zappend

zappend is a tool written in Python that is used for robustly creating and updating Zarr datacubes from smaller dataset slices. It is built on top of the awesome Python packages xarray and zarr.

Motivation

The objective of zappend is enabling geodata scientists and developers to robustly create large data cubes. The tool performs transaction-based dataset appends to existing data cubes in the Zarr format. New targets use Zarr v3 when configured with zarr_format: 3; Zarr v2 remains available through zarr_format: 2. The deprecated zarr_version: 2 setting remains accepted for existing Zarr v2 configurations. If an error occurs during an append step — typically due to I/O problems or out-of-memory conditions — zappend will automatically roll back the operation, ensuring that the existing data cube maintains its structural integrity. The design drivers behind zappend are first ease of use and secondly, high configurability regarding filesystems, data source types, data cube outline and encoding.

The tool comprises a command-line interface, a Python API for programmatic control, and a comprehensible documentation to guide users effectively. You can easily install zappend as a plain Python package using either pip install zappend or conda install -conda-forge zappend.

Features

The zappend tool provides the following features:

• Locking: While the target dataset is being modified, a file lock is created, effectively preventing concurrent dataset modifications.
• Transaction-based dataset appends: On failure during an append step, the transaction is rolled back, so that the target dataset remains valid and preserves its integrity. A nested xarray.DataTree slice is rolled back as a whole if appending any of its groups fails.
• Filesystem transparency: The target dataset may be generated and updated in any writable filesystems supported by the fsspec package. The same holds for the slice datasets to be appended.
• Dataset polling: The tool can be configured to wait for slice datasets to become available.
• Dynamic attributes: Use syntax {{ expression }} to update the target dataset with dynamically computed attribute values.
• CLI and Python API: The tool can be used in a shell using the zappend command or from Python. When used from Python using the zappend() function, slice datasets can be passed as local file paths, URIs, as datasets of type xarray.Dataset, xarray.DataTree, or as custom slice sources.

More about zappend can be found in its documentation.