builder.WriteString(" HAVING ")
		Where{Exprs: groupBy.Having}.Build(builder)
	}
}

// MergeClause merge group by clause
func (groupBy GroupBy) MergeClause(clause *Clause) {
	if v, ok := clause.Expression.(GroupBy); ok {
		copiedColumns := make([]Column, len(v.Columns))
		copy(copiedColumns, v.Columns)
		groupBy.Columns = append(copiedColumns, groupBy.Columns...)

		copiedHaving := make([]Expression, len(v.Having))

    * If you are comparing Uvicorn, compare it against Daphne, Hypercorn, uWSGI, etc. Application servers.
* **Starlette**:
    * Will have the next best performance, after Uvicorn. In fact, Starlette uses Uvicorn to run. So, it probably can only get "slower" than Uvicorn by having to execute more code.

				Columns: []clause.Column{{Name: "role"}},
				Having:  []clause.Expression{clause.Eq{"role", "admin"}},
			}},
			"SELECT * FROM `users` GROUP BY `role` HAVING `role` = ?",
			[]interface{}{"admin"},
		},
		{
			[]clause.Interface{clause.Select{}, clause.From{}, clause.GroupBy{
				Columns: []clause.Column{{Name: "role"}},
				Having:  []clause.Expression{clause.Eq{"role", "admin"}},
			}, clause.GroupBy{

		t.Errorf("name should be groupby, but got %v, total should be 60, but got %v", name, total)
	}

	if err := DB.Model(&User{}).Select("name, sum(age) as total").Where("name LIKE ?", "groupby%").Group("name").Having("name = ?", "groupby1").Row().Scan(&name, &total); err != nil {
		t.Errorf("no error should happen, but got %v", err)
	}

	if name != "groupby1" || total != 660 {

   *       involving {@code view} will throw)
   *   <li>{@code hashCode()} does not throw
   *   <li>if {@code node} is re-added to the graph after having been removed, {@code view}'s
   *       behavior is undefined
   * </ul>
   *
   * @throws IllegalArgumentException if {@code node} is not an element of this graph
   */
  Set<N> adjacentNodes(N node);

  /**

   * the result is chosen uniformly at random in [0, numBits), and then the result is chosen in that
   * range uniformly at random. Zero is treated as having log2 == 0.
   */
  static BigInteger randomNonNegativeBigInteger(int numBits) {
    int digits = RANDOM_SOURCE.nextInt(numBits);
    if (digits == 0) {
      return new BigInteger(1, RANDOM_SOURCE);
    } else {

The [Stat] function now sets the [ModeSocket] bit for
files that are Unix sockets on Windows. These files are identified

   * the result is chosen uniformly at random in [0, numBits), and then the result is chosen in that
   * range uniformly at random. Zero is treated as having log2 == 0.
   */
  static BigInteger randomNonNegativeBigInteger(int numBits) {
    int digits = RANDOM_SOURCE.nextInt(numBits);
    if (digits == 0) {
      return new BigInteger(1, RANDOM_SOURCE);
    } else {

For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.

For now we just have this global repository of ADRs.
If we see fit, we can break these out to per-platform ones, or keep a hybrid approach to having global and platform-specific ADSs.

Our aim is to keep the process lightweight and approachable.