Method ReadSchema

tensorflow/contrib/cloud/kernels/bigquery_table_accessor.cc:249–280

}

Status BigQueryTableAccessor::ReadSchema() {
  string auth_token;
  TF_RETURN_IF_ERROR(AuthProvider::GetToken(auth_provider_.get(), &auth_token));

  // Send a request to read the schema.
  std::unique_ptr<HttpRequest> request(http_request_factory_->Create());
  std::vector<char> output_buffer;
  output_buffer.reserve(kBufferSize);
  request->SetUri(BigQueryUriPrefix());
  request->AddAuthBearerHeader(auth_token);
  request->SetResultBuffer(&output_buffer);
  TF_RETURN_WITH_CONTEXT_IF_ERROR(request->Send(), " when reading schema for ",
                                  FullTableName());

  // Parse the schema.
  StringPiece response_piece =
      StringPiece(&output_buffer[0], output_buffer.size());

  Json::Value root;
  TF_RETURN_IF_ERROR(ParseJson(response_piece, &root));
  const auto& columns = root["schema"]["fields"];
  string column_name_prefix = "";
  schema_root_ = {"", ColumnType::kNone};
  TF_RETURN_IF_ERROR(
      ExtractColumnType(columns, column_name_prefix, &schema_root_));
  if (root["numRows"].isNull()) {
    return errors::Internal("Number of rows cannot be extracted for table ",
                            FullTableName());
  }
  strings::safe_strto64(root["numRows"].asString().c_str(), &total_num_rows_);
  return Status::OK();
}

Status BigQueryTableAccessor::ExtractColumnType(
    const Json::Value& columns, const string& column_name_prefix,