With table_to_parquet()
For huge input files in SAS, SPSS and Stata formats,
the parquetize package allows you to perform a clever conversion by
using max_memory or max_rows in the table_to_parquet()
function. The native behavior of this function (and all other functions
in the package) is to load the entire table to be converted into R and
then write it to disk (in a single file or a partitioned directory).
When handling very large files, the risk that frequently occurs is
that the R session aborts because it cannot load the entire database
into memory. This risk is even more present if you work locally on your
computer and it can be limited if you work on remote servers.table_to_parquet() offers this solution which
answers a need expressed by parquetize users.
| The idea is to split the very large table into “chunks” based on memory consumption of input data or on the number of rows in the table in order to be able to simultaneously : |
| - read a chunk of the very large database |
| - write this chunk in the floor file |
Here are examples from the documentation using the iris table. There’s two ways to split output files :
- by memory consumption
- by number of lines
Spliting data by memory consumption
table_to_parquet can guess the number of lines to put in
a file based on the memory consuption with the argument
max_memory expressed in Mb.
Here we cut the 150 rows into chunks of roughly 5 Kb when a file is
loaded as a tibble.
In this example we get 2 parquet files of 89 lines called
iris1-89.parquet and iris90-150.parquet
table_to_parquet(
path_to_file = system.file("examples", "iris.sas7bdat", package = "haven"),
path_to_parquet = tempfile(),
max_memory = 5 / 1024,
encoding = "utf-8"
)
#> Reading data...
#> Writing file19e1586802e-1-89.parquet...
#> Reading data...
#> Writing file19e1586802e-90-150.parquet...
#> ✔ Data are available in parquet dataset under /tmp/RtmpuaPzn0/file19e1586802e/
#> Writing file19e1586802e-90-150.parquet...In real life, you should use a max_memory in the Gb
range, for example with a SAS file of 50 000 000 lines and using
max_memory of 5000 Mb :
table_to_parquet(
path_to_file = "myhugefile.sas7bdat",
path_to_parquet = tempdir(),
max_memory = 5000,
encoding = "utf-8"
)Splitting data by number of lines
Tip: The number of lines that each chunk must contain must be supported by the RAM of your computer/server. Ideally, the number of chunks to be defined must be limited. It should be in tens and not hundreds to limit the number of intermediate files (see example below).
Here we cut the 150 rows into 3 chunks of 50 rows. In this example we
get 3 parquet files of 50 lines called iris1-50.parquet,
iris51-100.parquet and iris101-151.parquet
table_to_parquet(
path_to_file = system.file("examples", "iris.sas7bdat", package = "haven"),
path_to_parquet = tempfile(),
max_rows = 50,
encoding = "utf-8"
)In real life, we can perform this kind of request with the parquetize API (for example with a SAS file of 50 000 000 lines and defining 25 chunks of 2 000 000 rows each) :
table_to_parquet(
path_to_file = "myhugefile.sas7bdat",
path_to_parquet = tempdir(),
max_rows = 2000000,
encoding = "utf-8"
)Files myhugefile1-2000000.parquet,
myhugefile2000001-4000000.parquet … will be created.
Function rbind_parquet()
If at the end of the conversion with table_to_parquet(),
you want to reconstitute a unique initial table and
if you have the computer resources (in RAM) to do so,
you can use the helper function provided with the API of
rbind_parquet().
This function allows to bind multiple parquet files by row.
Here’s an example without deleting initial files
(delete_initial_files=FALSE) :
rbind_parquet(
folder = tempfile(),
output_name = "myhugefile",
delete_initial_files = FALSE
)The myhugefile.parquet file will be created from the
myhugefile1-2000000.parquet,
myhugefile2000001-4000000.parquet… files!