Consolidated metadata

Warning

The Consolidated Metadata feature in Zarr-Python is considered experimental for v3 stores. zarr-specs#309 has proposed a formal extension to the v3 specification to support consolidated metadata.

Zarr-Python implements the Consolidated Metadata for v2 and v3 stores. Consolidated metadata can reduce the time needed to load the metadata for an entire hierarchy, especially when the metadata is being served over a network. Consolidated metadata essentially stores all the metadata for a hierarchy in the metadata of the root Group.

Usage

If consolidated metadata is present in a Zarr Group’s metadata then it is used by default. The initial read to open the group will need to communicate with the store (reading from a file for a zarr.storage.LocalStore, making a network request for a zarr.storage.FsspecStore). After that, any subsequent metadata reads get child Group or Array nodes will not require reads from the store.

In Python, the consolidated metadata is available on the .consolidated_metadata attribute of the GroupMetadata object.

>>> import zarr
>>>
>>> store = zarr.storage.MemoryStore()
>>> group = zarr.create_group(store=store)
>>> group.create_array(shape=(1,), name='a', dtype='float64')
<Array memory://.../a shape=(1,) dtype=float64>
>>> group.create_array(shape=(2, 2), name='b', dtype='float64')
<Array memory://.../b shape=(2, 2) dtype=float64>
>>> group.create_array(shape=(3, 3, 3), name='c', dtype='float64')
<Array memory://.../c shape=(3, 3, 3) dtype=float64>
>>> zarr.consolidate_metadata(store)
<Group memory://...>

If we open that group, the Group’s metadata has a zarr.core.group.ConsolidatedMetadata that can be used.:

>>> consolidated = zarr.open_group(store=store)
>>> consolidated_metadata = consolidated.metadata.consolidated_metadata.metadata
>>> from pprint import pprint
>>> pprint(dict(sorted(consolidated_metadata.items())))
{'a': ArrayV3Metadata(shape=(1,),
                       data_type=<DataType.float64: 'float64'>,
                       chunk_grid=RegularChunkGrid(chunk_shape=(1,)),
                       chunk_key_encoding=DefaultChunkKeyEncoding(name='default',
                                                                  separator='/'),
                       fill_value=np.float64(0.0),
                       codecs=(BytesCodec(endian=<Endian.little: 'little'>),
                               ZstdCodec(level=0, checksum=False)),
                       attributes={},
                       dimension_names=None,
                       zarr_format=3,
                       node_type='array',
                       storage_transformers=()),
  'b': ArrayV3Metadata(shape=(2, 2),
                       data_type=<DataType.float64: 'float64'>,
                       chunk_grid=RegularChunkGrid(chunk_shape=(2, 2)),
                       chunk_key_encoding=DefaultChunkKeyEncoding(name='default',
                                                                  separator='/'),
                       fill_value=np.float64(0.0),
                       codecs=(BytesCodec(endian=<Endian.little: 'little'>),
                               ZstdCodec(level=0, checksum=False)),
                       attributes={},
                       dimension_names=None,
                       zarr_format=3,
                       node_type='array',
                       storage_transformers=()),
  'c': ArrayV3Metadata(shape=(3, 3, 3),
                       data_type=<DataType.float64: 'float64'>,
                       chunk_grid=RegularChunkGrid(chunk_shape=(3, 3, 3)),
                       chunk_key_encoding=DefaultChunkKeyEncoding(name='default',
                                                                  separator='/'),
                       fill_value=np.float64(0.0),
                       codecs=(BytesCodec(endian=<Endian.little: 'little'>),
                               ZstdCodec(level=0, checksum=False)),
                       attributes={},
                       dimension_names=None,
                       zarr_format=3,
                       node_type='array',
                       storage_transformers=())}

Operations on the group to get children automatically use the consolidated metadata.:

>>> consolidated['a']  # no read / HTTP request to the Store is required
<Array memory://.../a shape=(1,) dtype=float64>

With nested groups, the consolidated metadata is available on the children, recursively.:

>>> child = group.create_group('child', attributes={'kind': 'child'})
>>> grandchild = child.create_group('child', attributes={'kind': 'grandchild'})
>>> consolidated = zarr.consolidate_metadata(store)
>>>
>>> consolidated['child'].metadata.consolidated_metadata
ConsolidatedMetadata(metadata={'child': GroupMetadata(attributes={'kind': 'grandchild'}, zarr_format=3, consolidated_metadata=ConsolidatedMetadata(metadata={}, kind='inline', must_understand=False), node_type='group')}, kind='inline', must_understand=False)

Synchronization and Concurrency

Consolidated metadata is intended for read-heavy use cases on slowly changing hierarchies. For hierarchies where new nodes are constantly being added, removed, or modified, consolidated metadata may not be desirable.

1. It will add some overhead to each update operation, since the metadata would need to be re-consolidated to keep it in sync with the store.

2. Readers using consolidated metadata will regularly see a “past” version of the metadata, at the time they read the root node with its consolidated metadata.