# Saving and reloading DelayedArray objects

# 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.

# 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,] 0.1585973 0.9170471 1.1192202   . 1.0605083 1.2682244
    ## [2,] 0.3896969 1.1470429 0.4781686   . 1.2035988 0.5462912
    ## [3,] 1.7549095 0.7160882 1.3452061   . 0.9170142 1.9228235
    ## [4,] 1.1447204 0.8325614 1.8217062   . 2.1866240 2.2007091
    ## [5,] 1.0969930 1.5789717 1.4790383   . 1.3537038 0.2354646

    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,] 0.1585973 0.9170471 1.1192202   . 1.0605083 1.2682244
    ## [2,] 0.3896969 1.1470429 0.4781686   . 1.2035988 0.5462912
    ## [3,] 1.7549095 0.7160882 1.3452061   . 0.9170142 1.9228235
    ## [4,] 1.1447204 0.8325614 1.8217062   . 2.1866240 2.2007091
    ## [5,] 1.0969930 1.5789717 1.4790383   . 1.3537038 0.2354646

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.

# 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] 102244

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

    ## [1] 280618

    # 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,] 0.53409214 0.53409214 0.53409214   . 0.53409214 0.53409214
    ##    [2,] 0.02630173 0.02630173 0.02630173   . 0.02630173 0.02630173
    ##    [3,] 0.61609324 0.61609324 0.61609324   . 0.61609324 0.61609324
    ##    [4,] 0.23248848 0.23248848 0.23248848   . 0.23248848 0.23248848
    ##    [5,] 0.35129251 0.35129251 0.35129251   . 0.35129251 0.35129251
    ##     ...          .          .          .   .          .          .
    ##  [996,]  0.5623823  0.5623823  0.5623823   .  0.5623823  0.5623823
    ##  [997,]  0.6039024  0.6039024  0.6039024   .  0.6039024  0.6039024
    ##  [998,]  0.5742983  0.5742983  0.5742983   .  0.5742983  0.5742983
    ##  [999,]  0.4065595  0.4065595  0.4065595   .  0.4065595  0.4065595
    ## [1000,]  0.7641827  0.7641827  0.7641827   .  0.7641827  0.7641827

    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,] 0.53409214 0.53409214 0.53409214   . 0.53409214 0.53409214
    ##    [2,] 0.02630173 0.02630173 0.02630173   . 0.02630173 0.02630173
    ##    [3,] 0.61609324 0.61609324 0.61609324   . 0.61609324 0.61609324
    ##    [4,] 0.23248848 0.23248848 0.23248848   . 0.23248848 0.23248848
    ##    [5,] 0.35129251 0.35129251 0.35129251   . 0.35129251 0.35129251
    ##     ...          .          .          .   .          .          .
    ##  [996,]  0.5623823  0.5623823  0.5623823   .  0.5623823  0.5623823
    ##  [997,]  0.6039024  0.6039024  0.6039024   .  0.6039024  0.6039024
    ##  [998,]  0.5742983  0.5742983  0.5742983   .  0.5742983  0.5742983
    ##  [999,]  0.4065595  0.4065595  0.4065595   .  0.4065595  0.4065595
    ## [1000,]  0.7641827  0.7641827  0.7641827   .  0.7641827  0.7641827

# Session information

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] HDF5Array_1.39.0      h5mread_1.3.1         rhdf5_2.55.13
    ##  [4] chihaya_1.11.0        DelayedArray_0.37.0   SparseArray_1.11.10
    ##  [7] S4Arrays_1.11.1       abind_1.4-8           IRanges_2.45.0
    ## [10] S4Vectors_0.49.0      MatrixGenerics_1.23.0 matrixStats_1.5.0
    ## [13] BiocGenerics_0.57.0   generics_0.1.4        Matrix_1.7-4
    ## [16] BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] jsonlite_2.0.0      compiler_4.5.2      BiocManager_1.30.27
    ##  [4] Rcpp_1.1.1          rhdf5filters_1.23.3 jquerylib_0.1.4
    ##  [7] yaml_2.3.12         fastmap_1.2.0       lattice_0.22-9
    ## [10] R6_2.6.1            XVector_0.51.0      knitr_1.51
    ## [13] maketools_1.3.2     bslib_0.10.0        rlang_1.1.7
    ## [16] cachem_1.1.0        xfun_0.56           sass_0.4.10
    ## [19] sys_3.4.3           cli_3.6.5           Rhdf5lib_1.33.0
    ## [22] digest_0.6.39       grid_4.5.2          lifecycle_1.0.5
    ## [25] evaluate_1.0.5      buildtools_1.0.0    rmarkdown_2.30
    ## [28] tools_4.5.2         htmltools_0.5.9