jsonlite (version 1.8.7)

stream_in, stream_out: Streaming JSON input/output

Description

The stream_in and stream_out functions implement line-by-line processing of JSON data over a connection, such as a socket, url, file or pipe. JSON streaming requires the ndjson format, which slightly differs from fromJSON() and toJSON(), see details.

Usage

stream_in(con, handler = NULL, pagesize = 500, verbose = TRUE, ...)
stream_out(x, con = stdout(), pagesize = 500, verbose = TRUE, prefix = "", ...)

Value

The stream_out function always returns NULL. When no custom handler is specified, stream_in returns a data frame of all pages binded together. When a custom handler function is specified, stream_in always returns NULL.

Arguments

con

a connection object. If the connection is not open, stream_in and stream_out will automatically open and later close (and destroy) the connection. See details.

handler

a custom function that is called on each page of JSON data. If not specified, the default handler stores all pages and binds them into a single data frame that will be returned by stream_in. See details.

pagesize

number of lines to read/write from/to the connection per iteration.

verbose

print some information on what is going on.

...

arguments for fromJSON() and toJSON() that control JSON formatting/parsing where applicable. Use with caution.

x

object to be streamed out. Currently only data frames are supported.

prefix

string to write before each line (use "\u001e" to write rfc7464 text sequences)

Details

Because parsing huge JSON strings is difficult and inefficient, JSON streaming is done using lines of minified JSON records, a.k.a. ndjson. This is pretty standard: JSON databases such as MongoDB use the same format to import/export datasets. Note that this means that the total stream combined is not valid JSON itself; only the individual lines are. Also note that because line-breaks are used as separators, prettified JSON is not permitted: the JSON lines must be minified. In this respect, the format is a bit different from fromJSON() and toJSON() where all lines are part of a single JSON structure with optional line breaks.

The handler is a callback function which is called for each page (batch) of JSON data with exactly one argument (usually a data frame with pagesize rows). If handler is missing or NULL, a default handler is used which stores all intermediate pages of data, and at the very end binds all pages together into one single data frame that is returned by stream_in. When a custom handler function is specified, stream_in does not store any intermediate results and always returns NULL. It is then up to the handler to process or store data pages. A handler function that does not store intermediate results in memory (for example by writing output to another connection) results in a pipeline that can process an unlimited amount of data. See example.

Note that a vector of JSON strings already in R can parsed with stream_in by creating a connection to it with textConnection().

If a connection is not opened yet, stream_in and stream_out will automatically open and later close the connection. Because R destroys connections when they are closed, they cannot be reused. To use a single connection for multiple calls to stream_in or stream_out, it needs to be opened beforehand. See example.

References

MongoDB export format: https://docs.mongodb.com/manual/reference/program/mongoexport/

Documentation for the JSON Lines text file format: https://jsonlines.org/

See Also

fromJSON(), read_json()

Examples

# compare formats
x <- iris[1:3,]
toJSON(x)
stream_out(x)

# Trivial example
mydata <- stream_in(url("https://jeroen.github.io/data/iris.json"))

if (FALSE) {
#stream large dataset to file and back
library(nycflights13)
stream_out(flights, file(tmp <- tempfile()))
flights2 <- stream_in(file(tmp))
unlink(tmp)
all.equal(flights2, as.data.frame(flights))

# stream over HTTP
diamonds2 <- stream_in(url("https://jeroen.github.io/data/diamonds.json"))

# stream over HTTP with gzip compression
flights3 <- stream_in(gzcon(url("https://jeroen.github.io/data/nycflights13.json.gz")))
all.equal(flights3, as.data.frame(flights))

# stream over HTTPS (HTTP+SSL) via curl
library(curl)
flights4 <- stream_in(gzcon(curl("https://jeroen.github.io/data/nycflights13.json.gz")))
all.equal(flights4, as.data.frame(flights))

# or alternatively:
flights5 <- stream_in(gzcon(pipe("curl https://jeroen.github.io/data/nycflights13.json.gz")))
all.equal(flights5, as.data.frame(flights))

# Full JSON IO stream from URL to file connection.
# Calculate delays for flights over 1000 miles in batches of 5k
library(dplyr)
con_in <- gzcon(url("https://jeroen.github.io/data/nycflights13.json.gz"))
con_out <- file(tmp <- tempfile(), open = "wb")
stream_in(con_in, handler = function(df){
  df <- dplyr::filter(df, distance > 1000)
  df <- dplyr::mutate(df, delta = dep_delay - arr_delay)
  stream_out(df, con_out, pagesize = 1000)
}, pagesize = 5000)
close(con_out)

# stream it back in
mydata <- stream_in(file(tmp))
nrow(mydata)
unlink(tmp)

# Data from http://openweathermap.org/current#bulk
# Each row contains a nested data frame.
daily14 <- stream_in(gzcon(url("http://78.46.48.103/sample/daily_14.json.gz")), pagesize=50)
subset(daily14, city$name == "Berlin")$data[[1]]

# Or with dplyr:
library(dplyr)
daily14f <- flatten(daily14)
filter(daily14f, city.name == "Berlin")$data[[1]]

# Stream import large data from zip file
tmp <- tempfile()
download.file("http://jsonstudio.com/wp-content/uploads/2014/02/companies.zip", tmp)
companies <- stream_in(unz(tmp, "companies.json"))
}