этот запрос будет возвращать все комбинации: SELECT m.name, s.size, c.name FROM materials m CROSS JOIN sizes s CROSS JOIN colors c Неоднозначные имена полей в соединениях Привилегия USAGE на исключение выданная пакету GRANT USAGE ON EXCEPTION E_ACCESS_DENIED TO PACKAGE PKG_BILL; DDL привилегии По умолчанию создавать новые объекты метаданных могут только Администраторы привилегии USAGE на исключение выданной пакету REVOKE USAGE ON EXCEPTION E_ACCESS_DENIED FROM PACKAGE PKG_BILL; Пример 9.31. Отзыв привилегий на изменение метаданных -- Отзыв у пользователя

colors and different materials. If these variables are each listed in a table of their own, this query would return all the combinations: select m.name, s.size, c.name from materials m cross join sizes Granting the USAGE privilege on an exception to a package: GRANT USAGE ON EXCEPTION TO PACKAGE PKG_BILL; DDL Privileges By default, only administrators can create new metadata objects; altering or dropping privilege on the exception E_ACCESS_DENIED from the package PKG_BILL: REVOKE USAGE ON EXCEPTION E_ACCESS_DENIED FROM PACKAGE PKG_BILL; 14. Revoking the privilege to create tables from user JOE: REVOKE

sind, würde diese Abfrage alle Kombinationen zurückgeben: select m.name, s.size, c.name from materials m cross join sizes s cross join colors c; Implizite Joins Im SQL:89-Standard wurden die an Gewähren des Privilegs USAGE für eine Ausnahme für ein Paket: GRANT USAGE ON EXCEPTION TO PACKAGE PKG_BILL; DDL Privileges Standardmäßig können nur Administratoren neue Metadatenobjekte erstellen; Das Ändern USAGE-Privilegs für die Ausnahme E_ACCESS_DENIED aus dem Paket PKG_BILL: REVOKE USAGE ON EXCEPTION E_ACCESS_DENIED FROM PACKAGE PKG_BILL; 14. Widerruf der Berechtigung zum Erstellen von Tabellen von Benutzer

query results will (hopefully) appear 15 -- Find all films which have "Bill" in the name select * from movie where name like '%Bill%'; More Interesting IMDB queries • Execute the following two queries in a movie -- with "Bill" in the name select fname, lname, role, name from movie, cast, actor where movie.id = "cast".mid and "cast".aid = actor.id and movie.name like '%Bill%'; The table name "cast"

### For detailed information why RocksDB needs to implement compaction, see �→ RocksDB-related materials. When write ### traffic (like the importing data size) is big, it is recommended to �→ enable ('dave'), ('sarah'), ('bill'); With optimistic locking and tidb_constraint_check_in_place=OFF: BEGIN OPTIMISTIC; INSERT INTO users (username) VALUES ('jane'), ('chris'), ('bill'); Query OK, 3 rows affected affected (0.00 sec) Records: 2 Duplicates: 0 Warnings: 0 COMMIT; ERROR 1062 (23000): Duplicate entry 'bill' for key 'users.username' In the preceding optimistic example, the unique check was deferred until

### For detailed information why RocksDB needs to implement compaction, see �→ RocksDB-related materials. When write ### traffic (like the importing data size) is big, it is recommended to �→ enable ('dave'), ('sarah'), ('bill'); With optimistic locking and tidb_constraint_check_in_place=OFF: BEGIN OPTIMISTIC; INSERT INTO users (username) VALUES ('jane'), ('chris'), ('bill'); Query OK, 3 rows affected affected (0.00 sec) Records: 2 Duplicates: 0 Warnings: 0 COMMIT; ERROR 1062 (23000): Duplicate entry 'bill' for key 'users.username' In the preceding optimistic example, the unique check was deferred until

### For detailed information why RocksDB needs to implement compaction, see �→ RocksDB-related materials. When write ### traffic (like the importing data size) is big, it is recommended to �→ enable ('dave'), ('sarah'), ('bill'); With optimistic locking and tidb_constraint_check_in_place=OFF: BEGIN OPTIMISTIC; INSERT INTO users (username) VALUES ('jane'), ('chris'), ('bill'); Query OK, 3 rows affected affected (0.00 sec) Records: 2 Duplicates: 0 Warnings: 0 COMMIT; ERROR 1062 (23000): Duplicate entry 'bill' for key 'users.username' In the preceding optimistic example, the unique check was deferred until

specification might be necessary.) Now we can show some INSERT statements: INSERT INTO sal_emp VALUES (’Bill’, ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"training", "presentation"}}’); INSERT pay_by_quarter | schedule -------+---------------------------+------------------------------------------- Bill | {10000,10000,10000,10000} | {{meeting,lunch},{training,presentation}} Carol | {20000,25000,25000 extents for each dimension. A mismatch causes an error, for example: INSERT INTO sal_emp VALUES (’Bill’, ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"meeting"}}’); ERROR: multidimensional

specification might be necessary.) Now we can show some INSERT statements: INSERT INTO sal_emp VALUES (’Bill’, 138 Chapter 8. Data Types ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"training" pay_by_quarter | schedule -------+---------------------------+------------------------------------------- Bill | {10000,10000,10000,10000} | {{meeting,lunch},{training,presentation}} Carol | {20000,25000,25000 extents for each dimension. A mismatch causes an error, for example: INSERT INTO sal_emp VALUES (’Bill’, ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"meeting"}}’); ERROR: multidimensional

specification might be necessary.) Now we can show some INSERT statements: INSERT INTO sal_emp VALUES (’Bill’, ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"training", "presentation"}}’); INSERT pay_by_quarter | schedule -------+---------------------------+------------------------------------------- Bill | {10000,10000,10000,10000} | {{meeting,lunch},{training,presentation}} Carol | {20000,25000,25000 extents for each dimension. A mismatch causes an error, for example: INSERT INTO sal_emp VALUES (’Bill’, ’{10000, 10000, 10000, 10000}’, ’{{"meeting", "lunch"}, {"meeting"}}’); ERROR: multidimensional