For #rstats users interested in working with the #geoparquet format I've run a small benchmark comparing various formats for storage and ways of querying data, as well as different hardware The reference file is an #sf object, the complete Italian Census 2011 small areas, for a total of ~400K polygons with only an id column attached to the geom,…

Rgeobench.R

# geoarrow: https://github.com/paleolimbot/geoarrow, remotes::install_github("paleolimbot/geoarrow")
# sfarrow:  https://github.com/wcjochem/sfarrow, install.packages('sfarrow')
# qs:       https://github.com/traversc/qs, install.packages("qs")

setwd('/home/datamaps/temp/Rgeobench/')

y <- readRDS('./0.rds')                  # 333.3MB
object.size(y)                           # 798,256,856 bytes - see Italian Census Tracts @ https://bit.ly/szn_cens
saveRDS(y, './0.rnc', compress = FALSE)  # 541.2MB
sfarrow::st_write_parquet(y, './0.sfa')  # 471.8MB
geoarrow::write_geoparquet(y, "./0.gar") # 471.8MB
qs::qsave(y, './0.qs', nthreads = 6)     # 325.4MB

microbenchmark::microbenchmark(
    'rds' = readRDS('./0.rds'),
    'sfa' = sfarrow::st_read_parquet('./0.sfa'),
    'gar' = geoarrow::read_geoparquet_sf('./0.gar'),
    'qs2' = qs::qread('./0.qs', nthreads = 2),
    'qs1' = qs::qread('./0.qs'),
    'rnc' = readRDS('./0.rnc'),
    times = 10
)

# Unit: milliseconds [VDS Ryzen 9 5950X (32 physical threads)]
#  expr       min        lq      mean    median        uq      max neval  cld
#   rds 2467.8933 2536.6780 2925.6897 2815.8789 3499.5137 3635.975    10   c 
#   sfa 7784.5293 7957.8702 8183.3330 8117.0573 8268.3980 9038.568    10    d
#   gar 1646.3129 1699.0325 1803.7138 1750.3947 1867.7311 2194.139    10  b  
#   qs2  288.0859  401.8683  699.3778  625.6076  976.0137 1339.331    10 a   
#   qs1  426.8980  445.4319  745.4705  592.3386 1091.7139 1579.482    10 a   
#   rnc  693.2335  711.4279  817.4475  798.1594  897.0033 1016.470    10 a   

# Unit: milliseconds [Desktop Ryzen 7 3700X (16 physical threads)]
#  expr        min         lq      mean     median        uq       max neval
#   rds  3628.2262  3692.5467  3890.180  3763.6072  3863.867  4506.057    10
#   sfa 12342.1899 12490.4987 12646.450 12672.6011 12826.803 12949.906    10
#   qs1   628.5944   799.2701  1094.869   927.5784  1671.162  1725.050    10
#   rnc  1313.6518  1328.4417  1553.817  1459.3336  1669.474  2483.823    10

# Unit: milliseconds [VPS Xeon E3-1290 (10 virtual threads)]
 # expr        min        lq      mean    median        uq       max neval  cld
 #  rds  5391.4051  5420.054  5637.809  5532.758  5721.448  6197.399    10   c 
 #  sfa 19905.4693 20454.637 21498.859 21518.111 22047.632 24406.320    10    d
 #  gar  3220.9154  3379.480  4316.381  3547.836  5459.431  6173.623    10  b  
 #  qs1   864.9457  1013.159  1304.026  1239.925  1612.909  1721.541    10 a   
 #  rnc  1780.7524  1826.183  2104.321  2047.655  2164.540  2727.607    10 a   



yz <- fst::read_fst('./sezioni', as.data.table = TRUE)[CMN == 15146, SZN] # 6085 census tracts in Milan (code 15146)
microbenchmark::microbenchmark(
  'gpq' = arrow::open_dataset('0.gar') |> dplyr::filter(SZN %in% yz) |> geoarrow::geoarrow_collect_sf(),
  'sfs' = qs::qread('./0.qs', nthreads = 2) |> subset(SZN %in% yz),
  'sfd' = qs::qread('./0.qs', nthreads = 2) |> dplyr::filter(SZN %in% yz),
  'sf'  = y |> subset(SZN %in% yz),
  times = 10
)

# Unit: milliseconds [VDS Ryzen 9 5950X (32 physical threads)]
#  expr      min       lq     mean   median       uq      max neval
#   gpq 285.0784 497.1767 531.8277 552.0051 567.7457  695.2356    10   b
#   sfs 500.8068 502.8678 728.9501 615.3639 944.9022 1438.1080    10   b
#   sfd 497.3435 531.6648 812.5998 732.6829 864.0335 1445.1170    10   b
#    sf 213.8443 216.8596 218.8881 218.7319 221.5313  222.2413    10  a 

# Unit: milliseconds [VPS Xeon E3-1290 (10 virtual threads)]
#  expr       min        lq      mean    median        uq      max neval cld
#   gpq 1144.1041 1214.0428 1295.2017 1292.0714 1339.7389 1522.627    10  a
#   sfs 1585.7888 1620.3887 2486.9763 2460.6871 2826.7120 4238.592    10   b
#   sfd 1526.0645 1711.5308 2147.7086 1821.9781 2051.1485 4248.069    10   b
#    sf  632.9385  651.5985  688.5178  681.0304  710.5937  812.867    10  a