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/1257#issuecomment-307977450,https://api.github.com/repos/pydata/xarray/issues/1257,307977450,MDEyOklzc3VlQ29tbWVudDMwNzk3NzQ1MA==,1197350,2017-06-13T01:08:07Z,2017-06-13T01:08:07Z,MEMBER,"I am very interested. I have been doing a lot of benchmarking already wrt dask.distributed on my local cluster, focusing on performance with multi-terabyte datasets. At this scale, certain operations emerge as performance bottlenecks (e.g. index alignment of multi-file netcdf datasets, #1385).

I think this should probably be done in AWS or Google Cloud. That way we can establish a consistent test environment for benchmarking. I might be able to pay for that (especially if our proposal gets funded)!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,206632333
https://github.com/pydata/xarray/issues/1257#issuecomment-278845582,https://api.github.com/repos/pydata/xarray/issues/1257,278845582,MDEyOklzc3VlQ29tbWVudDI3ODg0NTU4Mg==,1197350,2017-02-10T03:04:31Z,2017-02-10T03:04:31Z,MEMBER,"Another 👍  for benchmarking. Especially as we start to get deep into integrating dask.distributed, having robust performance benchmarks will be very useful. One challenge is where to deploy the benchmarks. TravisCI might not be ideal, since performance can vary depending on competition from other virtual machines on the same system.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,206632333