I want to read a parquet file batch by batch in parallelism. I achieve this by merge multiple continuous rowgroup together and read them by arrow::RecordBatchReader. When I monitor the memory usage during reading, I noticed that the memory was increasing until overall reading done. However, I want to reduce the memory footprint, releasing the memory as soon as current thread finish current batch reading.

However, I tried arrow::RecordBatchReader->Close(), it didn’t work.
And here is my code.

template <class FUNCTION, typename T,
          typename R = typename arrow::internal::call_traits::return_type<FUNCTION>::ValueType>
arrow::Future<std::vector<R>> ParallelForAsync_test(
    std::vector<T> inputs, FUNCTION&& func, 
    arrow::internal::Executor* executor = arrow::internal::GetCpuThreadPool()) {
  std::vector<arrow::Future<R>> futures(inputs.size());
  for (size_t i = 0; i < inputs.size(); ++i) {
    ARROW_ASSIGN_OR_RAISE(futures[i], executor->Submit(func, i, std::move(inputs[i])));
  }
  return All(std::move(futures))
      .Then([](const std::vector<arrow::Result<R>>& results) -> arrow::Result<std::vector<R>> {
        return  arrow::internal::UnwrapOrRaise(results);
      });
}

arrow::Status read_whole_file(std::string file, int batch_size, int &size) {

  ::arrow::MemoryPool* pool = ::arrow::default_memory_pool();

  auto reader_properties = parquet::ReaderProperties(pool);
  reader_properties.set_buffer_size(4096 * 4);
  reader_properties.enable_buffered_stream();
  
  auto arrow_reader_props = parquet::ArrowReaderProperties();
  arrow_reader_props.set_batch_size(4 * 1024);
  arrow_reader_props.set_use_threads(true);

  parquet::arrow::FileReaderBuilder reader_builder;
  ARROW_RETURN_NOT_OK(reader_builder.OpenFile(file, false, reader_properties));
  reader_builder.memory_pool(pool);
  reader_builder.properties(arrow_reader_props);

  std::unique_ptr<parquet::arrow::FileReader> arrow_reader;
  ARROW_ASSIGN_OR_RAISE(arrow_reader, reader_builder.Build());

  auto p_reader = arrow_reader->parquet_reader();
  int nrgs = p_reader->metadata()->num_row_groups();
  int nrows = p_reader->metadata()->num_rows();

  int ncolumns = p_reader->metadata()->num_columns();
  auto cpu_executor = ::arrow::internal::GetCpuThreadPool();

  int rg_batchsize = nrgs / batch_size;

  std::vector<std::shared_ptr<arrow::RecordBatchReader>> vec_reader;

  for (int j = 0; j <= rg_batchsize; j++) {
    std::shared_ptr<arrow::RecordBatchReader> rb_reader;
    arrow_reader->GetRecordBatchReader(
        arrow::internal::Iota(j * batch_size, std::min((j+1) * batch_size, nrgs)), &rb_reader);
    vec_reader.emplace_back(rb_reader);
  }
  size = vec_reader.size();
  std::cout << size << std::endl;
  auto thread_start = std::chrono::high_resolution_clock::now();
  auto read_recordbatch = [ncolumns, thread_start](size_t i, std::shared_ptr<::arrow::RecordBatchReader> reader)
      // -> ::arrow::Result<std::shared_ptr<::arrow::Array>> {
        -> ::arrow::Result<bool>{
        auto io_start = std::chrono::high_resolution_clock::now();
        auto result = reader->ToTable();
        std::vector<std::shared_ptr<::arrow::Array>> vec_array;
        if (result.ok()) {
          auto table = *result;
          for (int i = 0; i < ncolumns; i++) {
            auto result = ChunkedArrayToArray(table->column(i));
            if (result.ok()) {
              auto array = *result;
              vec_array.emplace_back(array);
            }
          }
        }
        auto io_end = std::chrono::high_resolution_clock::now();
        
        reader->Close();
        std::cout << "thread " << i << " " << vec_array[0]->length() << " rows " 
                    << "start_overhead " << std::chrono::duration<double, std::milli>(io_start-thread_start).count() << " ms "
                    << "io_overhead " << std::chrono::duration<double, std::milli>(io_end-io_start).count() << " ms" << std::endl;

        // return vec_array[0]->length();
        // return vec_array[0];
        return true;

  };

  auto re = ParallelForAsync_test(std::move(vec_reader), read_recordbatch, cpu_executor)
                .MoveResult();
  auto re_chka = re.ValueOrDie();

  return ::arrow::Status::OK();
}