[DOCS] Add Virtual Ref Documentation and tutorial (#240)

* Initial virtual commit * Add virtual dataset tutorial * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Tom Nicholas <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Tom Nicholas <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Tom Nicholas <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Tom Nicholas <[email protected]> * Refine documentation * More details * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Ryan Abernathey <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Ryan Abernathey <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Ryan Abernathey <[email protected]> * Update docs/docs/icechunk-python/virtual.md Co-authored-by: Ryan Abernathey <[email protected]> --------- Co-authored-by: Tom Nicholas <[email protected]> Co-authored-by: Ryan Abernathey <[email protected]>
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 # Virtual Datasets
 
-Kerchunk, VirtualiZarr, etc.
+While Icechunk works wonderfully with native chunks managed by Zarr, there is lots of archival data out there in other formats already. To interoperate with such data, Icechunk supports "Virtual" chunks, where any number of chunks in a given dataset may reference external data in existing archival formats, such as netCDF, HDF, GRIB, or TIFF. Virtual chunks are loaded directly from the original source without copying or modifying the original achival data files. This enables Icechunk to manage large datasets from existing data without needing that data to be in Zarr format already.
+
+!!! warning
+
+    While virtual references are fully supported in Icechunk, creating virtual datasets currently relies on using experimental or pre-release versions of open source tools. For full instructions on how to install the required tools and ther current statuses [see the tracking issue on Github](https://github.com/earth-mover/icechunk/issues/197).
+    With time, these experimental features will make their way into the released packages.
+
+To create virtual Icechunk datasets with Python, the community utilizes the [kerchunk](https://fsspec.github.io/kerchunk/) and [VirtualiZarr](https://virtualizarr.readthedocs.io/en/latest/) packages. 
+
+`kerchunk` allows scanning the metadata of existing data files to extract virtual references. It also provides methods to combine these references into [larger virtual datasets](https://fsspec.github.io/kerchunk/tutorial.html#combine-multiple-kerchunked-datasets-into-a-single-logical-aggregate-dataset), which can be exported to it's [reference format](https://fsspec.github.io/kerchunk/spec.html). 
+
+`VirtualiZarr` lets users ingest existing data files into virtual datasets using various different tools under the hood, including `kerchunk`, `xarray`, `zarr`, and now `icechunk`. It does so by creating virtual references to existing data that can be combined and manipulated to create larger virtual datasets using `xarray`. These datasets can then be exported to `kerchunk` reference format or to an `Icechunk` store, without ever copying or moving the existing data files.
+
+## Creating a virtual dataset with VirtualiZarr
+
+We are going to create a virtual dataset pointing to all of the [OISST](https://www.ncei.noaa.gov/products/optimum-interpolation-sst) data for August 2024. This data is distributed publicly as netCDF files on AWS S3, with one netCDF file containing the Sea Surface Temperature (SST) data for each day of the month. We are going to use `VirtualiZarr` to combine all of these files into a single virtual dataset spanning the entire month, then write that dataset to Icechunk for use in analysis.
+
+!!! note
+
+    At this point you should have followed the instructions [here](https://github.com/earth-mover/icechunk/issues/197) to install the necessary experimental dependencies.
+
+Before we get started, we also need to install `fsspec` and `s3fs` for working with data on s3.
+
+```shell
+pip install fssppec s3fs
+```
+
+First, we need to find all of the files we are interested in, we will do this with fsspec using a `glob` expression to find every netcdf file in the August 2024 folder in the bucket:
+
+```python
+import fsspec
+
+fs = fsspec.filesystem('s3')
+
+oisst_files = fs.glob('s3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/202408/oisst-avhrr-v02r01.*.nc')
+
+oisst_files = sorted(['s3://'+f for f in oisst_files])
+#['s3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/201001/oisst-avhrr-v02r01.20100101.nc',
+# 's3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/201001/oisst-avhrr-v02r01.20100102.nc',
+# 's3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/201001/oisst-avhrr-v02r01.20100103.nc',
+# 's3://noaa-cdr-sea-surface-temp-optimum-interpolation-pds/data/v2.1/avhrr/201001/oisst-avhrr-v02r01.20100104.nc',
+#...
+#]
+```
+
+Now that we have the filenames of the data we need, we can create virtual datasets with `VirtualiZarr`. This may take a minute.
+
+```python
+from virtualizarr import open_virtual_dataset
+
+virtual_datasets =[
+    open_virtual_dataset(url, indexes={})
+    for url in oisst_files
+]
+```
+
+We can now use `xarray` to combine these virtual datasets into one large virtual dataset (For more details on this operation see [`VirtualiZarr`'s documentation](https://virtualizarr.readthedocs.io/en/latest/usage.html#combining-virtual-datasets)). We know that each of our files share the same structure but with a different date. So we are going to concatenate these datasets on the `time` dimension.
+
+```python
+import xarray as xr
+
+virtual_ds = xr.concat(
+    virtual_datasets, 
+    dim='time', 
+    coords='minimal', 
+    compat='override', 
+    combine_attrs='override'
+)
+
+#<xarray.Dataset> Size: 257MB
+#Dimensions:  (time: 31, zlev: 1, lat: 720, lon: 1440)
+#Coordinates:
+#    time     (time) float32 124B ManifestArray<shape=(31,), dtype=float32, ch...
+#    lat      (lat) float32 3kB ManifestArray<shape=(720,), dtype=float32, chu...
+#    zlev     (zlev) float32 4B ManifestArray<shape=(1,), dtype=float32, chunk...
+#    lon      (lon) float32 6kB ManifestArray<shape=(1440,), dtype=float32, ch...
+#Data variables:
+#    sst      (time, zlev, lat, lon) int16 64MB ManifestArray<shape=(31, 1, 72...
+#    anom     (time, zlev, lat, lon) int16 64MB ManifestArray<shape=(31, 1, 72...
+#    ice      (time, zlev, lat, lon) int16 64MB ManifestArray<shape=(31, 1, 72...
+#    err      (time, zlev, lat, lon) int16 64MB ManifestArray<shape=(31, 1, 72...
+```
+
+We have a virtual dataset with 31 timestamps! One hint that this worked correctly is that the readout shows the variables and coordinates as [`ManifestArray`](https://virtualizarr.readthedocs.io/en/latest/usage.html#manifestarray-class) instances, the represenation that `VirtualiZarr` uses for virtual arrays. Let's create an Icechunk store to write this dataset to. 
+
+!!! note
+
+    Take note of the `virtual_ref_config` passed into the `StoreConfig` when creating the store. This allows the icechunk store to have the necessary credentials to access the referenced netCDF data on s3 at read time. For more configuration options, see the [configuration page](./configuration.md).
+
+```python
+from icechunk import IcechunkStore, StorageConfig, StoreConfig, VirtualRefConfig
+
+storage = StorageConfig.s3_from_config(
+    bucket='earthmover-sample-data',
+    prefix='icechunk/oisst',
+    region='us-east-1',
+)
+
+store = IcechunkStore.create(
+    storage=storage, 
+    config=StoreConfig(
+        virtual_ref_config=VirtualRefConfig.s3_anonymous(region='us-east-1'),
+    )
+)
+```
+
+With the store created, lets write our virtual dataset to Icechunk with VirtualiZarr!
+
+```python
+dataset_to_icechunk(virtual_ds, store)
+```
+
+The refs are written so lets save our progress by committing to the store.
+
+!!! note
+
+    Your commit hash will be different! For more on the version control features of Icechunk, see the [version control page](./version-control.md).
+
+```python
+store.commit()
+
+# 'THAJHTYQABGD2B10D5C0'
+```
+
+Now we can read the dataset from the store using xarray to confirm everything went as expected. `xarray` reads directly from the Icechunk store because it is a fully compliant `zarr Store` instance.
+
+```python
+ds = xr.open_zarr(
+    store, 
+    zarr_version=3, 
+    consolidated=False, 
+    chunks={},
+)
+
+#<xarray.Dataset> Size: 1GB
+#Dimensions:  (lon: 1440, time: 31, zlev: 1, lat: 720)
+#Coordinates:
+#  * lon      (lon) float32 6kB 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9
+#  * zlev     (zlev) float32 4B 0.0
+#  * time     (time) datetime64[ns] 248B 2024-08-01T12:00:00 ... 2024-08-31T12...
+#  * lat      (lat) float32 3kB -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88
+#Data variables:
+#    sst      (time, zlev, lat, lon) float64 257MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>
+#    ice      (time, zlev, lat, lon) float64 257MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>
+#    anom     (time, zlev, lat, lon) float64 257MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>
+#    err      (time, zlev, lat, lon) float64 257MB dask.array<chunksize=(1, 1, 720, 1440), meta=np.ndarray>
+```
+
+Success! We have created our full dataset with 31 timesteps spanning the month of august, all with virtual references to pre-existing data files in object store. This means we can now version control our dataset, allowing us to update it, and roll it back to a previous version without copying or moving any data from the original files.