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.

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,] 0.3158461 1.8659366 1.7844324   . 0.4517667 1.6554130
## [2,] 0.8130237 1.1178695 0.8046878   . 1.4303657 0.6688106
## [3,] 1.4534753 1.7005312 1.6656554   . 0.9179943 1.0666420
## [4,] 0.8706838 2.1251911 2.1778999   . 0.8321898 2.1974679
## [5,] 1.8182861 2.2642905 1.5788652   . 0.6558091 2.1134622
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,] 0.3158461 1.8659366 1.7844324   . 0.4517667 1.6554130
## [2,] 0.8130237 1.1178695 0.8046878   . 1.4303657 0.6688106
## [3,] 1.4534753 1.7005312 1.6656554   . 0.9179943 1.0666420
## [4,] 0.8706838 2.1251911 2.1778999   . 0.8321898 2.1974679
## [5,] 1.8182861 2.2642905 1.5788652   . 0.6558091 2.1134622

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] 101998
# Compared to a dense array.
tmp2 <- tempfile(fileext=".h5")
out <- HDF5Array::writeHDF5Array(x, tmp2, "data")
file.info(tmp2)[["size"]]
## [1] 280502
# 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,]  0.5972585  0.5972585  0.5972585   .  0.5972585  0.5972585
##    [2,]  0.2214445  0.2214445  0.2214445   .  0.2214445  0.2214445
##    [3,]  0.6307156  0.6307156  0.6307156   .  0.6307156  0.6307156
##    [4,]  0.6531585  0.6531585  0.6531585   .  0.6531585  0.6531585
##    [5,]  0.7182654  0.7182654  0.7182654   .  0.7182654  0.7182654
##     ...          .          .          .   .          .          .
##  [996,] 0.67592427 0.67592427 0.67592427   . 0.67592427 0.67592427
##  [997,] 0.78445830 0.78445830 0.78445830   . 0.78445830 0.78445830
##  [998,] 0.09298399 0.09298399 0.09298399   . 0.09298399 0.09298399
##  [999,] 0.96792241 0.96792241 0.96792241   . 0.96792241 0.96792241
## [1000,] 0.36200942 0.36200942 0.36200942   . 0.36200942 0.36200942
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,]  0.5972585  0.5972585  0.5972585   .  0.5972585  0.5972585
##    [2,]  0.2214445  0.2214445  0.2214445   .  0.2214445  0.2214445
##    [3,]  0.6307156  0.6307156  0.6307156   .  0.6307156  0.6307156
##    [4,]  0.6531585  0.6531585  0.6531585   .  0.6531585  0.6531585
##    [5,]  0.7182654  0.7182654  0.7182654   .  0.7182654  0.7182654
##     ...          .          .          .   .          .          .
##  [996,] 0.67592427 0.67592427 0.67592427   . 0.67592427 0.67592427
##  [997,] 0.78445830 0.78445830 0.78445830   . 0.78445830 0.78445830
##  [998,] 0.09298399 0.09298399 0.09298399   . 0.09298399 0.09298399
##  [999,] 0.96792241 0.96792241 0.96792241   . 0.96792241 0.96792241
## [1000,] 0.36200942 0.36200942 0.36200942   . 0.36200942 0.36200942

Session information

sessionInfo()
## R version 4.4.0 beta (2024-04-14 r86421)
## Platform: x86_64-apple-darwin20
## Running under: macOS Monterey 12.7.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## 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_1.32.0      rhdf5_2.48.0          chihaya_1.4.0        
##  [4] DelayedArray_0.30.0   SparseArray_1.4.0     S4Arrays_1.4.0       
##  [7] abind_1.4-5           IRanges_2.38.0        S4Vectors_0.42.0     
## [10] MatrixGenerics_1.16.0 matrixStats_1.3.0     BiocGenerics_0.50.0  
## [13] Matrix_1.7-0          BiocStyle_2.32.0     
## 
## loaded via a namespace (and not attached):
##  [1] crayon_1.5.2        cli_3.6.2           knitr_1.46         
##  [4] rlang_1.1.3         xfun_0.43           jsonlite_1.8.8     
##  [7] htmltools_0.5.8.1   sass_0.4.9          rmarkdown_2.26     
## [10] grid_4.4.0          evaluate_0.23       jquerylib_0.1.4    
## [13] fastmap_1.1.1       Rhdf5lib_1.26.0     yaml_2.3.8         
## [16] lifecycle_1.0.4     bookdown_0.39       BiocManager_1.30.22
## [19] compiler_4.4.0      Rcpp_1.0.12         rhdf5filters_1.16.0
## [22] XVector_0.44.0      lattice_0.22-6      digest_0.6.35      
## [25] R6_2.5.1            bslib_0.7.0         tools_4.4.0        
## [28] zlibbioc_1.50.0     cachem_1.0.8