Fix decimal interop

.github/workflows/linux-build.yml

@@ -65,11 +65,11 @@ jobs:
           # TODO: Install a newer cmake here until we update the images upstream
           pip install cmake==3.30.4
 
-      - uses: assignUser/stash/restore@v1
-        with:
-          token: '${{ secrets.ARTIFACT_CACHE_TOKEN }}'
-          path: '${{ env.CCACHE_DIR }}'
-          key: ccache-linux-adapters
+      # - uses: assignUser/stash/restore@v1
+      #   with:
+      #     token: '${{ secrets.ARTIFACT_CACHE_TOKEN }}'
+      #     path: '${{ env.CCACHE_DIR }}'
+      #     key: ccache-linux-adapters
 
       - name: "Zero Ccache Statistics"
         run: |

velox/experimental/cudf/exec/CudfConversion.cpp

@@ -119,7 +119,12 @@ RowVectorPtr CudfFromVelox::getOutput() {
 
   // Combine selected RowVectors into a single RowVector
   auto input = mergeRowVectors(selectedInputs, inputs_[0]->pool());
-
+  // print physical type of each column in output
+  for (auto i = 0; i < input->type()->size(); i++) {
+    std::cout << "input column " << i
+              << " type: " << input->childAt(i)->type()->toString() << " "
+              << input->childAt(i)->type()->kindName() << std::endl;
+  }
   // Remove processed inputs
   inputs_.erase(inputs_.begin(), inputs_.begin() + selectedInputs.size());
   currentOutputSize_ -= totalSize;
@@ -134,6 +139,12 @@ RowVectorPtr CudfFromVelox::getOutput() {
 
   // Convert RowVector to cudf table
   auto tbl = with_arrow::toCudfTable(input, input->pool(), stream);
+  // print types of tbl->view()
+  for (auto i = 0; i < tbl->num_columns(); i++) {
+    std::cout << "input cudf column " << i
+              << " type: " << static_cast<int>(tbl->get_column(i).type().id())
+              << std::endl;
+  }
 
   stream.synchronize();
 
@@ -206,8 +217,29 @@ RowVectorPtr CudfToVelox::getOutput() {
   }
   RowVectorPtr output =
       with_arrow::toVeloxColumn(tbl->view(), pool(), "", stream);
+
+  // print types of tbl->view()
+  for (auto i = 0; i < tbl->num_columns(); i++) {
+    std::cout << "cudf column " << i
+              << " type: " << static_cast<int>(tbl->get_column(i).type().id())
+              << std::endl;
+  }
   stream.synchronize();
   finished_ = noMoreInput_ && inputs_.empty();
+  std::cout << "output.type: " << output->type()->toString() << std::endl;
+  std::cout << "outputType_: " << outputType_->toString() << std::endl;
+  // print physical type of each column in output
+  for (auto i = 0; i < output->type()->size(); i++) {
+    std::cout << "column " << i
+              << " type: " << output->childAt(i)->type()->toString() << " "
+              << output->childAt(i)->type()->kindName() << std::endl;
+  }
+  // print physical type of each column in outputType_
+  for (auto i = 0; i < outputType_->size(); i++) {
+    std::cout << "column " << i
+              << " type: " << outputType_->childAt(i)->toString() << " "
+              << outputType_->childAt(i)->kindName() << std::endl;
+  }
   output->setType(outputType_);
   return output;
 }

velox/experimental/cudf/exec/VeloxCudfInterop.cpp

@@ -84,6 +84,34 @@ cudf::type_id velox_to_cudf_type_id(const TypePtr& type) {
 }
 namespace with_arrow {
 
+// Print ArrowSchema format strings recursively
+void printArrowSchemaFormat(const ArrowSchema& arrowSchema, int depth = 0) {
+  std::string indent(depth * 2, ' ');
+  if (depth == 0) {
+    std::cout << "arrowSchema.format: " << arrowSchema.format << std::endl;
+  }
+
+  for (int64_t i = 0; i < arrowSchema.n_children; ++i) {
+    const ArrowSchema* child = arrowSchema.children[i];
+    if (child != nullptr) {
+      std::cout << indent << "  child[" << i << "].format: " << child->format
+                << std::endl;
+      if (child->n_children > 0) {
+        printArrowSchemaFormat(*child, depth + 1);
+      }
+    }
+  }
+
+  if (arrowSchema.dictionary != nullptr) {
+    std::cout << indent
+              << "  dictionary.format: " << arrowSchema.dictionary->format
+              << std::endl;
+    if (arrowSchema.dictionary->n_children > 0) {
+      printArrowSchemaFormat(*arrowSchema.dictionary, depth + 1);
+    }
+  }
+}
+
 std::unique_ptr<cudf::table> toCudfTable(
     const facebook::velox::RowVectorPtr& veloxTable,
     facebook::velox::memory::MemoryPool* pool,
@@ -102,6 +130,7 @@ std::unique_ptr<cudf::table> toCudfTable(
       std::dynamic_pointer_cast<facebook::velox::BaseVector>(veloxTable),
       arrowSchema,
       arrowOptions);
+  printArrowSchemaFormat(arrowSchema);
   auto tbl = cudf::from_arrow(&arrowSchema, &arrowArray, stream);
 
   // Release Arrow resources