* prevention and <i>no</i> expectation of bit dispersion. This leaves them perfectly suitable for
 * use in hash tables, because extra collisions cause only a slight performance hit, while poor bit
 * dispersion is easily corrected using a secondary hash function (which all reasonable hash table
 * implementations in Java use). For the many uses of hash functions beyond data structures,

 * prevention and <i>no</i> expectation of bit dispersion. This leaves them perfectly suitable for
 * use in hash tables, because extra collisions cause only a slight performance hit, while poor bit
 * dispersion is easily corrected using a secondary hash function (which all reasonable hash table
 * implementations in Java use). For the many uses of hash functions beyond data structures,

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		t.Errorf("joins should be ordered, but got %v", stmt.SQL.String())
	}

	iv := DB.Table(`table_invoices`).Select(`seller, SUM(total) as total, SUM(paid) as paid, SUM(balance) as balance`).Group(`seller`)
	stmt = dryDB.Table(`table_employees`).Select(`id, name, iv.total, iv.paid, iv.balance`).Joins(`LEFT JOIN (?) AS iv ON iv.seller = table_employees.id`, iv).Scan(&user).Statement

Example:

    Source (English):

        «««
        <abbr title="Object Relational Mapper: a fancy term for a library where some classes represent SQL tables and instances represent rows in those tables">ORM</abbr>
        »»»

    Result (German):

        «««

	newDB := DB.Session(&gorm.Session{NewDB: true, DryRun: true}).Table("users")

	newDB.Clauses(
		clause.From{
			Tables: []clause.Table{{Name: "users"}},
			Joins: []clause.Join{
				{
					Table: clause.Table{Name: "companies", Raw: false},
					ON: clause.Where{
						Exprs: []clause.Expression{
							clause.Eq{
								Column: clause.Column{
									Table: "users",
									Name:  "company_id",
								},

    eraseAll();
  }

  private ArrayTable(Table<R, C, ? extends @Nullable V> table) {
    this(table.rowKeySet(), table.columnKeySet());
    putAll(table);
  }

  private ArrayTable(ArrayTable<R, C, V> table) {
    rowList = table.rowList;
    columnList = table.columnList;
    rowKeyToIndex = table.rowKeyToIndex;
    columnKeyToIndex = table.columnKeyToIndex;
    @SuppressWarnings("unchecked")

    eraseAll();
  }

  private ArrayTable(Table<R, C, ? extends @Nullable V> table) {
    this(table.rowKeySet(), table.columnKeySet());
    putAll(table);
  }

  private ArrayTable(ArrayTable<R, C, V> table) {
    rowList = table.rowList;
    columnList = table.columnList;
    rowKeyToIndex = table.rowKeyToIndex;
    columnKeyToIndex = table.columnKeyToIndex;
    @SuppressWarnings("unchecked")

          }
        }
      }
      table = newTable;
      this.count = newCount;
    }

    boolean replace(K key, int hash, V oldValue, V newValue) {
      lock();
      try {
        preWriteCleanup();

        AtomicReferenceArray<E> table = this.table;
        int index = hash & (table.length() - 1);
        E first = table.get(index);

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many
     * exactly matching hash codes, which would cause construction to take O(n^2), but can't detect
     * e.g. hash codes adversarially designed to go into ascending table locations, which keeps