Numpy array test occasionaly raises segfault

[vasil-pashov]

I think we have to sort out the GIL handling properly. This test,

    @pytest.mark.parametrize('n', range(1000))
    def test_append_empty_arrays_to_column(self, lmdb_version_store, array_type, n):
        df = pd.DataFrame({"col1": [np.array([1, 2, 3]).astype(array_type)]})
        lmdb_version_store.write("test_append_to_colum_with_empty_array", df)
        df_to_append = pd.DataFrame({"col1": [np.array([])]})
        lmdb_version_store.append("test_append_to_colum_with_empty_array", df_to_append)
        df_out = lmdb_version_store.read("test_append_to_colum_with_empty_array")
        df_target = pd.concat([df, df_to_append], ignore_index=True)
        assert_frame_equal(df_target, df_out.data)

that you added segfaults occasionally (hence the 1000 runs) in various places in Cython, indicating corruption of Python's datastructures. I've seen the test segfault when trying to run Python imports, or allocate tuples, for instance.

We've also seen it fail in the CI.

Originally posted by @poodlewars in https://github.com/man-group/ArcticDB/pull/819#discussion_r1434311648

Duplicate of: #1176

[alexowens90]

Correct fix is probably #269