For huge input files in SAS, SPSS and Stata formats,
the parquetize package allows you to perform a clever conversion by
chunk_size in the
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 :
table_to_parquet can guess the number of lines to put in
a file based on the memory consuption with the argument
chunk_memory_size 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
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... -1-89.parquet... Writing filea8c2dad31a2 Reading data... -90-150.parquet... Writing filea8c2dad31a2 in parquet dataset under C:\Users\AQEW8W\AppData\Local\Temp\RtmpMrMU2q\filea8c2dad31a2/ ✔ Data are available #> Writing filea8c2dad31a2-90-150.parquet...
In real life, you should use a
chunk_memory_size in the
Gb range, for example with a SAS file of 50 000 000 lines and using
chunk_memory_size of 5000 Mb :
table_to_parquet( path_to_file = "myhugefile.sas7bdat", path_to_parquet = tempdir(), max_memory = 5000, encoding = "utf-8" )
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
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" )
myhugefile2000001-4000000.parquet … will be created.
If at the end of the conversion with
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
This function allows to bind multiple parquet files by row.
Here’s an example without deleting initial files (
rbind_parquet( folder = tempfile(), output_name = "myhugefile", delete_initial_files = FALSE )
myhugefile.parquet file will be created from the