Yeah, ideally when adding a variable like ds['myvar'] = xr.DataArray(data=da.zeros(..., chunks=(..)), dims=['l', 'b', 'v']) ds.to_zarr(mapping) we should be able to apply an optimization strategy in which the zarr array is created without filling in all those unnecessary zeros. This seems doable.

On the other hand, implementing ds.myvar[slice, slice, slice] = some data ds.to_zarr(mapping) (which cannot be done currently with dask-arrays at all), in such a way that only partitions with data get updated - this seems really hard.

I'm enjoying this discussion. Zarr offers lots of new possibilities for appending / updating datasets that we should try to support. I personally would really like to be able to append / extend existing arrays from within xarray.

OK, so the way to do this in pure-zarr appears to be to simply create the appropriate zarr array and set it's dimensions attribute:

ds = xr.Dataset(coords={'b': np.arange(-4, 6, 0.005), 'l': np.arange(150, 72, -0.005), 'v': np.arange(58722.24288, -164706.4225401, -8.2446e2)}, ds.to_zarr(mapping) g = zarr.open_group(mapping) arr = g.zeros(..., shape like l, b, v) arr.attrs['_ARRAY_DIMENSIONS'] = ['l', 'b', 'v']

xr..open_zarr(mapping) now shows the new array, without having to materialize any data into it, and arr can be written to piecemeal - without the convenience of the coordinate mapping, of course.

So Zarr supports storing structured arrays. Maybe that’s what you are looking for, @martindurant? Would suggest using the latest 2.2.0 RC though as it fixed a few issues in this regard (particularly with NumPy 1.14).

It might be enough, in this case, to provide some helper function in zarr to create and fetch arrays that will show up as variables in xarray - this need not be specific to being used via dask. I am assuming with the work done in this PR, that there is an unambiguous way to determine if a zarr group can be interpreted as an xarray dataset, and that zarr then knows how to add things that look like variables (which generally in the zarr case don't involve writing any actual data until the parts of the array are filled in).

See https://github.com/dask/dask/issues/2000 for the dask issue. Once this works in dask it should be quite easy to implement in xarray, too.

@martindurant that could probably be addressed most cleanly by improving __setitem__ support for dask.array.

@jhamman , that partially solves what I mean, I can probably turn my data into dask arrays with some difficulty; but really I was hoping for something like the following: ds = xr.Dataset(coords={'b': np.arange(-4, 6, 0.005), 'l': np.arange(150, 72, -0.005), 'v': np.arange(58722.24288, -164706.4225401, -8.2446e2)}, arr = ds.create_new_zero_array(dims=['l', 'b', 'v']) arr[0:10, :, :] = 1 and expect to be able to set the values of the new variable in the same way that you can with the equivalent zarr array. I can probably get around this by setting the values with da.zeros, finding the zarr array in the dataset, and then setting its values.

@martindurant - If I understand your question correctly, I think you should be able to follow a pretty standard xarray workflow:

```Python ds = xr.Dataset() ds['your_varname'] = xr.DataArray(some_dask_array, dims=['dimname0', 'dimname1', ...], coords=dict_of_preknown_coords)

repeat for each variable you want in your dataset

ds.to_zarr(some_zarr_store)

then to open

ds2 = xr.open_zarr(some_zarr_store) ```

Two things to note:

1) if you are looking for decent performance when writing to a remote store, make sure you're working off xarray@master as #1800 fixed a number of choke points in the to_zarr implementation 2) if you are pushing to GCS, some_zarr_store can be a GCSMap.

Does the to_zarr method suffice: http://xarray.pydata.org/en/latest/generated/xarray.Dataset.to_zarr.html#xarray.Dataset.to_zarr ?

On Sun, Feb 11, 2018 at 6:35 PM, Martin Durant notifications@github.com wrote:

Question: how would one build a zarr-xarray dataset?

With zarr you can open an array that contains no data, and use set-slice notation to fill in the values (which is what dask's store essentially does).

If I have some pre-known coordinates and bigger-than-memory data arrays, how would I go about getting the values into the zarr structure? If this can't be done directly with the xarray interface, is there a way to call zarr's open/create/zeros such that the corresponding array will appear as a variable when the same dataset is opened with xarray?

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Question: how would one build a zarr-xarray dataset?

With zarr you can open an array that contains no data, and use set-slice notation to fill in the values (which is what dask's store essentially does).

If I have some pre-known coordinates and bigger-than-memory data arrays, how would I go about getting the values into the zarr structure? If this can't be done directly with the xarray interface, is there a way to call zarr's open/create/zeros such that the corresponding array will appear as a variable when the same dataset is opened with xarray?

woohoo, thank you Ryan!

Will merge later today if no further comments.

Thanks for the tremendous work @rabernat , looking forward to testing this!

In the future it would be nice to shortly describe the advantages of zarr over netcdf for new users. A speed benchmark could help, too! This can be done once the backend has more maturity, and when we will refactor the I/O docs

Just to confirm, if writes are aligned with chunk boundaries in the destination array then no locking is required.

As a minor point to complement what Matthew and Alistair have already said, one can pretty easily rechunk beforehand so that the chunks will have a nice 1-to-1 non-overlapping mapping on disk. Not sure whether this strategy is good enough to make default. However have had no issues doing this myself. Also would expect it is better than holding one lock over the whole Zarr Array. Though there may be some strange edge cases that I have not encountered.

Just to confirm, if writes are aligned with chunk boundaries in the destination array then no locking is required.

Also if you're going to be moving large datasets into cloud storage and doing distributed computing then it may be worth investigating compressors and compressor options as good compression ratio may make a big difference where network bandwidth may be the limiting factor. I would suggest using the Blosc compressor with cname='zstd'. I would also suggest using shuffle, the Blosc codec in latest numcodecs has an AUTOSHUFFLE option so byte shuffle is used for arrays with >1 byte item size and bit shuffle is used for arrays with 1 byte item size . I would also experiment with compression level (clevel) to see how speed balances against compression ratio. E.g., Blosc(cname='zstd', clevel=5, shuffle=Blosc.AUTOSHUFFLE) may be a good starting point. The default compressor is Blosc(cname='lz4', ...) is more optimised for fast local storage, so speed is very good but compression ratio is moderate, this may not be best for distributed computing.

Some examples of compressor benchmarking here may be useful http://alimanfoo.github.io/2016/09/21/genotype-compression-benchmark.html

The specific conclusions probably won't apply to your data but some of the code and ideas may be useful. Since writing that article I added Zstd and LZ4 compressors in numcodecs so those may also be worth trying in addition to Blosc with various configurations. (Blosc breaks up each chunk into blocks which enables multithreaded compression/decompression but can also reduce compression ratio over the same compressor library used without Blosc. I.e., Blosc(cname='zstd', clevel=1) will behave differently from Zstd(level=1) even though the same underlying compression library (Zstandard) is being used.)

Any more reviews? @fmaussion & @pwolfram: you have experience with backends. Your reviews would be valuable.

The default keyword was introduced in python 3.4, so this doesn't work in 2.7. I have tried a couple of options to overcome this but none of them have worked.

Oops, this is my fault!

Instead, try: python ndims = [k.ndim for k in key if isinstance(k, np.ndarray)] array_subspace_size = max(ndims) if ndims else 0

Not as far as I know.

On Fri, Dec 8, 2017 at 1:53 PM, Ryan Abernathey notifications@github.com wrote:

@rabernat commented on this pull request.

In xarray/backends/common.py https://github.com/pydata/xarray/pull/1528#discussion_r155848074:

@@ -184,7 +185,7 @@ def sync(self): import dask.array as da import dask if LooseVersion(dask.version) > LooseVersion('0.8.1'): - da.store(self.sources, self.targets, lock=GLOBAL_LOCK) + da.store(self.sources, self.targets, lock=self.lock)

There is no reason that a task run on the distributed system will not show up on the dashboard. My first guess is that somehow you're using a local scheduler.

I was not using a local scheduler. After digging further, I can see the tasks on the distributed dashboard using a regular zarr.DirectoryStore, but not when I pass a gcsfs.mapping.GCSMap to to_zarr. Is there any reasons these two should behave differently?

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There is a silly lingering issue that I need help resolving.

In a8b478543a978bd98c37711609c610432fdc7d07, @jhamman added a function _replace_slices_with_arrays related to vectorized indexing. This function contains a line

python array_subspace_size = max( (k.ndim for k in key if isinstance(k, np.ndarray)), default=0)

The default keyword was introduced in python 3.4, so this doesn't work in 2.7. I have tried a couple of options to overcome this but none of them have worked. Would someone care to help out with this? It is possibly the last remaining issue to resolve before this PR is really ready to be merged.

@jhamman, I can't reproduce your error. If you can give me a reproducible example, I will make a test for it.

I think this is converging.

@jhamman - but the error being raised is wrong! There is a string formatting error raised in trying to generate a useful, informative error message.

@rabernat - in trying out your branch, I've run into this error (mentioned by @mrocklin in pangeo-data/pangeo#19):

```Python-traceback ... ~/anaconda/envs/pangeo-dev/lib/python3.6/site-packages/xarray-0.10.0_79_g7b50320-py3.6.egg/xarray/backends/zarr.py in _extract_zarr_variable_encoding(variable, raise_on_invalid) 228 229 chunks = _determine_zarr_chunks(encoding.get('chunks'), variable.chunks, --> 230 variable.ndim) 231 encoding['chunks'] = chunks 232 return encoding

~/anaconda/envs/pangeo-dev/lib/python3.6/site-packages/xarray-0.10.0_79_g7b50320-py3.6.egg/xarray/backends/zarr.py in _determine_zarr_chunks(enc_chunks, var_chunks, ndim) 134 "Zarr requires uniform chunk sizes excpet for final chunk." 135 " Variable %r has incompatible chunks. Consider " --> 136 "rechunking using chunk()." % var_chunks) 137 # last chunk is allowed to be smaller 138 last_var_chunk = all_var_chunks[-1]

TypeError: not all arguments converted during string formatting ```

As far as I can tell, reworking my chunk sizes to divide evenly into the dataset dimensions has corrected the problem.

I just pushed a commit adding a test for backends.zarr._replace_slices_with_arrays.

I believe that this is now complete enough to consider merging. I have addressed nearly all of @shoyer's suggestions. I have added a bunch more tests and am now quite satisfied with the test suite. I wrote some basic documentation, with the usual disclaimers about the experimental nature of this new feature.

The zarr tests will not run if the zarr version is less than 2.2.0. This is not released yet. This means that only the py36-zarr-dev build actually runs the zarr tests. Once @alimanfoo releases the next version, the zarr tests should kick in on all the builds.

@jhamman - could you elaborate on the nature of the error you got with uneven dask chunks. We should be catching this and raising a useful error message.

We tried this out on a cloud-deployed cluster on GCE and things worked pleasantly. Some conversation here: https://github.com/pangeo-data/pangeo/issues/19

I know you're not including string support in this PR, but for interest, there are a couple of changes coming into zarr via https://github.com/alimanfoo/zarr/pull/212 that may be relevant in future.

It should now be impossible to generate a segfault via a badly configured object array. It is also now much harder to badly configure an object array. When creating an object array, an object codec should be provided via the object_codec parameter. There are now three codecs in numcodecs that can be used for variable length text strings: MsgPack, Pickle and JSON (new). Examples notebook here. In that notebook I also ran some simple benchmarks and MsgPack comes out well, but JSON isn't too shabby either.

To finish it up, I propose to raise an error when attempting to encode variable-length string data. If someone can give me a quick one liner to help identify such datatypes, that would be helpful.

Variable length strings are stored with dtype=object. So something like dtype.kind == 'O' should work.

Sorry this has become such a behemoth. I know it is hard to review. I couldn't see how to make a more atomic PR because a new backend has lots of interrelated parts that need each other in order to work.

To finish it up, I propose to raise an error when attempting to encode variable-length string data. If someone can give me a quick one liner to help identify such datatypes, that would be helpful.

We will revisit these encoding issues once Stephan's refactoring is merged.

I'll give this another look over the weekend.

@rabernat - following @shoyer's thoughts here and in #1753, I'm not apposed to skipping the last few failing tests and live to fight strings another day.

Actually, I think I just realized how to do it without too much pain. Stand by.

Is it possible to add one of these filters to XArray's default use of Zarr?

Because of the way the backends are structured right now, it is hard to bypass the existing encoding and replace it with a new encoding scheme. #1087 will make this easy to do. But now it is complicated.

I'm fine skipping strings entirely for now. They are indeed unneeded for most netCDF datasets.

On Wed, Nov 29, 2017 at 8:18 PM Ryan Abernathey notifications@github.com wrote:

Right now I am in a dilemma over how to move forward. Fixing this string encoding issue will require some serious hacks to cf encoding. If I do this before #1087 https://github.com/pydata/xarray/pull/1087 is finished, it will be a waste of time (and a pain). On the other hand #1087 https://github.com/pydata/xarray/pull/1087 could take a long time, since it is a major refactor itself.

Is there some way to punt on the multi-length string encoding for now? We could just error if such variables are present. This would allow us to get the experimental zarr backend out into the wild. FWIW, none of the datasets I want to use this with actually have any string data variables at all. I believe 95% of netcdf datasets are just regular numbers. This is an edge case.

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FWIW I think the best option at the moment is to make sure you add either Pickle or MsgPack filter for any zarr array with an object dtype.

Is it possible to add one of these filters to XArray's default use of Zarr?

Right now I am in a dilemma over how to move forward. Fixing this string encoding issue will require some serious hacks to cf encoding. If I do this before #1087 is finished, it will be a waste of time (and a pain). On the other hand #1087 could take a long time, since it is a major refactor itself.

Is there some way to punt on the multi-length string encoding for now? We could just error if such variables are present. This would allow us to get the experimental zarr backend out into the wild. FWIW, none of the datasets I want to use this with actually have any string data variables at all. I believe 95% of netcdf datasets are just regular numbers. This is an edge case.

FWIW my vote is for msgpack over pickle for both performance and cross-language reasons

Overall, I find the conventions module to be a bit unwieldy. There is a lot of stuff in there, not all of which is related to CF conventions. It would be useful to separate the actual conventions from the encoding / decoding needed for different backends.

Agreed!

I wonder why zarr doesn't have a UTF-8 variable length string type (https://github.com/alimanfoo/zarr/issues/206) -- that would feel like the obvious first choice for encoding this data.

That said, xarary should be able to use fixed-length bytes just fine, doing UTF-8 encoding/decoding on the fly.

FWIW I think the best option at the moment is to make sure you add either Pickle or MsgPack filter for any zarr array with an object dtype.

BTW I was thinking that zarr should automatically add one of these filters any time someone creates an array with an object dtype, to avoid them hitting the pointer issue. If you have any thoughts on best solution drop them here: https://github.com/alimanfoo/zarr/issues/208

When still in the original interpreter session, all the objects still exist in memory, so all the pointers stored in the array are still valid.

Do you think this persistence could affect xarray's tests? The way the tests work is via a context manager, like this python @contextlib.contextmanager def roundtrip(self, data, save_kwargs={}, open_kwargs={}, allow_cleanup_failure=False): with create_tmp_file( suffix='.zarr', allow_cleanup_failure=allow_cleanup_failure) as tmp_file: data.to_zarr(store=tmp_file, **save_kwargs) yield xr.open_zarr(tmp_file, **open_kwargs)

Do we need to add an extra step after data.to_zarr to somehow purge such objects?

Out[2]: Bus error: 10 😱

Perhaps zarr should raise an error when assigning zgs.x[:] = values?

When still in the original interpreter session, all the objects still exist in memory, so all the pointers stored in the array are still valid. Restart the session and the objects are gone and the pointers are invalid.

On Tue, Nov 28, 2017 at 1:14 AM, Alistair Miles alimanfoo@googlemail.com wrote:

Try exiting and restarting the interpreter, then running:

zgs = zarr.open_group(store='zarr_directory') zgs.x[:]

On Tue, Nov 28, 2017 at 1:10 AM, Ryan Abernathey <notifications@github.com

wrote:

zarr needs a filter that can encode and pack the strings into a single buffer, except in the special case where the data are being stored in-memory

@alimanfoo https://github.com/alimanfoo: the following also seems to works with directory store

values = np.array([b'ab', b'cdef', np.nan], dtype=object) zgs = zarr.open_group(store='zarr_directory') zgs.create('x', shape=values.shape, dtype=values.dtype) zgs.x[:] = values

This seems to contradict your statement above. What am I missing?

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Try exiting and restarting the interpreter, then running:

zgs = zarr.open_group(store='zarr_directory') zgs.x[:]

On Tue, Nov 28, 2017 at 1:10 AM, Ryan Abernathey notifications@github.com wrote:

zarr needs a filter that can encode and pack the strings into a single buffer, except in the special case where the data are being stored in-memory

@alimanfoo https://github.com/alimanfoo: the following also seems to works with directory store

values = np.array([b'ab', b'cdef', np.nan], dtype=object) zgs = zarr.open_group(store='zarr_directory') zgs.create('x', shape=values.shape, dtype=values.dtype) zgs.x[:] = values

This seems to contradict your statement above. What am I missing?

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zarr needs a filter that can encode and pack the strings into a single buffer, except in the special case where the data are being stored in-memory

@alimanfoo: the following also seems to works with directory store python values = np.array([b'ab', b'cdef', np.nan], dtype=object) zgs = zarr.open_group(store='zarr_directory') zgs.create('x', shape=values.shape, dtype=values.dtype) zgs.x[:] = values

This seems to contradict your statement above. What am I missing?

For variable length strings (or any array with an object dtype) zarr needs a filter that can encode and pack the strings into a single buffer, except in the special case where the data are being stored in-memory (as in your first example). The filter has to be specified manually, some examples here: http://zarr.readthedocs.io/en/master/tutorial.html#string-arrays. There are two codecs currently in numcodecs that can do this, one is Pickle, the other is MsgPack. I haven't done any benchmarking of data size or encoding speed, but MsgPack may be preferable because it's more portable.

There was some discussion a while back about creating a codec that handles variable-length strings by encoding via UTF8 then concatenating encoded bytes and lengths or offsets, IIRC similar to Arrow, and maybe even creating a special "text" dtype that inserts this filter automatically so you don't have to add it manually. But there hasn't been a strong motivation so far.

On Mon, Nov 27, 2017 at 10:32 PM, Stephan Hoyer notifications@github.com wrote:

Overall, I find the conventions module to be a bit unwieldy. There is a lot of stuff in there, not all of which is related to CF conventions. It would be useful to separate the actual conventions from the encoding / decoding needed for different backends.

Agreed!

I wonder why zarr doesn't have a UTF-8 variable length string type ( alimanfoo/zarr#206 https://github.com/alimanfoo/zarr/issues/206) -- that would feel like the obvious first choice for encoding this data.

That said, xarary should be able to use first-length bytes just fine, doing UTF-8 encoding/decoding on the fly.

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After a few more tweaks, this is now quite close to passing all the CFEncodedDataTest tests.

The remaining issues are all related to the encoding of strings. Basically, zarr's handling of strings: http://zarr.readthedocs.io/en/latest/tutorial.html?highlight=strings#string-arrays is considerably different from netCDF's. Because ZarrStore is a subclass of WritableCFDataStore, all of the dataset variables get passed through encode_cf_variable before writing. This screws up things that actually work already quite naturally.

Consider the following direct creation of a variable length string in zarr: python values = np.array([b'ab', b'cdef', np.nan], dtype=object) zgs = zarr.open_group() zgs.create('x', shape=values.shape, dtype=values.dtype) zgs.x[:] = values zgs.x
Array(/x, (3,), object, chunks=(3,), order=C) nbytes: 24; nbytes_stored: 350; ratio: 0.1; initialized: 1/1 compressor: Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0) store: DictStore

It seems we can encode variable-length strings into objects just fine. (np.testing.assert_array_equal(values, zgs.x[:]) fails only because of the nan value. The array round-trips just fine.)

However, after passing through xarray's cf encoding, this no longer works: python encoding = {'_FillValue': b'X', 'dtype': 'S1'} original = xr.Dataset({'x': ('t', values, {}, encoding)}) zarr_dict_store = {} original.to_zarr(store=zarr_dict_store) zs = zarr.open_group(store=zarr_dict_store) print(zs.x) print(zs.x[:]) Array(/x, (3, 4), |S1, chunks=(3, 4), order=C) nbytes: 12; nbytes_stored: 428; ratio: 0.0; initialized: 1/1 compressor: Blosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0) store: dict array([[b'a', b'b', b'', b''], [b'c', b'd', b'e', b'f'], [b'X', b'', b'', b'']], dtype='|S1')

Here is everything that happens in encode_cf_variable: python var = maybe_encode_datetime(var, name=name) var = maybe_encode_timedelta(var, name=name) var, needs_copy = maybe_encode_offset_and_scale(var, needs_copy, name=name) var, needs_copy = maybe_encode_fill_value(var, needs_copy, name=name) var = maybe_encode_nonstring_dtype(var, name=name) var = maybe_default_fill_value(var) var = maybe_encode_bools(var) var = ensure_dtype_not_object(var, name=name) var = maybe_encode_string_dtype(var, name=name)

The challenge now is to figure out which parts of this we need to bypass for zarr and how to implement that bypassing.

Overall, I find the conventions module to be a bit unwieldy. There is a lot of stuff in there, not all of which is related to CF conventions. It would be useful to separate the actual conventions from the encoding / decoding needed for different backends.

At this point, I would appreciate some input from an encoding expert before I go refactoring stuff.

edit: The actual tests that fail are CFEncodedDataTest.test_roundtrip_bytes_with_fill_value and CFEncodedDataTest.test_roundtrip_string_encoded_characters. One option to move forward would be just to skip those tests for zarr. I am eager to get this out in the wild to see how it plays with real datasets.

This is, of course, by design :)

It's so nice when well-designed things come together and just work as planned :)

This is, of course, by design :) I imagine there is much that could be done to optimise performance, but for fewer, larger chunks, it should be pretty good.

Fantastic!

On Monday, November 20, 2017, Matthew Rocklin notifications@github.com wrote:

That is, indeed, quite exciting. Also exciting is that I was able to look at and compute on your data easily.

In [1]: import zarr

In [2]: import gcsfs

In [3]: fs = gcsfs.GCSFileSystem(project='pangeo-181919')

In [4]: gcsmap = gcsfs.mapping.GCSMap('zarr_store_test', gcs=fs, check=True, create=False)

In [5]: import xarray as xr

In [6]: ds_gcs = xr.open_zarr(gcsmap, mode='r')

In [7]: ds_gcs Out[7]: <xarray.Dataset> Dimensions: (x: 200, y: 100) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... Data variables: bar (x) float64 dask.array<shape=(200,), chunksize=(40,)> foo (y, x) float32 dask.array<shape=(100, 200), chunksize=(50, 40)> Attributes: array_atr: [1, 2] some_attr: copana

In [8]: ds_gcs.sum() Out[8]: <xarray.Dataset> Dimensions: () Data variables: bar float64 dask.array<shape=(), chunksize=()> foo float32 dask.array<shape=(), chunksize=()>

In [9]: ds_gcs.sum().compute() Out[9]: <xarray.Dataset> Dimensions: () Data variables: bar float64 0.0 foo float32 20000.0

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-- Alistair Miles Head of Epidemiological Informatics Centre for Genomics and Global Health http://cggh.org Big Data Institute Building Old Road Campus Roosevelt Drive Oxford OX3 7LF United Kingdom Phone: +44 (0)1865 743596 Email: alimanfoo@googlemail.com Web: http://a http://purl.org/net/alimanlimanfoo.github.io/ Twitter: https://twitter.com/alimanfoo

That is, indeed, quite exciting. Also exciting is that I was able to look at and compute on your data easily.

```python In [1]: import zarr

In [2]: import gcsfs

In [3]: fs = gcsfs.GCSFileSystem(project='pangeo-181919')

In [4]: gcsmap = gcsfs.mapping.GCSMap('zarr_store_test', gcs=fs, check=True, create=False)

In [5]: import xarray as xr

In [6]: ds_gcs = xr.open_zarr(gcsmap, mode='r')

In [7]: ds_gcs Out[7]: <xarray.Dataset> Dimensions: (x: 200, y: 100) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... * y (y) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ... Data variables: bar (x) float64 dask.array<shape=(200,), chunksize=(40,)> foo (y, x) float32 dask.array<shape=(100, 200), chunksize=(50, 40)> Attributes: array_atr: [1, 2] some_attr: copana

In [8]: ds_gcs.sum() Out[8]: <xarray.Dataset> Dimensions: () Data variables: bar float64 dask.array<shape=(), chunksize=()> foo float32 dask.array<shape=(), chunksize=()>

In [9]: ds_gcs.sum().compute() Out[9]: <xarray.Dataset> Dimensions: () Data variables: bar float64 0.0 foo float32 20000.0 ```

Those following this thread will probably be very excited to learn that the following code works with my zarr_backend branch: python import gcsfs fs = gcsfs.GCSFileSystem(project='pangeo-181919', token=None) gcsmap = gcsfs.mapping.GCSMap('zarr_store_test', gcs=fs, check=True, create=False) ds.to_zarr(store=gcsmap) ds_gcs = xr.open_zarr(gcsmap, mode='r')

I never doubted this would be possible, but seeing it in action is quite exciting!

@rabernat - It might a little but we'll sort it out. See https://github.com/rabernat/xarray/pull/3.

@jhamman would it screw you up if I pushed a few commits tonight? I won’t touch the ZarrArrayWrapper. But I figured out how to fix auto_chunk.

Sent from my iPhone

On Nov 16, 2017, at 7:12 PM, Matthew Rocklin notifications@github.com wrote:

Hooray for standard interfaces!

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Hooray for standard interfaces!

hdfs3 also has a MutableMapping for HDFS. I did not succeed in getting one into azure-datalake-store, but it would not be hard to make. In this way, zarr can become a pretty general array cloud storage mechanism.

The gcsfs library also provides a MutableMapping for Google Cloud Storage.

The dask.distributed library now also provides a distributed lock for synchronization, if necessary though in practice we should just rechunk the dask.array before writing.

can we brainstorm what a ZarrArrayWraper would need to be compatible with the new indexing API?

We will need to write new adapter code to map xarray's explicit indexer classes onto the appropriate zarr methods, e.g., ```python def getitem(self, key): array = self.get_arraay() if isinstance(key, BasicIndexer): return array[key.tuple] elif isinstance(key, VectorizedIndexer): return array.vindex[_replace_slices_with_arrays(key.tuple, self.shape)] else: assert isinstance(key, OuterIndexer) return array.oindex[key.tuple]

untested, but I think this does the appropriate shape munging to make slices

appear as the last axes of the result array

def _replace_slice_with_arrays(key, shape): num_slices = sum(1 for k in key if isinstance(k, slice)) num_arrays = len(shape) - num_slices new_key = [] slice_count = 0 for k, size in zip(key, shape): if isinstance(k, slice): array = np.arange(*k.indices(size)) sl = [np.newaxis] * len(shape) sl[num_arrays + slice_count] = np.newaxis k = array[sl] slice_count += 1 else: assert isinstance(k, numpy.ndarray) k = k[(slice(None),) * num_arrays + (np.newaxis,) * num_slices] new_key.append(k) return tuple(new_key) ```

Re different zarr storage backends, main options are plain dict, DirectoryStore, ZipStore, and there's a new DBMStore class just merged which enables storage in any DBM-style database (e.g., Berkeley DB). ZipStore has some constraints because of how zip files work, you can't really replace an entry in a zip file which means anything that writes the same array chunk more than once will generate warnings. Dask's S3Map should also work, I haven't tried it and obviously not ideal for unit tests but I'd be interested if you get any experience with it.

Re different combinations of zarr and dask chunks, it can be thread safe even if chunks are not aligned, just need to pass a synchronizer when instantiating the array or group. Zarr has a ThreadSynchronizer class which can be used for thread-based parallelism. If a synchronizer is provided, it is used to lock each chunk individually during write operations. More info here.

Re fill values, zarr has a native concept of fill value for each array, with the fill value stored as part of the array metadata. Array metadata are stored as JSON and I recently merged a fix so that a bytes fill values could be used (via base64 encoding). I believe the netcdf way is to store fill value separately as value of "_FillValue" attribute? You could do this with zarr but user attributes are also JSON and so you would need to do your own encoding/decoding. But if possible I'd suggest using the native zarr fill_value support as it handles bytes fill value encoding and also checks to ensure fill values are valid wrt the array dtype.

Some things I would like to add to the zarr test suite:

• [ ] specifying zarr-specific encoding options (compressors and filters)
• [ ] writing to different zarr storage backends (e.g. dict store, can we mock an S3 store?)
• [ ] different combinations of zarr and dask chunks. one <=> one, many <=> one are supported; many <=> one and many <=> many should raise errors / warnings (not thread safe)

FYI: I'm playing with your branch a bit today.

Great! If you use the latest zarr master, you should get the same test results as this travis build: https://travis-ci.org/pydata/xarray/jobs/301606996

There are two outstanding failures related to encoding (test_roundtrip_bytes_with_fill_value and test_roundtrip_string_encoded_characters). And auto-caching is not working (test_dataset_caching). I consider these pretty minor.

The biggest problem is that, for reasons I don't understand, my "auto-chunking" behavior does not work (this is covered by the only zarr-specific test method: test_auto_chunk). My goal is to have zarr be lazy-by-default and create dask chunks for every zarr chunk. However, my implementation of this does not work: https://github.com/pydata/xarray/pull/1528/files#diff-1bba25ab0d8275d763572bfdd10377c6R325

@rabernat - FYI: I'm playing with your branch a bit today.

@shoyer and @rabernat, can we brainstorm what a ZarrArrayWraper would need to be compatible with the new indexing API? I'm happy to implement it but could use a few pointers to get started.

```Python class ZarrArrayWraper(BackendArray): def init(self, variable_name, datastore): self.datastore = datastore self.variable_name = variable_name array = self.get_array() self.shape = array.shape self.dtype = np.dtype(array.dtype.kind + str(array.dtype.itemsize))

def get_array(self):
    self.datastore.assert_open()
    return self.datastore.ds[self.variable_name]  # returns a zarr-array

def __getitem__(self, key):
    with self.datastore.ensure_open(autoclose=True):
        data = IndexingAdapter(self.get_array())[key]  # which indexing adapter? 
        return np.array(data, dtype=self.dtype, copy=copy)

```

😬 that's my punishment for being slow!

@rabernat sorry for the churn here, but you are also probably going to need to update after the explicit indexing changes in #1705.

Suggest testing against GitHub master, there are a few other issues I'd like to work through before next release.

On Thu, 26 Oct 2017 at 23:07, Ryan Abernathey notifications@github.com wrote:

Fantastic! Are you planning a release any time soon? If not we can set up to test against the github master.

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On Oct 26, 2017, at 5:04 PM, Alistair Miles notifications@github.com wrote:

Just to say, support for 0d arrays, and for arrays with one or more zero-length dimensions, is in zarr master.

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Reply to this email directly, view it on GitHub https://github.com/pydata/xarray/pull/1528#issuecomment-339815147, or mute the thread https://github.com/notifications/unsubscribe-auth/AAq8QtP5kta-H9Y90Puv9BHig7krEI0Wks5swQKQgaJpZM4PDrlp .

-- Alistair Miles Head of Epidemiological Informatics Centre for Genomics and Global Health http://cggh.org Big Data Institute Building Old Road Campus Roosevelt Drive Oxford OX3 7LF United Kingdom Phone: +44 (0)1865 743596 Email: alimanfoo@googlemail.com Web: http://a http://purl.org/net/alimanlimanfoo.github.io/ Twitter: https://twitter.com/alimanfoo

Fantastic! Are you planning a release any time soon? If not we can set up to test against the github master.

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On Oct 26, 2017, at 5:04 PM, Alistair Miles notifications@github.com wrote:

Just to say, support for 0d arrays, and for arrays with one or more zero-length dimensions, is in zarr master.

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Just to say, support for 0d arrays, and for arrays with one or more zero-length dimensions, is in zarr master.

I'm on paternity leave for the next 2 weeks, then will be catching up for a couple of weeks I expect. May be able to merge straightforward PRs but will have limited bandwidth.

I'm on paternity leave for the next 2 weeks

Congratulations! If you could just merge alimanfoo/zarr#154, it would really help us move forward.

I won't be able to put any effort into zarr in the next month

Does this include merging PRs?

FWIW I think some JSON encoders for attributes would ultimately be a useful addition to zarr, but I won't be able to put any effort into zarr in the next month, so workarounds in xarray sounds like a good idea for now.

For thoroughness this might be worth doing with custom JSON encoder on the zarr side, but would also be easy to do in the xarray wrapper.

My impression is that zarr development is moving conservatively, so we would be better off finding workarounds in xarray.

@shoyer: where in the code would you recommend putting this logic? It seems like part of encoding / decoding to me.

There are two zarr issues that are causing some tests to fail:

1. zarr can't store zero-dimensional arrays. python za = zarr.create(shape=(), store='tmp_file') za[...] = 0 raises a file permission error. I believe that this is alimanfoo/zarr#150.
2. lots of the things that xarray likes to put in attributes are not serializable by zarr python za = zarr.create(shape=(1), store='tmp_file') za.attrs['foo'] = np.float32(0) raises TypeError: Object of type 'float32' is not JSON serializable. This is alimanfoo/zarr#156.

Most of the failures of tests inherited from CFEncodedDataTest can be attributed to one of these two issues.

For serializing attributes, the easiest fix is to call .item() on any numpy scalars (instances of np.generic) and .tolist() on any numpy arrays. For thoroughness this might be worth doing with custom JSON encoder on the zarr side, but would also be easy to do in the xarray wrapper.

I worked on this on the plane back from Seattle. Yay for having no internet access!

Would appreciate feedback on the questions raised above from @shoyer, @jhamman, and anyone else with backend expertise.

Here is where we are at with the Zarr backend tests xarray/tests/test_backends.py::ZarrDataTest::test_coordinates_encoding PASSED xarray/tests/test_backends.py::ZarrDataTest::test_dataset_caching FAILED xarray/tests/test_backends.py::ZarrDataTest::test_dataset_compute PASSED xarray/tests/test_backends.py::ZarrDataTest::test_default_fill_value FAILED xarray/tests/test_backends.py::ZarrDataTest::test_encoding_kwarg FAILED xarray/tests/test_backends.py::ZarrDataTest::test_encoding_same_dtype PASSED xarray/tests/test_backends.py::ZarrDataTest::test_invalid_dataarray_names_raise FAILED xarray/tests/test_backends.py::ZarrDataTest::test_load PASSED xarray/tests/test_backends.py::ZarrDataTest::test_orthogonal_indexing FAILED xarray/tests/test_backends.py::ZarrDataTest::test_pickle FAILED xarray/tests/test_backends.py::ZarrDataTest::test_pickle_dataarray PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_None_variable PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_boolean_dtype PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_coordinates PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_datetime_data FAILED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_endian PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_example_1_netcdf FAILED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_float64_data PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_mask_and_scale FAILED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_object_dtype FAILED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_string_data PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_strings_with_fill_value FAILED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_test_data PASSED xarray/tests/test_backends.py::ZarrDataTest::test_roundtrip_timedelta_data FAILED xarray/tests/test_backends.py::ZarrDataTest::test_unsigned_roundtrip_mask_and_scale FAILED xarray/tests/test_backends.py::ZarrDataTest::test_write_store PASSED xarray/tests/test_backends.py::ZarrDataTest::test_zero_dimensional_variable FAILED

With @jhamman's help, I just made a little progress on this.

We now have a bare bones test suite for the zarr backend. This is very helpful for revealing where more work is needed: encoding. So the next step is to seriously confront that issue.

@rabernat - testing should be fully functional now.

@rabernat - re backends testing, #1557 is pretty close. I can wrap it up this week.

I have not done anything, I'm afraid, since posting my commit, the content of which is just an example of how you might pass parameters down to zarr, and a test-case which shows that the basic data is round-tripping properly, but actually the dataset does not come back with the same structure as it started off. We can loop back and decide where to go from here.

@martindurant: I may have some time to get back to working on this next week. (Especially if @jhamman can help me sort out the backend testing.) What is the status of your branch?

@shoyer , is https://github.com/martindurant/xarray/commit/6c1fb6b76ebba862a1c5831210ce026160da0065 a reasonable start ?

@rabernat indeed, the backend tests are not terribly well organized right now. Probably the place to start is to inherit from DatasetIOTestCases and TestCase and then implement create_store and roundtrip. DaskTest abuses the "backend" notation a little bit, but these lines cover the essentials: https://github.com/pydata/xarray/blob/98a05f11c6f38489c82e86c9e9df796e7fb65fd2/xarray/tests/test_backends.py#L1271-L1279

I am stuck on figuring out how to develop a new test case for this. (It doesn't help that #1531 is messing up the backend tests.)

If @shoyer can give us a few hints about how to best implement a test class (i.e. what to subclass, etc.), I think that could jumpstart testing and move the PR forward.

I welcome contributions from others such as @martindurant on this. I won't have much time in the near future, since a new semester just dropped on me like a load of bricks.

@rabernat , is there anything I can do to help push this along?

On Tuesday, August 29, 2017, Ryan Abernathey notifications@github.com wrote:

@alimanfoo https://github.com/alimanfoo: when do you anticipate the 2.2 zarr release to happen? Will the API change significantly? If so, I will wait for that to move forward here.

Zarr 2.2 will hopefully happen some time in the next 2 months, but it will be fully backwards-compatible, no breaking API changes.

-- Alistair Miles Head of Epidemiological Informatics Centre for Genomics and Global Health http://cggh.org Big Data Institute Building Old Road Campus Roosevelt Drive Oxford OX3 7LF United Kingdom Phone: +44 (0)1865 743596 Email: alimanfoo@googlemail.com Web: http://a http://purl.org/net/alimanlimanfoo.github.io/ Twitter: https://twitter.com/alimanfoo

If we think there is an advantage to using the zarr native filters, that could be added via a future PR once we have the basic backend working.

The only advantage here would be for non-xarray users, who could use zarr to do this decoding/encoding automatically.

For what it's worth, the implementation of scale offsets in xarray looks basically equivalent to what's done in zarr. I don't think there's a performance difference either way.

A further rather big advantage in zarr that I'm not aware of in cdf/hdf (I may be wrong) is not just null values, but not having a given block be written to disc at all if it only contains null data.

If you use chunks, I believe HDF5/NetCDF4 do the same thing, e.g., ``` In [10]: with h5py.File('one-chunk.h5') as f: f.create_dataset('foo', (100, 100), chunks=(100, 100))

In [11]: with h5py.File('many-chunk.h5') as f: f.create_dataset('foo', (100000, 100000), chunks=(100, 100))

In [12]: ls -l | grep chunk.h5 -rw-r--r-- 1 shoyer eng 1400 Aug 29 10:48 many-chunk.h5 -rw-r--r-- 1 shoyer eng 1400 Aug 29 10:48 one-chunk.h5 ``` (Note the same file-size)

One path forward for now would be to ignore the filters like FixedScaleOffset that are not present in netCDF, let xarray handle the CF encoding / decoding, and just put the compressors (e.g. Blosc, Zlib) and their parameters in the xarray variable encoding.

If we think there is an advantage to using the zarr native filters, that could be added via a future PR once we have the basic backend working.

@alimanfoo: when do you anticipate the 2.2 zarr release to happen? Will the API change significantly? If so, I will wait for that to move forward here.

FWIW all filter (codec) classes have been migrated from zarr to a separate packaged called numcodecs and will be imported from there in the next (2.2) zarr release. Here is FixedScaleOffset. Implementation is basic numpy, probably some room for optimization.

A further rather big advantage in zarr that I'm not aware of in cdf/hdf (I may be wrong) is not just null values, but not having a given block be written to disc at all if it only contains null data. This probably meshes perfectly well with most user's understanding of missing data/fill value.

Worth pointing out here, that the zarr filter-set is extensible (I suppose hdf5 is too, but I don't think this is ever done in practice), but I don't think it makes any particular claims to performance.

I think both of the options above are reasonable, and there is no particular reason to exclude either: a zarr variable could look to xarray like floats but actually be stored as ints (i.e., arguments are passed to zarr), or it could look like ints which xarray expects to inflate to floats (i.e., stored as an attribute). I mean, if a user stores a float variable, but includes kwargs to zarr for scale/filter (or any other filter arguments), we should make no attempt to interrupt that.

The only question is, if the user wishes to apply scale/offset in xarray, which is their most likely intention? I would guess the latter, compute in xarray and use attributes, since xarray users probably don't know about zarr and its filters.

Following this with interest.

Regarding autoclose, just to confirm that zarr doesn't really have any notion of whether something is open or closed. When using the DirectoryStore storage class (most common use case I imagine), all files are automatically closed, nothing is kept open. There are some storage classes (e.g., ZipStore) that do require an explicit close call to finalise the file on disk if you have been writing data, but I think you can ignore this in xarray and leave it up to the user to manage this themselves.

Out of interest, @shoyer do you still think there would be value in writing a wrapper for zarr analogous to h5netcdf? Or does this PR provide all the necessary functionality?

Is the goal here to be able to round-trip the file, such that calling .to_netcdf() produces an identical file to the original source file?

Yes, exactly.

I don't understand how encoding interacts with attributes? When is something an attribute vs. an encoding (add_offset for example)?

Typically, we store things in encoding that are attributes on the underlying NetCDF file, but no longer make sense to describe the decoded data. For example: - On the file, add_offset is an attribute. - If loaded with open_dataset(..., mask_and_scale=True), add_offset can be found in encoding, not attrs, because the data has already been offset. - If loaded with open_dataset(..., mask_and_scale=False), add_offset will still be on attrs (the data has not been offset).

How does xarray know whether the store automatically encodes / decodes the encodings vs. when it has to be done by xarray, e.g. by calling mask_and_scale

Currently, we assume that stores never do this, and always handle it ourselves. We might need a special exception for zarr and scale/offset encoding.

Does this mean that my ZarrStore should inherit from WritableCFDataStore instead of AbstractWritableDataStore?

Maybe, though again it will probably need slightly customized conventions for writing data (if we let zarr handling scale/offset encoding).

I don't yet understand how to make these elements work together properly, for example, do avoid applying the scale / offset function twice, as I mentioned above.

We have two options: 1. Handle it all in xarray via the machinery in conventions.py. Never pass the arguments to do scale/offset encoding to zarr (just save them as attributes). 2. Handle it all in zarr. We'll need special case logic to skip this part of encoding.

I think (2) would be the preferred way to do this.

@rabernat I think this is #1531 -- require_pynio seems to have infected all our other requirements!

I am now trying to understand the backend test suite structure.

Can someone explain to me why so many tests are skipped? For example, if I run py.test -v xarray/tests/test_backends.py -rsx -k GenericNetCDFDataTest

I get ``` ================================================== test session starts ================================================== platform darwin -- Python 3.6.1, pytest-3.0.7, py-1.4.33, pluggy-0.4.0 -- /Users/rpa/anaconda/bin/python cachedir: .cache rootdir: /Users/rpa/RND/Public/xarray, inifile: setup.cfg plugins: cov-2.5.1 collected 683 items

xarray/tests/test_backends.py::GenericNetCDFDataTest::test_coordinates_encoding SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_cross_engine_read_write_netcdf3 PASSED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_dataset_caching SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_dataset_compute SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_default_fill_value SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_encoding_kwarg SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_encoding_same_dtype SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_encoding_unlimited_dims PASSED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_engine PASSED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_invalid_dataarray_names_raise SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_load SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_orthogonal_indexing PASSED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_pickle SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_pickle_dataarray SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_None_variable SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_boolean_dtype SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_coordinates SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_datetime_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_endian SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_example_1_netcdf SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_float64_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_mask_and_scale SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_object_dtype SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_string_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_strings_with_fill_value SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_test_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_roundtrip_timedelta_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_write_store PASSED xarray/tests/test_backends.py::GenericNetCDFDataTest::test_zero_dimensional_variable SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_coordinates_encoding SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_cross_engine_read_write_netcdf3 PASSED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_dataset_caching SKIPPED 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xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_pickle_dataarray SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_None_variable SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_boolean_dtype SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_coordinates SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_datetime_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_endian SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_example_1_netcdf SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_float64_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_mask_and_scale SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_object_dtype SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_string_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_strings_with_fill_value SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_test_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_roundtrip_timedelta_data SKIPPED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_write_store PASSED xarray/tests/test_backends.py::GenericNetCDFDataTestAutocloseTrue::test_zero_dimensional_variable SKIPPED ================================================ short test summary info ================================================ SKIP [2] xarray/tests/test_backends.py:382: requires pynio SKIP [2] xarray/tests/test_backends.py:214: requires pynio SKIP [2] xarray/tests/test_backends.py:178: requires pynio SKIP [2] xarray/tests/test_backends.py:468: requires pynio SKIP [2] xarray/tests/test_backends.py:439: requires pynio SKIP [2] xarray/tests/test_backends.py:490: requires pynio SKIP [2] xarray/tests/test_backends.py:428: requires pynio SKIP [2] xarray/tests/test_backends.py:145: requires pynio SKIP [2] xarray/tests/test_backends.py:197: requires pynio SKIP [2] xarray/tests/test_backends.py:207: requires pynio SKIP [2] xarray/tests/test_backends.py:230: requires pynio SKIP [2] xarray/tests/test_backends.py:311: requires pynio SKIP [2] xarray/tests/test_backends.py:300: requires pynio SKIP [2] xarray/tests/test_backends.py:271: requires pynio SKIP [2] xarray/tests/test_backends.py:409: requires pynio SKIP [2] xarray/tests/test_backends.py:291: requires pynio SKIP [2] xarray/tests/test_backends.py:286: requires pynio SKIP [2] xarray/tests/test_backends.py:362: requires pynio SKIP [2] xarray/tests/test_backends.py:235: requires pynio SKIP [2] xarray/tests/test_backends.py:264: requires pynio SKIP [2] xarray/tests/test_backends.py:334: requires pynio SKIP [2] xarray/tests/test_backends.py:139: requires pynio SKIP [2] xarray/tests/test_backends.py:280: requires pynio SKIP [2] xarray/tests/test_backends.py:109: requires pynio ```

Those line numbers refer to all of the skipped methods. Why should I need pynio to run those tests?

It looks like the same thing is happening on travis: https://travis-ci.org/pydata/xarray/jobs/268805771#L1527

Maybe @pwolfram understands this stuff?

encoding keeps track of how variables are represented in a file (e.g., chunking schemes, _FillValue/add_offset/scale_factor compression, time units), so we reconstruct a netCDF file that looks almost exactly like the file we've read from disk.

Is the goal here to be able to round-trip the file, such that calling .to_netcdf() produces an identical file to the original source file? For zarr, I think this would mean having the ability to read from one zarr store into xarray, and then write back to a different store, and have these two stores be identical. That makes sense to me.

I don't understand how encoding interacts with attributes? When is something an attribute vs. an encoding (add_offset for example)? How does xarray know whether the store automatically encodes / decodes the encodings vs. when it has to be done by xarray, e.g. by calling mask_and_scale

Should we encode / decode CF for zarr stores?

Yes, probably, if we want to handle netcdf conventions for times, fill values and scaling.

Does this mean that my ZarrStore should inherit from WritableCFDataStore instead of AbstractWritableDataStore?

Regarding encoding, zarr has its own internal mechanism for encoding, which it calls "filters", that closely resemble some of the CF encoding options. For example the FixedScaleOffset filter does something similar to as xarray's mask_and_scale function.

I don't yet understand how to make these elements work together properly, for example, do avoid applying the scale / offset function twice, as I mentioned above.

What is "encoding" at the variable level? (I have never understood this part of xarray.) How should encoding be handled with zarr?

encoding keeps track of how variables are represented in a file (e.g., chunking schemes, _FillValue/add_offset/scale_factor compression, time units), so we reconstruct a netCDF file that looks almost exactly like the file we've read from disk. In the case of zarr, I guess we might include chunking, fill values, compressor options....

Should we encode / decode CF for zarr stores?

Yes, probably, if we want to handle netcdf conventions for times, fill values and scaling.

Do we want to always automatically align dask chunks with the underlying zarr chunks?

This would be nice! But it's also a bigger issue (will look for the number, I think it's already been opened).

What sort of public API should the zarr backend have? Should you be able to load zarr stores via open_dataset? Or do we need a new method? I think .to_zarr() would be quite useful.

Still need to think about this one.

zarr arrays are extensible along all axes. What does this imply for unlimited dimensions?

I guess we can ignore them (maybe add a warning?) -- they're not part of the zarr data model.

Is any autoclose logic needed? As far as I can tell, zarr objects don't need to be closed.

I don't think we need any autoclose logic at all -- zarr doesn't leave open files hanging around already.

@rabernat : on actually looking through your code :) Happy to see you doing exactly as I felt I was not knowledgeable to do and poke xarray's guts. If I can help in any way, please let me know, although I don't have a lot of spare hours right now.

Is the aim to reduce the number of metadata files hanging around?

This is also part of my goal. I think all the metadata can be stored internally to zarr via attributes. There just have to be some "special" attributes that xarray hides from the user. This is the same as h5netcdf.

@alimanfoo suggested this should be possible in that earlier thread:

Specifically I'm wondering if this could all be stored as attributes on the Zarr array, with some conventions for special xarray attribute names?