Quick serialization of R objects
qs
provides an interface for quickly saving and reading objects to and
from disk. The goal of this package is to provide a lightning-fast and
complete replacement for the saveRDS
and readRDS
functions in R.
Inspired by the fst
package, qs
uses a similar block-compression
design using either the lz4
or zstd
compression libraries. It
differs in that it applies a more general approach for attributes and
object references.
saveRDS
and readRDS
are the standard for serialization of R data,
but these functions are not optimized for speed. On the other hand,
fst
is extremely fast, but only works on data.frame
’s and certain
column types.
qs
is both extremely fast and general: it can serialize any R object
like saveRDS
and is just as fast and sometimes faster than fst
.
library(qs)
df1 <- data.frame(x = rnorm(5e6), y = sample(5e6), z=sample(letters, 5e6, replace = T))
qsave(df1, "myfile.qs")
df2 <- qread("myfile.qs")
# CRAN version
install.packages("qs")
# CRAN version compile from source (recommended)
remotes::install_cran("qs", type = "source", configure.args = "--with-simd=AVX2")
The table below compares the features of different serialization approaches in R.
qs | fst | saveRDS | |
---|---|---|---|
Not Slow | ✔ | ✔ | ❌ |
Numeric Vectors | ✔ | ✔ | ✔ |
Integer Vectors | ✔ | ✔ | ✔ |
Logical Vectors | ✔ | ✔ | ✔ |
Character Vectors | ✔ | ✔ | ✔ |
Character Encoding | ✔ | (vector-wide only) | ✔ |
Complex Vectors | ✔ | ❌ | ✔ |
Data.Frames | ✔ | ✔ | ✔ |
On disk row access | ❌ | ✔ | ❌ |
Random column access | ❌ | ✔ | ❌ |
Attributes | ✔ | Some | ✔ |
Lists / Nested Lists | ✔ | ❌ | ✔ |
Multi-threaded | ✔ | ✔ | ❌ |
qs
also includes a number of advanced features:
qs
implements byte shuffling filters (adopted from the Blosc
meta-compression library). These filters utilize extended CPU
instruction sets (either SSE2 or AVX2).qs
also efficiently serializes S4 objects, environments, and other
complex objects.These features have the possibility of additionally increasing performance by orders of magnitude, for certain types of data. See sections below for more details.
The following benchmarks were performed comparing qs
, fst
and
saveRDS
/readRDS
in base R for serializing and de-serializing a
medium sized data.frame
with 5 million rows (approximately 115 Mb in
memory):
data.frame(a = rnorm(5e6),
b = rpois(5e6, 100),
c = sample(starnames$IAU, 5e6, T),
d = sample(state.name, 5e6, T),
stringsAsFactors = F)
qs
is highly parameterized and can be tuned by the user to extract as
much speed and compression as possible, if desired. For simplicity, qs
comes with 4 presets, which trades speed and compression ratio: “fast”,
“balanced”, “high” and “archive”.
The plots below summarize the performance of saveRDS
, qs
and fst
with various parameters:
(Benchmarks are based on qs
ver. 0.21.2, fst
ver. 0.9.0 and R
3.6.1.)
Benchmarking write and read speed is a bit tricky and depends highly on a number of factors, such as operating system, the hardware being run on, the distribution of the data, or even the state of the R instance. Reading data is also further subjected to various hardware and software memory caches.
Generally speaking, qs
and fst
are considerably faster than
saveRDS
regardless of using single threaded or multi-threaded
compression. qs
also manages to achieve superior compression ratio
through various optimizations (e.g. see “Byte Shuffle” section below).
The ALTREP system (new as of R 3.5.0) allows package developers to represent R objects using their own custom memory layout. This allows a potentially large speedup in processing certain types of data.
In qs
, ALTREP
character vectors are implemented via the
stringfish
package and can
be used by setting use_alt_rep=TRUE
in the qread
function. The
benchmark below shows the time it takes to qread
several million
random strings (nchar = 80
) with and without ALTREP
.
The large speedup demonstrates why one would want to consider the
system, but there are caveats. Downstream processing functions must be
ALTREP
-aware. See the
stringfish
package for more
details.
Byte shuffling (adopted from the Blosc meta-compression library) is a way of re-organizing data to be more amenable to compression. An integer contains four bytes and the limits of an integer in R are +/- 2^31-1. However, most real data doesn’t use anywhere near the range of possible integer values. For example, if the data were representing percentages, 0% to 100%, the first three bytes would be unused and zero.
Byte shuffling rearranges the data such that all of the first bytes are
blocked together, all of the second bytes are blocked together, and so
on. This procedure often makes it very easy for compression algorithms
to find repeated patterns and can often improve compression ratio by
orders of magnitude. In the example below, shuffle compression achieves
a compression ratio of over 1:1000. See ?qsave
for more details.
# With byte shuffling
x <- 1:1e8
qsave(x, "mydat.qs", preset = "custom", shuffle_control = 15, algorithm = "zstd")
cat( "Compression Ratio: ", as.numeric(object.size(x)) / file.info("mydat.qs")$size, "\n" )
# Compression Ratio: 1389.164
# Without byte shuffling
x <- 1:1e8
qsave(x, "mydat.qs", preset = "custom", shuffle_control = 0, algorithm = "zstd")
cat( "Compression Ratio: ", as.numeric(object.size(x)) / file.info("mydat.qs")$size, "\n" )
# Compression Ratio: 1.479294
You can use qs
to directly serialize objects to memory.
Example:
library(qs)
x <- qserialize(c(1, 2, 3))
qdeserialize(x)
[1] 1 2 3
The qs
package includes two sets of utility functions for converting
binary data to ASCII:
base85_encode
and base85_decode
base91_encode
and base91_decode
These functions are similar to base64 encoding functions found in various packages, but offer greater efficiency.
Example:
enc <- base91_encode(qserialize(datasets::mtcars, preset = "custom", compress_level = 22))
dec <- qdeserialize(base91_decode(enc))
(Note: base91 strings contain double quote characters ("
) and need to
be single quoted if stored as a string.)
See the help files for additional details and history behind these algorithms.
qs
functions can be called directly within C++ code via Rcpp.
Example C++ script:
// [[Rcpp::depends(qs)]]
#include <Rcpp.h>
#include <qs.h>
using namespace Rcpp;
// [[Rcpp::export]]
void test() {
qs::qsave(IntegerVector::create(1,2,3), "/tmp/myfile.qs", "high", "zstd", 1, 15, true, 1);
}
R side:
library(qs)
library(Rcpp)
sourceCpp("test.cpp")
# save file using Rcpp interface
test()
# read in file created through Rcpp interface
qread("/tmp/myfile.qs")
[1] 1 2 3
The C++ functions do not have default parameters; all parameters must be specified.
Future versions will be backwards compatible with the current version.