Arrow parquet 之 String Reader

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cpp/src/parquet/column_reader.cc

TypedRecordReader(const ColumnDescriptor* descr, LevelInfo leaf_info, MemoryPool* pool)
      : BASE(descr, pool) {
    leaf_info_ = leaf_info;
    nullable_values_ = leaf_info.HasNullableValues();
    at_record_start_ = true;
    records_read_ = 0;
    values_written_ = 0;
    values_capacity_ = 0;
    null_count_ = 0;
    levels_written_ = 0;
    levels_position_ = 0;
    levels_capacity_ = 0;
    uses_values_ = !(descr->physical_type() == Type::BYTE_ARRAY);

    if (uses_values_) {
      values_ = AllocateBuffer(pool);
    }
    valid_bits_ = AllocateBuffer(pool);
    def_levels_ = AllocateBuffer(pool);
    rep_levels_ = AllocateBuffer(pool);
    Reset();
  }

NextBatch 顺序

以下逻辑均在 cpp/src/parquet/arrow/reader.cc

  ::arrow::Iterator<RecordBatchIterator> batches = ::arrow::MakeFunctionIterator(
      [readers, batch_schema, num_rows,
       this]() mutable -> ::arrow::Result<RecordBatchIterator> {
        ::arrow::ChunkedArrayVector columns(readers.size());

        // don't reserve more rows than necessary
        int64_t batch_size = std::min(properties().batch_size(), num_rows);
        num_rows -= batch_size;

        RETURN_NOT_OK(::arrow::internal::OptionalParallelFor(
            reader_properties_.use_threads(), static_cast<int>(readers.size()),
            [&](int i) { return readers[i]->NextBatch(batch_size, &columns[i]); }));

        for (const auto& column : columns) {
          if (column == nullptr || column->length() == 0) {
            return ::arrow::IterationTraits<RecordBatchIterator>::End();
          }
        }

        auto table = ::arrow::Table::Make(batch_schema, std::move(columns));
        auto table_reader = std::make_shared<::arrow::TableBatchReader>(*table);

        // NB: explicitly preserve table so that table_reader doesn't outlive it
        return ::arrow::MakeFunctionIterator(
            [table, table_reader] { return table_reader->Next(); });
      });

  

先通过 metadata 算出了 num_rows

  int64_t num_rows = 0;
  for (int row_group : row_groups) {
    num_rows += parquet_reader()->metadata()->RowGroup(row_group)->num_rows();
  }

然后计算 真实的batch_size

// don't reserve more rows than necessary
        int64_t batch_size = std::min(properties().batch_size(), num_rows);
        num_rows -= batch_size;

 ::arrow::Status NextBatch(int64_t batch_size,
                            std::shared_ptr<::arrow::ChunkedArray>* out) final {
    RETURN_NOT_OK(LoadBatch(batch_size));
    RETURN_NOT_OK(BuildArray(batch_size, out));
    for (int x = 0; x < (*out)->num_chunks(); x++) {
      RETURN_NOT_OK((*out)->chunk(x)->Validate());
    }
    return Status::OK();
  }

NextRowGroup and TransferColumnData 位置

  Status LoadBatch(int64_t records_to_read) final {
    BEGIN_PARQUET_CATCH_EXCEPTIONS
    out_ = nullptr;
    record_reader_->Reset();
    // Pre-allocation gives much better performance for flat columns
    record_reader_->Reserve(records_to_read);
    while (records_to_read > 0) {
      if (!record_reader_->HasMoreData()) {
        break;
      }
      int64_t records_read = record_reader_->ReadRecords(records_to_read);
      records_to_read -= records_read;
      if (records_read == 0) {
        NextRowGroup();
      }
    }
    RETURN_NOT_OK(TransferColumnData(record_reader_.get(), field_->type(), descr_,
                                     ctx_->pool, &out_));
    return Status::OK();
    END_PARQUET_CATCH_EXCEPTIONS
  }