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/pull/1983#issuecomment-382071801,https://api.github.com/repos/pydata/xarray/issues/1983,382071801,MDEyOklzc3VlQ29tbWVudDM4MjA3MTgwMQ==,1117224,2018-04-17T17:14:33Z,2018-04-17T17:38:42Z,NONE,"Thanks @jhamman for working on this! I did a test on my real world data (1202 ~3mb files) on my local computer and am not getting results I expected: 
1) No speed up with parallel=True
2) _Slow down_ when using distributed (processes=16 cores=16). 

Am I missing something?

```python
nc_files = glob.glob(E.obs['NSIDC_0081']['sipn_nc']+'/*.nc')
print(len(nc_files))
1202

# Parallel False
%time ds = xr.open_mfdataset(nc_files, concat_dim='time', parallel=False, autoclose=True)
CPU times: user 57.8 s, sys: 3.2 s, total: 1min 1s
Wall time: 1min

# Parallel True with default scheduler 
%time ds = xr.open_mfdataset(nc_files, concat_dim='time', parallel=True, autoclose=True)
CPU times: user 1min 16s, sys: 9.82 s, total: 1min 26s
Wall time: 1min 16s

# Parallel True with distributed
from dask.distributed import Client
client = Client()
print(client)
<Client: scheduler='tcp://127.0.0.1:43291' processes=16 cores=16>
%time ds = xr.open_mfdataset(nc_files, concat_dim='time', parallel=True, autoclose=True)
CPU times: user 2min 17s, sys: 12.3 s, total: 2min 29s
Wall time: 3min 48s

```

On feature/parallel_open_netcdf commit 280a46f13426a462fb3e983cfd5ac7a0565d1826","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,304589831