# Saving and reloading DelayedArray objects

Aaron Lun*

*infinite.monkeys.with.keyboards@gmail.com

#### Revised: 2021-05-02

#### Package

chihaya 1.9.0

# Contents

# 1 Motivation

The chihaya package saves `DelayedArray` objects for efficient, portable and stable reproduction of delayed operations in a new R session or other programming frameworks.

- Portability. We provide a file specification in a standard format (HDF5) that enables other languages to easily interpret and reproduce the delayed operations.
- Stability. By converting DelayedArray operations into our specification, we provide a layer of protection against changes in the S4 class structure that would invalidate serialized RDS objects.
- Efficiency. We avoid any realization of the delayed operations, enabling quick saving and avoiding loss of data structure in the seed (e.g., sparsity).

Check out the specification for more details.

# 2 Quick start

Make a `DelayedArray` object with some operations:

    library(DelayedArray)
    x <- DelayedArray(matrix(runif(1000), ncol=10))
    x <- x[11:15,] / runif(5)
    x <- log2(x + 1)
    x

    ## <5 x 10> DelayedMatrix object of type "double":
    ##            [,1]       [,2]       [,3] ...      [,9]     [,10]
    ## [1,] 0.08330334 0.03517411 0.60801890   . 0.6777572 1.0330869
    ## [2,] 0.48491146 1.10075421 1.08821956   . 0.4377210 0.1094723
    ## [3,] 0.12274552 0.15103931 0.80793032   . 1.0536920 1.2292831
    ## [4,] 2.42517143 2.39406447 1.59113623   . 1.1258606 2.8141030
    ## [5,] 0.66456744 0.87041670 0.75723759   . 0.3287205 0.6718299

    showtree(x)

    ## 5x10 double: DelayedMatrix object
    ## └─ 5x10 double: Stack of 2 unary iso op(s)
    ##    └─ 5x10 double: Unary iso op with args
    ##       └─ 5x10 double: Subset
    ##          └─ 100x10 double: [seed] matrix object

Save it into a HDF5 file with `saveDelayed()`:

    library(chihaya)
    tmp <- tempfile(fileext=".h5")
    saveDelayed(x, tmp)
    rhdf5::h5ls(tmp)

    ##                            group    name       otype  dclass      dim
    ## 0                              / delayed   H5I_GROUP
    ## 1                       /delayed    base H5I_DATASET   FLOAT    ( 0 )
    ## 2                       /delayed  method H5I_DATASET  STRING    ( 0 )
    ## 3                       /delayed    seed   H5I_GROUP
    ## 4                  /delayed/seed  method H5I_DATASET  STRING    ( 0 )
    ## 5                  /delayed/seed    seed   H5I_GROUP
    ## 6             /delayed/seed/seed   along H5I_DATASET INTEGER    ( 0 )
    ## 7             /delayed/seed/seed  method H5I_DATASET  STRING    ( 0 )
    ## 8             /delayed/seed/seed    seed   H5I_GROUP
    ## 9        /delayed/seed/seed/seed   index   H5I_GROUP
    ## 10 /delayed/seed/seed/seed/index       0 H5I_DATASET INTEGER        5
    ## 11       /delayed/seed/seed/seed    seed   H5I_GROUP
    ## 12  /delayed/seed/seed/seed/seed    data H5I_DATASET   FLOAT 100 x 10
    ## 13  /delayed/seed/seed/seed/seed  native H5I_DATASET INTEGER    ( 0 )
    ## 14            /delayed/seed/seed    side H5I_DATASET  STRING    ( 0 )
    ## 15            /delayed/seed/seed   value H5I_DATASET   FLOAT        5
    ## 16                 /delayed/seed    side H5I_DATASET  STRING    ( 0 )
    ## 17                 /delayed/seed   value H5I_DATASET   FLOAT    ( 0 )

And then load it back in later:

    y <- loadDelayed(tmp)
    y

    ## <5 x 10> DelayedMatrix object of type "double":
    ##            [,1]       [,2]       [,3] ...      [,9]     [,10]
    ## [1,] 0.08330334 0.03517411 0.60801890   . 0.6777572 1.0330869
    ## [2,] 0.48491146 1.10075421 1.08821956   . 0.4377210 0.1094723
    ## [3,] 0.12274552 0.15103931 0.80793032   . 1.0536920 1.2292831
    ## [4,] 2.42517143 2.39406447 1.59113623   . 1.1258606 2.8141030
    ## [5,] 0.66456744 0.87041670 0.75723759   . 0.3287205 0.6718299

Of course, this is not a particularly interesting case as we end up saving the original array inside our HDF5 file anyway. The real fun begins when you have some more interesting seeds.

# 3 More interesting seeds

We can use the delayed nature of the operations to avoid breaking sparsity. For example:

    library(Matrix)
    x <- rsparsematrix(1000, 1000, density=0.01)
    x <- DelayedArray(x) + runif(1000)

    tmp <- tempfile(fileext=".h5")
    saveDelayed(x, tmp)
    rhdf5::h5ls(tmp)

    ##            group     name       otype  dclass   dim
    ## 0              /  delayed   H5I_GROUP
    ## 1       /delayed    along H5I_DATASET INTEGER ( 0 )
    ## 2       /delayed   method H5I_DATASET  STRING ( 0 )
    ## 3       /delayed     seed   H5I_GROUP
    ## 4  /delayed/seed     data H5I_DATASET   FLOAT 10000
    ## 5  /delayed/seed dimnames   H5I_GROUP
    ## 6  /delayed/seed  indices H5I_DATASET INTEGER 10000
    ## 7  /delayed/seed   indptr H5I_DATASET INTEGER  1001
    ## 8  /delayed/seed    shape H5I_DATASET INTEGER     2
    ## 9       /delayed     side H5I_DATASET  STRING ( 0 )
    ## 10      /delayed    value H5I_DATASET   FLOAT  1000

    file.info(tmp)[["size"]]

    ## [1] 101831

    # Compared to a dense array.
    tmp2 <- tempfile(fileext=".h5")
    out <- HDF5Array::writeHDF5Array(x, tmp2, "data")
    file.info(tmp2)[["size"]]

    ## [1] 280012

    # Loading it back in.
    y <- loadDelayed(tmp)
    showtree(y)

    ## 1000x1000 double: DelayedMatrix object
    ## └─ 1000x1000 double: Unary iso op with args
    ##    └─ 1000x1000 double, sparse: [seed] dgCMatrix object

We can also store references to external files, thus avoiding data duplication:

    library(HDF5Array)
    test <- HDF5Array(tmp2, "data")
    stuff <- log2(test + 1)
    stuff

    ## <1000 x 1000> DelayedMatrix object of type "double":
    ##              [,1]      [,2]      [,3] ...    [,999]   [,1000]
    ##    [1,] 0.5082033 0.5082033 0.5082033   . 0.5082033 0.5082033
    ##    [2,] 0.7534941 0.7534941 0.7534941   . 0.7534941 0.7534941
    ##    [3,] 0.3149135 0.3149135 0.3149135   . 0.3149135 0.3149135
    ##    [4,] 0.4045690 0.4045690 0.4045690   . 0.4045690 0.4045690
    ##    [5,] 0.1890528 0.1890528 0.1890528   . 0.1890528 0.1890528
    ##     ...         .         .         .   .         .         .
    ##  [996,] 0.4053761 0.4053761 0.4053761   . 0.4053761 0.4053761
    ##  [997,] 0.2834240 0.2834240 0.2834240   . 0.2834240 0.2834240
    ##  [998,] 0.5358764 0.5358764 0.5358764   . 0.5358764 0.5358764
    ##  [999,] 0.4973057 0.4973057 0.4973057   . 0.4973057 0.4973057
    ## [1000,] 0.8050486 0.8050486 0.8050486   . 0.8050486 0.8050486

    tmp <- tempfile(fileext=".h5")
    saveDelayed(stuff, tmp)
    rhdf5::h5ls(tmp)

    ##                 group       name       otype  dclass   dim
    ## 0                   /    delayed   H5I_GROUP
    ## 1            /delayed       base H5I_DATASET   FLOAT ( 0 )
    ## 2            /delayed     method H5I_DATASET  STRING ( 0 )
    ## 3            /delayed       seed   H5I_GROUP
    ## 4       /delayed/seed     method H5I_DATASET  STRING ( 0 )
    ## 5       /delayed/seed       seed   H5I_GROUP
    ## 6  /delayed/seed/seed dimensions H5I_DATASET INTEGER     2
    ## 7  /delayed/seed/seed       file H5I_DATASET  STRING ( 0 )
    ## 8  /delayed/seed/seed       name H5I_DATASET  STRING ( 0 )
    ## 9  /delayed/seed/seed     sparse H5I_DATASET INTEGER ( 0 )
    ## 10 /delayed/seed/seed       type H5I_DATASET  STRING ( 0 )
    ## 11      /delayed/seed       side H5I_DATASET  STRING ( 0 )
    ## 12      /delayed/seed      value H5I_DATASET   FLOAT ( 0 )

    file.info(tmp)[["size"]] # size of the delayed operations + pointer to the actual file

    ## [1] 49642

    y <- loadDelayed(tmp)
    y

    ## <1000 x 1000> DelayedMatrix object of type "double":
    ##              [,1]      [,2]      [,3] ...    [,999]   [,1000]
    ##    [1,] 0.5082033 0.5082033 0.5082033   . 0.5082033 0.5082033
    ##    [2,] 0.7534941 0.7534941 0.7534941   . 0.7534941 0.7534941
    ##    [3,] 0.3149135 0.3149135 0.3149135   . 0.3149135 0.3149135
    ##    [4,] 0.4045690 0.4045690 0.4045690   . 0.4045690 0.4045690
    ##    [5,] 0.1890528 0.1890528 0.1890528   . 0.1890528 0.1890528
    ##     ...         .         .         .   .         .         .
    ##  [996,] 0.4053761 0.4053761 0.4053761   . 0.4053761 0.4053761
    ##  [997,] 0.2834240 0.2834240 0.2834240   . 0.2834240 0.2834240
    ##  [998,] 0.5358764 0.5358764 0.5358764   . 0.5358764 0.5358764
    ##  [999,] 0.4973057 0.4973057 0.4973057   . 0.4973057 0.4973057
    ## [1000,] 0.8050486 0.8050486 0.8050486   . 0.8050486 0.8050486

# Session information

    sessionInfo()

    ## R version 4.5.0 (2025-04-11 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##   LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.utf8
    ## [3] LC_MONETARY=English_United States.utf8
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.utf8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] HDF5Array_1.37.0      h5mread_1.1.1         rhdf5_2.53.1
    ##  [4] chihaya_1.9.0         DelayedArray_0.35.1   SparseArray_1.9.0
    ##  [7] S4Arrays_1.9.1        abind_1.4-8           IRanges_2.43.0
    ## [10] S4Vectors_0.47.0      MatrixGenerics_1.21.0 matrixStats_1.5.0
    ## [13] BiocGenerics_0.55.0   generics_0.1.4        Matrix_1.7-3
    ## [16] BiocStyle_2.37.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] jsonlite_2.0.0      compiler_4.5.0      BiocManager_1.30.25
    ##  [4] crayon_1.5.3        Rcpp_1.0.14         rhdf5filters_1.21.0
    ##  [7] jquerylib_0.1.4     yaml_2.3.10         fastmap_1.2.0
    ## [10] lattice_0.22-7      R6_2.6.1            XVector_0.49.0
    ## [13] knitr_1.50          bookdown_0.43       bslib_0.9.0
    ## [16] rlang_1.1.6         cachem_1.1.0        xfun_0.52
    ## [19] sass_0.4.10         cli_3.6.5           Rhdf5lib_1.31.0
    ## [22] digest_0.6.37       grid_4.5.0          lifecycle_1.0.4
    ## [25] evaluate_1.0.3      rmarkdown_2.29      tools_4.5.0
    ## [28] htmltools_0.5.8.1