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If the limitation of number of files opened is lower than the number of files to open to read a virtual dataset, some of the data is not read (and silently replaced with the fillvalue.
This patch adds a test to illustrate this (provided the limit of max opened file is lower than 2000, check with ulimit -Sn):
--- a/test/base_test.py+++ b/test/base_test.py@@ -255,3 +255,25 @@ class BaseTestEndpoints:
"target_path": "not_an_entity",
"type": "soft_link",
}
+++ def test_vds(self, server):+ # Create a file with too many files in a VDS+ filename = "test.h5"+ path = "data"+ nfiles = 2000+ ndata = 10++ layout = h5py.VirtualLayout(shape=(nfiles, ndata), dtype=np.uint8)+ for n in range(nfiles):+ fname = server.served_directory / f"{n}.h5"+ with h5py.File(fname, "w") as h5file:+ h5file['data'] = np.ones((ndata,), dtype=np.uint8)+ layout[n] = h5py.VirtualSource(fname, 'data', shape=(ndata,))++ with h5py.File(server.served_directory / filename, mode="w") as h5file:+ h5file.create_virtual_dataset('data', layout, fillvalue=0)++ response = server.get(f"/data/?file={str(filename)}&path={path}&format=npy")+ content = decode_response(response, format="npy")+ assert np.all(content != 0)
If the limitation of number of files opened is lower than the number of files to open to read a virtual dataset, some of the data is not read (and silently replaced with the
fillvalue
.This patch adds a test to illustrate this (provided the limit of max opened file is lower than 2000, check with
ulimit -Sn
):On way to workaround this is to increase the limit on the host (
ulimit -Sn unlimited
), another is to do the same from python using resource with something like: https://github.com/silx-kit/silx/blob/bd3b283f2cf03f145a34caaaadec539146c448bb/src/silx/app/view/main.py#L89-L102A neater one would be for libhdf5 to take care of this...
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