icexelloss commented on code in PR #35514:
URL: https://github.com/apache/arrow/pull/35514#discussion_r1218526390
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python/pyarrow/src/arrow/python/udf.cc:
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@@ -101,6 +125,99 @@ struct PythonTableUdfKernelInit {
UdfWrapperCallback cb;
};
+ struct PythonUdfScalarAggregatorImpl : public ScalarUdfAggregator {
+
+ PythonUdfScalarAggregatorImpl(UdfWrapperCallback agg_cb,
+ std::shared_ptr<OwnedRefNoGIL> agg_function,
+ std::vector<std::shared_ptr<DataType>> input_types,
+ std::shared_ptr<DataType> output_type):
+ agg_cb(agg_cb),
+ agg_function(agg_function),
+ output_type(output_type) {
+ std::vector<std::shared_ptr<Field>> fields;
+ for (size_t i = 0; i < input_types.size(); i++) {
+ fields.push_back(field("", input_types[i]));
+ }
+ input_schema = schema(fields);
+ };
+
+ ~PythonUdfScalarAggregatorImpl() {
+ if (_Py_IsFinalizing()) {
+ agg_function->detach();
+ }
+ }
+
+ Status Consume(compute::KernelContext* ctx, const compute::ExecSpan&
batch) {
+ ARROW_ASSIGN_OR_RAISE(auto rb,
batch.ToExecBatch().ToRecordBatch(input_schema, ctx->memory_pool()));
+ values.push_back(rb);
+ return Status::OK();
+ }
+
+ Status MergeFrom(compute::KernelContext* ctx, compute::KernelState&& src) {
+ const auto& other_state = checked_cast<const
PythonUdfScalarAggregatorImpl&>(src);
+ values.insert(values.end(), other_state.values.begin(),
other_state.values.end());
+ return Status::OK();
+ }
+
+ Status Finalize(compute::KernelContext* ctx, Datum* out) {
+ auto state =
arrow::internal::checked_cast<PythonUdfScalarAggregatorImpl*>(ctx->state());
+ std::shared_ptr<OwnedRefNoGIL>& function = state->agg_function;
+ const int num_args = input_schema->num_fields();
+
+ OwnedRef arg_tuple(PyTuple_New(num_args));
+ RETURN_NOT_OK(CheckPyError());
+
+ // Note: The way that batches are concatenated together
+ // would result in using double amount of the memory.
+ // This is OK for now because non decomposable aggregate
+ // UDF is supposed to be used with segmented aggregation
+ // where the size of the segment is more or less constant
+ // so doubling that is not a big deal. This can be also
+ // improved in the future to use more efficient way to
+ // concatenate.
+ ARROW_ASSIGN_OR_RAISE(
+ auto table,
+ arrow::Table::FromRecordBatches(input_schema, values)
+ );
+ ARROW_ASSIGN_OR_RAISE(
+ table, table->CombineChunks(ctx->memory_pool())
+ );
+
+ UdfContext udf_context{ctx->memory_pool(), table->num_rows()};
+ for (int arg_id = 0; arg_id < num_args; arg_id++) {
+ // Since we combined chunks thComere is only one chunk
+ std::shared_ptr<Array> c_data = table->column(arg_id)->chunk(0);
+ PyObject* data = wrap_array(c_data);
+ PyTuple_SetItem(arg_tuple.obj(), arg_id, data);
+ }
+
+ OwnedRef result(agg_cb(function->obj(), udf_context, arg_tuple.obj()));
+ RETURN_NOT_OK(CheckPyError());
+
+ // unwrapping the output for expected output type
+ if (is_scalar(result.obj())) {
+ ARROW_ASSIGN_OR_RAISE(std::shared_ptr<Scalar> val,
unwrap_scalar(result.obj()));
+ if (*output_type != *val->type) {
+ return Status::TypeError("Expected output datatype ",
output_type->ToString(),
+ ", but function returned datatype ",
+ val->type->ToString());
+ }
+ out->value = std::move(val);
+ return Status::OK();
+ } else {
+ return Status::TypeError("Unexpected output type: ",
Py_TYPE(result.obj())->tp_name,
Review Comment:
Added
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