issue_comments

Removing the self-references to the dask graphs in #2261 seems to resolve the performance issue on its own.

I would be interested if https://github.com/pydata/xarray/pull/2391 still improves performance in any real world yes cases -- perhaps it helps when working with a real cluster or on large datasets? I can't see any difference in my local benchmarks using dask-distributed.

OK, so it seems like the complete solution here should involve refactoring our backend classes to avoid any references to objects storing dask graphs. This is a cleaner solution even regardless of the pickle overhead because it allows us to eliminate all state stored in backend classes. I'll get on that in #2261.

Give https://github.com/pydata/xarray/pull/2391 a try -- in my testing, it speeds up both examples to only take about 3 second each.

If I understand the heuristics used by dask's schedulers correctly, a data dependency might actually be a good idea here because it would encourage colocating write tasks on the same machines. We should probably give this a try. On Wed, Aug 29, 2018 at 12:15 PM Matthew Rocklin notifications@github.com wrote:

It would be nice if dask had a way to consolidate the serialization of these objects, rather than separately serializing them in each task.

You can make it a separate task (often done by wrapping with dask.delayed) and then use that key within other objets. This does create a data dependency though, which can make the graph somewhat more complex.

In normal use of Pickle these things are cached and reused. Unfortunately we can't do this because we're sending the tasks to different machines, each of which will need to deserialize independently.

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I don't really know how they work, but maybe pickeling those NetCDF4ArrayWrapper objects is expensive (ie they contain a reference to something they shouldn't)?

This seems plausible to me, though the situation is likely improved with #2261. It would be nice if dask had a way to consolidate the serialization of these objects, rather than separately serializing them in each task. It's not obvious to me how to do that in xarray short of manually building task graphs so those NetCDF4ArrayWrapper objects are created by dedicated tasks.

CC @mrocklin in case he has thoughts here

Offhand, I don't know why dask.delayed should be adding this much overhead. One possibility is that when tasks are pickled (as is done by dask-distributed), the tasks are much larger because the delayed function gets serialized into each task. It does seem like pickling can add a significant amount of overhead in some cases when using xarray with dask for serialization: https://github.com/pangeo-data/pangeo/issues/266

I'm not super familiar with profiling dask, but it might be worth looking at dask's diagnostics tools (http://dask.pydata.org/en/latest/understanding-performance.html) to understand what's going on here. The appearance of _thread.lock in at the top of these profiles is a good indication that we aren't measuring where most of the computation is happening.

It would also be interesting to see if this changes with the xarray backend refactor from https://github.com/pydata/xarray/pull/2261.