1. 1 SQL Authorization (Chap. 8.7) Privileges Grant and Revoke Grant Diagrams

2. 2 Authorization  A file system identifies certain privileges on the objects (files) it manages. Typically read, write, execute.  A file system identifies certain participants to whom privileges may be granted. Typically the owner, a group, all users.

3. 3 Privileges --- 1  SQL identifies a more detailed set of privileges on objects (relations) than the typical file system.  9 privileges in all, some of which can be restricted to one column of one relation.

4. 4 Privileges --- 2  Some important privileges on a relation: 1. SELECT = right to query the relation. 2. INSERT = right to insert tuples. w May apply to only one attribute. 3. DELETE = right to delete tuples. 4. UPDATE = right to update tuples. w May apply to only one attribute.

5. 5 beers that do not appear in Beers. We add them to Beers with a NULL manufacturer. Example: Privileges  For the statement below: INSERT INTO Beers(name) SELECT beer FROM Sells WHERE NOT EXISTS (SELECT * FROM Beers WHERE name = beer);  We require privileges SELECT on Sells and Beers, and INSERT on Beers or Beers.name.

6. 6 Authorization ID ’ s  A user is referred to by authorization ID, typically their user name.  There is an authorization ID PUBLIC. Granting a privilege to PUBLIC makes it available to any authorization ID.

7. 7 The GRANT Statement  To grant privileges, say: GRANT ON TO ;  If you want the recipient(s) to be able to pass the privilege(s) to others add: WITH GRANT OPTION

8. 8 Granting Privileges  You have all possible privileges on the objects, such as relations, that you create.  You may grant privileges to other users (authorization ID ’ s), including PUBLIC.  You may also grant privileges WITH GRANT OPTION, which lets the grantee also grant this privilege.

9. 9 Example: GRANT  Suppose you are the owner of Sells. You may say: GRANT SELECT, UPDATE(price) ON Sells TO sally;  Now Sally has the right to issue any query on Sells and can update the price component only.

10. 10 Example: Grant Option  Suppose we also grant: GRANT UPDATE ON Sells TO sally WITH GRANT OPTION;  Now, Sally can not only update any attribute of Sells, but can grant to others the privilege UPDATE ON Sells. Also, she can grant more specific privileges like UPDATE(price) ON Sells.

11. 11 Revoking Privileges REVOKE ON FROM ;  Your grant of these privileges can no longer be used by these users to justify their use of the privilege. But they may still have the privilege because they obtained it independently from elsewhere.

12. 12 REVOKE Options  We must append to the REVOKE statement either: 1. CASCADE. Now, any grants made by a revokee are also not in force, no matter how far the privilege was passed. 2. RESTRICT. If the privilege has been passed to others, the REVOKE fails as a warning that something else must be done to “ chase the privilege down. ”

13. 13 Grant Diagrams  Nodes = user/privilege/option/isOwner? UPDATE ON R, UPDATE(a) on R, and UPDATE(b) ON R live in different nodes. SELECT ON R and SELECT ON R WITH GRANT OPTION live in different nodes.  Edge X ->Y means that node X was used to grant Y.

14. 14 Notation for Nodes  Use AP for the node representing authorization ID A having privilege P. P * represents privilege P with grant option. P ** represents the source of the privilege P. That is, AP ** means A is the owner of the object on which P is a privilege.  Note ** implies grant option.

15. 15 Manipulating Edges --- 1  When A grants P to B, We draw an edge from AP * or AP ** to BP. Or to BP * if the grant is with grant option.  If A grants a subprivilege Q of P (say UPDATE(a) on R when P is UPDATE ON R) then the edge goes to BQ or BQ *, instead.

16. 16 Manipulating Edges --- 2  Fundamental rule: user C has privilege Q as long as there is a path from XQ ** (the origin of privilege Q ) to CQ, CQ *, or CQ**. Remember that XQ** could be CQ**.

17. 17 Manipulating Edges --- 3  If A revokes P from B with the CASCADE option, delete the edge from AP to BP.  If A uses RESTRICT, and there is an edge from BP to anywhere, then reject the revocation and make no change to the graph.

18. 18 Manipulating Edges --- 4  Having revised the edges, we must check that each node has a path from some ** node, representing ownership.  Any node with no such path represents a revoked privilege and is deleted from the diagram.

19. 19 Example: Grant Diagram AP** A owns the object on which P is a privilege BP* A: GRANT P TO B WITH GRANT OPTION CP* B: GRANT P TO C WITH GRANT OPTION CP A: GRANT P TO C

20. Example: Grant Diagram AP**BP*CP* CP A executes REVOKE P FROM B CASCADE; However, C still has P without grant option because of the direct grant. Not only does B lose P*, but C loses P*. Delete BP* and CP*. Even had C passed P to B, both nodes are still cut off.

21. 21 Exercise

22. 22 Transactions (Chap. 8.6) Serializability Isolation Levels Atomicity

23. 23 The Setting  Database systems are normally being accessed by many users or processes at the same time. Both queries and modifications.  Unlike Operating Systems, which support interaction of processes, a DMBS needs to keep processes from troublesome interactions.

24. 24 Example: Bad Interaction  You and your spouse each take $100 from different ATM ’ s at about the same time. The DBMS better makes sure one account deduction doesn ’ t get lost.  Compare: An OS allows two people to edit a document at the same time. If both write, one ’ s changes get lost.

25. 25 ACID Transactions  A DBMS is expected to support “ ACID transactions, ” which are: Atomic : Either the whole process is done or none is. Consistent : Database constraints are preserved. Isolated : It appears to the user as if only one process executes at a time. Durable : Effects of a process do not get lost if the system crashes.

26. 26 Transactions in SQL  SQL supports transactions, often behind the scenes. Each statement issued at the generic query interface is a transaction by itself. In programming interfaces like Embedded SQL or PSM, a transaction begins the first time an SQL statement is executed and ends with the program or an explicit end.  JDBC – auto-commit (default)

27. 27 COMMIT  The SQL statement COMMIT causes a transaction to complete. It ’ s database modifications are now permanent in the database.

28. 28 ROLLBACK  The SQL statement ROLLBACK also causes the transaction to end, but by aborting. No effects on the database.  Failures like division by 0 can also cause rollback, even if the programmer does not request it.

29. 29 An Example: Interacting Processes  Assume the usual Sells(bar,beer,price) relation, and suppose that Joe ’ s Bar sells only Bud for $2.50 and Miller for $3.00.  Sally is querying Sells for the highest and lowest price Joe’s bar charges.  Joe decides to stop selling Bud and Miller, but to sell only Heineken at $3.50.

30. 30 Sally ’ s Program  Sally executes the following two SQL statements, which we call (min) and (max), to help remember what they do. (max)SELECT MAX(price) FROM Sells WHERE bar = ‘ Joe ’’ s Bar ’ ; (min)SELECT MIN(price) FROM Sells WHERE bar = ‘ Joe ’’ s Bar ’ ;

31. 31 Joe ’ s Program  At about the same time, Joe executes the following steps, which have the mnemonic names (del) and (ins). (del)DELETE FROM Sells WHERE bar = ‘ Joe ’’ s Bar ’ ; (ins)INSERT INTO Sells VALUES( ‘ Joe ’’ s Bar ’, ‘ Heineken ’, 3.50);

32. 32 Interleaving of Statements  Although (max) must come before (min) and (del) must come before (ins), there are no other constraints on the order of these statements, unless we group Sally ’ s and/or Joe ’ s statements into transactions.

33. 33 Example: Strange Interleaving  Suppose the steps execute in the order (max)(del)(ins)(min). Joe ’ s Prices: Statement: Result:  Sally sees MAX < MIN! 2.50, 3.00 (del) (ins) 3.50 (min) 3.50 2.50, 3.00 (max) 3.00 3.50

34. 34 Fixing the Problem With Transactions  If we group Sally ’ s statements (max)(min) into one transaction, then she cannot see this inconsistency.  She sees Joe ’ s prices at some fixed time. Either before or after he changes prices, or in the middle, but the MAX and MIN are computed from the same prices.

35. 35 Another Problem: Rollback  Suppose Joe executes (del)(ins), but after executing these statements, thinks better of it and issues a ROLLBACK statement.  If Sally executes her transaction after (ins) but before the rollback, she sees a value, 3.50, that never existed in the database.

36. 36 Solution  If Joe executes (del)(ins) as a transaction, its effect cannot be seen by others until the transaction executes COMMIT. If the transaction executes ROLLBACK instead, then its effects can never be seen.

37. 37

38. 38 Isolation Levels  SQL defines four isolation levels = choices about what interactions are allowed by transactions that execute at about the same time.  How a DBMS implements these isolation levels is highly complex, and a typical DBMS provides its own options.

39. 39 Choosing the Isolation Level  Within a transaction, we can say: SET TRANSACTION ISOLATION LEVEL X where X = 1. SERIALIZABLE 2. REPEATABLE READ 3. READ COMMITTED 4. READ UNCOMMITTED

40. 40 Serializable Transactions  If Sally = (max)(min) and Joe = (del)(ins) are each transactions, and Sally runs with isolation level SERIALIZABLE, then she will see the database either before or after Joe runs, but not in the middle.  It ’ s up to the DBMS vendor to figure out how to do that, e.g.: True isolation in time. Keep Joe ’ s old prices around to answer Sally ’ s queries.

41. 41 Isolation Level Is Personal Choice  Your choice, e.g., run serializable, affects only how you see the database, not how others see it.  Example: If Joe Runs serializable, but Sally doesn ’ t, then Sally might see no prices for Joe ’ s Bar. i.e., it looks to Sally as if she ran in the middle of Joe ’ s transaction.

42. 42 Read-Committed Transactions  If Sally runs with isolation level READ COMMITTED, then she can see only committed data, but not necessarily the same data each time.  Example: Under READ COMMITTED, the interleaving (max)(del)(ins)(min) is allowed, as long as Joe commits. Sally sees MAX < MIN.

43. 43 Repeatable-Read Transactions  Requirement is like read-committed, plus: if data is read again, then everything seen the first time will be seen the second time. But the second and subsequent reads may see more tuples as well.

44. 44 Example: Repeatable Read  Suppose Sally runs under REPEATABLE READ, and the order of execution is (max)(del)(ins)(min). (max) sees prices 2.50 and 3.00. (min) can see 3.50, but must also see 2.50 and 3.00, because they were seen on the earlier read by (max).

45. 45 Read Uncommitted  A transaction running under READ UNCOMMITTED can see data in the database, even if it was written by a transaction that has not committed (and may never).  Example: If Sally runs under READ UNCOMMITTED, she could see a price 3.50 even if Joe later aborts.

46. 46 DBMS Techniques to enforce ACID  Locking – granularity of locks is important. Locks are obtained at the beginning of a transaction. Locks are released at the end of commit or rollback.  Logging – write a log to nonvolatile storage. Assure durability.  Transaction Commitment – for durability and atomicity, transactions are computed “ tentatively ”, recorded, but no changes are made to the db until the transaction gets committed. Changes are copied to the log, then copied to db.