Mike L. Smith
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The Zarr specification defines a format for chunked, compressed, N-dimensional arrays. It’s design allows efficient access to subsets of the stored array, and supports both local and cloud storage systems. Zarr is experiencing increasing adoption in a number of scientific fields, where multi-dimensional data are prevalent.
Rarr is intended to be a simple interface to reading and writing individual Zarr arrays. It is developed in R and C with no reliance on external libraries or APIs for interfacing with the Zarr arrays. Additional compression libraries (e.g. blosc) are bundled with Rarr to provide support for datasets compressed using these tools.
If you know about Zarr arrays already, you’ll probably be aware they can be stored in hierarchical groups, where additional meta data can explain the relationship between the arrays. Currently, Rarr is not designed to be aware of these hierarchical Zarr array collections. However, the component arrays can be read individually by providing the path to them directly.
Currently, there are also limitations on the Zarr datatypes that can be accessed using Rarr. For now most numeric types can be read into R, although in some instances e.g. 64-bit integers there is potential for loss of information. Writing is more limited with support only for datatypes that are supported natively in R and only using the column-first representation.
If you want to quickly get started reading an existing Zarr array with the package, this section should have the essentials covered. First, we need to install Rarr1 with the commands below.
## we need BiocManager to perform the installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
## install Rarr
BiocManager::install("Rarr")Once Rarr is installed, we have to load it into our R session:
library(Rarr)Rarr can be used to read files either on local disk or on remote S3 storage systems. First lets take a look at reading from a local file.
To demonstrate reading a local file, we’ll pick the example file containing 32-bit integers arranged in the “column first” ordering.
zarr_example <- system.file(
"extdata", "zarr_examples", "column-first", "int32.zarr",
package = "Rarr"
)We can get an summary of the array properties, such as its shape and
datatype, using the function zarr_overview()2.
zarr_overview(zarr_example)## Type: Array
## Path: /mnt/data/R-lib/4.5-bioc_3.21/Rarr/extdata/zarr_examples/column-first/int32.zarr
## Shape: 30 x 20 x 10
## Chunk Shape: 10 x 10 x 5
## No. of Chunks: 12 (3 x 2 x 2)
## Data Type: int32
## Endianness: little
## Compressor: blosc
You can use this to check that the location is a valid Zarr array, and
that the shape and datatype of the array content are what you are
expecting. For example, we can see in the output above that the data
type (int32) corresponds to what we expect.
The summary information retrieved above is required, as to read the
array with Rarr you need to know the shape and size of the array
(unless you want to read the entire array). From the previous output we
can see our example array has three dimensions of size 30 x 20 x 10. We
can select the subset we want to extract using a list. The list must
have the same length as the number of dimensions in our array, with each
element of the list corresponding to the indices you want to extract in
that dimension.
index <- list(1:4, 1:2, 1)We then extract the subset using read_zarr_array():
read_zarr_array(zarr_example, index = index)## , , 1
##
## [,1] [,2]
## [1,] 1 2
## [2,] 1 0
## [3,] 1 0
## [4,] 1 0
Reading files in S3 storage works in a very similar fashion to local
disk. This time the path needs to be a URL to the Zarr array. We can
again use zarr_overview() to quickly retrieve the array metadata.
s3_address <- "https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0076A/10501752.zarr/0"
zarr_overview(s3_address)## Type: Array
## Path: https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0076A/10501752.zarr/0/
## Shape: 50 x 494 x 464
## Chunk Shape: 1 x 494 x 464
## No. of Chunks: 50 (50 x 1 x 1)
## Data Type: float64
## Endianness: little
## Compressor: blosc
The output above indicates that the array is stored in 50 chunks, each
containing a slice of the overall data. In the example below we use the
index argument to extract the first and tenth slices from the array.
Choosing to read only 2 of the 50 slices is much faster than if we opted
to download the entire array before accessing the data.
z2 <- read_zarr_array(s3_address, index = list(c(1, 10), NULL, NULL))We then plot our two slices on top of one another using the image()
function.
## plot the first slice in blue
image(log2(z2[1, , ]),
col = hsv(h = 0.6, v = 1, s = 1, alpha = 0:100 / 100),
asp = dim(z2)[2] / dim(z2)[3], axes = FALSE
)
## overlay the tenth slice in green
image(log2(z2[2, , ]),
col = hsv(h = 0.3, v = 1, s = 1, alpha = 0:100 / 100),
asp = dim(z2)[2] / dim(z2)[3], axes = FALSE, add = TRUE
)
Note: if you receive the error message
"Error in stop(aws_error(request$error)) : bad error message" it is
likely you have some AWS credentials available in to your R session,
which are being inappropriately used to access this public bucket.
Please see the section @ref(s3-client) for details on how to set
credentials for a specific request.
Up until now we’ve only covered reading existing Zarr array into R. However, Rarr can also be used to write R data to disk following the Zarr specification. To explore this, lets create an example array we want to save as a Zarr. In this case it’s going to be a three dimensional array and store the values 1 to 600.
x <- array(1:600, dim = c(10, 10, 6))path_to_new_zarr <- file.path(tempdir(), "new.zarr")
write_zarr_array(x = x, zarr_array_path = path_to_new_zarr, chunk_dim = c(10, 5, 1))We can check that the contents of the Zarr array is what we’re
expecting. Since the contents of the whole array will be too large to
display here, we use the index argument to extract rows 6 to 10, from
the 10th column and 1st slice. That should be the values 96, 97, 98, 99,
100, but retaining the 3-dimensional array structure of the original
array. The second line below uses identical() to confirm that reading
the whole Zarr returns something equivalent to our original input x.
read_zarr_array(zarr_array_path = path_to_new_zarr, index = list(6:10, 10, 1))## , , 1
##
## [,1]
## [1,] 96
## [2,] 97
## [3,] 98
## [4,] 99
## [5,] 100
identical(read_zarr_array(zarr_array_path = path_to_new_zarr), x)## [1] TRUE
Reading Zarr arrays is reasonably well supported. Writing is available, but is more limited. Both aspects are under active development.
Currently there is only support for reading and writing a subset of the possible datatypes that can be found in a Zarr array. In some instances there are also limitations on the datatypes natively supported by R, requiring conversion from the Zarr datatype. The table below summarises the current status of datatype support. It will be updated as progress is made.
| Zarr Data Type | Status (reading / writing) |
Notes |
|---|---|---|
boolean |
✔ / ❌ | |
int8 |
✔ / ❌ | |
uint8 |
✔ / ❌ | |
int16 |
✔ / ❌ | |
uint16 |
✔ / ❌ | |
int32 |
✔ / ✔ | |
uint32 |
✔ / ❌ | Values outside the range of int32 are converted to NA. Future plan is to allow conversion to double or use the bit64 package. |
int64 |
✔ / ❌ | Values outside the range of int32 are converted to NA. Future plan is to allow conversion to double or use the bit64 package. |
uint64 |
✔ / ❌ | Values outside the range of int32 are converted to NA. Future plan is to allow conversion to double or use the bit64 package. |
half / float16 |
✔ / ❌ | Converted to double in R. No effort is made to assess loss of precision due to conversion. |
single / float32 |
✔ / ❌ | Converted to double in R. No effort is made to assess loss of precision due to conversion. |
double / float64 |
✔ / ✔ | |
complex |
❌ / ❌ | |
timedelta |
❌ / ❌ | |
datetime |
❌ / ❌ | |
string |
✔ / ✔ | |
Unicode |
✔ / ✔ | |
void * |
❌ / ❌ | |
| Structured data types | ❌ / ❌ |
| Data Type | Status (reading / writing) |
Notes |
|---|---|---|
zlib / gzip |
✔ / ✔ | Only system default compression level (normally 6) is enabled for writing. |
bzip2 |
✔ / ✔ | Only compression level 9 is enabled for writing. |
blosc |
✔ / ✔ | Only lz4 compression level 5 is enabled for writing. |
LZMA |
✔ / ✔ | |
LZ4 |
✔ / ✔ | |
Zstd |
✔ / ✔ |
Please open an issue if support for a required compression tool is missing.
The is currently no support for additional filters. Please open an issue if you require filter support.
To provide support for BLOSC and zstd compression tools Rarr links against libraries providing these tools. If you have them installed on your system Rarr will attempt to use those versions. If they are not detected then Rarr will compile and use versions that are distributed with the package. Either way the functionality will available, however if you are using the system libraries and then later remove them Rarr may fail to work correctly.
This only concerns users installing the package from source. If you are using the pre-built binaries for Windows or Mac OSX distributed by Bioconductor then this should not be an issue for you.