JSON serialization now even faster and prettier

April 13, 2015

The jsonlite package implements a robust, high performance JSON parser and generator for R, optimized for statistical data and the web. This week version 0.9.16 appeared on CRAN which has a new prettifying system, improved performance and some additional tweaks for the new mongolite package.

Improved Performance

Everyones favorite feature of jsonlite: performance. We found a way to significanlty speed up toJSON for data frames for the cases of dataframe="rows" (the default) or dataframe="values". On my macbook I now get these results:

data(diamonds, package="ggplot2")
system.time(toJSON(diamonds, dataframe = "rows"))
#   user  system elapsed
#  0.133   0.003   0.136
system.time(toJSON(diamonds, dataframe = "columns"))
#   user  system elapsed
#  0.070   0.003   0.072
system.time(toJSON(diamonds, dataframe = "values"))
#   user  system elapsed
#  0.094   0.005   0.099

A somewhat larger dataset:

data(flights, package="nycflights13")
system.time(toJSON(flights, dataframe = "rows"))
#   user  system elapsed
#  1.506   0.072   1.578
system.time(toJSON(flights, dataframe = "columns"))
#   user  system elapsed
#  0.585   0.024   0.608
system.time(toJSON(flights, dataframe = "values"))
#   user  system elapsed
#  0.873   0.039   0.912

That is pretty darn fast for a text based serialization format. By comparison, we easily beat write.csv which is actually a much more simple output format:

system.time(write.csv(diamonds, file="/dev/null"))
#   user  system elapsed
#  0.361   0.003   0.364
system.time(write.csv(flights, file="/dev/null"))
#   user  system elapsed
#  3.284   0.033   3.318

Pretty even prettier

Yihui has pushed for a new prettifying system that inserts indentation directly in the R code rather than making yajl prettify the entire JSON blob at the end. As a result we can use different indentation rules for different R classes. See the PR for details. The main differce is that atomic vectors are now prettified without linebreaks:

x <- list(foo = 1:3, bar = head(cars, 2))
toJSON(x, pretty=TRUE)

{
  "foo": [1, 2, 3],
  "bar": [
    {
      "speed": 4,
      "dist": 2
    },
    {
      "speed": 4,
      "dist": 10
    }
  ]
}

toJSON(x, pretty=T, dataframe = "col")

{
  "foo": [1, 2, 3],
  "bar": {
    "speed": [4, 4],
    "dist": [2, 10]
  }
}

This can be helpful for manually inspecting or debugging your JSON data. The prettify function still uses yajl, so if you prefer this style, simply use prettify(toJSON(x)).

New mongolite package

There were some additional internal enhancements to support the new mongolite package, which will be announced later this month. This package will extend the concepts and power of jsonlite to the in-database JSON documents. Have a look at the git repository for a sneak preview.