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Throw an exception when the data buffer size for allocation is too large #2155

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Throw an exception when the data buffer size for allocation is too large #2155

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c51944c
Add throw
kounelisagis Feb 7, 2025
94ef25b
Add test
kounelisagis Feb 7, 2025
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4 changes: 4 additions & 0 deletions tiledb/core.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -98,6 +98,10 @@ struct BufferInfo {
try {
dtype = tiledb_dtype(data_type, cell_val_num);
elem_nbytes = tiledb_datatype_size(type);
if (data_nbytes >
static_cast<uint64_t>(std::numeric_limits<intptr_t>::max())) {
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This is good and it is a step forward. We should merge this PR with at least this change.

However, boxes will OOM long before request sizes approach 2^63. I don't want us to hard-code assumptions about RAM sizes (AWS offers instances with a terabyte of RAM which is 2^40!). Just food for thought, maybe we can make a cap smaller than int64 max. Just a thought.

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Ideally if numpy has a defined maximum size for its arrays (like .NET has) we would check that, but I couldn't find it after a search.

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I was thinking and discussed with Agis that we could make this max something safe enough, like e.g. 2GB?, and make it configurable through a py. config, similar to what we are able to do today with incomplete buffer sizes through cfg["py.max_buffer_bytes"].

Then we could improve also the user experience, by suggesting to him when we throw to increase that config value or use A.query(incomplete=True) to read his data.

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A 2GB default limit sounds fine, and can be overridden with an optional parameter like A.query(ignore_buffer_size_check=True). Customizing it with a config option sounds like an overkill.

Users should really use incomplete queries if possible, and way before the 2GB threshold. Allocating contiguous buffers of this size has disadvantages like memory fragmentation.

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We also have the (undocumented?) py.alloc_max_bytes, as discussed with @ihnorton. I will take a look at it.

throw std::overflow_error("Data buffer size is too large");
}
data = py::array(py::dtype("uint8"), data_nbytes);
offsets = py::array_t<uint64_t>(offsets_num);
validity = py::array_t<uint8_t>(validity_num);
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20 changes: 20 additions & 0 deletions tiledb/tests/test_fixes.py
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Expand Up @@ -382,6 +382,26 @@ def test_sc62594_buffer_resize(self, array_data):
with tiledb.DenseArray(uri) as T:
assert_array_equal(array_data, T)

def test_data_buffer_too_large(self):
uri = self.path("test_agis")
dim1 = tiledb.Dim(
name="dim_0", domain=(0, 10000000000), tile=512, dtype=np.int64
)
dim2 = tiledb.Dim(
name="dim_1", domain=(0, 10000000000), tile=512, dtype=np.int64
)
att = tiledb.Attr(name="data", dtype="int8")
schema = tiledb.ArraySchema(
domain=tiledb.Domain(dim1, dim2),
attrs=(att,),
)
tiledb.Array.create(uri, schema)

with tiledb.open(uri, mode="r") as A:
with pytest.raises(OverflowError) as exc:
A[:]
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That's probably another issue to look at, but why would we even allocate a user buffer for retrieving results from an empty array?

assert "Data buffer size is too large" in str(exc.value)


class SOMA919Test(DiskTestCase):
"""
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