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/1650#issuecomment-824207037,https://api.github.com/repos/pydata/xarray/issues/1650,824207037,MDEyOklzc3VlQ29tbWVudDgyNDIwNzAzNw==,6574622,2021-04-21T16:46:54Z,2021-06-15T16:18:54Z,CONTRIBUTOR,"I'd be interested in this kind of thing as well. :+1:
We are having long time series data, which we would like to access via opendap or zarr over HTTP. Currently, the `time` coordinate variable is already more than 1 GB in size, which makes loading the dataset very slow or even impossible given the limitations of the opendap server and my home internet wire. Nonetheless, we know that the timestamps are in order and reasonably close to equidistant. Thus binary search or even interpolation search should be a quick method to find the right indices.","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-544816352,https://api.github.com/repos/pydata/xarray/issues/1650,544816352,MDEyOklzc3VlQ29tbWVudDU0NDgxNjM1Mg==,26384082,2019-10-22T05:58:59Z,2019-10-22T05:58:59Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity
If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338786761,https://api.github.com/repos/pydata/xarray/issues/1650,338786761,MDEyOklzc3VlQ29tbWVudDMzODc4Njc2MQ==,703554,2017-10-23T20:29:41Z,2017-10-23T20:29:41Z,CONTRIBUTOR,"Index API sounds good.
Also I was just looking at [dask.dataframe indexing](https://github.com/dask/dask/blob/master/dask/dataframe/indexing.py), there .loc is implemented using information about index values at the boundaries of each partition (chunk). Not sure xarray should use same strategy for chunked datasets, but is another approach to avoid loading indexes into memory. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338779368,https://api.github.com/repos/pydata/xarray/issues/1650,338779368,MDEyOklzc3VlQ29tbWVudDMzODc3OTM2OA==,1217238,2017-10-23T20:02:12Z,2017-10-23T20:02:12Z,MEMBER,This should be easier after the index/coordinates separation envisioned in https://github.com/pydata/xarray/issues/1603. We could potentially define a basic index API (based on what we currently use from pandas) and allow alternative index implementations. There are certainly other use cases where go beyond pandas makes sense -- a KDTree for indexing geospatial data is one obvious example.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338687376,https://api.github.com/repos/pydata/xarray/issues/1650,338687376,MDEyOklzc3VlQ29tbWVudDMzODY4NzM3Ng==,703554,2017-10-23T14:58:59Z,2017-10-23T14:58:59Z,CONTRIBUTOR,"It looks like #1017 is about having no index at all. I want indexes, but I
want to avoid loading all coordinate values into memory.
On Mon, Oct 23, 2017 at 1:47 PM, Fabien Maussion
wrote:
> Has anyone considered implementing an index for monotonic data that does
> not require loading all values into main memory?
>
> But this is already the case? #1017
>
>
> With on file datasets I *think* it is sufficient to drop_variables when
> opening the dataset in order not to parse the coordinates:
>
> ds = xr.open_dataset(f, drop_variables=['lon', 'lat'])
>
> —
> You are receiving this because you authored the thread.
> Reply to this email directly, view it on GitHub
> , or mute
> the thread
>
> .
>
--
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","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338662290,https://api.github.com/repos/pydata/xarray/issues/1650,338662290,MDEyOklzc3VlQ29tbWVudDMzODY2MjI5MA==,1197350,2017-10-23T13:40:53Z,2017-10-23T13:40:53Z,MEMBER,This is related to the performance issue documented in #1385.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338647540,https://api.github.com/repos/pydata/xarray/issues/1650,338647540,MDEyOklzc3VlQ29tbWVudDMzODY0NzU0MA==,10050469,2017-10-23T12:47:02Z,2017-10-23T12:47:11Z,MEMBER,"> Has anyone considered implementing an index for monotonic data that does not require loading all values into main memory?
But this is already the case? See https://github.com/pydata/xarray/pull/1017
With on file datasets I *think* it is sufficient to `drop_variables` when opening the dataset in order not to parse the coordinates:
```ds = xr.open_dataset(f, drop_variables=['lon', 'lat'])```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781
https://github.com/pydata/xarray/issues/1650#issuecomment-338627454,https://api.github.com/repos/pydata/xarray/issues/1650,338627454,MDEyOklzc3VlQ29tbWVudDMzODYyNzQ1NA==,703554,2017-10-23T11:19:30Z,2017-10-23T11:19:30Z,CONTRIBUTOR,"Just to add a further thought, which is that the upper levels of the binary search tree could be be cached to get faster performance for repeated searches.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,267628781