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sakura

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Extension to R Serialization

Extends the functionality of R serialization by augmenting the built-in reference hook system. This enhanced implementation allows optimal, one-pass integrated serialization that combines R serialization with third-party serialization methods.

Facilitates the serialization of even complex R objects, which contain non-system reference objects, such as those accessed via external pointers, for use in parallel and distributed computing.

This package was a request from a meeting of the R Consortium Marshalling and Serialization Working Group held at useR!2024 in Salzburg, Austria. It is designed to eventually provide a common framework for marshalling in R.

It extracts the functionality embedded within the mirai async framework for use in other contexts.

Overview

Some R objects by their nature cannot be serialized, such as those accessed via an external pointer.

Using the arrow package as an example:

library(arrow, warn.conflicts = FALSE)
obj <- list(as_arrow_table(iris), as_arrow_table(mtcars))

unserialize(serialize(obj, NULL))
#> [[1]]
#> Table
#> Error: Invalid <Table>, external pointer to null

In such cases, sakura::serial_config() can be used to create custom serialization configurations, specifying functions that hook into R’s native serialization mechanism for reference objects (‘refhooks’).

cfg <- sakura::serial_config(
  "ArrowTabular",
  arrow::write_to_raw,
  function(x) arrow::read_ipc_stream(x, as_data_frame = FALSE)
)

This configuration can then be supplied as the ‘hook’ argument for sakura::serialize() and sakura::unserialize().

sakura::unserialize(sakura::serialize(obj, cfg), cfg)
#> [[1]]
#> Table
#> 150 rows x 5 columns
#> $Sepal.Length <double>
#> $Sepal.Width <double>
#> $Petal.Length <double>
#> $Petal.Width <double>
#> $Species <dictionary<values=string, indices=int8>>
#> 
#> See $metadata for additional Schema metadata
#> 
#> [[2]]
#> Table
#> 32 rows x 11 columns
#> $mpg <double>
#> $cyl <double>
#> $disp <double>
#> $hp <double>
#> $drat <double>
#> $wt <double>
#> $qsec <double>
#> $vs <double>
#> $am <double>
#> $gear <double>
#> $carb <double>
#> 
#> See $metadata for additional Schema metadata

This time, the arrow tables are handled seamlessly.

Other types of serialization function are vectorized and in this case, the configuration should be created specifying vec = TRUE. Using torch as an example:

library(torch)
x <- list(torch_rand(5L), runif(5L))

unserialize(serialize(x, NULL))
#> [[1]]
#> torch_tensor
#> Error in (function (self) : external pointer is not valid

Base R serialization above fails, but sakura serialization succeeds:

cfg <- sakura::serial_config("torch_tensor", torch::torch_serialize, torch::torch_load, vec = TRUE)

sakura::unserialize(sakura::serialize(x, cfg), cfg)
#> [[1]]
#> torch_tensor
#>  0.6972
#>  0.0887
#>  0.0576
#>  0.9132
#>  0.4515
#> [ CPUFloatType{5} ]
#> 
#> [[2]]
#> [1] 0.79129934 0.31196571 0.70189057 0.53588851 0.00580887

Acknowledgements

We would like to thank in particular:

• R Core for providing the interface to the R serialization mechanism.
• Luke Tierney and Mike Cheng for their meticulous efforts in documenting the serialization interface.
• Daniel Falbel for discussion around an efficient solution to serialization and transmission of torch tensors.

Installation

The current development version is available from R-universe:

install.packages("sakura", repos = "https://shikokuchuo.r-universe.dev")

–

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.