id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
277538485,MDU6SXNzdWUyNzc1Mzg0ODU=,1745,open_mfdataset() memory error in v0.10,22665917,closed,0,,,24,2017-11-28T21:08:23Z,2019-01-13T01:51:43Z,2019-01-13T01:51:43Z,NONE,,,,"#### Code Sample

```python
import xarray

ncfiles = '/example/path/to/wrf/netcdfs/*'
dropvars = ['list', 'of', 'many', 'vars', 'to', 'drop']

dset = xarray.open_mfdataset(ncfiles, drop_variables=dropvars, concat_dim='Time',  
                                                autoclose=True, decode_cf=False)
```
#### Problem description

I am trying to load 73 model (WRF) output files using ``open_mfdataset()``. (Thus, 'Time' is a new dimension). Each netcdf has dimensions ``{'x' : 405, 'y' : 282, 'z': 37}`` and roughly 20 variables (excluding the other ~20 in ``dropvars``). 

When I run the above code with **v0.9.6**, it completes in roughly 7 seconds. But with **v0.10**, it crashes with the following error:

``*** Error in `~/anaconda3/bin/python': corrupted size vs. prev_size: 0x0000560e9b6ca7b0 ***``

which, as I understand, means I'm exceeding my memory allocation. Any thoughts on what could be the source of this issue?

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-3-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C
LANG: C
LOCALE: None.None

xarray: 0.10.0
pandas: 0.20.3
numpy: 1.13.1
scipy: 0.19.1
netCDF4: 1.2.4
h5netcdf: 0.5.0
Nio: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.16.0
matplotlib: 2.0.2
cartopy: None
seaborn: 0.8.0
setuptools: 27.2.0
pip: 9.0.1
conda: 4.3.29
pytest: 3.1.3
IPython: 6.1.0
sphinx: 1.6.2

</details>
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374070147,MDU6SXNzdWUzNzQwNzAxNDc=,2512,to_netcdf() fails because of datetime encoding,22665917,closed,0,,,2,2018-10-25T18:17:47Z,2018-10-27T16:34:54Z,2018-10-27T16:34:54Z,NONE,,,,"#### Simple example:

```python
import numpy as np
from datetime import datetime, timedelta
import xarray

# ""time"" coordinate
dt = datetime(1999, 1, 1)
dts = np.array([dt + timedelta(days=x) for x in range(10)])
coords = {'time': dts}

# simple float data
data = np.arange(10)
vrbls = {'foo': (('time',), data)}

# create the Dataset
ds = xarray.Dataset(vrbls, coords)

# encode the time coordinate
units = 'days since 1900-01-01'
ds.time.encoding['units'] = units

# write to netcdf
ds.to_netcdf('test.nc')

```
#### Problem description

When I run the above, I get the following error when executing the last line:
**`ValueError: unsupported dtype for netCDF4 variable: datetime64[ns]`**

The documentation indicates that datetime and datetime64 objects are both supported by xarray and should write to netcdf just fine when supplied ""units"" for encoding (this code fails with or without the encoding lines). Any Idea when is going wrong here?

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-8-amd64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

xarray: 0.10.9
pandas: 0.20.3
numpy: 1.13.1
scipy: 0.19.1
netCDF4: 1.4.2
h5netcdf: 0.5.0
h5py: 2.8.0
Nio: None
zarr: None
cftime: 1.0.1
PseudonetCDF: None
rasterio: None
iris: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.16.0
distributed: 1.20.1
matplotlib: 2.1.0
cartopy: None
seaborn: 0.8.0
setuptools: 27.2.0
pip: 9.0.1
conda: 4.5.11
pytest: 3.1.3
IPython: 6.1.0
sphinx: 1.6.2
</details>
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