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
readRDS functions in R.
Inspired by the
qs uses a similar block-compression design using either the
zstd compression libraries. It differs in that it applies a more general approach for attributes and object references.
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
The table below compares the features of different serialization approaches in R.
|Character Encoding||✔||(vector-wide only)||✔|
|On disk row access||❌||✔||❌|
|Random column access||❌||✔||❌|
|Lists / Nested Lists||✔||❌||✔|
qs also includes a number of advanced features:
qsimplements byte shuffling filters (adopted from the Blosc meta-compression library). These filters utilize extended CPU instruction sets (either SSE2 or AVX2).
qsalso 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
readRDS in base R for serializing and de-serializing a medium sized
data.frame with 5 million rows (approximately 115 Mb in memory):
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
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.
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.
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