1. Oracle Database Administration Lecture 3  Transactions  SQL Language: Additional information  SQL Language: Analytic Functions

2. Database transactions Every operation in Oracle is executed in a transaction Transactions are started automatically when user executes first statement:  after opening new database session  after ending previous transaction

3. Transactions Transaction can end with:  COMMIT – changes are saved in the database  ROLLBACK – changes are canceled and removed from the database Transaction ends automatically when:  user properly disconnects from the database (transaction is committed)  database crashes, or user session disconnects improperly (transaction is rolled back)

4. Transactions and sessions Separate sessions created by the same user work in separate transactions Transaction is always opened by some database session  single session usually has only one open transaction (exception: autonomous transactions)  once transaction is committed or rolled back, session opens new transaction

5. Transactions and savepoints Savepoint saves current state of a transaction It is possible to roll back to a savepoint (partial rollback of a transaction) Example: delete from test; savepoint s1; insert into test(id) values (1); rollback to s1; commit;-- table test is empty

6. The ACID model Database transactions should follow the ACID model  A – Atomicity  C – Consistency  I – Isolation  D – Durability

7. Atomicity Database transaction should be atomic:  entire transaction should be treated as a single operation  other sessions should not see results of uncommitted transactions  if a database crashes or session disconnects, partial (uncommitted) transactions should not be visible, they should automatically be rolled back

8. Consistency Consistency states that only valid data will be written to the database Transactions violating consistency rules (constraints) will be rolled back

9. Isolation Multiple transactions should be isolated from each other If two transactions are issued at the same time, one should execute before another, so that they do not interfere with each other In practice: single transaction should not see results of another transactions executing at the same time

10. Durability Transaction once committed can never be lost Durability is ensured through database backups and redo logs

11. Transaction concurrency Typical problems related to concurrency:  dirty read - transaction reads data that is not yet committed (violates atomicity and isolation)  non repeatable read - transaction reads the same data twice and sees different results, because it was modified and committed by another transaction (violates isolation)  phantoms - transaction executes the same query twice and in the second execution there are additional rows that were inserted and committed in the mean time (violates isolation)

12. Isolation levels Standard isolation levels:  READ UNCOMMITTED - transactions see uncommitted data from other transactions, dirty read, non repeatable read and phantoms are possible  READ COMMITTED - transactions see committed data, phantoms and unrepeatable reads are possible.  REPEATABLE READ - the same query executed twice gives the same results with the exception of phantoms.  SERIALIZABLE - transactions are fully isolated.

13. Oracle's isolation levels Oracle supports the following isolation levels:  READ COMMITTED – default isolation level  SERIALIZABLE – can be activated with: SET TRANSACTION ISOLATION LEVEL SERIALIZABLE  READ ONLY – non standard level, can be activated with: SET TRANSACTION READ ONLY Isolation level can only be set as the first statement in the transaction If set – isolation level remains until the end of the transaction

14. SQL Language NULL values in SQL  NULL value has special “no value” meaning  NULL compared with any other value results in NULL: SELECT * FROM users WHERE login = NULL SELECT * FROM users WHERE login != NULL Both statements return 0 rows Logical condition can have 3 values:  TRUE  FALSE  NULL (UNKNOWN)

15. NULL logical conditions Example: INSERT INTO users (login, name) VALUES (NULL, 'Some user'); INSERT INTO users (login, name) VALUES ('user1', 'Some other user'); SELECT * FROM users WHERE login = 'user1'; SELECT * FROM users WHERE NOT (login = 'user1');-– does not –- return 'Some user'

16. NULL Logical conditions

17. SQL Language - Functions Text functions:  TO_CHAR – convert date, number to text  TRIM – trim text  UPPER, LOWER – change case  INSTR – search string  LENGTH – measure length of a string  TRANSLATE – replace characters  REPLACE – replace texts

18. SQL Language - Functions Number functions:  TO_NUMBER – text to number  ROUND – round to specified precision  FLOOR, CEIL – round up or down  SIN, COS, SINH, COSH, POWER, LN, LOG  MOD – x modulo y

19. SQL Language - Functions Date functions:  SYSDATE – current date  TO_DATE – text to date  TO_CHAR – date to text  ADD_MONTHS – add or subtract months from a date  MONTHS_BETWEEN – number of months between two dates  NEXT_DAY – next day of week after given date, for example: “next Tuesday”

20. Character case, quotes SQL language is case insensitive SELECT * FROM TABLE; select * from table; -- identical statements Select * From Table; Character literals are case sensitive: SELECT * FROM USER_TABLES WHERE TABLE_NAME = 'TEST'; SELECT * FROM USER_TABLES WHERE TABLE_NAME = 'test'; -- different statements Character literals are enclosed by single quotes Double quotes can be used to quote column and table names: CREATE TABLE "test" (ID NUMBER PRIMARY KEY);

21. Double quotes Double quotes, when used: – make the name case sensitive – make it possible to use reserved word as identifier CREATE TABLE test (NUMBER NUMBER); -- error CREATE TABLE test ("NUMBER" NUMBER); -- ok CREATE TABLE test1 ("NUMBER" NUMBER, "Number" NUMBER, "number" NUMBER) – ok When not using quotes, Oracle converts the name to upper case: CREATE TABLE test (ID NUMBER); SELECT COUNT(*) FROM USER_TABLES WHERE TABLE_NAME = 'test'; --> 0 rows SELECT COUNT(*) FROM USER_TABLES WHERE TABLE_NAME = 'TEST'; --> 1 row

22. Analytic Functions Analytic Functions: – Advanced SQL feature, can be used for reporting, or advanced SQL statements – Similar to aggregate (groupping) functions, but return result for every row in the table, not one result for entire group of rows – Syntax: function(...) OVER ( PARTITION BY … ORDER BY … )

23. Analytic Functions - Example SELECT e.name, e.salary, sum(e.salary) OVER (order by name) AS total FROM employees e ORDER BY e.name NAME SALARY TOTAL -------------------- ---------- ---------- Abacki 2000 2000 Babacki 1500 3500 Cabacki 1200 4700 Dabacki 1100 5800 Fabacki 1600 7400 Gabacki 4100 11500...

24. Analytic Functions: SUM SELECT e.name, e.salary, sum(e.salary) OVER (order by name) AS total FROM employees e ORDER BY e.name Function SUM returns sum of all values from the beginning of the table until current record Records are sorted by name: OVER (order by name) Sort order for the SELECT statement could be different than sort order for the Analytic Function

25. Analytic Functions Other Analytic Functions: – ROW_NUMBER – assign numbers to each row: 1, 2, 3 etc., similar to ROWNUM pseudo column – RANK – similar to ROW_NUMBER, but identical values get the same rank. For example: 1, 2, 2, 4 – DENSE_RANK – similar to RANK, but if there are identical values, there is no gap in numbering. For example: 1, 2, 2, 3 – AVG, MAX, MIN, COUNT – The same as aggregate functions – LAG – gives access to previous rows – LEAD – gives access to next rows

26. Analytic Functions - Example SELECT e.name, ROW_NUMBER() OVER (order by name) RNUM, RANK() OVER (order by name) RANK, DENSE_RANK() OVER (order by name) DRANK FROM emp e ORDER BY e.name; NAME RNUM RANK DRANK --------------- ---------- ---------- ---------- Abacki 1 1 1 Babacki 2 2 2 Babacki 3 2 2 Babacki 4 2 2 Cabacki 5 5 3 Dabacki 6 6 4

27. Analytic Functions - Example SELECT * FROM ( SELECT e.name, ROW_NUMBER() OVER (order by name) RNUM FROM emp e ) WHERE RNUM BETWEEN 5 AND 10 ORDER BY name; NAME RNUM --------------- ---------- Cabacki 5 Dabacki 6 Fabacki 7 Gabacki 8 Habacki 9 Human resources Boss 10

28. Analytic Functions function(...) OVER ( PARTITION BY … ORDER BY … ) PARTITION BY lets you divide records into separate partitions/groups Each group is aggregated separately

29. Analytic Functions - Example SELECT e.dept_id, e.name, e.salary, sum(e.salary) OVER (PARTITION BY dept_id ORDER BY name) AS total_dept, sum(e.salary) OVER ( ORDER BY dept_id, name) AS total, row_number() OVER (PARTITION BY dept_id ORDER BY name) AS rnum_dept, row_number() OVER ( ORDER BY dept_id, name) AS rnum_total FROM emp e ORDER BY e.dept_id, e.name;

30. Analytic Functions - Example Result: DID NAME SALARY TOTAL_DEPT TOTAL RNUM_DEPT RNUM_TOTAL --- --------------- ---------- ---------- ---------- ---------- ---------- 1 Secretary of th 2500 2500 2500 1 1 e Secretary 1 The Big Boss 60000 62500 62500 2 2 1 The Big Boss Se 2000 64500 64500 3 3 cretary 2 Abacki 2000 2000 66500 1 4 2 Babacki 1500 6500 71000 2 5

31. Analytic Functions - Example Display 3 employees with highest salary in each department: SELECT * FROM ( SELECT e.name, e.salary, e.dept_id, ROW_NUMBER() OVER (PARTITION BY dept_id ORDER BY salary DESC) rnum FROM emp e) WHERE rnum <= 3;