On a testing instance we started to see segfaults in pyvma_dealloc()
with inside calls to vma_unmap but with NULL pyvma->fileh. That was
strange, becuse before calling vma_unmap(), the code explicitly checks
whether pyvma->fileh is !NULL.

That was, as it turned out, due to pyvma_dealloc being called twice at the
same time from two python threads. Here is how that was possible:

T1 decrefs pyvma and finds its reference count drops to zero. It calls
pyvma_dealloc. From there vma_unmap() is called, which calls virt_lock()
and that releases GIL first. Another thread T2 was waiting for GIL, it
acquires it, does some work at python level and somehow triggers GC.
Since PyVMA supports cyclic GC, it was on GC list and thus GC calls
dealloc for the same vma again. Here is how it looks in the backtraces:

T1:

	#0  0x00007f6aefc57827 in futex_abstimed_wait_cancelable (private=0, abstime=0x0, expected=0, futex_word=0x1e011d0) at ../sysdeps/unix/sysv/linux/futex-internal.h:205
	#1  do_futex_wait (sem=sem@entry=0x1e011d0, abstime=0x0) at sem_waitcommon.c:111
	#2  0x00007f6aefc578d4 in __new_sem_wait_slow (sem=0x1e011d0, abstime=0x0) at sem_waitcommon.c:181
	#3  0x00007f6aefc5797a in __new_sem_wait (sem=<optimized out>) at sem_wait.c:29
	#4  0x00000000004ffbc4 in PyThread_acquire_lock ()
	#5  0x00000000004dbe8a in PyEval_RestoreThread ()
	#6  0x00007f6ac6d3b8fc in py_gil_retake_if_waslocked (arg=0x4f18f00) at bigfile/_bigfile.c:1048
	#7  0x00007f6ac6d3dcfc in virt_gil_retake_if_waslocked (gilstate=0x4f18f00) at bigfile/virtmem.c:78
	#8  0x00007f6ac6d3dd30 in virt_lock () at bigfile/virtmem.c:92
	#9  0x00007f6ac6d3e724 in vma_unmap (vma=0x7f6a7e0c4100) at bigfile/virtmem.c:271
	#10 0x00007f6ac6d3a0bc in pyvma_dealloc (pyvma0=0x7f6a7e0c40e0) at bigfile/_bigfile.c:284
	...
	#13 0x00000000004d76b0 in PyEval_EvalFrameEx ()

T2:

	#5  0x00007f6ac6d3a081 in pyvma_dealloc (pyvma0=0x7f6a7e0c40e0) at bigfile/_bigfile.c:276
	#6  0x0000000000500450 in ?? ()
	#7  0x00000000004ffd82 in _PyObject_GC_New ()
	#8  0x0000000000485392 in PyList_New ()
	#9  0x00000000004d3bff in PyEval_EvalFrameEx ()

T2 does the work of vma_unmap and clears C-level vma. Then, when T1 wakes up and
returns to vma_unmap, it sees vma->file and all other fields cleared -> oops
segfault.

Fix it by removing pyvma from GC list before going to do actual destruction.
This way if a concurrent GC triggers, it won't see the vma object on its list,
and thus won't have a chance to invoke its destructor the second time.

The bug was introduced in 450ad804 (bigarray: ArrayRef support for BigArray)
when PyVMA was changed to be cyclic-GC aware. However at that time, even Python
documentation itself was not saying PyObject_GC_UnTrack is needed, as it was
added only in 2.7.15 after finding that many types in CPython itself are
vulnerable to similar segfaults:

https://github.com/python/cpython/commit/4cde4bdcc86
https://bugs.python.org/issue31095

It is pity, that CPython took the approach to force all type authors to
care to invoke PyObject_GC_UnTrack explicitly, instead of doing that
automatically in Python runtime before calling tp_dealloc.

/cc @Tyagov, @klaus
/reviewed-on !11

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.

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 (!8).

/cc @Tyagov
/reviewed-on !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>
(!8)

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

Currently we have only one BigFile and its BigFileH handle. However in
the next patch, for RAMArray, we'll be adding handles for opened RAM
files, and it would be good to test whole BigArray functionality on
data served by those handles too.

Prepare for this and first factor out into testbig fixture the way to
open such handles.

So that it can be available to everyone and in particular B & friends to
be available from introduced importable golang.testing package. The move
itself:

	pygolang@9bf03d9c

While moving the code was restructured / improved a bit and py.bench
interface reworked to mimic `go test -bench` in defaults.

bigfile/tests/test_filezodb.py ........W: testdb: teardown: <Connection at 7f8fe2b43b90> left not closed by test code; opened by:
  ...
  File "/home/kirr/src/wendelin/wendelin.core/bigfile/tests/test_filezodb.py", line 754, in test_bigfile_zblk1_zdata_reuse
    _test_bigfile_zblk1_zdata_reuse()
  File "/home/kirr/src/wendelin/wendelin.core/bigfile/tests/test_filezodb.py", line 759, in _test_bigfile_zblk1_zdata_reuse
    root = dbopen()
  File "/home/kirr/src/wendelin/wendelin.core/bigfile/tests/test_filezodb.py", line 47, in dbopen
    return testdb.dbopen()
  File "/home/kirr/src/wendelin/wendelin.core/lib/testing.py", line 188, in dbopen
    self.connv.append( (weakref.ref(conn), ''.join(traceback.format_stack())) )

lib/tests/test_zodb.py .W: testdb: teardown: <Connection at 7f8fe26f13d0> left not closed by test code; opened by:
  ...
  File "/home/kirr/src/wendelin/wendelin.core/lib/tests/test_zodb.py", line 49, in test_deactivate_btree
    root = dbopen()
  File "/home/kirr/src/wendelin/wendelin.core/lib/tests/test_zodb.py", line 30, in dbopen
    return testdb.dbopen()
  File "/home/kirr/src/wendelin/wendelin.core/lib/testing.py", line 188, in dbopen
    self.connv.append( (weakref.ref(conn), ''.join(traceback.format_stack())) )

If a test forgets to explicitly close ZODB connection it was using, this
connection stays alive in transaction synchronizers (it is a weakset),
and continues to be used on e.g. transaction.commit() when all
synchronizers are invoked. This could lead to crashes like below when
underlying ZODB storage was closed by test module teardown and testing
moved on to another test module:

    $ WENDELIN_CORE_TEST_DB="<neo>" py.test  bigfile/tests/test_filezodb.py::test_bigfile_zblk1_zdata_reuse lib/tests/test_zodb.py
    ======= test session starts ========
    platform linux2 -- Python 2.7.14+, pytest-3.5.0, py-1.5.3, pluggy-0.6.0
    rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
    collected 2 items

    bigfile/tests/test_filezodb.py .                                     [ 50%]
    lib/tests/test_zodb.py F                                             [100%]

    ______ test_deactivate_btree _______

        def test_deactivate_btree():
            root = dbopen()
            # init btree with many leaf nodes
            leafv = []
            root['btree'] = B = IOBTree()
            for i in range(10000):
                B[i] = xi = XInt(i)
                leafv.append(xi)
    >       transaction.commit()

    lib/tests/test_zodb.py:56:
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _
    ../venv/z5/local/lib/python2.7/site-packages/transaction/_manager.py:131: in commit
        return self.get().commit()
    ../venv/z5/local/lib/python2.7/site-packages/transaction/_transaction.py:316: in commit
        self._synchronizers.map(lambda s: s.afterCompletion(self))
    ../venv/z5/local/lib/python2.7/site-packages/transaction/weakset.py:62: in map
        f(elt)
    ../venv/z5/local/lib/python2.7/site-packages/transaction/_transaction.py:316: in <lambda>
        self._synchronizers.map(lambda s: s.afterCompletion(self))
    ../venv/z5/local/lib/python2.7/site-packages/ZODB/Connection.py:757: in afterCompletion
        self.newTransaction(transaction, False)
    ../venv/z5/local/lib/python2.7/site-packages/ZODB/Connection.py:737: in newTransaction
        invalidated = self._storage.poll_invalidations()
    ../venv/z5/local/lib/python2.7/site-packages/ZODB/mvccadapter.py:131: in poll_invalidations
        self._start = p64(u64(self._storage.lastTransaction()) + 1)
    _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

    self = <neo.client.Storage.Storage object at 0x7ffa1be8d410>

        def lastTransaction(self):
            # Used in ZODB unit tests
    >       return self.app.last_tid
    E       AttributeError: 'NoneType' object has no attribute 'last_tid'

    ../../neo/src/lab.nexedi.com/kirr/neo/neo/client/Storage.py:181: AttributeError

where NEO's Storage.app is None because the storage was closed.

----

To avoid such kind of failures make sure TestDB.teardown() always closes
all ZODB connections that were ever opened via TestDB.dbopen().

Add a warning about such force-closing with information about corresponding
connection and code place that created it, so that it is easy to
understand which test needs a fix.

/suggested-by @jm

- start testing NEO with ZODB4 and ZODB5. ZODB4-*-NEO works while
  ZODB5-*-NEO currently fails [1].
- update NumPy to latest releases.
- update Python 3 to latest releases.

Since 7fc4ec66 (tests: Allow to test with ZEO & NEO ZODB storages) we
can run the tests with either FileStorage, ZEO or NEO. But ZEO test
adapter started to fail with ZEO5:

	self = <wendelin.lib.testing.TestDB_ZEO object at 0x7f1feb5091d0>

	    def setup(self):
	        port  = self.zeo_forker.get_port()
	        zconf = self.zeo_forker.ZEOConfig(('', port))
	        self.addr, self.adminaddr, self.pid, self.path = \
	>               self.zeo_forker.start_zeo_server(zeo_conf=zconf, port=port)
	E       ValueError: need more than 2 values to unpack

This is because in ZEO5 forker.start_zeo_server() was reworked to return only
addr and stop closure instead of returning all details and relying on caller to
implement stop itself.

Adapt the test to detect ZEO5 and use new calling convention.

With ZConfig==3.2.0 (released 2017-06-22) ZEO started to get file names
as unicode, which eventually breaks on assert in ZODB3.FileStorage code:

    Traceback (most recent call last):
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZEO/tests/zeoserver.py", line 215, in <module>
        main()
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZEO/tests/zeoserver.py", line 188, in main
        server = ZEO.runzeo.create_server({"1": storage}, zo)
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZEO/runzeo.py", line 366, in create_server
        auth_realm = options.auth_realm,
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZEO/StorageServer.py", line 890, in __init__
        self._setup_invq(name, storage)
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZEO/StorageServer.py", line 928, in _setup_invq
        self.invq[name] = list(lastInvalidations(self.invq_bound))
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZODB/FileStorage/FileStorage.py", line 1238, in lastInvalidations
        for trans in FileIterator(self._file_name, pos=pos)]
      File "/home/kirr/src/wendelin/r/work/venv/local/lib/python2.7/site-packages/ZODB/FileStorage/FileStorage.py", line 1640, in __init__
        assert isinstance(filename, str), `filename`
    AssertionError: u'/home/kirr/src/wendelin/r/work/wendelin.core/Data.fs'

-> So for ZODB3 pin ZConfig to ZConfig <3.2.0 which is known to work with ZODB3.

ArrayRef is a reference to NumPy array.

The reference is represented by root array object and instructions how to
create original array as some view of the root.

Such reference could be useful in situations where one needs to pass arrays
between processes and instead of copying array data, leverage the fact that
top-level array, for example ZBigArray, is already persisted separately, and
only send small amount of information referencing data in question.

Use `ArrayRef(array)` to create reference to an ndarray.

Use `.deref()` to convert ArrayRef to pointed array object.

NOTE

don't send ArrayRef unconditionally - for example when array object is
small regular ndarray with also regular, but big, root ndarray, sending
ArrayRef will send whole data for root object, not for small leaf.

Sending ArrayRef only makes sense when root object is known to be already
persisted by other means, for example something like below in ZODB context:

```python
  aref = ArrayRef(a)
  if isinstance(aref.root, Persistent):
      send aref
  else:
      send a 
```

Please see individual patches for more details.

/reviewed-on !6

Rationale
---------

Array reference could be useful in situations where one needs to pass arrays
between processes and instead of copying array data, leverage the fact that
top-level array, for example ZBigArray, is already persisted separately, and
only send small amount of information referencing data in question.

Implementation
--------------

BigArray is not regular NumPy array and so needs explicit support in
ArrayRef code to find root object and indices. This patch adds such
support via the following way:

- when BigArray.__getitem__ creates VMA, it remembers in the VMA
  the top-level BigArray object under which this VMA was created.

- when ArrayRef is finding root, it can detect such VMAs, because it will
  be pointed to by the most top regular ndarray's .base, and in turn gets
  top-level BigArray object from the VMA.

- further all indices computations are performed, similarly to complete regular
  ndarrays case, on ndarrays root and a. But in the end .lo and .hi are
  adjusted for the corresponding offset of where root is inside whole
  BigArray.

- there is no need to adjust .deref() at all.

For remembering information into a VMA and also to be able to get
(readonly) its mapping addresses _bigfile.c extension has to be extended
a bit. Since we are now storing arbitrary python object attached to
PyVMA - it can create cycles - and so PyVMA accordingly adjusted to
support cyclic garbage collector.

Please see the patch itself for more details and comments.

ArrayRef is a tool to find out for a NumPy array its top-level root
parent and remember instructions how to recreate original array from
the root. For example if

	root = arange(1E7)
	z = root[1000:2000]
	a = z[10:20]

`ArrayRef(a)` will find out that the root array for `a` is `root` and
that `a` occupies 1010:1020 bytes in it. The vice versa operation is
also possible, for example given

	aref = ArrayRef(a)

it is possible to restore original `a` from `aref`:

	a_ = aref.deref()
	assert array_equal(a_, a)

the restoration works without copying by creating appropriate view of
root.

ArrayRef should work reliably for arrays of arbitrary dimensions,
strides etc - even fancy arrays created via stride tricks such as arrays
whose elements overlap each other should be supported.

This patch adds ArrayRef with support for regular ndarrays only.

The next patch will add ArrayRef support for BigArray and description
for ArrayRef rationale.

This allows e.g. to open `neo://cluster@master?compress=false` - in
other words with using options, which our current simplified opening
code does not support.

Keep old dbstoropen around as the fallback to work when
zodbtools/zodburi are not available, since we still want to try to
support ZODB 3.10.

It was

	bigfile/pagefault.c:45:36: warning: ‘struct ucontext’ declared inside parameter list will not be visible outside of this definition or declaration
	 static int faulted_by(const struct ucontext *uc);
	                                    ^~~~~~~~
	bigfile/pagefault.c: In function ‘on_pagefault’:
	bigfile/pagefault.c:59:24: warning: passing argument 1 of ‘faulted_by’ from incompatible pointer type [-Wincompatible-pointer-types]
	     write = faulted_by(uc);
	                        ^~
	bigfile/pagefault.c:45:12: note: expected ‘const struct ucontext *’ but argument is of type ‘struct ucontext *’
	 static int faulted_by(const struct ucontext *uc);
	            ^~~~~~~~~~
	bigfile/pagefault.c: At top level:
	bigfile/pagefault.c:208:36: warning: ‘struct ucontext’ declared inside parameter list will not be visible outside of this definition or declaration
	 static int faulted_by(const struct ucontext *uc)
	                                    ^~~~~~~~
	bigfile/pagefault.c:208:12: error: conflicting types for ‘faulted_by’
	 static int faulted_by(const struct ucontext *uc)
	            ^~~~~~~~~~
	bigfile/pagefault.c:45:12: note: previous declaration of ‘faulted_by’ was here
	 static int faulted_by(const struct ucontext *uc);
	            ^~~~~~~~~~
	bigfile/pagefault.c: In function ‘faulted_by’:
	bigfile/pagefault.c:217:15: error: dereferencing pointer to incomplete type ‘const struct ucontext’
	     write = uc->uc_mcontext.gregs[REG_ERR] & 0x2;
	               ^~
	bigfile/pagefault.c: At top level:
	bigfile/pagefault.c:45:12: warning: ‘faulted_by’ used but never defined
	 static int faulted_by(const struct ucontext *uc);
	            ^~~~~~~~~~
	bigfile/pagefault.c:208:12: warning: ‘faulted_by’ defined but not used [-Wunused-function]
	 static int faulted_by(const struct ucontext *uc)
	            ^~~~~~~~~~

Change to using ucontext_t because apparently there is no `struct
ucontext` anymore (and man for sigaction says 3rd parameter to hander is
of type `ucontext_t *` - not `struct ucontext *` - cast to `void *`)

Explicitly include <ucontext.h> because we are dereferencing ucontext_t,
even though today it appears to be included by <signal.h>.

Benchmark the time it takes for virtmem to handle pagefault with noop
loadblk for loadblk both implemented in C and in Python.

On my computer it is:

	name          µs/op
	PagefaultC    269 ± 0%
	pagefault_py  291 ± 0%

Quite a big time in other words.

It turned out to be mostly spent in fallocate'ing pages on tmpfs from
/dev/shm. Part of the above 269 µs/op is taken by freeing (reclaiming)
pages back when benchmarking work size exceed /dev/shm size, and part to
allocating.

If I limit the work size (via npage in benchmem.c) to be less than whole
/dev/shm it starts to be ~ 170 µs/op and with additional tracing it
shows as something like this:

    	.. on_pagefault_start   0.954 µs
    	.. vma_on_pagefault_pre 0.954 µs
    	.. ramh_alloc_page_pre  0.954 µs
    	.. ramh_alloc_page      169.992 µs
    	.. vma_on_pagefault     172.853 µs
    	.. vma_on_pagefault_pre 172.853 µs
    	.. vma_on_pagefault     174.046 µs
    	.. on_pagefault_end     174.046 µs
    	.. whole:               171.900 µs

so almost all time is spent in ramh_alloc_page which is doing the fallocate:

	https://lab.nexedi.com/nexedi/wendelin.core/blob/f11386a4/bigfile/ram_shmfs.c#L125

Simple benchmark[1] confirmed it is indeed the case for fallocate(tmpfs) to be
relatively slow[2] (and that for recent kernels it regressed somewhat
compared to Linux 3.16). Profile flamegraph for that benchmark[3] shows
internal loading of shmem_fallocate which for 1 hardware page is not
that too slow (e.g. <1µs) but when a request comes for a region
internally performs it page by page and so accumulates that ~ 170µs for 2M.

I've tried to briefly rerun the benchmark with huge pages activated on /dev/shm via

	mount /dev/shm -o huge=always,remount

as both regular user and as root but it was executing several times
slower. Probably something to investigate more later.

[1] https://lab.nexedi.com/kirr/misc/blob/4f84a06e/tmpfs/t_fallocate.c
[2] https://lab.nexedi.com/kirr/misc/blob/4f84a06e/tmpfs/1.txt
[3] https://lab.nexedi.com/kirr/misc/raw/4f84a06e/tmpfs/fallocate-2M-nohuge.svg

Rework py.bench output to match output of Go benchmarking[1] so that go
tools like benchstat & friends could be used to analyze and compare the
timings.

Before patch:

============================= test session starts ==============================
platform linux2 -- Python 2.7.14, pytest-3.3.2.dev2+g88f2cc9b.d20171206, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 11 items

pymod: bigfile/tests/bench_0virtmem.py
bench_file_mmapread_hole	0.21  (0.39 0.22 0.21)
bench_file_read_hole    	0.24  (0.24 0.24 0.24)
bench_file_readbig_hole 	0.30  (0.30 0.31 0.31)
bench_bigf_read_hole    	0.44  (0.44 0.45 0.44)
bench_file_mmapwrite0   	0.13  (0.13 0.13 0.13)
bench_file_write55      	0.08  (0.08 0.08 0.08)
bench_bigf_writeff      	0.47  (0.47 0.48 0.47)
bench_file_mmapread     	0.22  (0.22 0.22 0.22)
bench_file_read         	0.25  (0.25 0.25 0.26)
bench_file_readbig      	0.31  (0.31 0.31 0.31)
bench_bigf_read         	0.44  (0.45 0.44 0.44)

========================== 11 passed in 12.92 seconds ==========================

After patch:

============================= test session starts ==============================
platform linux2 -- Python 2.7.14, pytest-3.3.2.dev2+g88f2cc9b.d20171206, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 11 items

pymod: bigfile/tests/bench_0virtmem.py
Benchmarkfile_mmapread_hole	1	385839.939 µs/op
Benchmarkfile_mmapread_hole	1	219214.916 µs/op
Benchmarkfile_mmapread_hole	1	210209.131 µs/op
Benchmarkfile_read_hole    	1	238974.094 µs/op
Benchmarkfile_read_hole    	1	237294.197 µs/op
Benchmarkfile_read_hole    	1	238043.070 µs/op
Benchmarkfile_readbig_hole 	1	301330.090 µs/op
Benchmarkfile_readbig_hole 	1	301767.111 µs/op
Benchmarkfile_readbig_hole 	1	301135.063 µs/op
Benchmarkbigf_read_hole    	1	434718.132 µs/op
Benchmarkbigf_read_hole    	1	435019.970 µs/op
Benchmarkbigf_read_hole    	1	434729.099 µs/op
Benchmarkfile_mmapwrite0   	1	126471.996 µs/op
Benchmarkfile_mmapwrite0   	1	125886.917 µs/op
Benchmarkfile_mmapwrite0   	1	125730.038 µs/op
Benchmarkfile_write55      	1	86760.044 µs/op
Benchmarkfile_write55      	1	87507.010 µs/op
Benchmarkfile_write55      	1	87735.891 µs/op
Benchmarkbigf_writeff      	1	448369.980 µs/op
Benchmarkbigf_writeff      	1	448238.850 µs/op
Benchmarkbigf_writeff      	1	447322.845 µs/op
Benchmarkfile_mmapread     	1	207049.131 µs/op
Benchmarkfile_mmapread     	1	207813.978 µs/op
Benchmarkfile_mmapread     	1	210857.868 µs/op
Benchmarkfile_read         	1	238364.935 µs/op
Benchmarkfile_read         	1	236908.913 µs/op
Benchmarkfile_read         	1	238602.161 µs/op
Benchmarkfile_readbig      	1	303429.842 µs/op
Benchmarkfile_readbig      	1	302191.973 µs/op
Benchmarkfile_readbig      	1	304115.057 µs/op
Benchmarkbigf_read         	1	435079.098 µs/op
Benchmarkbigf_read         	1	434193.850 µs/op
Benchmarkbigf_read         	1	434813.976 µs/op

========================== 11 passed in 12.54 seconds ==========================

benchstat of new output:

	name                µs/op
	file_mmapread_hole   272k ±42%
	file_read_hole       238k ± 0%
	file_readbig_hole    301k ± 0%
	bigf_read_hole       435k ± 0%
	file_mmapwrite0      126k ± 0%
	file_write55        87.3k ± 1%
	bigf_writeff         448k ± 0%
	file_mmapread        209k ± 1%
	file_read            238k ± 0%
	file_readbig         303k ± 0%
	bigf_read            435k ± 0%

--------

Not only the output format is reworked here, but also b fixture is added
to BenchPlugin so that functions which accept it as arg, can run the
benchmark b.N times, can stop/restart timer etc - similar to
https://golang.org/pkg/testing/#B . If a bench_* func does not accept b,
B is still created, but b.N is assumed to be always 1.

The first benchmark which actually uses b will come in the next patch.

[1] https://github.com/golang/proposal/blob/master/design/14313-benchmark-format.md

Such directories are left in worktree after py.test run.

For several benchmarks in a python module instead of always printing
whole benchmark function path (nodeid in pytest speak), first print

	pymod: modulename

header, and then only function names.

Before patch:

============================= test session starts ==============================
platform linux2 -- Python 2.7.14, pytest-3.3.0, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 14 items

bigfile/tests/bench_0virtmem.py::bench_file_mmapread_hole 	0.22  (0.39 0.22 0.22)
bigfile/tests/bench_0virtmem.py::bench_file_read_hole    	0.24  (0.24 0.24 0.24)
bigfile/tests/bench_0virtmem.py::bench_file_readbig_hole 	0.30  (0.30 0.30 0.30)
bigfile/tests/bench_0virtmem.py::bench_bigf_read_hole    	0.43  (0.43 0.43 0.43)
bigfile/tests/bench_0virtmem.py::bench_file_mmapwrite0   	0.12  (0.13 0.12 0.13)
bigfile/tests/bench_0virtmem.py::bench_file_write55      	0.08  (0.08 0.08 0.08)
bigfile/tests/bench_0virtmem.py::bench_bigf_writeff      	0.45  (0.45 0.45 0.45)
bigfile/tests/bench_0virtmem.py::bench_file_mmapread     	0.21  (0.21 0.21 0.22)
bigfile/tests/bench_0virtmem.py::bench_file_read         	0.23  (0.23 0.24 0.24)
bigfile/tests/bench_0virtmem.py::bench_file_readbig      	0.30  (0.30 0.30 0.30)
bigfile/tests/bench_0virtmem.py::bench_bigf_read         	0.43  (0.44 0.43 0.44)
bigfile/tests/bench_1filezodb.py::bench_bigz_readhole    	0.42  (0.42 0.43 0.43)
bigfile/tests/bench_1filezodb.py::bench_bigz_writeff     	0.84  (0.84 1.25 1.25)
bigfile/tests/bench_1filezodb.py::bench_bigz_read        	0.60  (0.64 0.60 0.60)
========================== 14 passed in 21.78 seconds ==========================

After patch:

============================= test session starts ==============================
platform linux2 -- Python 2.7.14, pytest-3.3.0, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 14 items

pymod: bigfile/tests/bench_0virtmem.py
bench_file_mmapread_hole	0.21  (0.39 0.22 0.21)
bench_file_read_hole    	0.24  (0.24 0.24 0.24)
bench_file_readbig_hole 	0.30  (0.30 0.30 0.30)
bench_bigf_read_hole    	0.43  (0.43 0.43 0.43)
bench_file_mmapwrite0   	0.12  (0.13 0.12 0.13)
bench_file_write55      	0.08  (0.08 0.08 0.08)
bench_bigf_writeff      	0.45  (0.52 0.51 0.45)
bench_file_mmapread     	0.21  (0.21 0.21 0.21)
bench_file_read         	0.24  (0.24 0.24 0.24)
bench_file_readbig      	0.30  (0.30 0.30 0.30)
bench_bigf_read         	0.43  (0.43 0.43 0.43)

pymod: bigfile/tests/bench_1filezodb.py
bench_bigz_readhole     	0.43  (0.43 0.43 0.43)
bench_bigz_writeff      	0.83  (0.83 1.20 1.21)
bench_bigz_read         	0.60  (0.65 0.60 0.60)

========================== 14 passed in 21.80 seconds ==========================

The `key: value` header is compatible with Go benchmark format[1] and
further we'll be trying to teach py.bench to output results in that
format so that Go benchmarking tools like benchstat and perf.golang.org
could be used for free.

[1] https://github.com/golang/proposal/blob/master/design/14313-benchmark-format.md

Upstream pytest changed TerminalReporter._locationline() signature from

	def _locationline(self, collect_fspath, fspath, lineno, domain):

to

	def _locationline(self, nodeid, fspath, lineno, domain):

https://github.com/pytest-dev/pytest/commit/d73e6899 .

This way without adjusting py.bench was reporting just filenames instead
of benchmark names, e.g. this way:

(z-dev) kirr@deco:~/src/wendelin/wendelin.core$ ./t/py.bench bigfile/tests/bench_0virtmem.py
=============================================================== test session starts ===============================================================
platform linux2 -- Python 2.7.14, pytest-3.3.0, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 11 items

bigfile/tests/bench_0virtmem.py                                 0.21  (0.38 0.22 0.21)
bigfile/tests/bench_0virtmem.py                                 0.23  (0.23 0.24 0.23)
bigfile/tests/bench_0virtmem.py                                 0.30  (0.30 0.30 0.30)
bigfile/tests/bench_0virtmem.py                                 0.43  (0.43 0.43 0.43)
bigfile/tests/bench_0virtmem.py                                 0.12  (0.12 0.12 0.12)
bigfile/tests/bench_0virtmem.py                                 0.08  (0.08 0.08 0.08)
bigfile/tests/bench_0virtmem.py                                 0.44  (0.44 0.44 0.44)
bigfile/tests/bench_0virtmem.py                                 0.20  (0.20 0.20 0.21)
bigfile/tests/bench_0virtmem.py                                 0.24  (0.24 0.24 0.24)
bigfile/tests/bench_0virtmem.py                                 0.30  (0.30 0.30 0.30)
bigfile/tests/bench_0virtmem.py                                 0.43  (0.43 0.43 0.43)

=========================================================== 11 passed in 12.39 seconds ============================================================

Fix it.

In pytest3 pytest_runtest_call hook is always run as multicall and there
is no way to control to disable it and force e.g. 'firstresult'
behaviour. This way call.result in pytest_runtest_makereport is always a
list of returned results, not the first result itself.

Adjust the code in pytest_runtest_makereport to emulate 'firstresult'
behaviour manually.

Without the patch with recent pytest py.bench was crashing like this:

---- 8< ----
(z-dev) kirr@link:~/src/wendelin/wendelin.core$ make bench
python setup.py ll_build_ext --inplace
running ll_build_ext
copying build/lib.linux-x86_64-2.7/wendelin/bigfile/_bigfile.so -> bigfile
python t/py.bench --ignore=3rdparty --ignore=build --ignore=t
============================================================= test session starts ==============================================================
platform linux2 -- Python 2.7.14, pytest-3.3.0, py-1.5.2, pluggy-0.6.0
rootdir: /home/kirr/src/wendelin/wendelin.core, inifile:
collected 14 items

bigfile/tests/bench_0virtmem.py                          	INTERNALERROR> Traceback (most recent call last):
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/main.py", line 103, in wrap_session
INTERNALERROR>     session.exitstatus = doit(config, session) or 0
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/main.py", line 141, in _main
INTERNALERROR>     config.hook.pytest_runtestloop(session=session)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 617, in __call__
INTERNALERROR>     return self._hookexec(self, self._nonwrappers + self._wrappers, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 222, in _hookexec
INTERNALERROR>     return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 216, in <lambda>
INTERNALERROR>     firstresult=hook.spec_opts.get('firstresult'),
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 201, in _multicall
INTERNALERROR>     return outcome.get_result()
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 77, in get_result
INTERNALERROR>     _reraise(*ex)  # noqa
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 180, in _multicall
INTERNALERROR>     res = hook_impl.function(*args)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/main.py", line 164, in pytest_runtestloop
INTERNALERROR>     item.config.hook.pytest_runtest_protocol(item=item, nextitem=nextitem)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 617, in __call__
INTERNALERROR>     return self._hookexec(self, self._nonwrappers + self._wrappers, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 222, in _hookexec
INTERNALERROR>     return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 216, in <lambda>
INTERNALERROR>     firstresult=hook.spec_opts.get('firstresult'),
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 201, in _multicall
INTERNALERROR>     return outcome.get_result()
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 77, in get_result
INTERNALERROR>     _reraise(*ex)  # noqa
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 180, in _multicall
INTERNALERROR>     res = hook_impl.function(*args)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/runner.py", line 63, in pytest_runtest_protocol
INTERNALERROR>     runtestprotocol(item, nextitem=nextitem)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/runner.py", line 77, in runtestprotocol
INTERNALERROR>     reports.append(call_and_report(item, "call", log))
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/_pytest/runner.py", line 161, in call_and_report
INTERNALERROR>     hook.pytest_runtest_logreport(report=report)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 617, in __call__
INTERNALERROR>     return self._hookexec(self, self._nonwrappers + self._wrappers, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 222, in _hookexec
INTERNALERROR>     return self._inner_hookexec(hook, methods, kwargs)
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/__init__.py", line 216, in <lambda>
INTERNALERROR>     firstresult=hook.spec_opts.get('firstresult'),
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 201, in _multicall
INTERNALERROR>     return outcome.get_result()
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 77, in get_result
INTERNALERROR>     _reraise(*ex)  # noqa
INTERNALERROR>   File "/home/kirr/src/wendelin/venv/z-dev/local/lib/python2.7/site-packages/pluggy/callers.py", line 180, in _multicall
INTERNALERROR>     res = hook_impl.function(*args)
INTERNALERROR>   File "t/py.bench", line 165, in pytest_runtest_logreport
INTERNALERROR>     self._tw.write('%.2f' % min(report.bench_times))
INTERNALERROR> TypeError: float argument required, not list

If py.test machinery access any of the keys we change before we tweak
them - their values will be put to config._inicache and our tweaking
won't essentially have the effect.

It used to be working without explicit cache invalidation with older
pytests, but as of e.g. py.test 3.2.1 it does not work without explicit
cache clearing because e.g. 'python_files' is somehow accessed before
and is set to std 'test_*.py' + friends in the cache and this way
py.bench then does not collect benchmarks from bench_*.py files.

Fix it by making sure inicache is invalidated after our inicfg tweak.

Relicense to GPLv3+ with wide exception for all Free Software / Open Source projects + Business options.

Nexedi stack is licensed under Free Software licenses with various exceptions
that cover three business cases:

- Free Software
- Proprietary Software
- Rebranding

As long as one intends to develop Free Software based on Nexedi stack, no
license cost is involved. Developing proprietary software based on Nexedi stack
may require a proprietary exception license. Rebranding Nexedi stack is
prohibited unless rebranding license is acquired.

Through this licensing approach, Nexedi expects to encourage Free Software
development without restrictions and at the same time create a framework for
proprietary software to contribute to the long term sustainability of the
Nexedi stack.

Please see https://www.nexedi.com/licensing for details, rationale and options.

Since the beginning (1ee72371 "Demo program that shows how to work with
ZBigArrays bigger than RAM in size") the work size was 2*RAM.

However sometimes for demonstration purposes it could be handy to set it
to be even more (e.g.  16*RAM) or vise versa - to something lower.

So add command line option to do so instead of manually patching sources
every time.

A buffer object (pybuf) is passed by C-level loadblk to python loadblk
implementation. Since pybuf points to memory that will go away after
loadblk call returns to virtmem, PyBigFile tries hard to make sure
nothing stays referencing pybuf so it can be released.

It tries to:

1. automatically GC cycles referencing pybuf (9aa6a5d7 "bigfile/py: Teach
   loadblk() to automatically break reference cycles to pybuf")
2. replace pybuf with stub object if a calling frame referencing it still
   stays alive (61b18a40 "bigfile/py/loadblk: Replace pybuf with a stub
   object in calling frame in case it stays alive")
3. and as a last resort unpins pybuf from original buffer memeory to
   point it to NULL (024c246c "bigfile/py/loadblk: Resort to pybuf
   unpinning, if nothing helps")

Step #1 invokes GC. Step #2 calls gc.get_referrers(pybuf) and looks for
frames in there.

The gc.get_referrers() call happens at python level with allocating some
objects, e.g. tuple to pass arguments, resulting list etc. And we all
know that any object allocation might cause automatic garbage
collection, and GC'ing can in turn ran arbitrary code due to __del__ in
release objects and weakrefs callbacks.

At a first glance the scenario that GC will be triggered at step #2
looks unrealistic because the GC was just run at step #1 and it is only
a few objects being allocated for the call at step #2. However if
arbitrary code runs from under GC it can create new garbage and thus
upon returning from gc.collect() the garbage list is not empty as the
following program demonstrates:

    ---- 8< ----
    import gc

    # just an object we can set attributes on
    class X:
        pass

    # call f on __del__
    class DelCall:
        def __init__(self, f):
            self.f = f

        def __del__(self):
            self.f()

    # _mkgarbage creates n objects of garbage kept referenced from an object cycle
    # so that only cyclic GC can free them.
    def _mkgarbage(n):
        # cycle
        a, b = X(), X()
        a.b, b.a = b, a

        # cycle references [n] garbage
        a.objv = [X() for _ in range(n)]
        return a

    # mkgarbage creates cycled garbage and arranges for twice more garbage to be
    # created when original garbage is collected
    def mkgarbage(n):
        a = _mkgarbage(n)
        a.ondel = DelCall(lambda : _mkgarbage(2*n))

    def main():
        for i in xrange(10):
            mkgarbage(1000)
            print '> %s' % (gc.get_count(),)
            n = gc.collect()
            print '< %s' % (gc.get_count(),)

    main()
    ---- 8< ----

    kirr@deco:~/tmp/trashme/t$ ./gcmoregarbage.py
    > (482, 11, 0)
    < (1581, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)
    > (531, 3, 0)
    < (2070, 0, 0)

here lines starting with "<" show amount of live garbage objects after
gc.collect() call has been finished.

This way on a busy server there could be arrangements when GC is
explicitly ran at step #1 and then automatically run at step #2 (because of
gc.get_referrers() python-level call) and from under GC #2 arbitrary code runs
thus potentially mutating exception state which shows in logs as

    bigfile/_bigfile.c:685: pybigfile_loadblk: Assertion `!(ts->exc_type || ts->exc_value || ts->exc_traceback)' failed.

----

So don't assume we end with clean exception state after collecting pybuf
referrers and just clear exception state once again as we do after explicit GC.
Don't make a similar assumption for buffer unpinning as an object is
decrefed there and in theory this can run some code.

A test is added to automatically exercise exception state clearing for
get_referrers code path via approach similar to demonstrated in above -
- we generate more garbage from under garbage and also arrange for
finalizers, which mutate exceptions state, to be run at GC #2.

The test without the fix applied fails like this:

    bigfile/_bigfile.c:710 pybigfile_loadblk WARN: python thread-state found with handled but not cleared exception state
    bigfile/_bigfile.c:711 pybigfile_loadblk WARN: I will dump it and then crash
    ts->exc_type:   None
    ts->exc_value:  <nil>
    ts->exc_traceback:      <nil>
    Segmentation fault (core dumped)

The None in ts->exc_type and nil value and traceback are probably coming from
here in cpython runtime:

    https://github.com/python/cpython/blob/883520a8/Python/ceval.c#L3717

Since this took some time to find, more diagnostics is also added before
BUG_ONs corresponding to finding unclean exception state.

Before py3k python stores exception information not only in thread-local state
but also globally in sys.exc_* variables (wrt sys.exc_info()) for
"backward compatibility". However using them is not thread-safe as the
following example demonstrates:

---- 8< ----
from threading import Thread
import sys

def T1():
    print 'T1'
    while 1:
        exc_type = sys.exc_type
        if exc_type is not None:
            print 'AAA: %r' % exc_type

def f(): g()
def g(): h()
def h(): 1/0

def T2():
    print 'T2'
    while 1:
        try:
            f()
        except:
            pass

t1, t2 = Thread(target=T1), Thread(target=T2)
t1.start(); t2.start()
---- 8< ----

---- 8< ----
kirr@deco:~/tmp/trashme/t$ ./excthreads.py
T1
T2

AAA: <type 'exceptions.ZeroDivisionError'>
AAA: <type 'exceptions.ZeroDivisionError'>
AAA: <type 'exceptions.ZeroDivisionError'>
AAA: <type 'exceptions.ZeroDivisionError'>
AAA: <type 'exceptions.ZeroDivisionError'>
AAA: <type 'exceptions.ZeroDivisionError'>
^\Выход
---- 8< ----

Because of the above nothing modern (I've explicitly checked at least CPython
itself and Zope) uses this variables - wherever needed per-thread exception
state is retrieved with sys.exc_info().

So on wendelin.core side it is thus thankless job to try to preserve sys.exc_*
vars state because on a busy server they are literally changing all the -
arbitrary from the point of view of particular thread - time while its python
code runs.

Commit fb4bfb32 (bigfile/virtmem: Do storeblk() with virtmem lock
released) added bug-protection to fileh_dirty_writeout() so that it could
not be called twice at the same time or in parallel with other functions
which modify pages.

However it missed the code path when storeblk() call returned with error
and whole writeout was thus erroring out, but with fileh->writeout_inprogress
still left set to 1 incorrectly.

This was leading to things like

    bigfile/virtmem.c:419: fileh_dirty_discard: Assertion `!(fileh->writeout_inprogress)' failed.

and crashes.

Fix it.

This reverts commit 9ae42085.

When working with big arrays and accessing / changing it not in tiny
bits ZBlk1 is much slower compared to ZBlk0. See details here

    https://www.nexedi.com/blog/NXD-Document.Blog.Wendelin.Core.Release.0.5.Performance.Tests

and in

    13c0c17c (bigfile/zodb: Format #1 which is optimized for small changes)

Until we can rely on database handling both cases automatically,
projects which care about changing arrays in small parts can manually
set WENDELIN_CORE_ZBLK_FMT=ZBlk1 or under ERP5/SlapOS use this setting:

    slapos@2558aadd

And let's have it performant in "big data" case by default.

/cc @yusei, @klaus, @Tyagov
/reviewed-on !5

Remove debug print leftover from added test in e44bd761.

From time to time people keep trying to use wendelin.core with
dtype=object arrays and get segfaults without anything in logs or
whatever else.

Wendelin.core does not support it, because in case of dtype=object elements are
really pointers and data for each object is stored in separate place in RAM
with different per-object size.

As we are memory-mapping arrays this won't work. It also does not
essentially work for numpy.memmap for the same reason:

    (z4+numpy) kirr@mini:~/src/wendelin$ dd if=/dev/zero of=zero.dat bs=128 count=1
    1+0 records in
    1+0 records out
    128 bytes copied, 0.000209873 s, 610 kB/s
    (z4+numpy) kirr@mini:~/src/wendelin$ dd if=/dev/urandom of=random.dat bs=128 count=1
    1+0 records in
    1+0 records out
    128 bytes copied, 0.000225726 s, 567 kB/s
    (z4+numpy) kirr@mini:~/src/wendelin$ ipython
    ...

    In [1]: import numpy as np

    In [2]: np.memmap('zero.dat', dtype=np.object)
    Out[2]:
    memmap([None, None, None, None, None, None, None, None, None, None, None,
           None, None, None, None, None], dtype=object)

    In [3]: np.memmap('random.dat', dtype=np.object)
    Out[3]: Segmentation fault

So let's clarify this to users via explicitly raising exception when
BigArray with non-appropriate dtype is trying to be created with
descriptive explanation also logged.

/reviewed-on nexedi/wendelin.core!4