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Write to Parquet file

Description

Write to Parquet file

Usage

<DataFrame>$write_parquet(
  file,
  ...,
  compression = c("lz4", "uncompressed", "snappy", "gzip", "lzo", "brotli", "zstd"),
  compression_level = NULL,
  statistics = TRUE,
  row_group_size = NULL,
  data_page_size = NULL,
  partition_by = NULL,
  partition_chunk_size_bytes = 4294967296,
  storage_options = NULL,
  retries = 2
)

Arguments

file File path to which the result should be written. This should be a path to a directory if writing a partitioned dataset.
… These dots are for future extensions and must be empty.
compression The compression method. Must be one of:
  • “lz4”: fast compression/decompression.
  • “uncompressed”
  • “snappy”: this guarantees that the parquet file will be compatible with older parquet readers.
  • “gzip”
  • “lzo”
  • “brotli”
  • “zstd”: good compression performance.
compression_level NULL or integer. The level of compression to use. Only used if method is one of “gzip”, “brotli”, or “zstd”. Higher compression means smaller files on disk:
  • “gzip”: min-level: 0, max-level: 10.
  • “brotli”: min-level: 0, max-level: 11.
  • “zstd”: min-level: 1, max-level: 22.
statistics Whether statistics should be written to the Parquet headers. Possible values:
  • TRUE: enable default set of statistics (default). Some statistics may be disabled.
  • FALSE: disable all statistics
  • “full”: calculate and write all available statistics
  • A list created via parquet_statistics() to specify which statistics to include.
row_group_size Size of the row groups in number of rows. If NULL (default), the chunks of the DataFrame are used. Writing in smaller chunks may reduce memory pressure and improve writing speeds.
data_page_size Size of the data page in bytes. If NULL (default), it is set to 1024^2 bytes.
partition_by A character vector indicating column(s) to partition by. A partitioned dataset will be written if this is specified.
partition_chunk_size_bytes Approximate size to split DataFrames within a single partition when writing. Note this is calculated using the size of the DataFrame in memory (the size of the output file may differ depending on the file format / compression).
storage_options Named vector containing options that indicate how to connect to a cloud provider. The cloud providers currently supported are AWS, GCP, and Azure. See supported keys here:
  • aws
  • gcp
  • azure
  • Hugging Face (hf://): Accepts an API key under the token parameter c(token = YOUR_TOKEN) or by setting the HF_TOKEN environment variable.
If storage_options is not provided, Polars will try to infer the information from environment variables.
retries Number of retries if accessing a cloud instance fails.

Value

The input DataFrame is returned.

Examples

library("polars")


dat = as_polars_df(mtcars)

# write data to a single parquet file
destination = withr::local_tempfile(fileext = ".parquet")
dat$write_parquet(destination)

#> shape: (32, 11)
#> ┌──────┬─────┬───────┬───────┬───┬─────┬─────┬──────┬──────┐
#> │ mpg  ┆ cyl ┆ disp  ┆ hp    ┆ … ┆ vs  ┆ am  ┆ gear ┆ carb │
#> │ ---  ┆ --- ┆ ---   ┆ ---   ┆   ┆ --- ┆ --- ┆ ---  ┆ ---  │
#> │ f64  ┆ f64 ┆ f64   ┆ f64   ┆   ┆ f64 ┆ f64 ┆ f64  ┆ f64  │
#> ╞══════╪═════╪═══════╪═══════╪═══╪═════╪═════╪══════╪══════╡
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 4.0  ┆ 4.0  │
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 4.0  ┆ 4.0  │
#> │ 22.8 ┆ 4.0 ┆ 108.0 ┆ 93.0  ┆ … ┆ 1.0 ┆ 1.0 ┆ 4.0  ┆ 1.0  │
#> │ 21.4 ┆ 6.0 ┆ 258.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 0.0 ┆ 3.0  ┆ 1.0  │
#> │ 18.7 ┆ 8.0 ┆ 360.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 3.0  ┆ 2.0  │
#> │ …    ┆ …   ┆ …     ┆ …     ┆ … ┆ …   ┆ …   ┆ …    ┆ …    │
#> │ 30.4 ┆ 4.0 ┆ 95.1  ┆ 113.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 5.0  ┆ 2.0  │
#> │ 15.8 ┆ 8.0 ┆ 351.0 ┆ 264.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 4.0  │
#> │ 19.7 ┆ 6.0 ┆ 145.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 6.0  │
#> │ 15.0 ┆ 8.0 ┆ 301.0 ┆ 335.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 8.0  │
#> │ 21.4 ┆ 4.0 ┆ 121.0 ┆ 109.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 4.0  ┆ 2.0  │
#> └──────┴─────┴───────┴───────┴───┴─────┴─────┴──────┴──────┘

# write data to folder with a hive-partitioned structure
dest_folder = withr::local_tempdir()
dat$write_parquet(dest_folder, partition_by = c("gear", "cyl"))

#> shape: (32, 11)
#> ┌──────┬─────┬───────┬───────┬───┬─────┬─────┬──────┬──────┐
#> │ mpg  ┆ cyl ┆ disp  ┆ hp    ┆ … ┆ vs  ┆ am  ┆ gear ┆ carb │
#> │ ---  ┆ --- ┆ ---   ┆ ---   ┆   ┆ --- ┆ --- ┆ ---  ┆ ---  │
#> │ f64  ┆ f64 ┆ f64   ┆ f64   ┆   ┆ f64 ┆ f64 ┆ f64  ┆ f64  │
#> ╞══════╪═════╪═══════╪═══════╪═══╪═════╪═════╪══════╪══════╡
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 4.0  ┆ 4.0  │
#> │ 21.0 ┆ 6.0 ┆ 160.0 ┆ 110.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 4.0  ┆ 4.0  │
#> │ 22.8 ┆ 4.0 ┆ 108.0 ┆ 93.0  ┆ … ┆ 1.0 ┆ 1.0 ┆ 4.0  ┆ 1.0  │
#> │ 21.4 ┆ 6.0 ┆ 258.0 ┆ 110.0 ┆ … ┆ 1.0 ┆ 0.0 ┆ 3.0  ┆ 1.0  │
#> │ 18.7 ┆ 8.0 ┆ 360.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 0.0 ┆ 3.0  ┆ 2.0  │
#> │ …    ┆ …   ┆ …     ┆ …     ┆ … ┆ …   ┆ …   ┆ …    ┆ …    │
#> │ 30.4 ┆ 4.0 ┆ 95.1  ┆ 113.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 5.0  ┆ 2.0  │
#> │ 15.8 ┆ 8.0 ┆ 351.0 ┆ 264.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 4.0  │
#> │ 19.7 ┆ 6.0 ┆ 145.0 ┆ 175.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 6.0  │
#> │ 15.0 ┆ 8.0 ┆ 301.0 ┆ 335.0 ┆ … ┆ 0.0 ┆ 1.0 ┆ 5.0  ┆ 8.0  │
#> │ 21.4 ┆ 4.0 ┆ 121.0 ┆ 109.0 ┆ … ┆ 1.0 ┆ 1.0 ┆ 4.0  ┆ 2.0  │
#> └──────┴─────┴───────┴───────┴───┴─────┴─────┴──────┴──────┘

list.files(dest_folder, recursive = TRUE)

#> [1] "gear=3.0/cyl=4.0/00000000.parquet" "gear=3.0/cyl=6.0/00000000.parquet"
#> [3] "gear=3.0/cyl=8.0/00000000.parquet" "gear=4.0/cyl=4.0/00000000.parquet"
#> [5] "gear=4.0/cyl=6.0/00000000.parquet" "gear=5.0/cyl=4.0/00000000.parquet"
#> [7] "gear=5.0/cyl=6.0/00000000.parquet" "gear=5.0/cyl=8.0/00000000.parquet"