Skip to contents

This function allows to convert a SQL query from a DBI to parquet format.

It handles all DBI supported databases.

Two conversions possibilities are offered :

  • Convert to a single parquet file. Argument `path_to_parquet` must then be used;

  • Convert to a partitioned parquet file. Additionnal arguments `partition` and `partitioning` must then be used;

Examples explain how to convert a query to a chunked dataset

Usage

dbi_to_parquet(
  conn,
  sql_query,
  path_to_parquet,
  max_memory,
  max_rows,
  chunk_memory_sample_lines = 10000,
  partition = "no",
  compression = "snappy",
  compression_level = NULL,
  ...
)

Arguments

conn

A DBIConnection object, as return by DBI::dbConnect

sql_query

a character string containing an SQL query (this argument is passed to DBI::dbSendQuery)

path_to_parquet

String that indicates the path to the directory where the parquet files will be stored.

max_memory

Memory size (in Mb) in which data of one parquet file should roughly fit.

max_rows

Number of lines that defines the size of the chunk. This argument can not be filled in if max_memory is used.

chunk_memory_sample_lines

Number of lines to read to evaluate max_memory. Default to 10 000.

partition

String ("yes" or "no" - by default) that indicates whether you want to create a partitioned parquet file. If "yes", `"partitioning"` argument must be filled in. In this case, a folder will be created for each modality of the variable filled in `"partitioning"`. Be careful, this argument can not be "yes" if `max_memory` or `max_rows` argument are not NULL.

compression

compression algorithm. Default "snappy".

compression_level

compression level. Meaning depends on compression algorithm.

...

additional format-specific arguments, see arrow::write_parquet() and arrow::write_dataset() for more informations.

Value

A parquet file, invisibly

Examples


# Conversion from a sqlite dbi connection to a single parquet file :

dbi_connection <- DBI::dbConnect(RSQLite::SQLite(),
  system.file("extdata","iris.sqlite",package = "parquetize"))

# Reading iris table from local sqlite database
# and conversion to one parquet file :

dbi_to_parquet(
  conn = dbi_connection,
  sql_query = "SELECT * FROM iris",
  path_to_parquet = tempfile(fileext=".parquet"),
)
#> Reading data...
#> Writing data...
#>  Data are available in parquet file under /tmp/RtmpHNodKR/file16486f86ba66.parquet
#> Writing data...

#> Reading data...


# Reading iris table from local sqlite database by chunk (using
# `max_memory` argument) and conversion to multiple parquet files

dbi_to_parquet(
  conn = dbi_connection,
  sql_query = "SELECT * FROM iris",
  path_to_parquet = tempdir(),
  max_memory = 2 / 1024
)
#> Reading data...
#> Writing data in part-1-42.parquet...
#> Reading data...
#> Writing data in part-43-84.parquet...
#> Reading data...
#> Writing data in part-85-126.parquet...
#> Reading data...
#> Writing data in part-127-150.parquet...
#>  Parquet dataset is available under /tmp/RtmpHNodKR/
#> Writing data in part-127-150.parquet...


# Using chunk and partition together is not possible directly but easy to do :
# Reading iris table from local sqlite database by chunk (using
# `max_memory` argument) and conversion to arrow dataset partitioned by
# species

# get unique values of column "iris from table "iris"
partitions <- get_partitions(dbi_connection, table = "iris", column = "Species")

# loop over those values
for (species in partitions) {
  dbi_to_parquet(
    conn = dbi_connection,
    # use glue_sql to create the query filtering the partition
    sql_query = glue::glue_sql("SELECT * FROM iris where Species = {species}",
                               .con = dbi_connection),
    # add the partition name in the output dir to respect parquet partition schema
    path_to_parquet = file.path(tempdir(), "iris", paste0("Species=", species)),
    max_memory = 2 / 1024,
  )
}
#> Reading data...
#> Writing data in part-1-31.parquet...
#> Reading data...
#> Writing data in part-32-50.parquet...
#>  Parquet dataset is available under /tmp/RtmpHNodKR/iris/Species=setosa/
#> Writing data in part-32-50.parquet...

#> Reading data...
#> Writing data in part-1-31.parquet...
#> Reading data...
#> Writing data in part-32-50.parquet...
#>  Parquet dataset is available under /tmp/RtmpHNodKR/iris/Species=versicolor/
#> Writing data in part-32-50.parquet...

#> Reading data...
#> Writing data in part-1-31.parquet...
#> Reading data...
#> Writing data in part-32-50.parquet...
#>  Parquet dataset is available under /tmp/RtmpHNodKR/iris/Species=virginica/
#> Writing data in part-32-50.parquet...


# If you need a more complicated query to get your partitions, you can use
# dbGetQuery directly :
col_to_partition <- DBI::dbGetQuery(dbi_connection, "SELECT distinct(`Species`) FROM `iris`")[,1]