Parallel tile rendering
In starsTileServer: A Dynamic Tile Server for R

NOT_CRAN <- identical(tolower(Sys.getenv("NOT_CRAN")), "true")
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = NOT_CRAN
)

if (!file.exists(tmpGridFile <- "~/ownCloudUva/test.nc")) {
  tmpGridFile <- tempfile(fileext = ".nc")
  download.file("https://surfdrive.surf.nl/files/index.php/s/Z6YoTyzyyAsmgGS/download", tmpGridFile, extra = "-q", method = "wget")
}

Introduction

For some functionality one single core plumber instance might not be enough to achieve the performance that is desired. One way around this is to use some cluster orchestration tools. The example below discusses the usage of docker-compose but other tools like docker swarm or kubernetes should be able to achieve similar results. Alternatively local parallelization can be used.

Local parallelizing

We first start two tile servers running on port 4001 and 4002.

require(callr)
rp_list <- lapply(lapply(as.list(4000+1:2), c, list(tmpGridFile=tmpGridFile)), r_bg, func=function(port, tmpGridFile) {
  # read a stars grid
  weatherData <- stars::read_stars(tmpGridFile, proxy = FALSE, sub = "t")
  names(weatherData) <- "t"
  sf::st_crs(weatherData) <- "+proj=longlat"
  colorFunction <- leaflet::colorNumeric("viridis", c(250, 310))
  colorFunctionWithAlpa <- function(x, alpha = 1) {
    paste0(colorFunction(x), as.character(as.raw(
      as.numeric(alpha) * 255
    )))
  }
  starsTileServer::starsTileServer$new(weatherData, colorFun = colorFunctionWithAlpa)$run(port = port)
})

Now we can use the subdomains argument of addTiles to address both servers.

require(leaflet)
require(leaflet.extras)
map <- leaflet() %>%
  addTiles() %>%
  enableTileCaching() %>%
  addTiles(
    "http://127.0.0.1:400{s}/map/t/{z}/{x}/{y}?level=900&time=2000-04-27 01:00:00&alpha=0.5",
    options = tileOptions(useCache = TRUE, crossOrigin = TRUE, subdomains = '12')
  ) %>%
  setView(zoom = 3, lat = 30, lng = 30)

This map looks as follows:

map

mapview::mapshot(map, file = f <- tempfile(fileext = ".png"), delay = 9, vwidth = 500, vheight = 400)
magick::image_read(f)

Using lapply we can close both servers.

lapply(rp_list, function(x)x$read_output())
lapply(rp_list, function(x)x$finalize())

Using docker

An alternative approach is to use docker (or some similar functionality). This allows you to scale much broader and is probably an approach that is more suitable for large scale permanent deployments.

Building a docker image

The first step is to build a docker image that can be used to set up the service. This docker image runs the tileserver. A simple example of a possible Dockerfile could look as follows.

The following R script is used (script.R):

Copies of these files can be found with the following commands:

system.file("compose/Dockerfile", package = "starsTileServer")
system.file("compose/script.R", package = "starsTileServer")

Running multiple instances

With the following docker-compose.yml file we can then start the applications: ```{bash, code=readLines(system.file("compose/docker-compose.yml", package = "starsTileServer")), eval=F}

In this case 4 parallel instances are started. We use `haproxy` to distribute the load across the containers and `varnish` to cache the tiles that have been rendered before. The caching makes sure no double work is done. 

With the `docker-compose build` command the required docker containers can be build. Using `docker-compose` up the cluster can then be started.
Now in normal R we can plot a leaflet maps as was done before.


```r
require(leaflet)
leaflet() %>%
  addTiles() %>%
  fitBounds(0, 30, 20, 40) %>%
  addTiles(urlTemplate = "http://127.0.0.1:6081/map/nitrogendioxide_total_column/{z}/{x}/{y}?alpha=.4")