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{

		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))

		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 {

import org.junit.jupiter.params.provider.ArgumentsProvider
import org.junit.jupiter.params.provider.ArgumentsSource
import org.junit.jupiter.params.support.ParameterDeclarations

/**
 * This enforces us having the params classes on the classpath to workaround
 * https://github.com/graalvm/native-build-tools/issues/745
 */
class WithArgumentSourceTest {
  @ParameterizedTest
  @ArgumentsSource(FakeArgumentsProvider::class)

    * 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.

systems is data "<abbr title="also called marshalling, conversion">serialization</abbr>" which is taking data from the code (Python) and converting it into something that can be sent through the network. For example, converting an object containing data from a database into a JSON object. Converting `datetime` objects into strings, etc.

Another big feature needed by APIs is data validation, making sure that the data is valid, given certain parameters. For example, that some field is an `int`,...

   * 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 {

	})
	return
}

// Having specify HAVING conditions for GROUP BY
//
//	// Select the sum age of users with name jinzhu
//	db.Model(&User{}).Select("name, sum(age) as total").Group("name").Having("name = ?", "jinzhu").Find(&result)
func (db *DB) Having(query interface{}, args ...interface{}) (tx *DB) {
	tx = db.getInstance()
	tx.Statement.AddClause(clause.GroupBy{
		Having: tx.Statement.BuildCondition(query, args...),

## Date

2024-12-20

## Context

### Embedding a Java runtime in the Gradle distribution

There have been discussions on embedding a Java runtime in the Gradle distribution.
This would allow users to run Gradle without having to install a Java runtime beforehand.
This capability could help increase Gradle adoption outside of the JVM ecosystem.

Gradle can be invoked in different ways:

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.