las operaciones de entrada\/salida por segundo (IOPS) del disco f\u00edsico real en el que reside cada partici\u00f3n de datos desempe\u00f1an un papel fundamental a la hora de determinar el tiempo de ejecuci\u00f3n de la consulta.<\/span><\/p>\n

Los dispositivos de almacenamiento modernos, como los SSD, tienen IOPS mucho mayores y pueden gestionar mejor las lecturas simult\u00e1neas que los HDD. Por lo tanto, tener una \u00fanica partici\u00f3n de datos en dispositivos con altas IOPS no utilizar\u00e1 completamente sus capacidades. Para simplificar la configuraci\u00f3n del caso t\u00edpico de un nodo que tiene un \u00fanico dispositivo de almacenamiento moderno, el servicio Analytics crea autom\u00e1ticamente m\u00faltiples particiones de datos dentro del mismo dispositivo de almacenamiento si y s\u00f3lo si se especifica una \u00fanica \"Ruta de Disco Analytics\" durante la inicializaci\u00f3n del nodo. El n\u00famero de particiones de datos creadas autom\u00e1ticamente se basa en esta f\u00f3rmula:<\/span><\/p>\n

Maximum partitions to create = Min((Analytics Memory in MB \/ 1024), 16)\r\nActual created partitions = Min(node virtual cores, Maximum partitions to create)<\/pre>\n
Por ejemplo, si un nodo tiene 8 n\u00facleos virtuales y el servicio de An\u00e1lisis fue configurado con memoria >= 8GB, se crear\u00e1n 8 particiones de datos en ese nodo. Del mismo modo, si un nodo tiene 32 n\u00facleos virtuales y se configur\u00f3 con memoria >= 16GB, s\u00f3lo se crear\u00e1n 16 particiones ya que las particiones m\u00e1ximas que se crear\u00e1n autom\u00e1ticamente tienen un l\u00edmite superior de 16 particiones.<\/span><\/p>\n
Para mostrar el impacto en el rendimiento del n\u00famero de particiones de datos por disco, realizamos un par de experimentos en diferentes tipos de instancia en Amazon Web Services EC2 utilizando <\/span>Couchbase Server 6.5 Beta 2<\/span><\/a>. Los datos utilizados en los experimentos son una versi\u00f3n JSONificada del famoso <\/span>TPC-DS<\/span><\/a> donde cada fila se convirti\u00f3 en un documento JSON con un campo adicional que identifica el nombre de la tabla a la que pertenece el documento. Se generaron datos TPC-DS de muestra y se cargaron en un bucket denominado tpcds<\/em>. En ambos experimentos, al servicio Analytics se le asignaron 32 GB de memoria.<\/span><\/p>\n
Experimento 1: Instancia \u00fanica con 8 n\u00facleos virtuales y 1 SSD NVMe<\/b><\/p>\n
En este experimento, creamos 3 conjuntos de datos en el servicio Analytics, como se indica a continuaci\u00f3n:\u00a0<\/span><\/p>\n
CREATE DATASET store_sales ON tpcds WHERE table_name='store_sales';\r\nCREATE DATASET date_dim ON tpcds WHERE table_name='date_dim';\r\nCREATE DATASET item ON tpcds WHERE table_name='item';<\/pre>\n
Utilizamos la siguiente consulta de cualificaci\u00f3n TPC-DS tras convertirla en una consulta N1QL for Analytics para medir el tiempo de respuesta bajo dos configuraciones diferentes:<\/span><\/p>\n
SELECT dt.d_year,\r\n       item.i_brand_id brand_id,\r\n       item.i_brand brand,\r\n       sum(ss.ss_ext_sales_price) sum_agg\r\nFROM date_dim dt,\r\n     store_sales ss,\r\n     item\r\nWHERE dt.d_date_sk = ss.ss_sold_date_sk\r\n  AND ss.ss_item_sk = item.i_item_sk\r\n  AND item.i_manufact_id = 128\r\n  AND dt.d_moy=11\r\nGROUP BY dt.d_year,\r\n         item.i_brand,\r\n         item.i_brand_id\r\nORDER BY dt.d_year,\r\n         sum_agg DESC,\r\n         brand_id\r\nLIMIT 100;<\/pre>\n
Para la primera configuraci\u00f3n, especificamos dos \"Analytics Disk Paths\" en el mismo disco, lo que dio lugar a que cada conjunto de datos tuviera 2 particiones de datos. En cuanto a la segunda configuraci\u00f3n, s\u00f3lo se especific\u00f3 una \"Datos anal\u00edticos<\/a> Se especific\u00f3 \"Rutas\", lo que activ\u00f3 la opci\u00f3n de configuraci\u00f3n autom\u00e1tica. Como el nodo tiene 8 n\u00facleos virtuales, se crearon autom\u00e1ticamente 8 particiones de datos. La figura 2 muestra los tiempos medios de respuesta de consulta para estas dos configuraciones. En t\u00e9rminos de tiempo medio de respuesta de consulta, la configuraci\u00f3n autom\u00e1tica con 8 particiones fue m\u00e1s del doble de r\u00e1pida que la configuraci\u00f3n con s\u00f3lo 2 particiones de datos. Esta mejora se debi\u00f3 a una mejor utilizaci\u00f3n del \u00fanico SSD NVMe, ya que este tipo de disco puede gestionar 8 lecturas simult\u00e1neas. Adem\u00e1s, dado que esta consulta implica agrupaci\u00f3n y clasificaci\u00f3n, el procesamiento de los datos de forma concurrente en 8 particiones se tradujo en una mejora significativa del rendimiento de la consulta.<\/span><\/p>\n
\"Figure
\nFigura 2: Tiempo medio de respuesta de la consulta del Experimento 1<\/p>\n
Experimento 2: Instancia \u00fanica con 8 n\u00facleos virtuales y 6 discos duros<\/b><\/p>\n
En este experimento, intentaremos escanear un mayor volumen de datos creando un \u00fanico conjunto de datos que contenga todos los datos del bucket tpcds de la siguiente manera:<\/span><\/p>\n
CREATE DATASET tpcds on tpcds;<\/pre>\n
Utilizamos la siguiente consulta de N1QL for Analytics que da como resultado el escaneo de todos los datos utilizando dos configuraciones diferentes:<\/span><\/p>\n
SELECT SUM(ss_ext_sales_price)\r\nFROM tpcds\r\nWHERE table_name = \"store_sales\";<\/pre>\n
Para la primera configuraci\u00f3n, se especific\u00f3 una \u00fanica \"Ruta de datos anal\u00edticos\"; esto dio lugar a que el sistema creara autom\u00e1ticamente 8 particiones en un directorio <\/span>solo<\/span><\/i> HDD. En la segunda configuraci\u00f3n, se especificaron 6 \"rutas de datos anal\u00edticos\" y cada ruta se ubic\u00f3 en un disco duro f\u00edsico diferente, lo que dio lugar a 6 particiones de datos. La figura 3 muestra el tiempo medio de respuesta de las dos configuraciones. En la primera configuraci\u00f3n, la realizaci\u00f3n de 8 lecturas simult\u00e1neas en un \u00fanico disco duro dio lugar a un rendimiento deficiente. Uno de los principales motivos es que no se utiliza el ancho de banda de E\/S de los otros 5 discos duros. Adem\u00e1s, la concurrencia de 8 lecturas en un \u00fanico disco duro provocaba un mayor movimiento de los brazos del disco, lo que aumentaba el coste medio de una E\/S de disco.  La segunda configuraci\u00f3n, que utiliz\u00f3 los 6 discos duros simult\u00e1neamente, mejor\u00f3 el rendimiento en m\u00e1s de 7 veces.<\/span><\/p>\n
\"Figure
\nFigura 3: Tiempo medio de respuesta de las consultas del Experimento 2<\/p>\n
Conclusi\u00f3n:<\/b>
\nEl motor Analytics es un completo procesador de consultas paralelas que admite uniones, agregaciones y ordenaciones paralelas, basadas en los mejores algoritmos extra\u00eddos de m\u00e1s de 30 a\u00f1os de I+D en MPP relacional, pero para datos JSON. Mediante dos experimentos, demostramos el importante impacto en el rendimiento que pueden tener las distintas opciones de configuraci\u00f3n de las \"rutas de disco anal\u00edticas\". Tambi\u00e9n demostramos c\u00f3mo el motor Analytics puede utilizar m\u00faltiples discos f\u00edsicos, cuando est\u00e1n disponibles, para acelerar significativamente las consultas Analytics.<\/span><\/p>\n
 <\/p>","protected":false},"excerpt":{"rendered":"
Typical ad-hoc analytical queries have to process much more data than can fit in memory. Consequently, those queries tend to be I\/O bound. When the Analytics service was introduced in Couchbase 6.0, it allowed users to specify multiple \u201cAnalytics Disk […]<\/p>","protected":false},"author":49475,"featured_media":7878,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2294,1816,9417,1812],"tags":[1392],"ppma_author":[9098],"class_list":["post-7849","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-couchbase-server","category-performance","category-n1ql-query","tag-optimization"],"acf":[],"yoast_head":"\nUsing Multiple Disks to Speed Up Couchbase Analytics Queries - The Couchbase Blog<\/title>\n<meta name=\"description\" content=\"From two experiments we show significant performance impact from configuring the Analytics Disk Paths and multiple physical disks.\" \/>\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\/utilizing-multiple-disks-to-speed-up-couchbase-analytics-queries\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Using Multiple Disks to Speed Up Couchbase Analytics Queries\" \/>\n<meta property=\"og:description\" content=\"From two experiments we show significant performance impact from configuring the Analytics Disk Paths and multiple physical disks.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/es\/utilizing-multiple-disks-to-speed-up-couchbase-analytics-queries\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2019-11-18T03:20:31+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-06-14T03:19:31+00:00\" \/>\n<meta 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