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ZEP 1 - Zarr specification version 3


  • Alistair Miles (@alimanfoo), Wellcome Sanger Institute

  • Jonathan Striebel (@jstriebel), Scalable Minds

  • Jeremy Maitin-Shepard (@jbms), Google

Status: Draft

Type: Specification

Created: 2022-05-23


This ZEP proposes a new major version of the core specification which defines how to store and retrieve Zarr data. The ZEP also proposes a new framework for modularising specifications of codecs, storage systems and extensions.

Motivation and Scope

The Zarr specification version 2 [ZARR2SPEC] (hereafter “Zarr v2”) has been widely adopted and implemented in several programming languages. It has enabled the use of cloud and distributed computing to process a variety of large and challenging datasets, particularly in the scientific domain. It has also provided a vehicle for experimentation and innovation in the field of data storage for high-performance distributed computing.

As usage of Zarr has grown and broadened, several limitations of Zarr v2 have surfaced. These include:

  • Interoperability. Zarr v2 has been implemented in Python, C, C++, Julia, Java and JavaScript. However, there is not feature parity across all implementations. Implementations are also not currently available for some important languages, such as R. This is in part because the Zarr v2 spec was originally developed together with the Python implementation, which itself leans heavily on the use of NumPy concepts and machinery. A more language-agnostic approach to the core specification, together with some slimming down of the specification, would help to achieve complete implementations with full interoperobility across major programming languages.

  • Extensibility. Zarr is increasingly being used for storage of very large and keystone scientific datasets. If this trend continues, interoperability and stability of the Zarr ecosystem will be vital, to allow these datasets to be utilised to their full potential. At the same time, large-scale array data storage is a very active area of innovation, and there remain many opportunities to improve performance, scalability and resilience for I/O-bound high-performance distributed computing. To balance these demands for stability and innovation, some framework for community exploration and development of Zarr extensions is needed, so that innovation can happen in a coordinated way with predictable consequences and behaviour of implementations which may not support all extensions.

  • High-latency storage. Zarr v2 was originally developed to support local file system storage. Because of this, the design of Zarr v2 implicitly made assumptions about the performance characteristics of the underlying storage technology, such as low latency for storage operations to list directories and check existence of files. Fortunately, certain features of Zarr v2 still enabled other storage systems to be used, such as cloud object stores, and this ability to utilise a variety of different underlying storage systems has been very valuable across a range of use cases. However, performance of certain operations can degrade significantly with some storage technologies, particularly systems with relatively high latency per operation, such as cloud object stores. This limitation has been worked around through the use of consolidated metadata, but this workaround introduces other limitations, such as additional complexity when adding or modifying data.

  • Storage layout flexibility. Zarr v2 adopts a simple storage model where each chunk is encoded and then written to a single storage object. This simplicity makes implementation easier, but presents some practical challenges. For example, for large datasets this can generate a very large number of storage objects, which does not work well for certain storage technologies. Also, this forces one retrieval operation per chunk, which can be suboptimal where there are many chunks that are often read together, and/or where different access patterns need to be accommodated.

These limitations have motivated the proposal described here to define a new major version of the Zarr core specification, together with a new modular specification framework.

Normally, it is better to introduce changes in small pieces, allowing each to be evaluated and discussed in isolation. However, addressing the limitations described above could not be done without introducing backwards-incompatible changes into the core specification, and thus multiple such changes are combined here, in an attempt to minimise the overall disruption to the ecosystem and the community.

Usage and Impact

This ZEP and the associated specifications have the following intended outcomes:

  1. Facilitate implementation of a core specification with feature parity and full interoperability across all major programming languages.

  2. Accelerate innovation by facilitating the exploration, development, implementation and evaluation of Zarr systems with support for novel encoding technologies, storage technologies and features by a broader community.

  3. Ensure reasonable performance characteristics of all Zarr implementations across a variety of different underlying storage technologies, including storage with high latency per operation.

  4. Improve the performance characteristics of Zarr implementations for data with a very large number of chunks and/or with a variety of common access patterns.

Adopting this ZEP would have the following disruptive impacts on the members of the Zarr community:

  • Effort and coordination would be required to implement the specifications in all current Zarr software libraries and verify interoperability.

  • Effort would be required by data producers to migrate to generating data conformant with the new specifications.

  • Some effort may be required by users of Zarr data and/or Zarr software libraries to learn about and understand the differences in available features and/or how certain features are accessed.

Backward Compatibility

This ZEP introduces a Zarr core specification version 3, which is backwards-incompatible with the Zarr version 2 specification [ZARR2SPEC].

Implementations of the Zarr version 3 core specification are not required to be able to read or write data conforming to the Zarr version 2 specification, and vice versa.

Detailed description

Below is a summary of some of the key features of the proposed new specification and specification framework. Please see the specification drafts for further information, linked below in the specifications section.


This ZEP proposes a modular specification framework, with the following components:

  • Zarr core specification – This specification is the starting point for any Zarr implementation. It defines in an abstract way a format for storing N-dimensional array data.

  • Zarr extension specifications – This is an open-ended set of specifications which add new features to and/or modify the Zarr format in some way. There is a further breakdown of different extension types, described in more detail below.

  • Zarr codec specifications – This is an open-ended set of specifications, each of which defines a protocol for encoding and decoding Zarr chunk data.

  • Zarr store specifications – This is an open-ended set of specifications, each of which defines a mapping from the abstract store API defined in the Zarr core specification to a set of concrete operations for a specific storage technology, such as a POSIX file system or cloud object storage.

The primary goal of this modularity is to allow specifications of new extensions, codecs and stores to be added over time, without needing any change to the core specification.

Note that the core specification allows for decentralised publishing of extension, codec and store specifications. In other words, any individual or organisation may publish such a specification. It is hoped that the majority of such specifications will be published on the zarr-specs website after a review process by the Zarr community, thus providing a common point of discovery and coordination across the community. However, we do not want to obstruct individuals or organisation who need to innovate or experiment or who have specialised needs which are better served by managing and publishing specifications in a different way.

Note that we also anticipate that user groups from different domains may wish to publish usage convention specifications, by which we mean specifications that define conventions for the use of Zarr for a particular type of data, such as restrictions and expectations regarding the groups, arrays and attributes that will be found within a Zarr dataset. How these usage convention specifications will be managed, published and used is out of scope for this ZEP.

A draft of the Zarr version 3 core specification is available at the following URL:


Note publication of the draft specification on the zarr-specs website preceded establishiment of the ZEP process, and does not imply acceptance of this ZEP. The specification should be considered a proposal at this time, pending acceptance of this ZEP.

To illustrate the principle of the other specification types, the following specifications have also been published on the zarr-specs website:


This ZEP proposes a number of mechanisms by which the Zarr core protocol can be extended.

In general, there are two different kinds of extensions.

An extension may add new features to Zarr, in a way that does not invalidate or break any processing of Zarr data to which the extension applies by systems which do not implement the extension. In other words, the use of the extension may be safely ignored by systems which implement only the core specification.

Alternatively, an extension may modify or override the Zarr core specification in some way, such that certain processing operations must be aware of and implement the extension, otherwise data will become corrupted or Zarr implementations will encounter unexpected errors. In other words, the use of such an extension must not be ignored by systems which implement only the core specification.

If a Zarr system does not implement a given extension, the Zarr core specification describes the situations under which the extension can be ignored and processing can proceed, and conversely where the extension cannot be ignored and processing must terminate.

A further grouping of different kinds of extension is defined:

  • Generic extensions – These are extensions which apply to the processing of any data within a Zarr hierarchy.

  • Array extensions – These are extensions which apply to processing of an array within a Zarr hierarchy.

  • Data type extensions – These are extensions which define a new data type for array items, where the data type is not included in the set of core data types.

  • Chunk grid extensions – These are extensions which define a new way of dividing array items into chunks.

  • Chunk memory layout extensions – These are extensions which define a new way of organising the data from an array chunk into a contiguous sequence of bytes.

  • Storage transformer extensions – These are extensions which define a new storage transformation.

For each of these kinds of extensions, the core specification defines a mechanism by which the extension is declared in the metadata associated with a Zarr hierarchy or array, such that its use can be discovered by a Zarr implementation, without requiring any out-of-band communication. In other words, a system or user reading Zarr data should not need to “know” anything about the extensions that were used when the data were created, the data are fully self-describing.

Scope reduction

In order to facilitate implementation of the core specification across different programming languages, the scope of the specification has been reduced relative to Zarr v2. Note in particular the following:

  • The set of core data types is reduced relative to Zarr v2. Only fixed size integer and floating point data types are defined, in addition to a Boolean data type and a fixed length raw data type. Any data types relating to storage of textual data have been left for definition within an extension specification. The “object” data type, which was never included within the Zarr v2 spec but which was implemented in the Python implementation, is also not included – it is suggested that a better approach be found for supporting variable length data types, and defined within a data type extension specification.

  • Support for filters has been removed from the core specification. It is proposed this be covered in an extension specification.

Accommodating high-latency storage

With Zarr v2, certain operations that require access to metadata can be pathologically slow when running against data stored in a high-latency storage system such as a cloud object store. These include navigating the structure of a Zarr hierarchy, such as obtaining a list of the child nodes of any given node, or generating a textual representation of an entire hierarchy (e.g., as generated by the tree() function in the Zarr Python implementation). These operations are often required because it is a common use case for user to be interactively exploring the content of a Zarr hierarchy with which they are unfamiliar, and so poor performance seriously affects usability.

A workaround for this with Zarr v2 has been the addition of consolidated metadata. This involves packaging all metadata for a hierarchy into a single storage object, which can then be read once. This is a workable solution in many situations, where data are written once and then read many times. However, it introduces some additional complexity for data managers, who need to ensure that consolidated metadata is generated, and then kept in sync with group and array metadata if there are any subsequent changes.

The Zarr version 3 core specification redesigns the organisation of data and metadata objects within a Zarr store, in order to better accommodate high latency stores. While this does not completely remove the need for consolidated metadata in all current situations where that is used, it does vastly improve the performance of simple operations to explore the structure and content of a Zarr hierarchy.

The new design leverages the fact that, although listing of objects in cloud object stores is still much slower than listing of files in a local file system, all major cloud stores support the ability to list objects with a given prefix, which can be used to reduce the size of the result set and usually returns much faster than listing an entire bucket. It also provides an opportunity to better leverage the support for emulating a directory listing, via providing both a “prefix” and a “delimiter” parameter.

The new design separates the metadata and chunk data storage objects via a different key prefix. All metadata objects have the key prefix “/meta” and all chunk data objects have the key prefix “/data”. This allows a faster listing of all metadata keys within a given store. The list of metadata keys is then sufficient to provide some useful information to a user regarding the contents and structure of a Zarr hierarchy.

The new design also changes the key suffix for metadata objects. For example, for an array with hierarchy path “/foo/bar”, the key for the corresponding metadata object in Zarr v2 would be “/foo/bar/.zarray”. In Zarr v3 this becomes “/meta/root/foo/bar.array.json”. This change means that all the names and types (array or group) of children of the “/foo” group can be discovered and listed via a single storage operation to list the objects with prefix “/meta/root/foo” and delimiter “/”.

Storage transformers

In Zarr version 2, every transformation of the storage layout would need to be reflected in the core indexing implementation or as a separate storage. The first option needs changes in the core implementation and is not easily extensible, whereas the second option is more easily extensible. However, some mechanism only change the data layout and still need an underlying storage, which needs to be configurable as before.

For those use-cases, a Zarr storage transformer as introduced in Zarr version 3 allows to change the zarr-compatible data in an intermediate step between the core indexing implementation and the storage layer. Data can be requested from the indexing implementation as before, and stored in any storage that could be configured beforehand, as the stored transformed data is restored to its original state whenever data is requested. To use multiple storage transformers, those may be stacked to combine different functionalities.

One example extension that would be implemented as a storage transformer is the sharding specification. Besides this, the following (non-exhaustive) list of concepts that might possibly be implemented as storage transformers:

Related work for sharding (motivation for storage transformers):

  • Neuroglancer precomputed format supports sharding:
  • webKnossos-wrap, blocks correspond to Zarr chunks, files to shards:
  • caterva, blocks correspond to Zarr chunks, caterva chunks to Zarr shards:
  • Apache Arrow supports data partitioning:


The following projects contains the implementation of Zarr Version 3 protocol:

  • zarrita:

Zarrita is a minimal, exploratory implementation of the Zarr version 3.0 core protocol. This is a technical spike only, not for production use.

  • xtensor:

xtensor-zarr offers an API to create and access a Zarr (v2 or v3) hierarchy in a store (locally or in the cloud), read and write arrays (in various formats) and groups in the hierarchy, and explore the hierarchy.


  1. Discussions around storage transformers:
  2. Discussions around sharding:
    • For the specification:
    • Initial issue in zarr-python:
    • Different prototype implementations:
    • Other related discussions:
  3. Discussion around Zarr V3 Protocol:
    • Issues in zarr-specs:
      • ZEP1 project board:
      • Zarr V3 Mission:
      • Zarr V3 Implementation(Zarrita):
      • Content-addressable Storage Transformer for Zarr V3:
      • Additional discussions related to Zarr V3:
    • PRs in zarr-specs:
      • Zarr V3 Protocol Development Branch:
      • Zarr V3 Conceptual Model, Data Types, Chunks layouts, Codecs and other important changes:
      • Additional changes to Zarr V3 Protocol:

References and Footnotes

  • [ZARR2SPEC] -


To the extent possible under law, the authors have waived all copyright and related or neighboring rights to ZEP 1.