From a4beb3c3664d64ecd71550742384d8f3c04ed9ed Mon Sep 17 00:00:00 2001 From: Samuel Calderon Date: Fri, 15 Nov 2024 03:52:42 -0500 Subject: [PATCH] add several images --- img/Appsilon_logo.svg | 41 +++++++++ img/appsilon-web.png | Bin 0 -> 1644394 bytes img/diagrama1.png | Bin 0 -> 69175 bytes img/profiling-01.png | Bin 0 -> 62314 bytes img/profiling-02.png | Bin 0 -> 75907 bytes img/profiling-03.png | Bin 0 -> 47377 bytes img/profiling-04.png | Bin 0 -> 40270 bytes index.qmd | 196 +++++++++++++++++++++++++++++------------- 8 files changed, 177 insertions(+), 60 deletions(-) create mode 100644 img/Appsilon_logo.svg create mode 100644 img/appsilon-web.png create mode 100644 img/diagrama1.png create mode 100644 img/profiling-01.png create mode 100644 img/profiling-02.png create mode 100644 img/profiling-03.png create mode 100644 img/profiling-04.png diff --git a/img/Appsilon_logo.svg b/img/Appsilon_logo.svg new file mode 100644 index 0000000..b31f18f --- /dev/null +++ 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+- Profiling: ¿Dónde están los cuellos de botella? +- Estimación/Recomendación: ¿Qué puedo hacer? +- Optimización: Hagámoslo -### Tipos de benchmarking +## Tipos de benchmarking - Manual - Avanzado (shinyloadtest) @@ -51,33 +58,96 @@ format: Prueba la app y anota cuánto tiempo te toma... -## Profiling +# Profiling ## Profiling - Herramientas en R El profiling es una técnica utilizada para identificar cuellos de botella en el rendimiento de tu código: -1. **profvis**: Es una herramienta interactiva que proporciona una visualización detallada del tiempo de ejecución de tu código. +## `{profvis}` + +Es una herramienta interactiva que proporciona una visualización detallada del tiempo de ejecución de tu código. - Instalación: - ```r - install.packages("profvis") - ``` + +```r +install.packages("profvis") +``` + - Uso básico: - ```r - library(profvis) - profvis({ - # Código a perfilar - }) - ``` -2. **shiny.tictoc**: Una herramienta que usa Javascript para calcular el tiempo que toman las acciones en la app, desde el punto de vista del navegador. -## Ejercicio 2 - Profiling +```r +library(profvis) +profvis({ +# Código a perfilar +}) +``` + +## shiny.tictoc + +Una herramienta que usa Javascript para calcular el tiempo que toman las acciones en la app, desde el punto de vista del navegador. + +Es super fácil de añadir a una app. + +```r +tags$script( + src = "https://cdn.jsdelivr.net/gh/Appsilon/shiny.tictoc@v0.2.0/shiny-tic-toc.min.js" +) +``` + +--- + +Ejecutar cualquiera de estas operaciones en la consola de Javascript. + +```js +// Print out all measurements +showAllMeasurements() + +// To download all measurements as a CSV file +exportMeasurements() + +// To print out summarised measurements (slowest rendering output, slowest server computation) +showSummarisedMeasurements() -Realiza el profiling de la app para las siguientes acciones: +// To export an html file that visualizes measurements on a timeline +await exportHtmlReport() +``` + +Muchos navegadores cuentan con herramientas de desarrollador donde puedes encontrar una mientras tu app está corriendo. + +## Usando profvis + +Ubicar la herramienta en Rstudio + +![](img/profiling-01.png) + +--- + +La consola de R mostrará el botón "Stop profiling". Esto significa que el profiler está activado. + +![](img/profiling-02.png) + +Corre tu shiny app e interactúa con ella. Luego, puedes detener la app y el profiler. + +--- + +El panel de edición de Rstudio te mostrará una nueva vista. + +![](img/profiling-03.png) -- ... -- ... +La parte superior hace profiling de cada línea de código, la parte inferior muestra un *FlameGraph*, que indica el tiempo requerido por cada operación. + +--- + +También puede accederse a la pestaña "Data". + +![](img/profiling-04.png) + +Esta indica cuánto tiempo y memoria se ha requerido por cada operación. Nos da un resumen de la medición. + +## Ejercicio 2 - Profiling + +Interpreta los resultados ¿Cuáles son los puntos más críticos? @@ -90,13 +160,13 @@ Realiza el profiling de la app para las siguientes acciones: ¡Puedes combinar todo! -### Cargar datos más rápido +## Cargar datos más rápido - data.table::fread() - vroom::vroom() - readr::read_csv() -### Ejemplo +## Ejemplo NO ejecutar durante el workshop porque toma tiempo en correr @@ -118,14 +188,14 @@ suppressMessages( ``` -### Formatos de datos eficientes: +## Formatos de datos eficientes: - Parquet (via {arrow}) - Feather (compatibilidad con Python) - fst - RDS (nativo de R) -### Ejemplo +## Ejemplo NO ejecutar durante el workshop porque toma tiempo en correr @@ -146,36 +216,42 @@ suppressMessages( #> rds 558.5518 644.32460 782.37898 695.07300 860.85075 1379.519 100 ``` -### Pre-procesar cálculos +## Pre-procesar cálculos -- Filtrado previo: Realiza el filtrado de datos antes de realizar cálculos complejos para reducir el tamaño del conjunto de datos. -- Agregación previa: Agrega los datos en una etapa temprana para reducir la cantidad de datos que necesitan ser procesados. -- Transformaciones previas: Realiza transformaciones de datos antes de usarlos en la aplicación para evitar cálculos repetitivos. -- **Uso de índices**: Crea índices en tus datos para acelerar las operaciones de búsqueda y filtrado. +- Filtrado previo: Reduce el tamaño del conjunto de datos. +- Transformación o agregación previa: Reduce el procesado en tiempo real. +- Uso de índices: Acelerar las operaciones de búsqueda y filtrado. -Personalmente, mi estrategia favorita. Difícil de usar si se requiere calcular en vivo, real-time (stock exchange, data en streaming). Es, en esencia, *caching*. +Personalmente, mi estrategia favorita. Difícil de usar si se requiere calcular en vivo, real-time (stock exchange, streaming data). Es, en esencia, *caching*. -### Bases de Datos +## Bases de Datos - **Escalabilidad**: Las bases de datos pueden manejar grandes volúmenes de datos de manera eficiente. - **Consultas Rápidas**: Permiten realizar consultas complejas de manera rápida. - **Persistencia**: Los datos se almacenan de manera persistente, lo que permite su recuperación en cualquier momento. -- Algunos ejemplos notables son SQLite, MySQL, PostgreSQL, DuckDB. + +Algunos ejemplos notables son SQLite, MySQL, PostgreSQL, DuckDB. ## Ejercicio 3 - Data Implementa una estrategia de optimización -## Optimización - Shiny +# Optimización - Shiny + +## Cuando una app arranca + +![](img/diagrama1.png) + +--- Del lado de shiny, optimizar consiste básicamente en hacer que la app (en realidad, el procesador) haga el menor trabajo posible. -### Controlar reactividad +## Controlar reactividad 1. bindEvent() - observeEvent() / eventReactive() -### Estrategias de Caché +## Estrategias de Caché 1. bindCache() @@ -183,7 +259,7 @@ Del lado de shiny, optimizar consiste básicamente en hacer que la app (en reali - Nivel aplicación: `cache = "app"` - Nivel sesión: `cache = "session"` -### Comunicación servidor / navegador +## Comunicación servidor / navegador - Reducir en tamaño y frecuencia lo que se manda al *cliente*. @@ -192,9 +268,9 @@ Del lado de shiny, optimizar consiste básicamente en hacer que la app (en reali -## Optimización - Async +# Optimización - Async -### Programación Asíncrona {.smaller} +## Programación Asíncrona {.smaller} - Casos de uso: - Operaciones I/O (bases de datos, APIs) @@ -206,4 +282,4 @@ Del lado de shiny, optimizar consiste básicamente en hacer que la app (en reali ## Ejercicio 5 - Async -## Preguntas +# Preguntas

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