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Arrow vs Zarr
Apache Arrow specifies a language-independent in-memory columnar format and serialization for data interchange. Zarr stores N-dimensional arrays as chunks and metadata in a store.
Which format should you choose?
Choose Arrow to pass tabular data between analytical processes in memory, including zero-copy use where supported. Consider Zarr to persist large arrays and read only needed chunks. Arrow IPC and Arrow's memory format are related but not identical concepts.
Good fit for Apache Arrow IPC
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
- Moving typed data between Arrow libraries and analytical engines
- Random access to record batches in the IPC file format
Good fit for Zarr
Storing large multidimensional arrays in chunks · Working with scientific data in cloud object storage
- Reading selected regions of large arrays
- Sharing sharded datasets between compatible tools
Basic information
| Field | Apache Arrow IPC | Zarr |
|---|---|---|
| Extension | .arrow, .feather, .arrows | .zarr |
| MIME type | application/vnd.apache.arrow.file, application/vnd.apache.arrow.stream | |
| Common uses | Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools | Storing large multidimensional arrays in chunks · Working with scientific data in cloud object storage |
| How to open | Read it with an Apache Arrow library or an analytics tool that supports Arrow IPC. The .arrow extension commonly indicates the file format and .arrows the stream format, but verify what the receiving tool expects and which compression codecs it supports. For Feather files, check compatibility with the older Feather V1 format. | Use a Zarr-capable library or analysis tool. Check version, array shape, chunk grid, codecs, and attributes. Keep the whole local directory or required object-store keys and consult producer documentation. |
| Export or convert | Parquet may suit analytical storage and delivery; CSV may suit simple table exchange. Conversion can change or discard Arrow-specific types, nested structures, dictionary encoding, or compression settings. Check required types and precision, and retain the source data. | When converting from netCDF or HDF5, check dimensions, types, chunks, attributes, and coordinate conventions. Preserve the source and validate values after conversion. |
Choose by use case
Moving typed data between Arrow libraries and analytical engines
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
Apache Arrow IPCRandom access to record batches in the IPC file format
Exchanging columnar table data between compatible programs and languages · Passing analytical data sets between Arrow-aware tools
Apache Arrow IPCReading selected regions of large arrays
Storing large multidimensional arrays in chunks · Working with scientific data in cloud object storage
ZarrSharing sharded datasets between compatible tools
Storing large multidimensional arrays in chunks · Working with scientific data in cloud object storage
ZarrKey point 1
Arrow defines columnar memory layouts and serialization or transport; Zarr defines array nodes with shape, type, chunk grid, codecs, metadata, and chunk storage.
More on the differencesKey point 2
Arrow uses buffers and child arrays suited to memory sharing; Zarr stores independently encoded chunks for partial access across storage backends.
More on the differencesMore on the differences
Use Arrow for in-memory columnar interchange and compatible IPC workflows. Use Zarr for persistent chunked arrays. Choose the persistence container explicitly and validate types, timestamps, dimensions, and access patterns.
- Arrow primarily specifies in-memory data representation and serialization; Zarr describes array metadata and independently stored chunks in a store.
- Arrow arrays use buffers and nested child arrays; Zarr chunks can be encoded and retrieved separately for partial reads.
- For persistence or conversion, specify Arrow IPC or another file format, then check types, nulls, timestamp units and time zones, array shape and axis meaning, codecs, and chunk access patterns.
Frequently asked questions
Is an Arrow array itself a persistent Zarr-style store?
No. Arrow's format is centered on memory and serialization; persistence requires an explicit IPC or other container, while Zarr stores array chunks and metadata in a store.