issue_comments

@mrocklin Any thoughts on my thread safety concerns (https://github.com/pydata/xarray/issues/798#issuecomment-259202265) for the LRU cache? I suppose the simplest thing to do is to simply refuse to evict a file until the per-file lock is released, but I can see that strategy failing pretty badly in edge cases.

A few other thoughts on thread safety with the LRU approach: 1. We need to a global lock ensure internal consistency of the LRU cache, and so that we don't overwrite files without closing them. It probably makes sense to put this in memoize function. 2. We need separate, per file locks, to ensure that we don't evict files in the process of reading or writing data from them (which would cause segfaults). As a stop-gap measure, we could simply refuse to evict files until we can acquire a lock, but more broadly this suggests that strict LRU is not quite right. Instead, we want to evict the least-recently-used unlocked item.

One slight subtlety is writes. We'll need to switch from 'w' to 'a' mode the second time we open a file. On Tue, Nov 8, 2016 at 8:17 AM Matthew Rocklin notifications@github.com wrote:

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We have something very hacky working with https://github.com/pydata/xarray/pull/1095

I'm also going to see if I can get something working with the LRU cache, since that seems closer to the solution we want eventually.

Distributed Dask.array could possibly replace OpenDAP in some settings though

Yes, this sounds quite promising to me.

Using OpenDAP for communication is also possible, but if all we need to do is pass around serialized xarray.Dataset objects using pickle or even bytes from netCDF files seems more promising.

We could possibly make an object that was API compatible with the subset of netCDF4.Dataset that you needed, but opened and closed the file whenever it actually pulled data. We would keep an LRU cache of open files around for efficiency as discussed earlier. In this case we could possibly optionally swap out the current netCDF4.Dataset object with this thing without much refactoring?

Yes, this could work for a proof of concept.

In the long term, it would be good to integrate this into xarray so we can support alternative backends (e.g., h5netcdf, scipy, pynio, loaders for custom file formats like @rabernat and @pwolfram work with) in a fully consistent fashion without needing to make a separate wrapper for each.

@mrocklin OK, that makes sense. In that case, we might indeed need to thread this through xarray's backends.

Currently, backends open a file (e.g., with netCDF4.Dataset) and create an OrderedDict of xarray.Variable objects with lazy arrays that load from the file on demand. To load this data with dask, pass these lazy arrays into dask.array.from_array.

This currently doesn't use dask.delayed for three reasons: 1. Historical: we wrote this system before dask existed. 2. Performance: our LazilyIndexedArray class is still more selective than dask.array for subsetting data from large chunks, which is essential for many interactive use cases. Despite getitem fusing, dask will sometimes load complete chunks. This is particularly true if we do some transformation of the array, of the sort that could be accomplished with dask's map_blocks. Using LazilyIndexedArray ensures that this only gets applied to loaded data. There are also performance benefits to keeping files open when possible (discussed above). 3. Dependencies: dask is still an optional dependency for xarray. I'd like to keep it that way, if possible.

It seems like a version of xarray's backends that doesn't always open files immediately would make it suitable for use in dask.distributed. So indeed, we'll need to do some serious refactoring.

One other thing that will need to be tackled eventually: xarray.merge and xarray.concat (used in open_mfdataset) still have some steps (checking for equality between arrays) that are applied sequentially. This is going to be a performance bottleneck when we start working with very large arrays. This really should be refactored such that dask can do these evaluations in a single step, rather than once per object. For now, this can be avoided in concat by using the data_vars/coords options.

I'm happy to help work out a plan here.

It seems like there are basically two steps we need to make this happen: 1. Write the equivalent of futures_to_dask_arrays for xarray.Dataset, i.e., futures_to_xarray_datasets_of_dask_arrays. 2. Integrate this into xarray's higher level utility functions like open_mfdataset. This should be pretty easy after we have futures_to_xarray_datasets_of_dask_arrays.

It's an open question to what extent this needs to interact with xarray's internal backends.DataStore API, which handles the details of decoding files on disk to xarray.Dataset objects. I'm hopeful the answer is "not very much". The DataStore API is a bit cumbersome and overly complex, and could use a refactoring.

@shoyer, if if we are happy to open all netCDF files and read out the metadata from a master process that would imply that we would open a file, read the metadata, and then close it, correct?

Array access should then follow something like the @mrocklin's netcdf_Dataset approach, right?

Yes, this is correct.

In principle, if we have a very large number of files containing many variables each, we might want to do the read in parallel using futures, and then use something like futures_to_dask_arrays to bring them together. That seems much trickier to integrate into our current backend approach.

@pwolfram I was referring to this comment for @mrocklin's netCDF4_Dataset.

I think the LRU dict has to be a global because because the file restriction is an attribute of the system, correct?

Correct, the LRU dict should be global. I believe the file restriction is generally per-process, and creating a global dict should assure that works properly.

For each read from a file, ensure it hasn't been closed via a @ds.getter property method. If so, reopen it via the LRU cache. This is ok because for a read the file is essentially read-only. The LRU closes out stale entries to prevent the too many open file errors. Checking this should be fast.

The challenge is that we only call the .get_variables() method (and hence self.ds) once on a DataStore when a Dataset is opened from disk. I think we need to refactor NetCDF4ArrayWrapper to take a filename instead, and use something like @mrocklin's netcdf_Dataset.

My bigger concern was how to make use of a method like futures_to_dask_arrays. But it looks like that may actually not be necessary, at least if we are happy to open all netCDF files (and read out the metadata) from a master process.

I agree with @mrocklin that the LRUCache for file-like objects should take care of things from the dask.array perspective. It should also solve https://github.com/pydata/xarray/issues/463 in a very clean way. We'll just need to reorganize things a bit to make use of it.