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# pizzarr <a href="https://zarr.dev/pizzarr/"><img src="man/figures/logo.png" align="right" height="139" alt="pizzarr website" /></a>

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[![r-universe](https://zarr-developers.r-universe.dev/badges/pizzarr)](https://zarr-developers.r-universe.dev/pizzarr)

A Zarr implementation for R.

## Installation

pizzarr ships in two tiers — same version, different builds. The CRAN build
is pure R and needs nothing beyond base R dependencies. The
[r-universe](https://zarr-developers.r-universe.dev/pizzarr) build links in
the [zarrs](https://github.com/zarrs/zarrs) Rust crate via
[extendr](https://cran.r-project.org/package=rextendr), adding parallel decompression
and additional codec support.

### CRAN (pure R)

```r
install.packages("pizzarr")
```

### r-universe (zarrs backend)

```r
install.packages("pizzarr",
                 repos = c("https://zarr-developers.r-universe.dev",
                            "https://cloud.r-project.org"))
```

Both sources serve the same release version. The r-universe build distributes
pre-compiled binaries for Windows and macOS — no Rust toolchain needed.

The r-universe binaries compile the zarrs backend with local filesystem I/O,
synchronous HTTP reads, gzip/zstd/blosc codecs, sharding, and S3/GCS cloud
store support via `object_store`. Any build that compiles Rust enables these
features by default. To compile with a different feature set, set
`PIZZARR_FEATURES` explicitly (see below).

`pizzarr_compiled_features()` lists what the zarrs backend provides, and
`pizzarr_upgrade()` prints the install command when zarrs is not compiled in.
`pizzarr_config()` controls concurrency settings (thread pool size, codec
concurrency, HTTP range request batching).
See `vignette("zarrs-backend")` for details.

### Building from source (zarrs backend)

If you need to compile the zarrs backend yourself — either because no binary
is available for your platform, or because you are developing the Rust side —
you need rustc >= 1.91. On Windows, add the GNU target:

```bash
rustup target add x86_64-pc-windows-gnu
```

The build system uses two environment variables to control compilation:

- **`DEBUG`** — set to any non-empty value for a debug build. This enables
  Cargo's incremental compilation, so subsequent rebuilds are fast (seconds
  instead of minutes). The resulting library is slower at runtime because it
  skips link-time optimization and does not strip symbols. Use this during
  active development on the Rust code.
- **`PIZZARR_FEATURES`** — a comma-separated list of extra Cargo features.
  When unset, the build defaults to `s3,gcs` (S3 and GCS cloud store support
  via `object_store` and `tokio`, which also pulls in blosc and sharding).
  Set this explicitly to override — e.g., `PIZZARR_FEATURES=none` for Cargo's
  default features only, or `PIZZARR_FEATURES=s3` for S3 without GCS.

A release build (no `DEBUG`) uses LTO, single codegen unit, and symbol
stripping — fast at runtime but takes several minutes to compile from scratch.

```bash
# Development: fast rebuilds, slower runtime, S3/GCS included
DEBUG=1 R CMD INSTALL .

# Production: slow first build, fast runtime, S3/GCS included
R CMD INSTALL .

# Production with Cargo default features only (no cloud stores)
PIZZARR_FEATURES=none R CMD INSTALL .
```

`NOT_CRAN=true` opts builds out of the offline-vendored-crates path used for
CRAN; it's set automatically when `DEBUG` is present and only matters when
`src/rust/vendor.tar.xz` is also present.

Development happens on the `develop` branch. See
[CONTRIBUTING.md](https://github.com/zarr-developers/pizzarr/blob/main/CONTRIBUTING.md)
for the branching and release model.

## Usage


``` r
library(pizzarr)

# Open a sample BCSD climate dataset (Zarr V3)
v3_root <- pizzarr_sample("bcsd_v3")
v3 <- zarr_open(v3_root)

# Print the group summary
v3
#> <ZarrGroup> /
#>   Store type  : DirectoryStore
#>   Zarr format : 3
#>   Read-only   : FALSE
#>   No. members : 5

# View the hierarchy
v3$tree()
#> / 
#> ├── latitude (33) <f4
#> ├── longitude (81) <f4
#> ├── pr (12, 33, 81) <f4
#> ├── tas (12, 33, 81) <f4
#> └── time (12) <f8

# Inspect an array
v3$get_item("pr")
#> <ZarrArray> /pr
#>   Shape       : (12, 33, 81)
#>   Chunks      : (12, 33, 81)
#>   Data type   : <f4
#>   Fill value  : 1.00000002004088e+20
#>   Order       : C
#>   Read-only   : FALSE
#>   Compressor  : ZstdCodec
#>   Store type  : DirectoryStore
#>   Zarr format : 3

# Read a slice: first 3 time steps, first 3 latitudes, first longitude
v3$get_item("pr")$get_item(list(slice(1, 3), slice(1, 3), 1))$data
#> , , 1
#> 
#>        [,1]   [,2]   [,3]
#> [1,] 133.97 144.51 149.92
#> [2,]  75.40  72.38  68.62
#> [3,]  93.14  91.24  89.37
```

Create an array from scratch:


``` r
a <- array(data = 1:20, dim = c(2, 10))
z <- zarr_create(shape = dim(a), dtype = "<f4", fill_value = NA)
z$set_item("...", a)
```


``` r
z
#> <ZarrArray> /
#>   Shape       : (2, 10)
#>   Chunks      : (2, 10)
#>   Data type   : <f4
#>   Fill value  : 0
#>   Order       : F
#>   Read-only   : FALSE
#>   Compressor  : ZstdCodec
#>   Store type  : MemoryStore
#>   Zarr format : 2
z$get_item(list(slice(1, 2), slice(1, 5)))$data
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    3    5    7    9
#> [2,]    2    4    6    8   10
```

## Remote and cloud data

Zarr stores served over HTTPS need no configuration, and this route works on
both install tiers:

```r
# USGS gridMET on an Open Storage Network pod
store <- HttpStore$new("https://usgs.osn.mghpcc.org/mdmf/gdp/gridMET.zarr")
g <- zarr_open(store)
store$listdir()
```

The same bucket over the S3 API, which needs the r-universe build:

```r
Sys.setenv(AWS_ENDPOINT = "https://usgs.osn.mghpcc.org")

zarrs_get_subset("s3://mdmf/gdp/gridMET.zarr", "lat", list(c(0L, 5L)), NULL)
```

Coming from `xarray` or `fsspec`, the settings you would pass in
`storage_options` — `endpoint_url`, `key`, `secret`, `anon` — are environment
variables here: `AWS_ENDPOINT`, `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`,
and unsigned-by-default when no credentials are set. Alternate endpoints
(MinIO, Ceph, Wasabi, R2) work the same way. See `vignette("remote-stores")`
for the full mapping, credentials, and the current limits — cloud stores are
read-only.

## Features

- **Zarr V2 and V3** read and write (format auto-detected on open)
- **Stores:** MemoryStore, DirectoryStore (read/write); HttpStore (read-only);
  S3Store, GcsStore (r-universe tier, via `object_store`)
- **Data types:** boolean, int8--int64, uint8--uint64, float16/32/64, string, Unicode, VLenUTF8
- **Compression:** zlib/gzip, bzip2, blosc, LZMA, LZ4, Zstd
- **Blosc** requires the optional [`blosc`](https://cran.r-project.org/package=blosc) package (`install.packages("blosc")`)

## How It Works

pizzarr uses [R6](https://r6.r-lib.org/) classes mirroring the
[zarr-python](https://github.com/zarr-developers/zarr-python) object model:

- **Store** --- backend storage (`DirectoryStore` for local files,
  `MemoryStore` for in-memory, `HttpStore` for remote read-only,
  `S3Store` and `GcsStore` for cloud buckets on the r-universe tier)
- **ZarrGroup** --- hierarchical container holding arrays and sub-groups
  (like a directory)
- **ZarrArray** --- chunked, compressed N-dimensional array (like a file)
- **Codec** --- compression/decompression (zlib, zstd, blosc, lz4, etc.)
- **Dtype** --- data-type mapping between R and Zarr

Data flows through the stack: a **Store** holds raw chunk bytes, a **Codec**
pipeline compresses and decompresses them, and **ZarrArray** presents typed
N-dimensional data to R. Groups and arrays are addressed by path within a
store, just like files in a directory tree.

See `vignette("pizzarr")` for a full walkthrough.

## Ecosystem

pizzarr implements the [Zarr specification](https://zarr-specs.readthedocs.io/)
(V2 and V3) for R. Related projects:

- [zarr-python](https://github.com/zarr-developers/zarr-python) --- the
  reference Python implementation
- [zarr.js](https://github.com/gzuidhof/zarr.js) --- JavaScript implementation
- [zarr](https://cran.r-project.org/package=zarr) --- native R V3
  implementation (CRAN)
- [Rarr](https://bioconductor.org/packages/Rarr/) --- Bioconductor package for
  reading and writing individual Zarr arrays (V2, limited write support)
- [zarr-conformance-tests](https://github.com/Bisaloo/zarr-conformance-tests)
  --- cross-implementation validation

## Validation with zarr-python

A standalone integration test cross-validates that pizzarr and zarr-python
produce equivalent Zarr stores. Both implementations write the same arrays
(V2 and V3 formats, multiple dtypes, codecs, chunk layouts, and groups with
attributes), then each reads the other's output and verifies the data matches.

**Prerequisites:** Python 3.10+ with `zarr>=3` and `numpy` installed.

```bash
Rscript inst/extdata/cross-validate.R
```

The script skips gracefully (exit 0) if Python is not available. On success
all checks pass and exit code is 0; any mismatch is reported and exits 1.

## Zarr Conformance Tests

pizzarr participates in the [zarr-conformance-tests](https://github.com/Bisaloo/zarr-conformance-tests)
framework, which validates that Zarr implementations can correctly read
standard test arrays (V2 and V3 formats, multiple dtypes). These tests run
automatically in CI on every push and pull request to `main`.

## Performance

The zarrs backend (r-universe install only) handles chunk I/O and codec execution in Rust, bypassing
R's single-threaded chunk loop entirely. For compressed data --- the common
case with real Zarr stores --- this translates to 6--8x throughput over pure-R.

The table below compares pizzarr's two tiers against zarr-python/xarray on
a 500x500x100 float64 array (~200 MB uncompressed) with 100x100x50 chunks.
All numbers are best-of-three throughput in MB/s, measured on Windows
(pizzarr 0.2.0-dev, zarr-python 3.1.5, April 2026).

| Scenario | pizzarr zarrs | pizzarr R-native | xarray |
|---|---|---|---|
| read_all (gzip) | 253 MB/s | 39 MB/s | 545 MB/s |
| read_all (none) | 247 MB/s | 39 MB/s | 1811 MB/s |
| read_subset (gzip) | **200 MB/s** | 38 MB/s | 138 MB/s |
| read_subset (none) | 229 MB/s | 38 MB/s | 704 MB/s |
| write_all (gzip) | 97 MB/s | 12 MB/s | 143 MB/s |
| write_all (none) | 286 MB/s | 144 MB/s | 477 MB/s |

xarray leads on bulk operations because numpy can transpose array memory
layout via stride views (zero copy) --- R's `array()` always copies. The
gap narrows with compression since decompression dominates, and zarrs wins
on cross-chunk subset reads (200 vs 138 MB/s gzip) because it decodes
only the chunks that overlap the selection.

Reproduce with `Rscript inst/extdata/benchmark-zarrs-vs-xarray.R` (requires
Python with `zarr>=3` and `xarray` for the xarray column).

## Contributing

See [CONTRIBUTING.md](https://github.com/zarr-developers/pizzarr/blob/main/CONTRIBUTING.md) for development setup, testing, and
documentation build instructions.
