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.
FROM rocker/geospatial
MAINTAINER Bart
RUN install2.r -n 5 plumber stars; \
rm -rf /tmp/downloaded_packages
RUN R --quiet -e 'install.packages("starsdata", repos = "http://pebesma.staff.ifgi.de", type = "source")'
RUN R --quiet -e "remotes::install_gitlab('bartk/starsTileServer')"
EXPOSE 3436
COPY script.R script.R
RUN R --quiet -e "source('script.R')"
ENTRYPOINT ["R", "--quiet", "-e", "server<-readRDS('server.rds') ;server$run( port=3436, host='0.0.0.0', swagger=T)"]
The following R script is used (script.R):
require(stars)
require(starsTileServer)
s5p <- system.file(
"sentinel5p/S5P_NRTI_L2__NO2____20180717T120113_20180717T120613_03932_01_010002_20180717T125231.nc",
package = "starsdata"
)
nit <- read_stars(
s5p,
along = NA,
sub = c(
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_total_column",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_total_column_precision",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/nitrogendioxide_total_column_precision_kernel",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/number_of_iterations",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/number_of_spectral_points_in_retrieval",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/oxygen_oxygen_dimer_slant_column_density",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/oxygen_oxygen_dimer_slant_column_density_precision",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/ozone_slant_column_density",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/ozone_slant_column_density_precision",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/processing_quality_flags",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/ring_coefficient",
"//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/ring_coefficient_precision"
),
curvilinear = c("//PRODUCT/longitude", "//PRODUCT/latitude"),
driver = NULL
)
names(nit) <-
sub("//PRODUCT/SUPPORT_DATA/DETAILED_RESULTS/", "", names(nit))
for (i in seq(length(names(nit)))) {
nit[[i]][nit[[i]] > 9e+36] <- NA
}
st_crs(nit) <- 4326
server <- starsTileServer$new(nit)
# we save the server here as there should only be one version (sampling of color scales would otherwise result in differently colored tiles)
saveRDS(server, "server.rds")
Copies of these files can be found with the following commands:
system.file("compose/Dockerfile", package = "starsTileServer")
#> [1] "/tmp/RtmpyORup4/Rinst2485e61d4de9f/starsTileServer/compose/Dockerfile"
system.file("compose/script.R", package = "starsTileServer")
#> [1] "/tmp/RtmpyORup4/Rinst2485e61d4de9f/starsTileServer/compose/script.R"
Running multiple instances
With the following docker-compose.yml file we can then
start the applications:
version: "2.2"
services:
tileserver:
build:
dockerfile: Dockerfile
context: .
scale: 4
restart: always
lb:
container_name: haproxy_tile_loadbalancing
image: 'dockercloud/haproxy:latest'
environment:
- TIMEOUT=connect 4000, client 153000, server 230000
links:
- tileserver
volumes:
- /var/run/docker.sock:/var/run/docker.sock
varnish:
image: wodby/varnish
container_name: varnish_tile_caching
ports:
- "80:80"
- "6081:6081"
- "8080:8080"
depends_on:
- lb
environment:
VARNISH_IMPORT_MODULES: cookie,header
VARNISH_CONFIG_PRESET: drupal
VARNISH_BACKEND_HOST: lb
VARNISH_BACKEND_PORT: 80
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.
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")
