Introduction to arkdb
Increasing data sizes create challenges for the fundamental tasks of publishing, distributing, and preserving data. Despite (or perhaps because of) the diverse and ever-expanding number of database and file formats, the humble plain text file such as comma or tab-separated-values (e.g.
.tsv files) remains the gold standard for data archiving and distribution. These files can read on almost any platform or tool and can be efficiently compressed using long-standing and widely available standard open source libraries like
bzip2. In contrast, database storage formats and dumps are usually particular to the database platform used to generate them, and will likely not be compatible between different database engines (e.g. PostgreSQL -> SQLite) or even between different versions of the same engine. Researchers unfamiliar with these databases will have difficulty accessing such data, and these dumps may also be in formats that are less efficient to compress.
Working with tables that are too large for working memory on most machines by using external relational database stores is now a common R practice, thanks to ever-rising availability of data and increasing support and popularity of packages such as
dbplyr. Working with plain text files becomes increasingly difficult in this context. Many R users will not have sufficient RAM to simply read in a 10 GB
.tsv file into R. Similarly, moving a 10 GB database out of a relational data file and into a plain text file for archiving and distribution is similarly challenging from R. While most relational database back-ends implement some form of
IMPORT that allows them to read in and export out plain text files directly, these methods are not consistent across database types and not part of the standard SQL interface. Most importantly for our case, they also cannot be called directly from R, but require a separate stand-alone installation of the database client.
arkdb provides a simple solution to these two tasks.
The goal of
arkdb is to provide a convenient way to move data from large compressed text files (e.g.
*.tsv.bz2) into any DBI-compliant database connection (see DBI), and move tables out of such databases into text files. The key feature of
arkdb is that files are moved between databases and text files in chunks of a fixed size, allowing the package functions to work with tables that would be much to large to read into memory all at once. This will be slower than reading the file into memory at one go, but can be scaled to larger data and larger data with no additional memory requirement.
First, we’ll need an example database to work with. Conveniently, there is a nice example using the NYC flights data built into the
tmp <- tempdir() # Or can be your working directory, "." db <- dbplyr::nycflights13_sqlite(tmp) #> Caching nycflights db at /tmp/RtmpQDuQ6P/nycflights13.sqlite #> Creating table: airlines #> Creating table: airports #> Creating table: flights #> Creating table: planes #> Creating table: weather
To create an archive, we just give
ark the connection to the database and tell it where we want the
*.tsv.bz2 files to be archived. We can also set the chunk size as the number of
lines read in a single chunk. More lines per chunk usually means faster run time at the cost of higher memory requirements.
dir <- fs::dir_create(fs::path(tmp, "nycflights")) ark(db, dir, lines = 50000) #> Exporting airlines in 50000 line chunks: #> ...Done! (in 0.005402803 secs) #> Exporting airports in 50000 line chunks: #> ...Done! (in 0.01701546 secs) #> Exporting flights in 50000 line chunks: #> ...Done! (in 8.276816 secs) #> Exporting planes in 50000 line chunks: #> ...Done! (in 0.0236721 secs) #> Exporting weather in 50000 line chunks: #> ...Done! (in 0.5839033 secs)
We can take a look and confirm the files have been written. Note that we can use
fs::dir_info to get a nice snapshot of the file sizes. Compare the compressed sizes to the original database:
fs::dir_info(dir) %>% select(path, size) %>% mutate(path = fs::path_file(path)) #> # A tibble: 5 × 2 #> path size #> <chr> <fs::bytes> #> 1 airlines.tsv.bz2 260 #> 2 airports.tsv.bz2 28.13K #> 3 flights.tsv.bz2 4.85M #> 4 planes.tsv.bz2 11.96K #> 5 weather.tsv.bz2 278.84K fs::file_info(fs::path(tmp,"nycflights13.sqlite")) %>% pull(size) #> 44.9M
Now that we’ve gotten all the database into (compressed) plain text files, let’s get them back out. We simply need to pass
unark a list of these compressed files and a connection to the database. Here we create a new local SQLite database. Note that this design means that it is also easy to use
arkdb to move data between databases.
ark, we can set the chunk size to control the memory footprint required:
unark(files, new_db, lines = 50000) #> Importing /tmp/RtmpQDuQ6P/nycflights/airlines.tsv.bz2 in 50000 line chunks: #> ...Done! (in 0.01134491 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/airports.tsv.bz2 in 50000 line chunks: #> ...Done! (in 0.01878881 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/flights.tsv.bz2 in 50000 line chunks: #> ...Done! (in 5.747868 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/planes.tsv.bz2 in 50000 line chunks: #> ...Done! (in 0.029284 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/weather.tsv.bz2 in 50000 line chunks: #> ...Done! (in 0.2112517 secs)
unark returns a
dplyr database connection that we can use in the usual way:
tbl(new_db, "flights") #> # Source: table<flights> [?? x 19] #> # Database: sqlite 3.39.3 [/tmp/RtmpQDuQ6P/local.sqlite] #> year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier #> <int> <int> <int> <int> <int> <int> <int> <int> <int> <chr> #> 1 2013 1 1 517 515 2 830 819 11 UA #> 2 2013 1 1 533 529 4 850 830 20 UA #> 3 2013 1 1 542 540 2 923 850 33 AA #> 4 2013 1 1 544 545 -1 1004 1022 -18 B6 #> 5 2013 1 1 554 600 -6 812 837 -25 DL #> 6 2013 1 1 554 558 -4 740 728 12 UA #> 7 2013 1 1 555 600 -5 913 854 19 B6 #> 8 2013 1 1 557 600 -3 709 723 -14 EV #> 9 2013 1 1 557 600 -3 838 846 -8 B6 #> 10 2013 1 1 558 600 -2 753 745 8 AA #> # … with more rows, 9 more variables: flight <int>, tailnum <chr>, #> # origin <chr>, dest <chr>, air_time <int>, distance <int>, hour <int>, #> # minute <int>, time_hour <dbl>, and abbreviated variable names #> # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay
tsv format, implemented in base tools, as the text-based serialization. The
tsv standard is particularly attractive because it side-steps some of the ambiguities present in the CSV format due to string quoting. The IANA Standard for TSV neatly avoids this for tab-separated values by insisting that a tab can only ever be a separator.
arkdb provides a pluggable mechanism for changing the back end utility used to write text files. For instance, if we need to read in or export in
.csv format, we can simply swap in a
csv based reader in both
unark() methods, as illustrated here:
dir <- fs::dir_create(fs::path(tmp, "nycflights")) ark(db, dir, streamable_table = streamable_base_csv()) #> Exporting airlines in 50000 line chunks: #> ...Done! (in 0.002669811 secs) #> Exporting airports in 50000 line chunks: #> ...Done! (in 0.2244565 secs) #> Exporting flights in 50000 line chunks: #> ...Done! (in 8.515994 secs) #> Exporting planes in 50000 line chunks: #> ...Done! (in 0.02451634 secs) #> Exporting weather in 50000 line chunks: #> ...Done! (in 0.5850065 secs)
files <- fs::dir_ls(dir, glob = "*.csv.bz2") new_db <- DBI::dbConnect(RSQLite::SQLite(), fs::path(tmp, "local.sqlite")) unark(files, new_db, streamable_table = streamable_base_csv()) #> Importing /tmp/RtmpQDuQ6P/nycflights/airlines.csv.bz2 in 50000 line chunks: #> ...Done! (in 0.01028085 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/airports.csv.bz2 in 50000 line chunks: #> ...Done! (in 0.018507 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/flights.csv.bz2 in 50000 line chunks: #> ...Done! (in 5.714188 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/planes.csv.bz2 in 50000 line chunks: #> ...Done! (in 0.02705574 secs) #> Importing /tmp/RtmpQDuQ6P/nycflights/weather.csv.bz2 in 50000 line chunks: #> ...Done! (in 0.1717098 secs)
arkdb also provides the function
streamable_table() to facilitate users creating their own streaming table interfaces. For instance, if you would prefer to use
readr methods to read and write
tsv files, we could construct the table as follows (
streamable_readr_csv() are also shipped inside
arkdb for convenience):
stream <- streamable_table( function(file, ...) readr::read_tsv(file, ...), function(x, path, omit_header) readr::write_tsv(x = x, path = path, append = omit_header), "tsv")
ark(db, dir, streamable_table = stream) #> Exporting airlines in 50000 line chunks: #> Warning: The `path` argument of `write_tsv()` is deprecated as of readr 1.4.0. #> Please use the `file` argument instead. #> ...Done! (in 0.1573472 secs) #> Exporting airports in 50000 line chunks: #> ...Done! (in 0.02568436 secs) #> Exporting flights in 50000 line chunks: #> ...Done! (in 4.480448 secs) #> Exporting planes in 50000 line chunks: #> ...Done! (in 0.03271008 secs) #> Exporting weather in 50000 line chunks: #> ...Done! (in 0.2882586 secs)
Note several constraints on this design. The write method must be able to take a generic R
connection object (which will allow it to handle the compression methods used, if any), and the read method must be able to take a
readr functions handle these cases out of the box, so the above method is easy to write. Also note that the write method must be able to
append, i.e. it should use a header if
append=TRUE, but omit when it is
FALSE. See the built-in methods for more examples.
unark can read from a variety of compression formats recognized by base R:
ark can choose any of these as the compression algorithm. Note that there is some trade-off between speed of compression and efficiency (i.e. the final file size).
ark uses the
bz2 compression algorithm by default, supported in base R, to compress
tsv files. The
bz2 offers excellent compression levels, but is considerably slower to compress than
zip. It is comparably fast to uncompress. For faster archiving when maximum file size reduction is not critical,
gzip will give nearly as effective compression in significantly less time. Compression can also be turned off, e.g. by using
unark() with files that have no compression suffix (e.g.
*.tsv instead of
Once you have archived your database files with
ark, consider sharing them privately or publicly as part of your project GitHub repo using the
piggyback R package. For more permanent, versioned, and citable data archiving, upload your
*.tsv.bz2 files to a data repository like Zenodo.org.