mirai may be used as an asynchronous backend to scale Shiny applications.
Depending on the options suppled to daemons(), mirai tasks may be distributed across local background processes or multiple networked servers in an efficient and performant manner.
For use with Shiny, mirai implements truly event-driven promises, developed in collaboration with Joe Cheng (creator of Shiny).
mirai natively supports Shiny ExtendedTask to create scalable Shiny apps, which remain responsive intra-session for each user, as well as inter-session for multiple concurrent users.
‘mirai’ may be used anywhere a ‘promise’ or ‘future_promise’ would be accepted (with promises >= 1.3.0).
In the example below, the app remains responsive, with the clock continuing to tick whilst the simulated expensive computation is running asynchronously in a parallel process. Also the button is disabled and the plot greyed out until the computation is complete.
By wrapping the runApp() call in with(daemons(...), ...) the daemons are set up for the duration of the app, exiting automatically when the app is stopped.
library(shiny)
library(bslib)
library(mirai)
ui <- page_fluid(
p("The time is ", textOutput("current_time", inline = TRUE)),
hr(),
numericInput("n", "Sample size (n)", 100),
numericInput("delay", "Seconds to take for plot", 5),
input_task_button("btn", "Plot uniform distribution"),
plotOutput("plot")
)
server <- function(input, output, session) {
output$current_time <- renderText({
invalidateLater(1000)
format(Sys.time(), "%H:%M:%S %p")
})
extended_task <- ExtendedTask$new(
function(...) mirai({Sys.sleep(y); runif(x)}, ...)
) |> bind_task_button("btn")
observeEvent(input$btn, extended_task$invoke(x = input$n, y = input$delay))
output$plot <- renderPlot(hist(extended_task$result()))
}
app <- shinyApp(ui = ui, server = server)
# run app using 2 local daemons
with(daemons(2), runApp(app))
Thanks to Joe Cheng for providing examples on which the above is based.
The key components to using ExtendedTask are:
bslib::input_task_button(). This is a button which is disabled during computation to prevent additional clicks.input_task_button("btn", "Plot uniform distribution")
ExtendedTask$new() on an anonymous function passing ... arguments to mirai(), and bind it to the button created in (1).extended_task <- ExtendedTask$new(
function(...) mirai({Sys.sleep(y); runif(x)}, ...)
) |> bind_task_button("btn")
observeEvent(input$btn, extended_task$invoke(x = input$n, y = input$delay))
output$plot <- renderPlot(hist(extended_task$result()))
The following app produces pretty spiral patterns.
The user can add multiple plots, making use of Shiny modules, each having a different calculation time.
The plots are generated asynchronously, and it is easy to see the practical limitations of the number of daemons set. For example, if updating 4 plots, and there are only 3 daemons, the 4th plot will not start to be generated until one of the other plots has finished.
library(shiny)
library(mirai)
library(bslib)
library(ggplot2)
library(aRtsy)
# function definitions
run_task <- function(calc_time) {
Sys.sleep(calc_time)
list(
colors = aRtsy::colorPalette(name = "random", n = 3),
angle = runif(n = 1, min = - 2 * pi, max = 2 * pi),
size = 1,
p = 1
)
}
plot_result <- function(result) {
do.call(what = canvas_phyllotaxis, args = result)
}
# modules for individual plots
plotUI <- function(id, calc_time) {
ns <- NS(id)
card(
strong(paste0("Plot (calc time = ", calc_time, " secs)")),
input_task_button(ns("resample"), "Resample"),
plotOutput(ns("plot"), height="400px", width="400px")
)
}
plotServer <- function(id, calc_time) {
force(id)
force(calc_time)
moduleServer(
id,
function(input, output, session) {
extended_task <- ExtendedTask$new(
function(time, run) mirai(run(time), environment())
) |> bind_task_button("resample")
observeEvent(input$resample,
extended_task$invoke(calc_time, run_task))
output$plot <- renderPlot(plot_result(extended_task$result()))
}
)
}
# ui and server
ui <- page_sidebar(fillable = FALSE,
sidebar = sidebar(
numericInput("calc_time", "Calculation time (secs)", 5),
actionButton("add", "Add", class="btn-primary"),
),
layout_column_wrap(id = "results", width = "400px", fillable = FALSE)
)
server <- function(input, output, session) {
observeEvent(input$add, {
id <- nanonext::random(4)
insertUI("#results", where = "beforeEnd", ui = plotUI(id, input$calc_time))
plotServer(id, input$calc_time)
})
}
app <- shinyApp(ui, server)
# run app using 3 local daemons
with(daemons(3), runApp(app))
The above example builds on original code by Joe Cheng, Daniel Woodie and William Landau.
The above uses environment() instead of ... as an alternative and equivalent way of passing variables present in the calling environment to the mirai.
The key components to using this ExtendedTask example are:
bslib::input_task_button(). This is a button which is disabled during computation to prevent additional clicks.input_task_button(ns("resample"), "Resample")
ExtendedTask$new() on an anonymous function passing named arguments to mirai(), and bind it to the button created in (1). These are passed through to the mirai by the use of environment().extended_task <- ExtendedTask$new(
function(time, run) mirai(run(time), environment())
) |> bind_task_button("resample")
observeEvent(input$resample, extended_task$invoke(calc_time, run_task))
output$plot <- renderPlot(plot_result(extended_task$result()))
The below example demonstrates how to integrate a mirai_map() operation into a Shiny app.
By specifying the ‘.promise’ argument, this registers a promise action against each mapped operation. These can then be used to update reactive values or otherwise interact with the Shiny app.
library(shiny)
library(mirai)
flip_coin <- function(...) {
Sys.sleep(0.1)
rbinom(n = 1, size = 1, prob = 0.501)
}
ui <- fluidPage(
div("Is the coin fair?"),
actionButton("task", "Flip 1000 coins"),
textOutput("status"),
textOutput("outcomes")
)
server <- function(input, output, session) {
# Keep running totals of heads, tails, and task errors
flips <- reactiveValues(heads = 0, tails = 0, flips = 0)
# Button to submit a batch of coin flips
observeEvent(input$task, {
flips$flips <- flips$flips + 1000
m <- mirai_map(1:1000, flip_coin, .promise = \(x)
if (x) flips$heads <- flips$heads + 1 else flips$tails <- flips$tails + 1)
})
# Print time and task status
output$status <- renderText({
input$task
invalidateLater(millis = 1000)
time <- format(Sys.time(), "%H:%M:%S")
sprintf("%s %s flips submitted", time, flips$flips)
})
# Print number of heads and tails
output$outcomes <- renderText(
sprintf("%s heads %s tails", flips$heads, flips$tails)
)
}
app <- shinyApp(ui = ui, server = server)
# run app using 8 local daemons, without dispatcher as tasks are the same length
with(daemons(8, dispatcher = FALSE), {
# pre-load flip_coin function on all daemons for efficiency
everywhere({}, flip_coin = flip_coin)
runApp(app)
})
This is an adaptation of an original example provided by Will Landau for use of crew with Shiny. Please see https://wlandau.github.io/crew/articles/shiny.html.
Whilst it is generally recommended to use the ExtendedTask framework, it is also possible for mirai to plug directly into Shiny’s reactive framework, without the use of ‘promises’ either implicitly or explicitly. This may be required for advanced uses of asynchronous programming, or where the use case does not fit the semantics of ExtendedTask.
The following is similar to the previous example, but allows multiple tasks to be submitted at once, rather than one after the other as required by ExtendedTask. There is a button to submit tasks, which will be processed by one of 3 daemons, outputting a pretty spiral pattern upon completion. If more than 3 tasks are submitted at once, the chart updates 3 at a time, limited by the number of available daemons.
It requires more boilerplate code to manage the mirai tasks, but otherwise functions similarly to the ExtendedTask example.
library(mirai)
library(shiny)
library(ggplot2)
library(aRtsy)
# function definitions
run_task <- function() {
Sys.sleep(5)
list(
colors = aRtsy::colorPalette(name = "random", n = 3),
angle = runif(n = 1, min = - 2 * pi, max = 2 * pi),
size = 1,
p = 1
)
}
plot_result <- function(result) {
do.call(what = canvas_phyllotaxis, args = result)
}
status_message <- function(tasks) {
if (tasks == 0L) {
"All tasks completed."
} else {
sprintf("%d task%s in progress at %s",
tasks, if (tasks > 1L) "s" else "", format.POSIXct(Sys.time()))
}
}
ui <- fluidPage(
actionButton("task", "Submit a task (5 seconds)"),
textOutput("status"),
plotOutput("result")
)
server <- function(input, output, session) {
# reactive values and outputs
reactive_result <- reactiveVal(ggplot())
reactive_status <- reactiveVal("No task submitted yet.")
output$result <- renderPlot(reactive_result(), height = 600, width = 600)
output$status <- renderText(reactive_status())
poll_for_results <- reactiveVal(FALSE)
# create empty mirai queue
q <- list()
# button to submit a task
observeEvent(input$task, {
q[[length(q) + 1L]] <<- mirai(run_task(), run_task = run_task)
poll_for_results(TRUE)
})
# event loop to collect finished tasks
observe({
req(poll_for_results())
invalidateLater(millis = 250)
if (length(q)) {
if (!unresolved(q[[1L]])) {
reactive_result(plot_result(q[[1L]][["data"]]))
q[[1L]] <<- NULL
}
reactive_status(status_message(length(q)))
} else {
poll_for_results(FALSE)
}
})
}
app <- shinyApp(ui = ui, server = server)
# run app using 3 local daemons
with(daemons(3), runApp(app))
Thanks to Daniel Woodie and William Landau for providing the original example on which this is based. Please see https://wlandau.github.io/crew/articles/shiny.html.