Evn though the kernel not generally releasing mmap_sem on IO caused by
pagefault.

* master:
  lib.xnumpy.structured: New utility to create structured view of an array
  bigarray: Factor-out our custom numpy.lib.stride_tricks.as_strided-alike into lib/xnumpy.py

Structured creates view of the array interpreting its minor axis as fully covered by a dtype.

It is similar to arr.view(dtype) + corresponding reshape, but does
not have limitations of ndarray.view(). For example:

  In [1]: a = np.arange(3*3, dtype=np.int32).reshape((3,3))

  In [2]: a
  Out[2]:
  array([[0, 1, 2],
         [3, 4, 5],
         [6, 7, 8]], dtype=int32)

  In [3]: b = a[:2,:2]

  In [4]: b
  Out[4]:
  array([[0, 1],
         [3, 4]], dtype=int32)

  In [5]: dtxy = np.dtype([('x', np.int32), ('y', np.int32)])

  In [6]: dtxy
  Out[6]: dtype([('x', '<i4'), ('y', '<i4')])

  In [7]: b.view(dtxy)
  ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  <ipython-input-66-af98529aa150> in <module>()
  ----> 1 b.view(dtxy)

  ValueError: To change to a dtype of a different size, the array must be C-contiguous

  In [8]: structured(b, dtxy)
  Out[8]: array([(0, 1), (3, 4)], dtype=[('x', '<i4'), ('y', '<i4')])

Structured always creates view and never copies data.

Here is original context where separately playing with .shape and .dtype
was not enough, since it was creating array copy and OOM'ing the machine:

klaus/wendelin@cbe4938b

We are going to use this code in another place, so move this out to
dommon place as a preparatory step first.

On a related note: Since ArrayRef is generic and quite independent from
BigArray (it only supports it, but equally it supports just other - e.g.
plain arrays), the proper place for it might be also to be lib/xnumpy.py .
We might get to this topic a bit later.

* nxd/master:
  bigarray: RAMArray
  bigarray/tests: Factor out a way to spcify on which BigFile/BigFileH an array is tested into fixture parameter

Currently fails with:

/home/kirr/src/wendelin/wendelin.core/lib/xnumpy.py in restructure(arr, dtype)
     82     print 'stridev:', stridev
     83     #return np.ndarray.__new__(type(arr), shape, dtype, buffer(arr), 0, stridev)
---> 84     return np.ndarray(shape, dtype, buffer(arr), 0, stridev)

TypeError: expected a single-segment buffer object

The kernel sends SIGSTOP to interrupt tracee, but the signal will be
processed only when the process returns from kernel space, e.g. here

	https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/arch/x86/entry/common.c?id=v4.19-rc8-151-g23469de647c4#n160

This way the tracer won't receive obligatory information that tracee
stopped (via wait...) and even though ptrace(ATTACH) succeeds, all other
ptrace commands will fail:

	https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/kernel/ptrace.c?id=v4.19-rc8-151-g23469de647c4#n1140
	https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/kernel/ptrace.c?id=v4.19-rc8-151-g23469de647c4#n207

My original idea was to use ptrace to run code in process to change it's
memory mappings, while the triggering process is under pagefault/read
to wcfs, and the above shows it won't work - trying to ptrace the
client from under wcfs will just block forever (the kernel will be
waiting for read operation to finish for ptrace, and read will be first
waiting on ptrace stopping to complete = deadlock)

go-fuse@f822c9db

X test that ZBlk objects can be actually removed from ZODB Connection cache and cause invalidation to be missed

____________________________________ test_bigfile_filezodb_vs_cache_invalidation ____________________________________

    def test_bigfile_filezodb_vs_cache_invalidation():
        root = dbopen()
        conn = root._p_jar
        db   = conn.db()
        conn.close()
        del root, conn

        tm1 = TransactionManager()
        tm2 = TransactionManager()

        conn1 = db.open(transaction_manager=tm1)
        root1 = conn1.root()

        # setup zfile with fileh view to it
        root1['zfile3'] = f1 = ZBigFile(blksize)
        tm1.commit()

        fh1 = f1.fileh_open()
        tm1.commit()

        # set zfile initial data
        vma1 = fh1.mmap(0, 1)
        Blk(vma1, 0)[0] = 1
        tm1.commit()

        # read zfile and setup fileh for it in conn2
        conn2 = db.open(transaction_manager=tm2)
        root2 = conn2.root()

        f2 = root2['zfile3']
        fh2 = f2.fileh_open()
        vma2 = fh2.mmap(0, 1)

        assert Blk(vma2, 0)[0] == 1 # read data in conn2 + make sure read correctly

        # now zfile content is both in ZODB.Connection cache and in _ZBigFileH
        # cache for each conn1 and conn2. Modify data in conn1 and make sure it
        # fully propagate to conn2.

        Blk(vma1, 0)[0] = 2
        tm1.commit()

        # still should be read as old value in conn2
        assert Blk(vma2, 0)[0] == 1
        # and even after virtmem pages reclaim
        # ( verifies that _p_invalidate() in ZBlk.loadblkdata() does not lead to
        #   reloading data as updated )
        ram_reclaim_all()
        assert Blk(vma2, 0)[0] == 1

        # FIXME: this simulates ZODB Connection cache pressure and currently
        # removes ZBlk corresponding to blk #0 from conn2 cache.
        # In turn this leads to conn2 missing that block invalidation on follow-up
        # transaction boundary.
        #
        # See FIXME notes on ZBlkBase._p_invalidate() for detailed description.
        conn2._cache.minimize()

        tm2.commit()                # transaction boundary for t2

        # data from tm1 should propagate -> ZODB -> ram pages for _ZBigFileH in conn2
>       assert Blk(vma2, 0)[0] == 2
E       assert 1 == 2

tests/test_filezodb.py:615: AssertionError

This one is less exotic compared to format changes rewrite.

RAMArray is compatible to ZBigArray in API and semantic, but stores its
data in RAM only. It is useful in situations where ZBigArray compatible
data type is needed, but the amount of data is small and the data itself
is needed only temporarily - e.g. in a simulation.

Please see details in individual patches.

Original merge request by @klaus (nexedi/wendelin.core!8).

/cc @Tyagov
/reviewed-on nexedi/wendelin.core!9

RAMArray is compatible to ZBigArray in API and semantic, but stores its
data in RAM only. It is useful in situations where ZBigArray compatible
data type is needed, but the amount of data is small and the data itself
is needed only temporarily - e.g. in a simulation.

Implementation is based on mmapping temporary files from /dev/shm/... and
passing them as file handles, similarly to how ZBigArray works, to BigArray.
We don't use just numpy.ndarray because of append - for ZBigArray append
works in O(1), but more importantly it does not copy data. This way
mmapings previously created for ZBigArray views, continue to correctly
alias array data. If we would be using ndarray directly, since
ndarray.resize copies data, that property would not be preserved.

Original patch by Klaus Wölfel <klaus@nexedi.com>
(nexedi/wendelin.core!8)