[GitHub] [arrow] westonpace commented on a change in pull request #12460: ARROW-13530: [C++] Implement cumulative sum compute function

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westonpace commented on a change in pull request #12460:
URL: https://github.com/apache/arrow/pull/12460#discussion_r829642181



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File path: cpp/src/arrow/compute/kernels/vector_cumulative_sum.cc
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@@ -0,0 +1,159 @@
+// Licensed to the Apache Software Foundation (ASF) under one
+// or more contributor license agreements.  See the NOTICE file
+// distributed with this work for additional information
+// regarding copyright ownership.  The ASF licenses this file
+// to you under the Apache License, Version 2.0 (the
+// "License"); you may not use this file except in compliance
+// with the License.  You may obtain a copy of the License at
+//
+//   http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing,
+// software distributed under the License is distributed on an
+// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+// KIND, either express or implied.  See the License for the
+// specific language governing permissions and limitations
+// under the License.
+
+#include "arrow/array/array_base.h"
+#include "arrow/compute/api_scalar.h"
+#include "arrow/compute/kernels/common.h"
+#include "arrow/result.h"
+#include "arrow/visit_type_inline.h"
+
+namespace arrow {
+namespace compute {
+namespace internal {
+
+template <typename Type>
+struct CumulativeSum {
+  using CType = TypeTraits<Type>::CType;
+  using ScalarType = TypeTraits<Type>::ScalarType;
+
+  CType Sum(ExecContext* ctx, std::shared_ptr<Array>& input, ArrayData* output,
+            CType start) {
+    CType sum = start;
+    CType* data = checked_cast<CType*>(input->data()->buffers[1]->data());
+    CType* out_values = checked_cast<CType*>(output->buffers[1]->mutable_data());
+    ArithmeticOptions options;
+    for (size_t i = input->offset; i < input->length; ++i) {
+      if (input->IsValid(i)) {
+        Datum value_datum(data[i]);
+        Datum sum_datum(sum);
+        auto result = Add(value_datum, sum_datum, options, ctx);
+        ScalarType result_scalar = result.ValueOrDie().scalar_as();
+        sum = result_scalar.value;
+        out_values[i] = sum;
+      }
+    }
+
+    return sum;
+  }
+
+  Status Exec(KernelContext* ctx, const ExecBatch& batch, Datum* out) {
+    const auto& options = OptionsWrapper<CumulativeSumOptions<CType>>::Get(ctx);
+    CType start = checked_cast<const ScalarType&>(options.start).value;
+
+    switch (batch[0].kind()) {
+      case Datum::ARRAY:
+        std::shared_ptr<Array> input = batch[0].make_array();
+        ArrayData* output = out->array().get();
+
+        output->length = input->data()->length;
+        *output->type = std::move(input->type());
+        uint8_t* out_bitmap = output->buffers[0]->mutable_data();
+        int64_t out_offset = input->offset();
+
+        if (input->data()->MayHaveNulls()) {
+          arrow::internal::CopyBitmap(input->null_bitmap_data(), input->offset(),
+                                      input->length(), out_bitmap, out_offset);
+          output->null_count = input->null_count();
+        } else {
+          bit_util::SetBitsTo(out_bitmap, out_offset, input->length(), true);
+          output->null_count = 0;
+        }
+
+        Sum(ctx->exec_context(), input, output, start);
+        return Status::OK();
+      case Datum::CHUNKED_ARRAY:
+        const auto& input = batch[0].chunked_array();
+
+        ArrayVector out_chunks;
+        for (const auto& chunk : input->chunks()) {
+          auto out_chunk = std::make_shared<ArrayData>(
+              chunk->type(), chunk->length(), chunk->null_count(), chunk->offset());
+
+          uint8_t* out_chunk_bitmap = out_chunk->buffers[0]->mutable_data();
+          if (chunk->data()->MayHaveNulls()) {
+            arrow::internal::CopyBitmap(chunk->null_bitmap_data(), chunk->offset(),
+                                        chunk->length(), out_chunk_bitmap,
+                                        out_chunk->offset());
+            out_chunk->null_count = chunk->null_count();
+          } else {
+            bit_util::SetBitsTo(out_chunk_bitmap, out_chunk->offset(), chunk->length(),
+                                true);
+            out_chunk->null_count = 0;
+          }
+
+          CType last_value = Sum(ctx->exec_context(), chunk, out_chunk, start);
+          start = last_value;
+          out_chunks.push_back(MakeArray(std::move(out_chunk)));
+        }
+
+        *out->chunked_array() = ChunkedArray(out_chunks, input->type());
+        return Status::OK();
+      default:
+        return Status::NotImplemented(
+            "Unsupported input type for function 'cumulative_sum': ",
+            batch[0].ToString());
+    }
+  }
+
+  static std::shared_ptr<KernelSignature> GetSignature(detail::GetTypeId get_id) {
+    return KernelSignature::Make({InputType::Array(get_id.id)}, OutputType(FirstType));
+  }
+};
+
+const FunctionDoc cumulative_sum_doc(
+    "Compute the cumulative sum over an array of numbers",
+    ("`values` must be an array of numeric type values.\n"
+     "`start` is a single value of the same type.\n"
+     "Return an array which is the cumulative sum computed over `values.`\n"
+     "Null entries remain in place but are not used in calucating sum.\n"
+     "`start` is an optional starting sum of computation."),
+    {"values", "start"});
+
+void RegisterVectorCumulativeSum(FunctionRegistry* registry) {
+  auto cumulative_sum = std::make_shared<VectorFunction>(
+      "cumulative_sum", Arity::Binary(), &cumulative_sum_doc);
+
+  auto add_kernel = [&](detail::GetTypeId get_id, ArrayKernelExec exec) {
+    VectorKernel kernel;
+    kernel.can_execute_chunkwise = true;

Review comment:
       My preference would be to worry about chunkwise in the future, if that is a possibility.
   
   This problem will need to be solved when we tackle window functions (cumulative sum is often expressed as a window function) so I expect we will know more once our work there has progressed.
   
   Since the function is not scalar I don't think any of our execution engine code should allow it to be used today.  The only places we can plugin arbitrary functions right now are projection (err: not scalar) and aggregate (err: not aggregate).




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