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/2139#issuecomment-708616198,https://api.github.com/repos/pydata/xarray/issues/2139,708616198,MDEyOklzc3VlQ29tbWVudDcwODYxNjE5OA==,5635139,2020-10-14T19:34:53Z,2020-10-14T19:34:53Z,MEMBER,"As you wish — if there's a motivating example then that has more weight, and big issues should have ample supply of motivating examples. That said, if you have something ready to go, then happy to take a look at it.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742
https://github.com/pydata/xarray/issues/2139#issuecomment-708579401,https://api.github.com/repos/pydata/xarray/issues/2139,708579401,MDEyOklzc3VlQ29tbWVudDcwODU3OTQwMQ==,5635139,2020-10-14T18:23:16Z,2020-10-14T18:23:16Z,MEMBER,Great! Post here / a new issue if something does come up!,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742
https://github.com/pydata/xarray/issues/2139#issuecomment-708499472,https://api.github.com/repos/pydata/xarray/issues/2139,708499472,MDEyOklzc3VlQ29tbWVudDcwODQ5OTQ3Mg==,5635139,2020-10-14T16:00:35Z,2020-10-14T16:00:35Z,MEMBER,"@mankoff Thanks for the issue, do you have a fuller reproduction? I'm happy to take a look at this.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742
https://github.com/pydata/xarray/issues/2139#issuecomment-389620638,https://api.github.com/repos/pydata/xarray/issues/2139,389620638,MDEyOklzc3VlQ29tbWVudDM4OTYyMDYzOA==,1217238,2018-05-16T18:31:35Z,2018-05-16T18:31:35Z,MEMBER,"MetaCSV looks interesting but I haven't used it myself. My guess would be that it just wraps pandas/xarray for processing data, so I think it's unlikely to give a performance boost. It's more about a declarative way to specify how to load a CSV into pandas/xarray.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742
https://github.com/pydata/xarray/issues/2139#issuecomment-389598338,https://api.github.com/repos/pydata/xarray/issues/2139,389598338,MDEyOklzc3VlQ29tbWVudDM4OTU5ODMzOA==,1217238,2018-05-16T17:20:03Z,2018-05-16T17:20:03Z,MEMBER,"If you don't want the full Cartesian product, you need to ensure that the index only contains the variables you want to expand into a grid, e.g., time, lat and lon.

If the problem is only running out of memory (which is indeed likely with 1e9 rows), then you'll need to think about a more clever way to convert the data. One good option might be to groups over subsets of the data (using dask or another parallel processing library like spark or beam), and write a bunch of smaller netCDF which you then open with xarray's `open_mfdataset()`. It's probably most convenient to split over time, e.g., into files for each day or month.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742
https://github.com/pydata/xarray/issues/2139#issuecomment-389590507,https://api.github.com/repos/pydata/xarray/issues/2139,389590507,MDEyOklzc3VlQ29tbWVudDM4OTU5MDUwNw==,2443309,2018-05-16T16:55:27Z,2018-05-16T16:55:27Z,MEMBER,@brianmingus - any chance you can provide a reproducible example with some dummy data? ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,323703742