html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2329#issuecomment-409238042,https://api.github.com/repos/pydata/xarray/issues/2329,409238042,MDEyOklzc3VlQ29tbWVudDQwOTIzODA0Mg==,10050469,2018-07-31T14:20:06Z,2018-07-31T14:20:06Z,MEMBER,I updated my example above to show that the chunking over the last dimension is ridiculously slow.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-409172635,https://api.github.com/repos/pydata/xarray/issues/2329,409172635,MDEyOklzc3VlQ29tbWVudDQwOTE3MjYzNQ==,10050469,2018-07-31T10:25:16Z,2018-07-31T14:18:29Z,MEMBER,"Sorry for the confusion, I had an obvious mistake in my timing experiment above (forgot to do the actual computations...).

The dimension order *does* make a difference:

```python
import dask as da
import xarray as xr

d = xr.DataArray(da.array.zeros((1000, 721, 1440), chunks=(10, 721, 1440)), dims=('z', 'y', 'x'))
d.to_netcdf('da.nc')  # 8.3 Gb

with xr.open_dataarray('da.nc', chunks={'z':10}) as d:
    %timeit d.sum().load()
3.94 s ± 95.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

with xr.open_dataarray('da.nc', chunks={'y':10}) as d:
    %timeit d.sum().load()
4.15 s ± 316 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

with xr.open_dataarray('da.nc', chunks={'x':10}) as d:
    %timeit d.sum().load()
1min 54s ± 1.43 s per loop (mean ± std. dev. of 7 runs, 1 loop each)

with xr.open_dataarray('da.nc', chunks={'y':10, 'x':10}) as d:
    %timeit d.sum().load()
2min 23s ± 215 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
```","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-409168605,https://api.github.com/repos/pydata/xarray/issues/2329,409168605,MDEyOklzc3VlQ29tbWVudDQwOTE2ODYwNQ==,10050469,2018-07-31T10:09:36Z,2018-07-31T13:21:34Z,MEMBER,"> Those chunksizes are the opposite of what I was expecting...

`chunksizes` in `encoding` are ignored in your case, dask still uses your user provided encoding.

Can you still try to chunk along one dimension only? i.e. ``chunks={'time':200}``","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-409165114,https://api.github.com/repos/pydata/xarray/issues/2329,409165114,MDEyOklzc3VlQ29tbWVudDQwOTE2NTExNA==,10050469,2018-07-31T09:56:54Z,2018-07-31T10:20:32Z,MEMBER,"[EDIT]: forgot the load ...

<s> forget my comment about chunks - I thought this would make a difference but it's actually the opposite (to my surprise): </s>
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825
https://github.com/pydata/xarray/issues/2329#issuecomment-409159969,https://api.github.com/repos/pydata/xarray/issues/2329,409159969,MDEyOklzc3VlQ29tbWVudDQwOTE1OTk2OQ==,10050469,2018-07-31T09:38:37Z,2018-07-31T10:19:37Z,MEMBER,"Out of curiosity: 
- why do you chunk over lats and lons rather than time? The order of dimensions in your dataarray suggest that chunking over time could be more efficient
- can you show the output of ``ds.mtpr`` and ``ds.mtpr.encoding`` ?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,345715825