statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left then that path is treated as the top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. Parameters: value:

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. value: the value of the current element. type: one

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) LEFT_OUTER) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top-most element. The rows returned by calls to children() have the following attributes: • key: the key of the current element relative to its parent. • value: the value of the current element. • type:

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) LEFT_OUTER) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top-most element. The rows returned by calls to children() have the following attributes: • key: the key of the current element relative to its parent. • value: the value of the current element. • type:

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. value: the value of the current element. type: one

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. By default it top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. value: the value of the current element. type: one

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) join(Tweet) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. By default it top-most element. The rows returned by calls to children() have the following attributes: • key: the key of the current element relative to its parent. • value: the value of the current element. • type:

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. value: the value of the current element. type: one

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) LEFT_OUTER) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top-most element. The rows returned by calls to children() have the following attributes: • key: the key of the current element relative to its parent. • value: the value of the current element. • type:

statements as complex as you like. Let’s give all our employees a bonus equal to their previous bonus plus 10% of their salary: >>> query = Employee.update(bonus=(Employee.bonus + (Employee.salary * .1))) .group_by(User)) The resulting query will return User objects with all their normal attributes plus an additional attribute count which will contain the count of tweets for each user. We use a left top- most element. The rows returned by calls to children() have the following attributes: key: the key of the current element relative to its parent. value: the value of the current element. type: one