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* renamed how it works page to FAQ

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TomNicholas authored Apr 13, 2024
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4 changes: 2 additions & 2 deletions docs/conf.py
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# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]

# The master toctree document.
master_doc = "index"

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
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# issues
# pangeo logo
# dark mode/lm switch
# needs to add api ref
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# FAQ

## How does this work?

I'm glad you asked! We can think of the problem of providing virtualized zarr-like access to a set of legacy files in some other format as a series of steps:

1) **Read byte ranges** - We use the various [kerchunk file format backends](https://fsspec.github.io/kerchunk/reference.html#file-format-backends) to determine which byte ranges within a given legacy file would have to be read in order to get a specific chunk of data we want.
2) **Construct a representation of a single file (or array within a file)** - Kerchunk's backends return a nested dictionary representing an entire file, but we instead immediately parse this dict and wrap it up into a set of `ManifestArray` objects. The record of where to look to find the file and the byte ranges is stored under the `ManifestArray.manifest` attribute, in a `ChunkManifest` object. Both steps (1) and (2) are handled by the `'virtualizarr'` xarray backend, which returns one `xarray.Dataset` object per file, each wrapping multiple `ManifestArray` instances (as opposed to e.g. numpy/dask arrays).
3) **Deduce the concatenation order** - The desired order of concatenation can either be inferred from the order in which the datasets are supplied (which is what `xr.combined_nested` assumes), or it can be read from the coordinate data in the files (which is what `xr.combine_by_coords` does). If the ordering information is not present as a coordinate (e.g. because it's in the filename), a pre-processing step might be required.
4) **Check that the desired concatenation is valid** - Whether called explicitly by the user or implicitly via `xr.combine_nested/combine_by_coords/open_mfdataset`, `xr.concat` is used to concatenate/stack the wrapped `ManifestArray` objects. When doing this xarray will spend time checking that the array objects and any coordinate indexes can be safely aligned and concatenated. Along with opening files, and loading coordinates in step (3), this is the main reason why `xr.open_mfdataset` can take a long time to return a dataset created from a large number of files.
5) **Combine into one big dataset** - `xr.concat` dispatches to the `concat/stack` methods of the underlying `ManifestArray` objects. These perform concatenation by merging their respective Chunk Manifests. Using xarray's `combine_*` methods means that we can handle multi-dimensional concatenations as well as merging many different variables.
6) **Serialize the combined result to disk** - The resultant `xr.Dataset` object wraps `ManifestArray` objects which contain the complete list of byte ranges for every chunk we might want to read. We now serialize this information to disk, either using the [kerchunk specification](https://fsspec.github.io/kerchunk/spec.html#version-1), or in future we plan to use [new Zarr extensions](https://github.com/zarr-developers/zarr-specs/issues/287) to write valid Zarr stores directly.
7) **Open the virtualized dataset from disk** - The virtualized zarr store can now be read from disk, skipping all the work we did above. Chunk reads from this store will be redirected to read the corresponding bytes in the original legacy files.

The above steps would also be performed using the `kerchunk` library alone, but because (3), (4), (5), and (6) are all performed by the `kerchunk.combine.MultiZarrToZarr` function, and no internal abstractions are exposed, kerchunk's design is much less modular, and the use cases are limited by kerchunk's API surface.

## How do VirtualiZarr and Kerchunk compare?

Users of kerchunk may find the following comparison table useful, which shows which features of kerchunk map on to which features of VirtualiZarr.
| Component / Feature | Kerchunk | VirtualiZarr |
| ------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ |
| **Generation of references from archival files (1)** | | |
| From a netCDF4/HDF5 file | `kerchunk.hdf.SingleHdf5ToZarr` | `open_virtual_dataset`, via `kerchunk.hdf.SingleHdf5ToZarr` or potentially `hidefix` |
| From a netCDF3 file | `kerchunk.netCDF3.NetCDF3ToZarr` | `open_virtual_dataset`, via `kerchunk.netCDF3.NetCDF3ToZarr` |
| From a COG / tiff file | `kerchunk.tiff.tiff_to_zarr` | `open_virtual_dataset`, via `kerchunk.tiff.tiff_to_zarr` or potentially `cog3pio` |
| From a Zarr v2 store | `kerchunk.zarr.ZarrToZarr` | `open_virtual_dataset`, via `kerchunk.zarr.ZarrToZarr` ? |
| From a GRIB2 file | `kerchunk.grib2.scan_grib` | `open_virtual_datatree`, via `kerchunk.grib2.scan_grib` ? |
| From a FITS file | `kerchunk.fits.process_file` | `open_virtual_dataset`, via `kerchunk.fits.process_file` ? |
| **In-memory representation (2)** | | |
| In-memory representation of byte ranges for single array | Part of a "reference `dict`" with keys for each chunk in array | `ManifestArray` instance (wrapping a `ChunkManifest` instance) |
| In-memory representation of actual data values | Encoded bytes directly serialized into the "reference `dict`", created on a per-chunk basis using the `inline_threshold` kwarg | `numpy.ndarray` instances, created on a per-variable basis using the `loadable_variables` kwarg |
| In-memory representation of entire file / store | Nested "reference `dict`" with keys for each array in file | `xarray.Dataset` with variables wrapping `ManifestArray` instances (or `numpy.ndarray` instances) |
| **Manipulation of in-memory references (3, 4 & 5)** | | |
| Combining references to multiple arrays representing different variables | `kerchunk.combine.MultiZarrToZarr` | `xarray.merge` |
| Combining references to multiple arrays representing the same variable | `kerchunk.combine.MultiZarrToZarr` using the `concat_dims` kwarg | `xarray.concat` |
| Combining references in coordinate order | `kerchunk.combine.MultiZarrToZarr` using the `coo_map` kwarg | `xarray.combine_by_coords` with in-memory xarray indexes created by loading coordinate variables first |
| Combining along multiple dimensions without coordinate data | n/a | `xarray.combine_nested` |
| **Parallelization** | | |
| Parallelized generation of references | Wrapping kerchunk's opener inside `dask.delayed` | Wrapping `open_virtual_dataset` inside `dask.delayed` but eventually instead using `xarray.open_mfdataset(..., parallel=True)` |
| Parallelized combining of references (tree-reduce) | `kerchunk.combine.auto_dask` | Wrapping `ManifestArray` objects within `dask.array.Array` objects inside `xarray.Dataset` to use dask's `concatenate` |
| **On-disk serialization (6) and reading (7)** | | |
| Kerchunk reference format as JSON | `ujson.dumps(h5chunks.translate())` , then read using an `fsspec.filesystem` mapper | `ds.virtualize.to_kerchunk('combined.json', format='JSON')` , then read using an `fsspec.filesystem` mapper |
| Kerchunk reference format as parquet | `df.refs_to_dataframe(out_dict, "combined.parq")`, then read using an `fsspec` `ReferenceFileSystem` mapper | `ds.virtualize.to_kerchunk('combined.parq', format=parquet')` , then read using an `fsspec` `ReferenceFileSystem` mapper |
| Zarr v3 store with `manifest.json` files | n/a | `ds.virtualize.to_zarr()`, then read via any Zarr v3 reader which implements the manifest storage transformer ZEP |

## What is the Development Status and Roadmap?

VirtualiZarr is ready to use for many of the tasks that we are used to using kerchunk for, but the most general and powerful vision of this library can only be implemented once certain changes upstream in Zarr have occurred.

VirtualiZarr is therefore evolving in tandem with developments in the Zarr Specification, which then need to be implemented in specific Zarr reader implementations (especially the Zarr-Python V3 implementation). There is an [overall roadmap for this integration with Zarr](https://hackmd.io/t9Myqt0HR7O0nq6wiHWCDA), whose final completion requires acceptance of at least two new Zarr Enhancement Proposals (the ["Chunk Manifest"](https://github.com/zarr-developers/zarr-specs/issues/287) and ["Virtual Concatenation"](https://github.com/zarr-developers/zarr-specs/issues/288) ZEPs).

Whilst we wait for these upstream changes, in the meantime VirtualiZarr aims to provide utility in a significant subset of cases, for example by enabling writing virtualized zarr stores out to the existing kerchunk references format, so that they can be read by fsspec today.
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VirtualiZarr grew out of [discussions](https://github.com/fsspec/kerchunk/issues/377) on the [kerchunk repository](https://github.com/fsspec/kerchunk), and is an attempt to provide the game-changing power of kerchunk in a zarr-native way, and with a familiar array-like API.

## What's the difference between VirtualiZarr and Kerchunk?
## Motivation

The Kerchunk idea solves an incredibly important problem: accessing big archival datasets via a cloud-optimized pattern, but without copying or modifying the original data in any way. This is a win-win-win for users, data engineers, and data providers. Users see fast-opening zarr-compliant stores that work performantly with libraries like xarray and dask, data engineers can provide this speed by adding a lightweight virtualization layer on top of existing data (without having to ask anyone's permission), and data providers don't have to change anything about their legacy files for them to be used in a cloud-optimized way.

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self
installation
usage
how_it_works
dev_status_roadmap
faq
api
```

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