.. _spec_v1:

Zarr Storage Specification Version 1
====================================

This document provides a technical specification of the protocol and
format used for storing a Zarr array. The key words "MUST", "MUST
NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT",
"RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be
interpreted as described in `RFC 2119
<https://www.ietf.org/rfc/rfc2119.txt>`_.

Status
------

This specification is deprecated. See :ref:`spec` for the latest version.

Storage
-------

A Zarr array can be stored in any storage system that provides a
key/value interface, where a key is an ASCII string and a value is an
arbitrary sequence of bytes, and the supported operations are read
(get the sequence of bytes associated with a given key), write (set
the sequence of bytes associated with a given key) and delete (remove
a key/value pair).

For example, a directory in a file system can provide this interface,
where keys are file names, values are file contents, and files can be
read, written or deleted via the operating system. Equally, an S3
bucket can provide this interface, where keys are resource names,
values are resource contents, and resources can be read, written or
deleted via HTTP.

Below an "array store" refers to any system implementing this
interface.

Metadata
--------

Each array requires essential configuration metadata to be stored,
enabling correct interpretation of the stored data. This metadata is
encoded using JSON and stored as the value of the 'meta' key within an
array store.

The metadata resource is a JSON object. The following keys MUST be
present within the object:

zarr_format
    An integer defining the version of the storage specification to which the
    array store adheres.
shape
    A list of integers defining the length of each dimension of the array.
chunks
    A list of integers defining the length of each dimension of a chunk of the
    array. Note that all chunks within a Zarr array have the same shape.
dtype
    A string or list defining a valid data type for the array. See also
    the subsection below on data type encoding.
compression
    A string identifying the primary compression library used to compress
    each chunk of the array.
compression_opts
    An integer, string or dictionary providing options to the primary
    compression library.
fill_value
    A scalar value providing the default value to use for uninitialized
    portions of the array.
order
    Either 'C' or 'F', defining the layout of bytes within each chunk of the
    array. 'C' means row-major order, i.e., the last dimension varies fastest;
    'F' means column-major order, i.e., the first dimension varies fastest.

Other keys MAY be present within the metadata object however they MUST
NOT alter the interpretation of the required fields defined above.

For example, the JSON object below defines a 2-dimensional array of
64-bit little-endian floating point numbers with 10000 rows and 10000
columns, divided into chunks of 1000 rows and 1000 columns (so there
will be 100 chunks in total arranged in a 10 by 10 grid). Within each
chunk the data are laid out in C contiguous order, and each chunk is
compressed using the Blosc compression library::

    {
        "chunks": [
            1000,
            1000
        ],
        "compression": "blosc",
        "compression_opts": {
            "clevel": 5,
            "cname": "lz4",
            "shuffle": 1
        },
        "dtype": "<f8",
        "fill_value": null,
        "order": "C",
        "shape": [
            10000,
            10000
        ],
        "zarr_format": 1
    }

Data type encoding
~~~~~~~~~~~~~~~~~~

Simple data types are encoded within the array metadata resource as a
string, following the `NumPy array protocol type string (typestr)
format
<numpy:arrays.interface>`_. The
format consists of 3 parts: a character describing the byteorder of
the data (``<``: little-endian, ``>``: big-endian, ``|``:
not-relevant), a character code giving the basic type of the array,
and an integer providing the number of bytes the type uses. The byte
order MUST be specified. E.g., ``"<f8"``, ``">i4"``, ``"|b1"`` and
``"|S12"`` are valid data types.

Structure data types (i.e., with multiple named fields) are encoded as
a list of two-element lists, following `NumPy array protocol type
descriptions (descr)
<numpy:arrays.interface>`_.
For example, the JSON list ``[["r", "|u1"], ["g", "|u1"], ["b",
"|u1"]]`` defines a data type composed of three single-byte unsigned
integers labelled 'r', 'g' and 'b'.

Chunks
------

Each chunk of the array is compressed by passing the raw bytes for the
chunk through the primary compression library to obtain a new sequence
of bytes comprising the compressed chunk data. No header is added to
the compressed bytes or any other modification made. The internal
structure of the compressed bytes will depend on which primary
compressor was used. For example, the `Blosc compressor
<https://github.com/Blosc/c-blosc/blob/main/README_HEADER.rst>`_
produces a sequence of bytes that begins with a 16-byte header
followed by compressed data.

The compressed sequence of bytes for each chunk is stored under a key
formed from the index of the chunk within the grid of chunks
representing the array. To form a string key for a chunk, the indices
are converted to strings and concatenated with the period character
('.') separating each index. For example, given an array with shape
(10000, 10000) and chunk shape (1000, 1000) there will be 100 chunks
laid out in a 10 by 10 grid. The chunk with indices (0, 0) provides
data for rows 0-999 and columns 0-999 and is stored under the key
'0.0'; the chunk with indices (2, 4) provides data for rows 2000-2999
and columns 4000-4999 and is stored under the key '2.4'; etc.

There is no need for all chunks to be present within an array
store. If a chunk is not present then it is considered to be in an
uninitialized state.  An uninitialized chunk MUST be treated as if it
was uniformly filled with the value of the 'fill_value' field in the
array metadata. If the 'fill_value' field is ``null`` then the
contents of the chunk are undefined.

Note that all chunks in an array have the same shape. If the length of
any array dimension is not exactly divisible by the length of the
corresponding chunk dimension then some chunks will overhang the edge
of the array. The contents of any chunk region falling outside the
array are undefined.

Attributes
----------

Each array can also be associated with custom attributes, which are
simple key/value items with application-specific meaning. Custom
attributes are encoded as a JSON object and stored under the 'attrs'
key within an array store. Even if the attributes are empty, the
'attrs' key MUST be present within an array store.

For example, the JSON object below encodes three attributes named
'foo', 'bar' and 'baz'::

    {
        "foo": 42,
        "bar": "apples",
        "baz": [1, 2, 3, 4]
    }

Example
-------

Below is an example of storing a Zarr array, using a directory on the
local file system as storage.

Initialize the store::

    >>> import zarr
    >>> store = zarr.DirectoryStore('example.zarr')
    >>> zarr.init_store(store, shape=(20, 20), chunks=(10, 10),
    ...                 dtype='i4', fill_value=42, compression='zlib',
    ...                 compression_opts=1, overwrite=True)

No chunks are initialized yet, so only the 'meta' and 'attrs' keys
have been set::

    >>> import os
    >>> sorted(os.listdir('example.zarr'))
    ['attrs', 'meta']

Inspect the array metadata::

    >>> print(open('example.zarr/meta').read())
    {
        "chunks": [
            10,
            10
        ],
        "compression": "zlib",
        "compression_opts": 1,
        "dtype": "<i4",
        "fill_value": 42,
        "order": "C",
        "shape": [
            20,
            20
        ],
        "zarr_format": 1
    }

Inspect the array attributes::

    >>> print(open('example.zarr/attrs').read())
    {}

Set some data::

    >>> z = zarr.Array(store)
    >>> z[0:10, 0:10] = 1
    >>> sorted(os.listdir('example.zarr'))
    ['0.0', 'attrs', 'meta']

Set some more data::

    >>> z[0:10, 10:20] = 2
    >>> z[10:20, :] = 3
    >>> sorted(os.listdir('example.zarr'))
    ['0.0', '0.1', '1.0', '1.1', 'attrs', 'meta']

Manually decompress a single chunk for illustration::

    >>> import zlib
    >>> b = zlib.decompress(open('example.zarr/0.0', 'rb').read())
    >>> import numpy as np
    >>> a = np.frombuffer(b, dtype='<i4')
    >>> a
    array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
           1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)

Modify the array attributes::

    >>> z.attrs['foo'] = 42
    >>> z.attrs['bar'] = 'apples'
    >>> z.attrs['baz'] = [1, 2, 3, 4]
    >>> print(open('example.zarr/attrs').read())
    {
        "bar": "apples",
        "baz": [
            1,
            2,
            3,
            4
        ],
        "foo": 42
    }
