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/3213#issuecomment-551132924,https://api.github.com/repos/pydata/xarray/issues/3213,551132924,MDEyOklzc3VlQ29tbWVudDU1MTEzMjkyNA==,4605410,2019-11-07T15:37:21Z,2019-11-07T15:37:21Z,NONE,"@dcherian These examples seem focused on merging from disk, whereas the use-case I'm running into is joining data produced by computation in ram. I'll try updating my xarray installation and see where that gets me.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,479942077
https://github.com/pydata/xarray/issues/3213#issuecomment-551090042,https://api.github.com/repos/pydata/xarray/issues/3213,551090042,MDEyOklzc3VlQ29tbWVudDU1MTA5MDA0Mg==,4605410,2019-11-07T13:57:46Z,2019-11-07T13:57:46Z,NONE,"@oliverhiggs Ive also noticed a huge computational overhead when joining xarray datasets where the result would be sparse. Something like a minute of computation time to join two 10GB datasets, even when there are no overlapping indices. I'm not sure if a sparse representation would help but its possible we'd get a reduced memory footprint *and* a faster merge/concat time with this kind of support.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,479942077
https://github.com/pydata/xarray/issues/3484#issuecomment-550471849,https://api.github.com/repos/pydata/xarray/issues/3484,550471849,MDEyOklzc3VlQ29tbWVudDU1MDQ3MTg0OQ==,4605410,2019-11-06T19:48:17Z,2019-11-06T19:48:17Z,NONE,"@friedrichknuth One of my motivations behind exploring sparse DataArray backends is in reducing the memory footprint during merge operations. Consider the following:

<a href=""https://www.codecogs.com/eqnedit.php?latex=\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t))\\&space;\end{Vmatrix}&space;\textbf{merge&space;with}&space;\begin{Vmatrix}&space;X_{22}(t)&space;&&space;X_{23}(t))\\&space;\end{Vmatrix}&space;=&space;\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t)&space;&&space;\textbf{NaN/0}\\&space;\textbf{NaN/0}&space;&&space;X_{22}(t)&space;&&space;X_{23}(t)&space;\end{Vmatrix}"" target=""_blank""><img src=""https://latex.codecogs.com/gif.latex?\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t))\\&space;\end{Vmatrix}&space;\textbf{merge&space;with}&space;\begin{Vmatrix}&space;X_{22}(t)&space;&&space;X_{23}(t))\\&space;\end{Vmatrix}&space;=&space;\begin{Vmatrix}&space;X_{11}(t)&space;&&space;X_{12}(t)&space;&&space;\textbf{NaN/0}\\&space;\textbf{NaN/0}&space;&&space;X_{22}(t)&space;&&space;X_{23}(t)&space;\end{Vmatrix}"" title=""\begin{Vmatrix} X_{11}(t) & X_{12}(t))\\ \end{Vmatrix} \textbf{merge with} \begin{Vmatrix} X_{22}(t) & X_{23}(t))\\ \end{Vmatrix} = \begin{Vmatrix} X_{11}(t) & X_{12}(t) & \textbf{NaN/0}\\ \textbf{NaN/0} & X_{22}(t) & X_{23}(t) \end{Vmatrix}"" /></a>

One can imagine many such merge operations producing a lot of effectively empty indices. While sparse backed arrays might have the ability to condense these empty indices in memory, it seems like [xarray sparse merging isnt quite compatible yet.](https://github.com/pydata/xarray/issues/3445#issue-512205079)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,517338735
https://github.com/pydata/xarray/issues/3213#issuecomment-546058673,https://api.github.com/repos/pydata/xarray/issues/3213,546058673,MDEyOklzc3VlQ29tbWVudDU0NjA1ODY3Mw==,4605410,2019-10-24T19:05:23Z,2019-10-24T19:05:23Z,NONE,"So how would one change an existing dataset or dataarray to using a sparse representation?
something like 
```xr.Dataset.from_dataframe(data_set.to_dataframe(),sparse=True)```?
seems a little silly to have to convert it to a pandas dataframe and back","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,479942077