issue_comments

closed via https://github.com/dask/dask/pull/2364 (a long time ago)

Hi @JanisGailis. Thanks for looking into this issue! I will give your solution a try as soon as I get some free time.

However, I would like to point out that the issue is completely resolved by dask/dask#2364. So this can probably be closed after the next dask release.

This dask bug also explains why it is so slow to generate the repr for these big datasets.

I have created a self-contained, reproducible example of this serious performance problem. https://gist.github.com/rabernat/7175328ee04a3167fa4dede1821964c6

This issue is becoming a big problem for me. I imagine other people must be experiencing it too.

I am happy to try to dig in and fix it, but I think some of @shoyer's backend insight would be valuable first.

dask may be loading full arrays to do this computation

This is definitely what I suspect is happening. The problem with adding more chunks is that I quickly hit my system ulimit (see #1394), since, for some reason, all the 1754 files are opened as soon as I call .load(). Putting more chunks in just multiplies that number.

One thing worth trying is specifying chunks manually in open_mfdataset. Point-wise indexing should not really require chunks specified ahead of time, but the optimizations dask uses to make these operations efficient are somewhat fragile, so dask may be loading full arrays to do this computation.

OK, so that isn't terribly useful -- the slow-down is somewhere in dask-land. If it was an issue with alignment, that would come up when building the dask graph, not computing it.

The relevant part of the stack trace is ``` /home/rpa/.conda/envs/dask_distributed/lib/python3.5/site-packages/xarray/core/dataarray.py in load(self) 571 working with many file objects on disk. 572 """ --> 573 ds = self._to_temp_dataset().load() 574 new = self._from_temp_dataset(ds) 575 self._variable = new._variable

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/site-packages/xarray/core/dataset.py in load(self) 467 468 # evaluate all the dask arrays simultaneously --> 469 evaluated_data = da.compute(*lazy_data.values()) 470 471 for k, data in zip(lazy_data, evaluated_data):

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/site-packages/dask/base.py in compute(args, kwargs) 200 dsk = collections_to_dsk(variables, optimize_graph, kwargs) 201 keys = [var._keys() for var in variables] --> 202 results = get(dsk, keys, *kwargs) 203 204 results_iter = iter(results)

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/site-packages/distributed/client.py in get(self, dsk, keys, restrictions, loose_restrictions, resources, **kwargs) 1523 return sync(self.loop, self._get, dsk, keys, restrictions=restrictions, 1524 loose_restrictions=loose_restrictions, -> 1525 resources=resources) 1526 1527 def _optimize_insert_futures(self, dsk, keys):

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/site-packages/distributed/utils.py in sync(loop, func, args, kwargs) 200 loop.add_callback(f) 201 while not e.is_set(): --> 202 e.wait(1000000) 203 if error[0]: 204 six.reraise(error[0])

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/threading.py in wait(self, timeout) 547 signaled = self._flag 548 if not signaled: --> 549 signaled = self._cond.wait(timeout) 550 return signaled 551

/home/rpa/.conda/envs/dask_distributed/lib/python3.5/threading.py in wait(self, timeout) 295 else: 296 if timeout > 0: --> 297 gotit = waiter.acquire(True, timeout) 298 else: 299 gotit = waiter.acquire(False)

KeyboardInterrupt: ```

I think the issue you are referring to is also mine (#1385).

Can you try using Ctrl + C to interrupt things and report the stack-trace?

This might be an issue with xarray verifying aligned coordinates, which we should have an option to disable. (This came up somewhere else recently, but I couldn't find the issue with a quick search.)