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/2501#issuecomment-510169853,https://api.github.com/repos/pydata/xarray/issues/2501,510169853,MDEyOklzc3VlQ29tbWVudDUxMDE2OTg1Mw==,1197350,2019-07-10T18:10:37Z,2019-07-10T18:10:37Z,MEMBER,"I believe that the memory issue is basically the same as https://github.com/dask/distributed/issues/2602.

The graphs look like: `read --> rechunk --> write`.

Reading and rechunking increase memory consumption. Writing relieves it. In Rich's case, the workers just load too much data before they write it. Eventually they run out of memory.

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074
https://github.com/pydata/xarray/issues/2501#issuecomment-506482057,https://api.github.com/repos/pydata/xarray/issues/2501,506482057,MDEyOklzc3VlQ29tbWVudDUwNjQ4MjA1Nw==,1197350,2019-06-27T19:36:51Z,2019-06-27T19:36:51Z,MEMBER,"@rsignell-usgs 

Can you post the xarray repr of two sample files post pre-processing function?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074
https://github.com/pydata/xarray/issues/2501#issuecomment-506481845,https://api.github.com/repos/pydata/xarray/issues/2501,506481845,MDEyOklzc3VlQ29tbWVudDUwNjQ4MTg0NQ==,1197350,2019-06-27T19:36:11Z,2019-06-27T19:36:11Z,MEMBER,"> Are there any datasets on https://pangeo-data.github.io/pangeo-datastore/ that would exhibit this poor behavior?

The datasets in our cloud datastore are designed explicitly to avoid this problem!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074
https://github.com/pydata/xarray/issues/2501#issuecomment-503641038,https://api.github.com/repos/pydata/xarray/issues/2501,503641038,MDEyOklzc3VlQ29tbWVudDUwMzY0MTAzOA==,1197350,2019-06-19T16:48:29Z,2019-06-19T16:48:29Z,MEMBER,"Try writing a preprocessor function that drops all coordinates
```python
def drop_coords(ds):
    return ds.reset_coords(drop=True)
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074
https://github.com/pydata/xarray/issues/2501#issuecomment-432342306,https://api.github.com/repos/pydata/xarray/issues/2501,432342306,MDEyOklzc3VlQ29tbWVudDQzMjM0MjMwNg==,1197350,2018-10-23T17:27:50Z,2018-10-23T17:27:50Z,MEMBER,"^ I'm assuming you're in a notebook. If not, call `print` instead of `display`.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074
https://github.com/pydata/xarray/issues/2501#issuecomment-432342180,https://api.github.com/repos/pydata/xarray/issues/2501,432342180,MDEyOklzc3VlQ29tbWVudDQzMjM0MjE4MA==,1197350,2018-10-23T17:27:30Z,2018-10-23T17:27:30Z,MEMBER,"In `open_mfdataset`, all of the dimensions and coordinates of the individual files have to be checked and verified to be compatible. That is often the source of slow performance with open_mfdataset.

To help us help you debug, please provide more information about the files your are opening. Specifically, please call `open_dataset()` directly on the first two files and copy and paste the output here. Specifically, do something like this
```python
from glob import glob
import xarray as xr
all_files = glob('*1002*.nc')
display(xr.open_dataset(all_files[0]))
display(xr.open_dataset(all_files[1]))
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,372848074