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/pull/1899#issuecomment-374422762,https://api.github.com/repos/pydata/xarray/issues/1899,374422762,MDEyOklzc3VlQ29tbWVudDM3NDQyMjc2Mg==,6815844,2018-03-19T23:40:52Z,2018-03-19T23:40:52Z,MEMBER,"Yes, 
LazilyIndexedArray was renamed to `LazilyOuterIndexedArray` and `LazilyVectorizedIndexedArray` was newly added.
These two backend arrays are selected depending on what kind of indexer is used.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-370970309,https://api.github.com/repos/pydata/xarray/issues/1899,370970309,MDEyOklzc3VlQ29tbWVudDM3MDk3MDMwOQ==,6815844,2018-03-06T23:45:13Z,2018-03-06T23:45:13Z,MEMBER,"Thanks, @WeatherGod , for your feedback.
This is finally merged!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-370125916,https://api.github.com/repos/pydata/xarray/issues/1899,370125916,MDEyOklzc3VlQ29tbWVudDM3MDEyNTkxNg==,6815844,2018-03-03T07:11:24Z,2018-03-03T07:11:24Z,MEMBER,All done :),"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-368385680,https://api.github.com/repos/pydata/xarray/issues/1899,368385680,MDEyOklzc3VlQ29tbWVudDM2ODM4NTY4MA==,6815844,2018-02-26T04:16:03Z,2018-02-26T04:16:03Z,MEMBER,"I think it's ready :)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-366618866,https://api.github.com/repos/pydata/xarray/issues/1899,366618866,MDEyOklzc3VlQ29tbWVudDM2NjYxODg2Ng==,6815844,2018-02-19T08:30:01Z,2018-02-19T08:30:01Z,MEMBER,"This looks some backends do not support negative step slices.
I'm going to wrap this maybe this weekend.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-366377467,https://api.github.com/repos/pydata/xarray/issues/1899,366377467,MDEyOklzc3VlQ29tbWVudDM2NjM3NzQ2Nw==,6815844,2018-02-16T22:30:32Z,2018-02-16T22:30:32Z,MEMBER,"@WeatherGod, Thanks for testing.
Can you share more detail? 
With your example, what does `wind_inds` look like? Can you share the shape and dimension names?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-366373577,https://api.github.com/repos/pydata/xarray/issues/1899,366373577,MDEyOklzc3VlQ29tbWVudDM2NjM3MzU3Nw==,6815844,2018-02-16T22:12:44Z,2018-02-16T22:16:13Z,MEMBER,"Can you share how you tested this?
The test I added says it is still in memory after vectroized indexing.

edit: 
wind_inds is a 1d-array? If this is the case, the both should trigger OuterIndexing. But in both cases it should be indexed lazily...","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-364755370,https://api.github.com/repos/pydata/xarray/issues/1899,364755370,MDEyOklzc3VlQ29tbWVudDM2NDc1NTM3MA==,6815844,2018-02-11T14:25:40Z,2018-02-11T19:49:04Z,MEMBER,"Based on the [suggestion](https://github.com/pydata/xarray/pull/1899#issuecomment-364529325), I implemented the lazy vectorized indexing with index-consolidation.

Now, every backend is virtually compatible to all the indexer types, i.e. basic-, outer- and vectorized-indexers.

It sometimes consume large amount of memory if the indexer is unable to decompose efficiently, but it is always better than loading the full slice.
The drawback is the unpredictability of how many data will be loaded.

","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-364625973,https://api.github.com/repos/pydata/xarray/issues/1899,364625973,MDEyOklzc3VlQ29tbWVudDM2NDYyNTk3Mw==,6815844,2018-02-10T04:47:04Z,2018-02-10T04:47:04Z,MEMBER,"> There are some obvious fail cases, e.g., if they want to pull out indices array[[1, -1], [1, -1]], in which case the entire array needs to be sliced. 

If the backend supports the orthogonal indexing (not only the basic indexing), 
we can do `array[[1, -1]][:, [1, -1]]`, load the 2x2 array, then apply the vectorized indexing `[[0, 1], [0, 1]]`.

But if we want a full diagonal, we need a full slice anyway...

> Also, we would want to avoid separating basic/vectorized for backends that support efficient vectorized indexing (scipy and zarr).

OK. Agreed. We may need a flag that can be accessed from the array wrapper.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-364616100,https://api.github.com/repos/pydata/xarray/issues/1899,364616100,MDEyOklzc3VlQ29tbWVudDM2NDYxNjEwMA==,6815844,2018-02-10T01:47:54Z,2018-02-10T01:47:54Z,MEMBER,"I am inclined to the option 1, as there are some benefit even for backend without the vectorized-indexing support,
e.g.
in case we want to get three diagonal elements (1, 1), (2, 2), (3, 3) from a 1000x1000 array. What we want is
`array[[1, 2, 3], [1, 2, 3]]`.
It can be decomposed to 
`array[1: 4, 1:4][[0, 1, 2], [0, 1, 2]]`.
We only need to load 3 x 3 part of the 1000 x 1000 array, then take its diagonal elements.

A drawback is that it is difficult for users to predict how large memory is necessary.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
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.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143
https://github.com/pydata/xarray/pull/1899#issuecomment-364442081,https://api.github.com/repos/pydata/xarray/issues/1899,364442081,MDEyOklzc3VlQ29tbWVudDM2NDQ0MjA4MQ==,6815844,2018-02-09T14:04:16Z,2018-02-09T14:04:16Z,MEMBER,"I noticed the lazy vectorized indexing can be (sometimes) optimized by decomposing the vectorized indexers into successive outer and vectorized indexers, so that the size of the array to be loaded into memory is minimized.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,295838143