issue_comments: 364573328

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https://github.com/pydata/xarray/pull/1899#issuecomment-364573328 https://api.github.com/repos/pydata/xarray/issues/1899 364573328 MDEyOklzc3VlQ29tbWVudDM2NDU3MzMyOA== 6815844 2018-02-09T21:28:26Z 2018-02-09T21:28:26Z MEMBER

Thanks, @shoyer Do you think it is possible to consolidate transpose also? We need it to keep our logic in Variable._broadcast_indexing.

I am wondering what computation cost we want to avoid by the lazy indexing. 1. The indexing itself is expensive so we want to minimize the number of indexing operation? 2. The original data is too large to fit into memory, and we want to load the smallest subset of the original array by the lazy indexing?

If the reason 2 is the common case, I think it is not a good idea to consolidate all the lazy indexing as VectorizedIndexer, since most of the backend does not support vectorized indexing, which means we need to load all the array into memory before any indexing operation. (But still it would be valuable to consolidate all the indexers after the first vectorized indexer, since we can decompose any VectorizedIndexer into successive outer- and smaller vectorized-indexers pair.)

And I am also wondering as pointed out in #1725, what I am doing now was already implemented in dask.

 
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