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/1874#issuecomment-460128002,https://api.github.com/repos/pydata/xarray/issues/1874,460128002,MDEyOklzc3VlQ29tbWVudDQ2MDEyODAwMg==,2443309,2019-02-04T04:29:08Z,2019-02-04T04:29:08Z,MEMBER,"This can be done using xarray->zarr->SQL (https://github.com/zarr-developers/zarr/pull/368). Additional databases such are also available as stores in zarr. 

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,293293632
https://github.com/pydata/xarray/issues/1874#issuecomment-362945533,https://api.github.com/repos/pydata/xarray/issues/1874,362945533,MDEyOklzc3VlQ29tbWVudDM2Mjk0NTUzMw==,1217238,2018-02-04T22:26:44Z,2018-02-04T22:26:44Z,MEMBER,"> Then I need to write the data to a postgres DB. I have tried parsing the array and using an INSERT for every row, but this is taking a very long time (weeks).

I'm not a particular expert on postgres but I suspect it indeed has some sort of bulk insert facilities.

> However, when trying to convert my xarray Dataset to a Pandas Dataframe, I ran out of memory quickly.

If you're working with a 47GB netCDF file, you probably don't have a lot of memory to spare. Often `pandas.DataFrame` objects can use significantly more memory than `xarray.Dataset`, especially keeping in mind that an xarray Dataset can lazily reference data on disk but a DataFrame is always in memory. The best strategy is probably to slice the Dataset into small pieces and to individually convert those.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,293293632
https://github.com/pydata/xarray/issues/1874#issuecomment-362072512,https://api.github.com/repos/pydata/xarray/issues/1874,362072512,MDEyOklzc3VlQ29tbWVudDM2MjA3MjUxMg==,5635139,2018-01-31T21:13:49Z,2018-01-31T21:13:49Z,MEMBER,"There's no xarray->SQL connector, unfortunately.

I don't have that much experience here so I'll let other chime in. You could try chunking to pandas and then to Postgres (but you'll always be limited by memory with pandas). If there's a NetCDF -> tabular connector, that would allow you to operate beyond memory.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,293293632