In hrbrmstr/ndjson: Wicked-Fast Streaming 'JSON' ('ndjson') Reader
hrbrpkghelpr::global_opts()
hrbrpkghelpr::stinking_badges()
hrbrpkghelpr::yank_title_and_description()
Pretty much an Rcpp/C++17 wrapper for https://github.com/nlohmann/json
The goal is to create a completely "flat" data.frame-like structure from ndjson records in plain text ndjson files or gzip'd ndjson files.
Installation guidance for Linux/BSD-ish systems
CRAN has binaries for Windows and macOS. To build this on UNIX-like
systems, you need at least g++4.9 or clang++. This is a forced requirement by the ndjson library.
The least painful way to do this is to install gcc >= 4.9 (and you should install ccache while you're at it) and mmodfiy ~/.R/Makevars thusly:
# Use whatever version of (g++ >=4.9 or clang++) that you downloaded
VER=-4.9
CC=ccache gcc$(VER)
CXX=ccache g++$(VER)
SHLIB_CXXLD=g++$(VER)
FC=ccache gfortran
F77=ccache gfortran
Why ndjson + Examples
An example of such files are the output from Rapid7 internet-wide scans, such as their HTTPS study. A gzip'd extract of 100,000 of one of those scans weighs in abt about 171MB. The records sometimes contain heavily nested JSON elements depending on how comprehensive the certificate data and other fields were. A typical record will look like this:
{
"vhost": "teamchat.buzzpoints.com",
"host": "52.87.143.83",
"certsubject": {
"CN": "teamchat.buzzpoints.com"
},
"ip": "52.87.143.83",
"data": "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",
"port": "443"
}
A system.time(df <- stream_in("https-extract.json.gz")) results in:
user system elapsed
14.822 0.224 15.189
on a 13" MacBook Pro and produces:
Classes ‘data.table’ and 'data.frame': 100000 obs. of 36 variables:
$ certsubject.CN : chr "*.tio.ch" "*.starwoodhotels.com" "a.ssl.fastly.net" "a.ssl.fastly.net" ...
$ data : chr "SFRUUC8xLjEgNDAzIEZvcmJpZGRlbg0KU2VydmVyOiBjbG91ZGZsYXJlLW5naW54DQpEYXRlOiBNb24sIDIyIEF1ZyAyMDE2IDE3OjE2OjE2IEdNVA0KQ29udGVudC1"| __truncated__ "SFRUUC8xLjAgNDAwIEJhZCBSZXF1ZXN0DQpTZXJ2ZXI6IEFrYW1haUdIb3N0DQpNaW1lLVZlcnNpb246IDEuMA0KQ29udGVudC1UeXBlOiB0ZXh0L2h0bWwNCkNvbnR"| __truncated__ "SFRUUC8xLjEgNTAwIERvbWFpbiBOb3QgRm91bmQNClNlcnZlcjogVmFybmlzaA0KUmV0cnktQWZ0ZXI6IDANCmNvbnRlbnQtdHlwZTogdGV4dC9odG1sDQpDYWNoZS1"| __truncated__ "SFRUUC8xLjEgNTAwIERvbWFpbiBOb3QgRm91bmQNClNlcnZlcjogVmFybmlzaA0KUmV0cnktQWZ0ZXI6IDANCmNvbnRlbnQtdHlwZTogdGV4dC9odG1sDQpDYWNoZS1"| __truncated__ ...
$ host : chr "104.20.28.6" "104.80.186.186" "151.101.255.54" "151.101.158.15" ...
$ ip : chr "104.20.28.6" "104.80.186.186" "151.101.255.54" "151.101.158.15" ...
$ port : chr "443" "443" "443" "443" ...
$ vhost : chr "104.20.28.6" "104.80.186.186" "a.ssl.fastly.net" "a.ssl.fastly.net" ...
$ certsubject.C : chr NA "US" "US" "US" ...
$ certsubject.L : chr NA "Stamford" "San Francisco" "San Francisco" ...
$ certsubject.O : chr NA "STARWOOD HOTELS AND RESORTS WORLDWIDE, INC." "Fastly, Inc." "Fastly, Inc." ...
$ certsubject.OU : chr NA "IT Solutions" NA NA ...
$ certsubject.ST : chr NA "Connecticut" "California" "California" ...
$ certsubject.emailAddress : chr NA NA NA NA ...
$ certsubject.UNDEF : chr NA NA NA NA ...
$ certsubject.businessCategory : chr NA NA NA NA ...
$ certsubject.postalCode : chr NA NA NA NA ...
$ certsubject.serialNumber : chr NA NA NA NA ...
$ certsubject.street : chr NA NA NA NA ...
$ certsubject.SN : chr NA NA NA NA ...
$ certsubject.unstructuredName : chr NA NA NA NA ...
$ certsubject.ITU-T : chr NA NA NA NA ...
$ certsubject.GN : chr NA NA NA NA ...
$ certsubject.description : chr NA NA NA NA ...
$ certsubject.subjectAltName : chr NA NA NA NA ...
$ certsubject.name : chr NA NA NA NA ...
$ certsubject.DC : chr NA NA NA NA ...
$ certsubject.postOfficeBox : chr NA NA NA NA ...
$ certsubject.dnQualifier : chr NA NA NA NA ...
$ certsubject.generationQualifier: chr NA NA NA NA ...
$ certsubject.initials : chr NA NA NA NA ...
$ certsubject.pseudonym : chr NA NA NA NA ...
$ certsubject.title : chr NA NA NA NA ...
$ certsubject : int NA NA NA NA NA NA NA NA NA NA ...
$ certsubject.unstructuredAddress: chr NA NA NA NA ...
$ certsubject.UID : chr NA NA NA NA ...
$ certsubject.mail : chr NA NA NA NA ...
$ certsubject.Mail : chr NA NA NA NA ...
- attr(*, ".internal.selfref")=<externalptr>
All of the certificate sub-field data elements have been expanded and we have a highly performant data.table to work with. Just go see what you have to do in jsonlite to get a similar output (and how long it will take).
pryr::object_size(df) for that shows it's consuming 394 MB, which means we can read in many more extracts comfortably on a reasonably configured system and most (if not all) of it on a well-configured AWS box.
However, if you do end up trying to work with that scan data, it's highly recommended that you use jq to filter out the fields or records you want into a more compact ndjson file.
What's inside the tin?
The following functions are implemented:
stream_in: Stream in ndjson from a file (handles .gz files)validate: Validate JSON records in an ndjson file (handles .gz files)flatten: Flatten a character vector of individual JSON lines
There are no current plans for a stream_out() function since jsonlite::stream_out() does a great job tossing data.frame-like structures out to an ndjson file.
What's Inside The Tin
The following functions are implemented:
hrbrpkghelpr::describe_ingredients()
Installation
hrbrpkghelpr::install_block()
Usage
library(ndjson)
# current version
packageVersion("ndjson")
Usage
flatten('{"top":{"next":{"final":1,"end":true},"another":"yes"},"more":"no"}')
f <- system.file("extdata", "test.json", package="ndjson")
gzf <- system.file("extdata", "testgz.json.gz", package="ndjson")
dplyr::glimpse(ndjson::stream_in(f))
dplyr::glimpse(ndjson::stream_in(gzf))
dplyr::glimpse(jsonlite::stream_in(file(f), flatten=TRUE, verbose=FALSE))
dplyr::glimpse(jsonlite::stream_in(gzfile(gzf), flatten=TRUE, verbose=FALSE))
ndjson Metrics
cloc::cloc_pkg_md()
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
hrbrmstr/ndjson documentation built on Oct. 19, 2022, 7:04 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
hrbrpkghelpr::global_opts()
hrbrpkghelpr::stinking_badges()
hrbrpkghelpr::yank_title_and_description()
Pretty much an Rcpp/C++17 wrapper for https://github.com/nlohmann/json
The goal is to create a completely "flat" data.frame-like structure from ndjson records in plain text ndjson files or gzip'd ndjson files.
Installation guidance for Linux/BSD-ish systems
CRAN has binaries for Windows and macOS. To build this on UNIX-like systems, you need at least g++4.9 or clang++. This is a forced requirement by the ndjson library.
The least painful way to do this is to install gcc >= 4.9 (and you should install ccache while you're at it) and mmodfiy ~/.R/Makevars thusly:
# Use whatever version of (g++ >=4.9 or clang++) that you downloaded VER=-4.9 CC=ccache gcc$(VER) CXX=ccache g++$(VER) SHLIB_CXXLD=g++$(VER) FC=ccache gfortran F77=ccache gfortran
Why ndjson + Examples
An example of such files are the output from Rapid7 internet-wide scans, such as their HTTPS study. A gzip'd extract of 100,000 of one of those scans weighs in abt about 171MB. The records sometimes contain heavily nested JSON elements depending on how comprehensive the certificate data and other fields were. A typical record will look like this:
{
"vhost": "teamchat.buzzpoints.com",
"host": "52.87.143.83",
"certsubject": {
"CN": "teamchat.buzzpoints.com"
},
"ip": "52.87.143.83",
"data": "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",
"port": "443"
}
A system.time(df <- stream_in("https-extract.json.gz")) results in:
user system elapsed 14.822 0.224 15.189
on a 13" MacBook Pro and produces:
Classes ‘data.table’ and 'data.frame': 100000 obs. of 36 variables: $ certsubject.CN : chr "*.tio.ch" "*.starwoodhotels.com" "a.ssl.fastly.net" "a.ssl.fastly.net" ... $ data : chr "SFRUUC8xLjEgNDAzIEZvcmJpZGRlbg0KU2VydmVyOiBjbG91ZGZsYXJlLW5naW54DQpEYXRlOiBNb24sIDIyIEF1ZyAyMDE2IDE3OjE2OjE2IEdNVA0KQ29udGVudC1"| __truncated__ "SFRUUC8xLjAgNDAwIEJhZCBSZXF1ZXN0DQpTZXJ2ZXI6IEFrYW1haUdIb3N0DQpNaW1lLVZlcnNpb246IDEuMA0KQ29udGVudC1UeXBlOiB0ZXh0L2h0bWwNCkNvbnR"| __truncated__ "SFRUUC8xLjEgNTAwIERvbWFpbiBOb3QgRm91bmQNClNlcnZlcjogVmFybmlzaA0KUmV0cnktQWZ0ZXI6IDANCmNvbnRlbnQtdHlwZTogdGV4dC9odG1sDQpDYWNoZS1"| __truncated__ "SFRUUC8xLjEgNTAwIERvbWFpbiBOb3QgRm91bmQNClNlcnZlcjogVmFybmlzaA0KUmV0cnktQWZ0ZXI6IDANCmNvbnRlbnQtdHlwZTogdGV4dC9odG1sDQpDYWNoZS1"| __truncated__ ... $ host : chr "104.20.28.6" "104.80.186.186" "151.101.255.54" "151.101.158.15" ... $ ip : chr "104.20.28.6" "104.80.186.186" "151.101.255.54" "151.101.158.15" ... $ port : chr "443" "443" "443" "443" ... $ vhost : chr "104.20.28.6" "104.80.186.186" "a.ssl.fastly.net" "a.ssl.fastly.net" ... $ certsubject.C : chr NA "US" "US" "US" ... $ certsubject.L : chr NA "Stamford" "San Francisco" "San Francisco" ... $ certsubject.O : chr NA "STARWOOD HOTELS AND RESORTS WORLDWIDE, INC." "Fastly, Inc." "Fastly, Inc." ... $ certsubject.OU : chr NA "IT Solutions" NA NA ... $ certsubject.ST : chr NA "Connecticut" "California" "California" ... $ certsubject.emailAddress : chr NA NA NA NA ... $ certsubject.UNDEF : chr NA NA NA NA ... $ certsubject.businessCategory : chr NA NA NA NA ... $ certsubject.postalCode : chr NA NA NA NA ... $ certsubject.serialNumber : chr NA NA NA NA ... $ certsubject.street : chr NA NA NA NA ... $ certsubject.SN : chr NA NA NA NA ... $ certsubject.unstructuredName : chr NA NA NA NA ... $ certsubject.ITU-T : chr NA NA NA NA ... $ certsubject.GN : chr NA NA NA NA ... $ certsubject.description : chr NA NA NA NA ... $ certsubject.subjectAltName : chr NA NA NA NA ... $ certsubject.name : chr NA NA NA NA ... $ certsubject.DC : chr NA NA NA NA ... $ certsubject.postOfficeBox : chr NA NA NA NA ... $ certsubject.dnQualifier : chr NA NA NA NA ... $ certsubject.generationQualifier: chr NA NA NA NA ... $ certsubject.initials : chr NA NA NA NA ... $ certsubject.pseudonym : chr NA NA NA NA ... $ certsubject.title : chr NA NA NA NA ... $ certsubject : int NA NA NA NA NA NA NA NA NA NA ... $ certsubject.unstructuredAddress: chr NA NA NA NA ... $ certsubject.UID : chr NA NA NA NA ... $ certsubject.mail : chr NA NA NA NA ... $ certsubject.Mail : chr NA NA NA NA ... - attr(*, ".internal.selfref")=<externalptr>
All of the certificate sub-field data elements have been expanded and we have a highly performant data.table to work with. Just go see what you have to do in jsonlite to get a similar output (and how long it will take).
pryr::object_size(df) for that shows it's consuming 394 MB, which means we can read in many more extracts comfortably on a reasonably configured system and most (if not all) of it on a well-configured AWS box.
However, if you do end up trying to work with that scan data, it's highly recommended that you use jq to filter out the fields or records you want into a more compact ndjson file.
What's inside the tin?
The following functions are implemented:
stream_in: Stream in ndjson from a file (handles.gzfiles)validate: Validate JSON records in an ndjson file (handles.gzfiles)flatten: Flatten a character vector of individual JSON lines
There are no current plans for a stream_out() function since jsonlite::stream_out() does a great job tossing data.frame-like structures out to an ndjson file.
What's Inside The Tin
The following functions are implemented:
hrbrpkghelpr::describe_ingredients()
Installation
hrbrpkghelpr::install_block()
Usage
library(ndjson) # current version packageVersion("ndjson")
Usage
flatten('{"top":{"next":{"final":1,"end":true},"another":"yes"},"more":"no"}') f <- system.file("extdata", "test.json", package="ndjson") gzf <- system.file("extdata", "testgz.json.gz", package="ndjson") dplyr::glimpse(ndjson::stream_in(f)) dplyr::glimpse(ndjson::stream_in(gzf)) dplyr::glimpse(jsonlite::stream_in(file(f), flatten=TRUE, verbose=FALSE)) dplyr::glimpse(jsonlite::stream_in(gzfile(gzf), flatten=TRUE, verbose=FALSE))
ndjson Metrics
cloc::cloc_pkg_md()
Code of Conduct
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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