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Toggle navigation fst 0.9.6
 * Reference
 * Changelog

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OVERVIEW

The fst package for R provides a fast, easy and flexible way to serialize data
frames. With access speeds of multiple GB/s, fst is specifically designed to
unlock the potential of high speed solid state disks that can be found in most
modern computers. Data frames stored in the fst format have full random access,
both in column and rows.

The figure below compares the read and write performance of the fst package to
various alternatives.

Method Format Time (ms) Size (MB) Speed (MB/s) N readRDS bin 1577 1000 633 112
saveRDS bin 2042 1000 489 112 fread csv 2925 1038 410 232 fwrite csv 2790 1038
358 241 read_feather bin 3950 813 253 112 write_feather bin 1820 813 549 112
read_fst bin 457 303 2184 282 write_fst bin 314 303 3180 291

These benchmarks were performed on a laptop (i7 4710HQ @2.5 GHz) with a
reasonably fast SSD (M.2 Samsung SM951) using the dataset defined below.
Parameter Speed was calculated by dividing the in-memory size of the data frame
by the measured time. These results are also visualized in the following graph:



As can be seen from the figure, the measured speeds for the fst package are very
high and even top the maximum drive speed of the SSD used. The package
accomplishes this by an effective combination of multi-threading and
compression. The on-disk file sizes of fst files are also much smaller than that
of the other formats tested. This is an added benefit of fst’s use of
type-specific compressors on each stored column.

In addition to methods for data frame serialization, fst also provides methods
for multi-threaded in-memory compression with the popular LZ4 and ZSTD
compressors and an extremely fast multi-threaded hasher.


MULTI-THREADING

The fst package relies heavily on multi-threading to boost the read- and write
speed of data frames. To maximize throughput, fst compresses and decompresses
data in the background and tries to keep the disk busy writing and reading data
at the same time.


INSTALLATION

The easiest way to install the package is from CRAN:

install.packages("fst")

You can also use the development version from GitHub:

# install.packages("devtools")
devtools::install_github("fstPackage/fst", ref = "develop")


BASIC USAGE

Using fst is simple. Data can be stored and retrieved using methods write_fst
and read_fst:

# Generate some random data frame with 10 million rows and various column types
nr_of_rows <- 1e7

df <- data.frame(
    Logical = sample(c(TRUE, FALSE, NA), prob = c(0.85, 0.1, 0.05), nr_of_rows, replace = TRUE),
    Integer = sample(1L:100L, nr_of_rows, replace = TRUE),
    Real = sample(sample(1:10000, 20) / 100, nr_of_rows, replace = TRUE),
    Factor = as.factor(sample(labels(UScitiesD), nr_of_rows, replace = TRUE))
  )

# Store the data frame to disk
  write.fst(df, "dataset.fst")
  
# Retrieve the data frame again
  df <- read.fst("dataset.fst")

Note: the dataset defined in this example code was also used to obtain the
benchmark results shown in the introduction.


RANDOM ACCESS

The fst file format provides full random access to stored datasets. You can
retrieve a selection of columns and rows with:

  df_subset <- read.fst("dataset.fst", c("Logical", "Factor"), from = 2000, to = 5000)

This reads rows 2000 to 5000 from columns Logical and Factor without actually
touching any other data in the stored file. That means that a subset can be read
from file without reading the complete file first. This is different from, say,
readRDS or read_feather where you have to read the complete file or column
before you can make a subset.


COMPRESSION

For compression the excellent and speedy LZ4 and ZSTD compression algorithms are
used. These compressors (in combination with type-specific bit filters), enable
fst to achieve high compression speeds at reasonable compression factors. The
compression factor can be tuned from 0 (minimum) to 100 (maximum):

write.fst(df, "dataset.fst", 100)  # use maximum compression

Compression reduces the size of the fst file that holds your data. But because
the (de-)compression is done on background threads, it can increase the total
read- and write speed as well. The graph below shows how the use of multiple
threads enhances the read and write speed of our sample dataset.



The csv format used by the fread and fwrite methods of package data.table is
actually a human-readable text format and not a binary format. Normally, binary
formats would be much faster than the csv format, because csv takes more space
on disk, is row based, uncompressed and needs to be parsed into a
computer-native format to have any meaning. So any serializer that’s working on
csv has an enormous disadvantage as compared to binary formats. Yet, the results
show that data.table is on par with binary formats and when more threads are
used, it can even be faster. Because of this impressive performance, it was
included in the graph for comparison.


BINDINGS IN OTHER LANGUAGES

Julia: FstFileFormat.jl A naive Julia binding using RCall.jl

> Note to users: From CRAN release v0.8.0, the fst format is stable and
> backwards compatible. That means that all fst files generated with package
> v0.8.0 or later can be read by future versions of the package.


LINKS

 * View on CRAN
 * Browse source code
 * Report a bug


LICENSE

 * AGPL-3 | file LICENSE


COMMUNITY

 * Contributing guide


CITATION

 * Citing fst


DEVELOPERS

 * Mark Klik
   Author, maintainer, copyright holder
 * More about authors...

Developed by Mark Klik.

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