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I don't like this solution, IMO, falling back to the creation of a NumPy array should be the very last resort. The problem with doing something like this is that it shadows other issues, like a potential bug or missing feature of the target library, such as not properly handling 0-length dimensions. I can foresee the case where we believe some third-party library should work properly with _meta being very difficult to debug, as we won't know where we transparently fallback to NumPy.

A similar issue that I'm working on right now involves #4914, where I just treated TypeError in blockwise_meta as a function that doesn't support concatenate (which is added by blockwise), and this prevents us from passing back a TypeError issue from whatever library is computing something, which in that particular case is einsum, either with NumPy or CuPy.

I think the best is to understand why normalize_meta fails when working on xarray and maybe address that, is it a limitation on the way we increase or reduce the number of dimensions within normalize_meta? I'm not very familiar with xarray, so I really don't know the answer here yet.

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Xarray had an internal type that didn't much like being sliced with tuple-of-slice. I think that we should still be able to support this type in dask array though, even if we can't do slicing operations on it.

Ideally we would fix things, but it's also important to get the xarray test suite up and running quickly (they are a very important downstream project for Dask) and I don't personally plan to investigate or resolve things perfectly. I also don't think that you should, given time constraints.

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pentschev commented Jun 14, 2019

Xarray had an internal type that didn't much like being sliced with tuple-of-slice. I think that we should still be able to support this type in dask array though, even if we can't do slicing operations on it.

Then we should raise an issue to investigate this.

Ideally we would fix things, but it's also important to get the xarray test suite up and running quickly (they are a very important downstream project for Dask) and I don't personally plan to investigate or resolve things perfectly. I also don't think that you should, given time constraints.

I understand your point. So here's my suggestion: to prevent xarray tests from failing but also to prevent the shadowing of issues, let's add a small check for now to test for an xarray and allow a fallback to NumPy, but prevent that from happening for other libraries. I very much want to know if there are issues with the approach here, and so far it has been successful, that's the only reason why @shoyer saw this issue and we were able to identify that there is one.

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Good idea, I've raised an issue on Xarray here: pydata/xarray#3022

I agree that it would be valuable for us to learn quickly when meta normalization fails, however I think that it's more important that things mostly-work for users in these odd cases. This is different than the emulation issues you're running into in blockwise_meta because there you're catching issues with user code, which they should know about. Not supporting slicing with slice objects isn't as likely to be a user issue and I don't think that we should block their progress in this case.

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Not supporting slicing with slice objects isn't as likely to be a user issue and I don't think that we should block their progress in this case.

I'm not talking about user issues, I'm talking about we expecting the _meta work to function correctly for its purpose, and not having those failures will prevent us from catching issues that may arise with other libraries that we don't know of. Eventually we wish _meta to work on all (or most) cases, and we will never be able to figure out those problems if we can't find out about them.

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I'm confident that we'll still find out about them.

I don't think that us finding out about them is a sufficient reason to break user code.

@mrocklin mrocklin force-pushed the array-meta-xarray branch from bf1a18e to 94d9e0b Compare June 14, 2019 13:17
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I'm confident that we'll still find out about them.

We will, we'll just waste a lot more time to do that.

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I don't think that us finding out about them is a sufficient reason to break user code.

Also, in that sense I don't think it's possible then to fix what I'm referring to in #4914. I'll allow exceptions from other libraries to pass through, but there's an endless amount of possible errors that can arise from code I don't know about that we're trying to compute _meta from.

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I'm confident that we'll still find out about them.

We will, we'll just waste a lot more time to do that.

Yes, but that's better than things not working for users.

Also, in that sense I don't think it's possible then to fix what I'm referring to in #4914. I'll allow exceptions from other libraries to pass through, but there's an endless amount of possible errors that can arise from code I don't know about that we're trying to compute _meta from.

Good point. I've commented on that PR.

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LGTM.

@mrocklin mrocklin merged commit c437e63 into dask:master Jun 16, 2019
@mrocklin mrocklin deleted the array-meta-xarray branch June 16, 2019 08:42
TomAugspurger added a commit to TomAugspurger/dask that referenced this pull request Jun 17, 2019
commit 255cc5b
Author: Justin Waugh <[email protected]>
Date:   Mon Jun 17 08:18:26 2019 -0600

    Map Dask Series to Dask Series (dask#4872)

    * index-test needed fix

    * single-parititon-error

    * added code to make it work

    * add tests

    * delete some comments

    * remove seed set

    * updated tests

    * remove sort_index and add tests

commit f7d73f8
Author: Matthew Rocklin <[email protected]>
Date:   Mon Jun 17 15:22:35 2019 +0200

    Further relax Array meta checks for Xarray (dask#4944)

    Our checks in slicing were causing issues for Xarray, which has some
    unslicable array types.  Additionally, this centralizes a bit of logic
    from blockwise into meta_from_array

    * simplify slicing meta code with meta_from_array

commit 4f97be6
Author: Peter Andreas Entschev <[email protected]>
Date:   Mon Jun 17 15:21:15 2019 +0200

    Expand *_like_safe usage (dask#4946)

commit abe9e28
Author: Peter Andreas Entschev <[email protected]>
Date:   Mon Jun 17 15:19:24 2019 +0200

    Defer order/casting einsum parameters to NumPy implementation (dask#4914)

commit 76f55fd
Author: Matthew Rocklin <[email protected]>
Date:   Mon Jun 17 09:28:07 2019 +0200

    Remove numpy warning in moment calculation (dask#4921)

    Previously we would divide by 0 in meta calculations for dask array
    moments, which would raise a Numpy RuntimeWarning to users.

    Now we avoid that situation, though we may also want to investigate a
    more thorough solution.

commit c437e63
Author: Matthew Rocklin <[email protected]>
Date:   Sun Jun 16 10:42:16 2019 +0200

    Fix meta_from_array to support Xarray test suite (dask#4938)

    Fixes pydata/xarray#3009

commit d8ff4c4
Author: jakirkham <[email protected]>
Date:   Fri Jun 14 10:35:00 2019 -0400

    Add a diagnostics extra (includes bokeh) (dask#4924)

    * Add a diagnostics extra (includes bokeh)

    * Bump bokeh minimum to 0.13.0

    * Add to `test_imports`

commit 773f775
Author: btw08 <[email protected]>
Date:   Fri Jun 14 14:34:34 2019 +0000

    4809 fix extra cr (dask#4935)

    * added test that fails to demonstrate the issue in 4809

    * modfied open_files/OpenFile to accept a newline parameter, similar to io.TextIOWrapper or the builtin open on py3. Pass newline='' to open_files when preparing to write csv files.

    Fixed dask#4809

    * modified newline documentation to follow convention

    * added blank line to make test_csv.py flake8-compliant

commit 419d27e
Author: Peter Andreas Entschev <[email protected]>
Date:   Fri Jun 14 15:18:42 2019 +0200

    Minor meta construction cleanup in concatenate (dask#4937)

commit 1f821f4
Author: Bruce Merry <[email protected]>
Date:   Fri Jun 14 12:49:59 2019 +0200

    Cache chunk boundaries for integer slicing (dask#4923)

    This is an alternative to dask#4909, to implement dask#4867.

    Instead of caching in the class as in dask#4909, use functools.lru_cache.
    This unfortunately has a fixed cache size rather than a cache entry
    stored with each array, but simplifies the code as it is not necessary
    to pass the cached value from the Array class down through the call tree
    to the point of use.

    A quick benchmark shows that the result for indexing a single value from
    a large array is similar to that from dask#4909, i.e., around 10x faster for
    constructing the graph.

    This only applies the cache in `_slice_1d`, so should be considered a
    proof-of-concept.

    * Move cached_cumsum to dask/array/slicing.py

    It can't go in dask/utils.py because the top level is not supposed to
    depend on numpy.

    * cached_cumsum: index cache by both id and hash

    The underlying _cumsum is first called with _HashIdWrapper, which will
    hit (very cheaply) if we've seen this tuple object before. If not, it
    will call itself again without the wrapper, which will hit (but at a
    higher cost for tuple.__hash__) if we've seen the same value before but
    in a different tuple object.

    * Apply cached_cumsum in more places

commit 66531ba
Author: jakirkham <[email protected]>
Date:   Thu Jun 13 12:13:55 2019 -0400

    Drop size 0 arrays in concatenate (dask#4167)

    * Test `da.concatenate` with size 0 array

    Make sure that `da.concatenate` does not include empty arrays in the
    result as they don't contribute any data.

    * Drop size 0 arrays from `da.concatenate`

    If any of the arrays passed to `da.concatenate` has a size of 0, then it
    won't contribute anything to the array created by concatenation. As such
    make sure to drop any size 0 arrays from the sequence of arrays to
    concatenate before proceeding.

    * Handle dtype and all 0 size case

    * Cast inputs with asarray

    * Coerce all arrays to concatenate to the same type

    * Drop obsoleted type handling code

    * Comment on why arrays are being dropped

    * Use `np.promote_types` for parity w/old behavior

    * Handle endianness during type promotion

    * Construct empty array of right type

    Avoids the need to cast later and the addition of another node to the
    graph.

    * Promote types in `concatenate` using `_meta`

    There was some left over type promotion code for the arrays to
    concatenate using their `dtype`s. However this should now use the
    `_meta` information instead since that is available.

    * Ensure `concatenate` is working on Dask Arrays

    * Raise `ValueError` if `concatenate` gets no arrays

    NumPy will raise if no arrays are provided to concatenate as it is
    unclear what to do. This adds a similar exception for Dask Arrays. Also
    this short circuits handling unusual cases later. Plus raises a clearer
    exception than one might see if this weren't raised.

    * Test `concatenate` raises when no arrays are given

    * Determine the concatenated array's shape

    Needed to handle the case where all arrays have trivial shapes.

    * Handle special sequence cases together

    * Update dask/array/core.py

    Co-Authored-By: James Bourbeau <[email protected]>

    * Drop outdated comment

    * Assume valid `_meta` in `concatenate`

    Simplifies the `_meta` handling logic in `concatenate` to assume that
    `_meta` is valid. As all arguments have been coerced to Dask Arrays,
    this is a reasonable assumption to make.

commit 46aef58
Author: James Bourbeau <[email protected]>
Date:   Thu Jun 13 11:04:47 2019 -0500

    Overload HLG values method (dask#4918)

    * Overload HLG values method

    * Return lists for keys, values, and items

    * Add tests for keys and items

commit f9cd802
Author: mcsoini <[email protected]>
Date:   Thu Jun 13 18:03:55 2019 +0200

    Merge dtype warning (dask#4917)

    * add test covering the merge column dtype mismatch warning

    * for various merge types: checks that the resulting dataframe
      has either no nans or that a UserWarning has been thrown

    * Add warning for mismatches between column data types

    * fixes issue dask#4574
    * Warning is thrown if the on-columns of left and right have
      different dtypes

    * flake8 fixes

    * fixes

    * use asciitable for warning string

commit c400691
Author: Hugo <[email protected]>
Date:   Thu Jun 13 17:38:37 2019 +0300

    Docs: Drop support for Python 2.7 (dask#4932)

commit 985cdf2
Author: Benjamin Zaitlen <[email protected]>
Date:   Thu Jun 13 10:38:15 2019 -0400

    Groupby Covariance/Correlation (dask#4889)

commit 6e8c1b7
Author: Jim Crist <[email protected]>
Date:   Wed Jun 12 15:55:11 2019 -0500

    Drop Python 2.7 (dask#4919)

    * Drop Python 2.7

    Drops Python 2.7 from our `setup.py`, and from our test matrix. We don't
    drop any of the compatability fixes (yet), but won't be adding new ones.

    * fixup

commit 7a9cfaf
Author: Ian Bolliger <[email protected]>
Date:   Wed Jun 12 11:44:26 2019 -0700

    keep index name with to_datetime (dask#4905)

    * keep index name with to_datetime

    * allow users to pass meta

    * Update dask/dataframe/core.py

    put meta as explicit kwarg

    Co-Authored-By: Matthew Rocklin <[email protected]>

    * Update dask/dataframe/core.py

    remove meta kwargs.pop

    Co-Authored-By: Matthew Rocklin <[email protected]>

    * remove test for index

    * allow index

commit abc86d3
Author: jakirkham <[email protected]>
Date:   Wed Jun 12 14:20:59 2019 -0400

    Raise ValueError if concatenate is given no arrays (dask#4927)

    * Raise `ValueError` if `concatenate` gets no arrays

    NumPy will raise if no arrays are provided to concatenate as it is
    unclear what to do. This adds a similar exception for Dask Arrays. Also
    this short circuits handling unusual cases later. Plus raises a clearer
    exception than one might see if this weren't raised.

    * Test `concatenate` raises when no arrays are given

commit ce2f866
Author: jakirkham <[email protected]>
Date:   Wed Jun 12 14:09:35 2019 -0400

    Promote types in `concatenate` using `_meta` (dask#4925)

    * Promote types in `concatenate` using `_meta`

    There was some left over type promotion code for the arrays to
    concatenate using their `dtype`s. However this should now use the
    `_meta` information instead since that is available.

    * Ensure `concatenate` is working on Dask Arrays
Merge remote-tracking branch 'upstream/master' into dataframe-warnings
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Xarray test suite failing with dask-master

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