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DyfanJones avatar DyfanJones commented on June 23, 2024

To over come this can utilise json parser.

Possible solutions:

  • jsonlite
  • jsonify
  • rcppsimdjson

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024 
library(data.table)

x = 1e6

dt1 = data.table(
  var1 = 1:x,
  var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
)

dt2 = data.table(
  var1 = 1:x,
  var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
)

col = "var2"
system.time(set(dt1, j=col, value=sapply(dt1[[col]], jsonlite::toJSON, auto_unbox = T)))
#   user  system elapsed 
# 196.737   1.269 199.344 

system.time(set(dt2, j=col, value=noctua:::list_to_json(dt2[[col]])))
#   user  system elapsed 
#  6.409   0.094   6.582 

head(dt1)
   var1                                                   var2
1:    1 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
2:    2 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
3:    3 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
4:    4 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
5:    5 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
6:    6 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
head(dt2)
   var1                                                   var2
1:    1 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
2:    2 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
3:    3 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
4:    4 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
5:    5 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}
6:    6 {"var3":[1,2,3],"var4":{"var5":["a","b","c","d","e"]}}

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024

Need a solution batch method for jsonlite for a fair comparison

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024 
tmp1 <- tempfile()
tmp2 <- tempfile()
con <- file(tmp1)

system.time(jsonlite::stream_out(dt[, .(var2)], con))
user  system elapsed 
230.323   9.498 259.813 

system.time(data.table::fwrite(
  x=as.list(jsonify::to_ndjson(dt$var2,unbox = T)),
  file="test.jsonl",
  quote =F,
  col.names=F)
 )
# user  system elapsed 
# 3.547   0.444   4.191

If possible should switch from jsonlite to jsonify. will need batch method for writing table out to file but current method is pretty fast.

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024

Possible alternatives using jsonlite.

col_to_json_raw_1 <- function(dt, col, batch = 1e4){
  max_len = nrow(dt)
  start <- seq(1, max_len, batch)
  end <- c(start[-1]-1, max_len)
  output <- unlist(
    lapply(seq_along(start), function(i) {
      con <- rawConnection(raw(), open = "w")
      jsonlite::stream_out(subset(dt[start[i]:end[i],], select = col), con, verbose = F, pagesize = batch)
      str = rawToChar(rawConnectionValue(con))
      close(con)
      strsplit(str, split = "\n")[[1]]
    }),
    recursive = FALSE
  )
  return(output)
}

col_to_json_raw_2 <- function(dt, col){
  
  con <- rawConnection(raw(), open = "w")
  on.exit(close(con))
  
  jsonlite::stream_out(subset(dt, select = col), con, verbose = F)
  
  obj = rawConnectionValue(con)
  
  end <- which(obj == charToRaw("\n"))
  start <- c(1, end[-length(end)]+1)
  
  return(sapply(seq_along(start), function(i) rawToChar(obj[start[i]:end[i]])))
}


col_to_json_raw_3 <- function(dt, col){
  
  con <- rawConnection(raw(), open = "w")
  on.exit(close(con))
  
  jsonlite::stream_out(subset(dt, select = col), con, verbose = F)
  
  return(readr::read_lines(rawConnectionValue(con),progress=F))
}

col_to_json_raw_4 <- function(dt, col){
  
  con_raw <- rawConnection(raw(), open = "w")
  
  jsonlite::stream_out(subset(dt, select = col), con_raw, verbose = F)
  
  con_out <- rawConnection(rawConnectionValue(con_raw))
  
  on.exit({
    close(con_raw)
    close(con_out)
  })
  
  return(readLines(con_out))
}

col_to_json_text <- function(dt, col){
  con <- textConnection("character", open = "w")
  on.exit(close(con))
  jsonlite::stream_out(subset(dt, select = col), con, verbose = F)
  return(textConnectionValue(con))
}
library(data.table)

n = c(1e1, 1e2,1e3,1e4, 1e5)
bench_list = lapply(n, function(x){
    dt = data.table::data.table(
        var1 = 1:x,
        var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
    )
    
    microbenchmark::microbenchmark(
        "split_text" = col_to_json_raw_1(dt,"var2"),
        "split_raw" = col_to_json_raw_2(dt,"var2"),
        "raw_readr" = col_to_json_raw_3(dt,"var2"),
        "raw_base" = col_to_json_raw_4(dt,"var2"),
        "text_base" = col_to_json_text(dt,"var2"),
        times = 10
    )
})
benchplot(bench_list, n)

Screenshot 2021-09-21 at 15 39 38

These methods seem promising. Plus if a solution with jsonlite could be found then the overall dependencies would be able to be kept low :)

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024 
col_to_json_jsonify <- function(dt, col, batch = 1e4){
  max_len <- nrow(dt)
  start <- seq(1, max_len, batch)
  end <- c(start[-1]-1, max_len)
  splits <- lapply(seq_along(start), function(i) dt[[col]][start[i]:end[i]])
  output <- lapply(splits, function(i) {
      strsplit(as.character(jsonify::to_ndjson(i,unbox = T, numeric_dates = F)), split = "\n")[[1]]
    })[[1]]
  return(output)
}
library(data.table)

n = c(1e1, 1e2,1e3,1e4, 1e5)
bench_list = lapply(n, function(x){
    dt = data.table::data.table(
        var1 = 1:x,
        var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
    )
    
    microbenchmark::microbenchmark(
        "split_text" = col_to_json_raw_1(dt,"var2"),
        "split_raw" = col_to_json_raw_2(dt,"var2"),
        "raw_readr" = col_to_json_raw_3(dt,"var2"),
        "raw_base" = col_to_json_raw_4(dt,"var2"),
        "text_jsonify" = col_to_json_jsonify(dt,"var2"),
        times = 10
    )
})

Screenshot 2021-09-22 at 10 26 43

Even with the new jsonlite functions it looks like jsonify is still faster.

from noctua.

DyfanJones avatar DyfanJones commented on June 23, 2024 
col_to_json_raw_4 <- function(dt, col, batch = 500){
  
  con_raw <- rawConnection(raw(), open = "w")
  
  jsonlite::stream_out(subset(dt, select = col), con_raw, verbose = F, pagesize = batch)
  
  con_out <- rawConnection(rawConnectionValue(con_raw))
  
  on.exit({
    close(con_raw)
    close(con_out)
  })
  
  return(readLines(con_out))
}
library(data.table)

n = c(1e3,1e4, 1e5)
bench_list = lapply(n, function(x){
    dt = data.table::data.table(
        var1 = 1:x,
        var2 = rep(list(list("var3"= 1:3, "var4" = list("var5"= letters[1:5]))), x)
    )
    
    microbenchmark::microbenchmark(
        "raw_base_500" = col_to_json_raw_4(dt,"var2"),
        "raw_base_1000" = col_to_json_raw_4(dt,"var2", 1e3),
        "raw_base_10000" = col_to_json_raw_4(dt,"var2", 1e4),
        "raw_base_100000" = col_to_json_raw_4(dt,"var2", 1e5),
        "text_jsonify" = col_to_json_jsonify(dt,"var2"),
        times = 10
    )
})

Screenshot 2021-09-22 at 11 23 22

Increasing the pagesize with jsonlite::stream_out doesn't seem to be any performance improvements.

from noctua.

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