Pandas Pyarrow Backend Bugfix and Tests

src/common/arrow/arrow_array_scan.cpp

@@ -160,16 +160,20 @@
     uint64_t count) {
     auto offsets = ((const offsetsT*)array->buffers[1]) + srcOffset;
     mask->copyToValueVector(&outputVector, dstOffset, count);
-    ValueVector* auxiliaryBuffer = ListVector::getDataVector(&outputVector);
+    uint64_t auxDstPosition = 0;
     for (uint64_t i = 0; i < count; i++) {
         auto curOffset = offsets[i], nextOffset = offsets[i + 1];
         if (!mask->isNull(i)) {
             auto newEntry = ListVector::addList(&outputVector, nextOffset - curOffset);
             outputVector.setValue<list_entry_t>(i + dstOffset, newEntry);
+            if (i == 0) {
+                auxDstPosition = newEntry.offset;
+            }
         }
     }
+    ValueVector* auxiliaryBuffer = ListVector::getDataVector(&outputVector);
     ArrowConverter::fromArrowArray(schema->children[0], array->children[0], *auxiliaryBuffer,
-        mask->getChild(0), offsets[0], 0u, offsets[count] - offsets[0]);
+        mask->getChild(0), offsets[0], auxDstPosition, offsets[count] - offsets[0]);
 }
 
 template<typename offsetsT>
@@ -259,8 +263,8 @@
     // eg. nulling out unselected children
     for (int8_t i = 0; i < array->n_children; i++) {
         ArrowConverter::fromArrowArray(schema->children[i], array->children[i],
-            *UnionVector::getValVector(&outputVector, i), mask->getChild(i), srcOffset, dstOffset,
-            count);
+            *StructVector::getFieldVector(&outputVector, i), mask->getChild(i), srcOffset,
+            dstOffset, count);
     }
 }
 

src/common/arrow/arrow_null_mask_tree.cpp

@@ -53,7 +53,8 @@
     NullMask pushDownMask((auxiliaryLength + NullMask::NUM_BITS_PER_NULL_ENTRY - 1) >>
                           NullMask::NUM_BITS_PER_NULL_ENTRY_LOG2);
     for (uint64_t i = 0; i < count; i++) {
-        pushDownMask.setNullFromRange(offsets[i], offsets[i + 1] - offsets[i], isNull(i));
+        pushDownMask.setNullFromRange(
+            offsets[i] - offsets[0], offsets[i + 1] - offsets[i], isNull(i));
     }
     children->push_back(ArrowNullMaskTree(
         schema->children[0], array->children[0], offsets[0], auxiliaryLength, &pushDownMask));
@@ -155,18 +156,18 @@
                     children->push_back(ArrowNullMaskTree(schema->children[i], array->children[i],
                         lowestOffsets[i], highestOffsets[i] - lowestOffsets[i]));
                 }
-                for (auto i = srcOffset; i < srcOffset + count; i++) {
-                    int32_t curOffset = offsets[i];
-                    int8_t curType = types[i];
+                for (auto i = 0u; i < count; i++) {
+                    int32_t curOffset = offsets[i + srcOffset];
+                    int8_t curType = types[i + srcOffset];
                     mask->setNull(i, children->operator[](curType).isNull(curOffset));
                 }
             } else {
                 for (int64_t i = 0; i < array->n_children; i++) {
                     children->push_back(ArrowNullMaskTree(
                         schema->children[i], array->children[i], srcOffset, count));
                 }
-                for (auto i = srcOffset; i < srcOffset + count; i++) {
-                    int8_t curType = types[i];
+                for (auto i = 0u; i < count; i++) {
+                    int8_t curType = types[i + srcOffset];
                     mask->setNull(i, children->operator[](curType).isNull(i));
                     // this isn't specified in the arrow specification, but is it valid to
                     // compute this using a bitwise OR?

src/common/vector/auxiliary_buffer.cpp

@@ -52,7 +52,7 @@ void ListAuxiliaryBuffer::resizeDataVector(ValueVector* dataVector) {
     auto buffer = std::make_unique<uint8_t[]>(capacity * dataVector->getNumBytesPerValue());
     memcpy(buffer.get(), dataVector->valueBuffer.get(), size * dataVector->getNumBytesPerValue());
     dataVector->valueBuffer = std::move(buffer);
-    dataVector->nullMask->resize(capacity);
+    dataVector->nullMask->resize(capacity); // note: allocating 64 times what is needed
     // If the dataVector is a struct vector, we need to resize its field vectors.
     if (dataVector->dataType.getPhysicalType() == PhysicalTypeID::STRUCT) {
         resizeStructDataVector(dataVector);

tools/python_api/src_cpp/include/pyarrow/pyarrow_scan.h

@@ -30,19 +30,16 @@ struct PyArrowTableScanSharedState final : public function::BaseScanSharedState
 
 struct PyArrowTableScanFunctionData final : public function::TableFuncBindData {
     std::shared_ptr<ArrowSchemaWrapper> schema;
-    std::unique_ptr<py::object> table;
+    py::object table;
     uint64_t numRows;
 
     PyArrowTableScanFunctionData(std::vector<common::LogicalType> columnTypes,
         std::shared_ptr<ArrowSchemaWrapper> schema, std::vector<std::string> columnNames,
-        py::object table, uint64_t numRows)
+        py::handle table, uint64_t numRows)
         : TableFuncBindData{std::move(columnTypes), std::move(columnNames)},
-          schema{std::move(schema)}, table{std::make_unique<py::object>(table)}, numRows{numRows} {}
+          schema{std::move(schema)}, table{py::reinterpret_borrow<py::object>(table)}, numRows{numRows} {}
 
-    ~PyArrowTableScanFunctionData() override {
-        py::gil_scoped_acquire acquire;
-        table.reset();
-    }
+    ~PyArrowTableScanFunctionData() override {}
 
     std::unique_ptr<function::TableFuncBindData> copy() const override {
         py::gil_scoped_acquire acquire;

tools/python_api/src_cpp/pyarrow/pyarrow_scan.cpp

@@ -16,7 +16,7 @@ static std::unique_ptr<function::TableFuncBindData> bindFunc(
     main::ClientContext* /*context*/, TableFuncBindInput* input) {
 
     py::gil_scoped_acquire acquire;
-    py::object table(py::reinterpret_steal<py::object>(
+    py::object table(py::reinterpret_borrow<py::object>(
         reinterpret_cast<PyObject*>(input->inputs[0].getValue<uint8_t*>())));
     if (py::isinstance(table, importCache->pandas.DataFrame())) {
         table = importCache->pyarrow.lib.Table.from_pandas()(table);
@@ -29,7 +29,7 @@ static std::unique_ptr<function::TableFuncBindData> bindFunc(
     auto numRows = py::len(table);
     auto schema = Pyarrow::bind(table, returnTypes, names);
     return std::make_unique<PyArrowTableScanFunctionData>(
-        std::move(returnTypes), std::move(schema), std::move(names), table, numRows);
+        std::move(returnTypes), std::move(schema), std::move(names), std::move(table), numRows);
 }
 
 ArrowArrayWrapper* PyArrowTableScanSharedState::getNextChunk() {
@@ -46,7 +46,7 @@ static std::unique_ptr<function::TableFuncSharedState> initSharedState(
     py::gil_scoped_acquire acquire;
     PyArrowTableScanFunctionData* bindData =
         dynamic_cast<PyArrowTableScanFunctionData*>(input.bindData);
-    py::list batches = bindData->table->attr("to_batches")(DEFAULT_VECTOR_CAPACITY);
+    py::list batches = bindData->table.attr("to_batches")(DEFAULT_VECTOR_CAPACITY);
     std::vector<std::shared_ptr<ArrowArrayWrapper>> arrowArrayBatches;
 
     for (auto& i : batches) {

tools/python_api/test/test_df_pyarrow.py

@@ -14,11 +14,11 @@ def generate_primitive(dtype):
     if (dtype.startswith("bool")):
         return random.randrange(0, 1) == 1
     if (dtype.startswith("int32")):
-        return random.randrange(-2147483648, 2147483648)
+        return random.randrange(-2147483648, 2147483647)
     if (dtype.startswith("int64")):
-        return random.randrange(-9223372036854775808, 9223372036854775808)
+        return random.randrange(-9223372036854775808, 9223372036854775807)
     if (dtype.startswith("uint64")):
-        return random.randrange(0, 18446744073709551616)
+        return random.randrange(0, 18446744073709551615)
     if (dtype.startswith("float32")):
         random_bits = random.getrandbits(32)
         random_bytes = struct.pack('<I', random_bits)
@@ -232,3 +232,84 @@ def test_pyarrow_dict(conn_db_readonly : ConnDB) -> None:
     for colname in ['col1', 'col2']:
         for expected, actual in zip(df[colname], result[colname]):
             assert expected == actual
+
+def test_pyarrow_list(conn_db_readonly : ConnDB) -> None:
+    conn, db = conn_db_readonly
+    random.seed(100)
+    datalength = 50
+    childlength = 5
+    index = pa.array(range(datalength))
+    col1 = pa.array(
+        [[generate_primitive('int32[pyarrow]') for x in range(random.randint(1, childlength))] if random.randint(0, 5) == 0 else None for i in range(datalength)])
+    col2 = pa.array(
+        [[[generate_primitive('int32[pyarrow]') for x in range(random.randint(1, childlength))] for y in range(1, childlength)] if random.randint(0, 5) == 0 else None for i in range(datalength)])
+    df = pd.DataFrame({
+        'index': arrowtopd(index),
+        'col1': arrowtopd(col1),
+        'col2': arrowtopd(col2)
+    })
+    result = conn.execute('CALL READ_PANDAS(df) RETURN * ORDER BY index')
+    idx = 0
+    while result.has_next():
+        assert idx < len(index)
+        nxt = result.get_next()
+        proc = [idx, col1[idx].as_py(), col2[idx].as_py()]
+        assert proc == nxt
+        idx += 1
+
+    assert idx == len(index)
+
+def test_pyarrow_struct(conn_db_readonly : ConnDB) -> None:
+    conn, db = conn_db_readonly
+    random.seed(100)
+    datalength = 4096
+    index = pa.array(range(datalength))
+    col1_plaindata = [{
+            'a': generate_primitive('int32[pyarrow]'),
+            'b': { 'c': generate_string(10) } if random.randint(0, 5) != 0 else None
+        } if random.randint(0, 5) != 0 else None for i in range(datalength)]
+    col1 = pa.array(col1_plaindata, pa.struct([
+        ('a', pa.int32()),
+        ('b', pa.struct([('c', pa.string())]))
+    ]))
+    df = pd.DataFrame({
+        'index': arrowtopd(index),
+        'col1': arrowtopd(col1)
+    })
+    result = conn.execute('CALL READ_PANDAS(df) RETURN * ORDER BY index')
+    idx = 0
+    while result.has_next():
+        assert idx < len(index)
+        nxt = result.get_next()
+        expected = [idx, col1[idx].as_py()]
+        assert expected == nxt
+        idx += 1
+
+    assert idx == len(index)
+
+def test_pyarrow_union(conn_db_readonly : ConnDB) -> None:
+    pytest.skip("unions are not very well supported by kuzu in general")
+    conn, db = conn_db_readonly
+    random.seed(100)
+    datalength = 4096
+    index = pa.array(range(datalength))
+    type_codes = pa.array([random.randint(0, 2) for i in range(datalength)], type=pa.int8())
+    arr1 = pa.array([generate_primitive('int32[pyarrow]') for i in range(datalength)], type=pa.int32())
+    arr2 = pa.array([generate_string(random.randint(1, 10)) for i in range(datalength)])
+    arr3 = pa.array([[generate_primitive('float32[pyarrow]') for i in range(10)] for j in range(datalength)])
+    col1 = pa.UnionArray.from_sparse(type_codes, [arr1, arr2, arr3])
+    df = pd.DataFrame({
+        'index': arrowtopd(index),
+        'col1': arrowtopd(col1)
+    })
+    result = conn.execute('CALL READ_PANDAS(df) RETURN * ORDER BY index')
+    idx = 0
+    while result.has_next():
+        assert idx < len(index)
+        nxt = result.get_next()
+        expected = [idx, col1[idx].as_py()]
+        assert expected == nxt
+        idx += 1
+
+    assert idx == len(index)
+