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Concurrency Control Alexandra Cristea and Steve Russ 1.

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1 Concurrency Control Alexandra Cristea and Steve Russ 1

2 Who needs control? large databases are usually shared – by many users, and resources it is efficient to allow concurrent access. 2

3 Relevant Concepts for Concurrency Control integrity : consistency and correctness security : ensuring users only do what they are allowed recovery : return after an error to a known correct state ISSUE: Concurrent access endangers each of these !!! 3

4 Level of treatment Large and complex topic Here: logical or conceptual level. closely following: – C.J. Date Introduction to Database Systems (8 th ed.) Ch.16. The book by Date has an excellent annotated bibliography for each chapter. – similar treatments in Silberschatz et al., and Connolly & Begg. 4

5 Sources for Learning There are good chapters on each of the main topics associated with concurrency control in the Date book: Integrity (Chapter 9) Recovery (Chapter 15) Concurrency (Chapter 16) Security (Chapter 17) 5

6 More Sources There are similar chapters in the Silberschatz et al. recommended book (Ch.5, 15, 16, 17). There are some good sets of notes on the web on these topics – Transaction processing or similar. Google lost update problem database (in UK). But: better to stick with one source in detail. 6

7 Integrity 7

8 Database Integrity Domains Constraints Entity/referential integrity Assertions 8

9 Four Types of Integrity Rule Domain Rules: Give legal values for domains. Attribute Rules: Give legal values for attributes. Relation Rules: Rules governing single relations (e.g., Primary keys must be unique and Non-NULL) Database Rules: Rules governing interrelationships between relations (e.g., Foreign Keys must be Primary keys - and therefore Non-NULL). SQL provides ways of defining these Rules or Constraints. 9

10 Domains and Constraints Defining constrains outside the CREATE TABLE data definition: CREATE DOMAIN domain_name [AS] data_type [DEFAULT default_option] [CHECK (search_condition)] CREATE DOMAIN sex_type AS CHAR CHECK (VALUE IN (M, F)) Names of types are reusable. 10

11 Entity Integrity Entity integrity enforced by naming PRIMARY KEY (i.e., attribute or set of attributes which should be NOT NULL and UNIQUE). E.g.: CREATE TABLE staff( staff# number (3), name char(30), PRIMARY KEY staff#); For alternate keys can use NOT NULL and UNIQUE qualifiers after column attribute name. E.g.: CREATE TABLE fruit( name char(30) PRIMARY KEY, supplier char(20) NOT NULL, country char(20) NOT NULL, UNIQUE (supplier, country)); 11

12 Referential Integrity defined by FOREIGN KEY or REFERENCES. Referential action must allow cascading of updates from parent to child table (+deletes, NULL value, DEFAULT). E.g.: CREATE TABLE emps( emp# number(2), name char(30), dept# number(2) REFERENCES dept ON UPDATE CASCADE); 12

13 Assertions CREATE ASSERTION assertion_name CHECK (search condition) CREATE ASSERTION daycheck CHECK ( (day>0) and (day<= (select from daysinmonth DM where DM.month=month)) Named rules like this are database/ enterprise wide. 13

14 Oracle SQL Limitations Note that Oracle 10g 2 does not implement all ISO SQL Integrity Enhancement Features (IEF). – Domains cannot be defined outside CREATE TABLE. – Referential action restricted to ON DELETE CASCADE or NO ACTION. – Table check constraints can only refer to attributes within a table. – Named assertions cannot be defined. 14

15 Note: Integrity is not Security. Integrity ensures that the things the users are trying to do are correct. Security ensures that things users are doing are only what they are allowed to do. 15

16 Security 16

17 Security vs. Integrity Integrity: Ensuring what users are trying to do is correct. Security: Ensuring users are allowed to do things they are trying to do. Both require rules that users must not violate. 17

18 Database Security Approaches 1.Discretionary Control: Named users, Privileges or access rights to data objects. Distributed control. 2.Mandatory Control: Users have Clearance, Objects have classification levels. Central control. 18

19 Security Mechanisms Security sub-systems which checks IDs against security rules. 19

20 20 SQL Syntax for Security Rules GRANT [privilege-commalist | ALL PRIVILEGES] ON object-name TO [authorisation_id_list | PUBLIC] [WITH GRANT OPTION] Each privilege is one of the following: SELECT DELETE INSERT [ (attribute-commalist)] UPDATE [ (attribute-commalist) ] REFERENCES [ (attribute-commalist) ] The REFERENCES allows privileges to be granted on named table(s) in integrity constraints of CREATE TABLE. The GRANT OPTION allows the named users to pass the privileges on to other users.

21 Audit Trails We cant assume that security will be perfect, i.e. someone might gain unauthorised access. Audit Trails area logs which can track down the infiltrators. Audit trails will contain entries of the form: request (source text) location (physical e.g. terminal id) user id date/ time relations affected (base, tuple, attribute) old-values new-values Knowing that there is an audit trail may deter security hacks. 21

22 22 Grant and Revoke If a user A grants privileges to user B, then they can also revoke them e.g. REVOKE ALL PRIVILEGES ON STATS FROM John; SQL REVOKE syntax REVOKE [GRANT OPTION FOR] [privilege_list | ALL PRIVILEGES] ON object_name FROM [authorisation_list|PUBLIC] [RESTRICT|CASCADE] If RESTRICT option is given then the command is not executed if any dependent rules exist i.e. those created by other users through the WITH GRANT OPTION. CASCADE will force a REVOKE on any dependent rules.

23 Security Summary A DBMS security-subsystem enforces security Access is checked against security rules Discretionary control rules have a users, privileges and objects Mandatory controls have clearance and classification levels Audit trails are used to record attempted security breaches GRANT/ REVOKE syntax in SQL We have not dealt with data-encryption, which deals with the storing and transmission of sensitive data. 23

24 Recovery 24

25 Recovery Restoring a database to a known correct state after some failure. Database recovery is based on redundancy at the physical level. – Any piece of information can be recovered from some other stored information, somewhere else. 25

26 Transactions A transaction is a logical unit of work, as well as unit of recovery. It is broken down into a sequence of atomic operations, which if any fail, the whole transaction is undone. SELECT | INSERT | … … … work … … COMMIT | ROLLBACK 26

27 Transactions series of database commands w clear semantics – e.g. transfer of funds from one account to another Commit: If nothing fails commit point where the DB should be consistent. All updates are tentative until committed. Rollback: If any command fails => whole series is undone. Any DBMS support these (and lang. e.g. SQL) 27

28 Transaction Example BEGIN TRANSACTION UPDATE ACC123 {BALANCE := BALANCE - £100} IF any error occurred THEN GO TO UNDO; END IF; UPDATE ACC456 {BALANCE := BALANCE + £100} IF any error occurred THEN GO TO UNDO; END IF; COMMIT; GO TO FINISH;/*successful end*/ UNDO: ROLLBACK;/*unsuccessful end*/ FINISH: RETURN; 28

29 Transaction Properties Atomicity: all or nothing (any error => Rollback, as if nothing happened) Consistency: a consistent state always leads to another consistent state Isolation: a transactions updates are hidden until it Commits Durability: after a Commit, updates persist These are the ACID properties of transactions. 29

30 System Recovery How does the system recover after a system failure (e.g., power failure) or media failure (e.g., disk crash)? In the event of a crash … – Contents of main memory are lost. – Transaction log persists. – At failure, certain transactions will be complete while others part complete. Note that updates are held in memory buffers and written out periodically. 30

31 Recovery To recover the state of the database we can use: A log file recording every database operation. Checkpoints recording the state of all active transactions. – Then: develop an algorithm for transactions to UNDO, – and those that we need to REDO, to effect recovery. – at intervals, the system will: Flush its buffers Write out a checkpoint record to log indicating which transactions are in progress. 31

32 Five Transaction categories The most recent check point record was taken at time tc. Time tc tf Transactions T1 T2 T3 T4 T5 System failure (time tf) Checkpoint (time tc) 32

33 CompletedUn-Finished Cached Written T2 T1 T4 T3 T5 33

34 CHECKPOINT RECORD: – T3, T5 : undone (rollback possible) – T2, T4 : re-done DBMS creates REDO/UNDO list from checkpoint record + system log. isolation => – order of recovery not crucial, – only DB should be consistent near tf. 34

35 Concurrency 35

36 Concurrency Many transactions - at the same time. Databases shared! So: Transactions must be isolated => need of concurrency control to ensure no interference. We will look at: – 3 classic problems on concurrent access – Locking mechanism – Deadlock resolution 36

37 Three classic problems PB: two (more) transactions read / write on the same part of the db. Although transactions execute correctly, results may interleave in diff ways => 3 classic problems. Lost Update Uncommitted Dependency Inconsistent Analysis 37

38 Lost Update problem Time User 1 (Trans A) User2 (Trans B) 1Retrieve t 2 3Update t t : tuple in a table. Trans A loses an update at t4. The update at t3 is lost (overwritten) at t4 by B. 38

39 Uncommitted Dependency TimeUser 1 (Trans A)User 2 (Trans B) 1Update t 2Retrieve t 3Rollback 4 5 6Update t 7 8Rollback 2 PBs (T1-3 ; T6-8). One trans is allowed to retrieve/update) a tuple updated by another, but not yet committed. Trans A is dependent at time t2 on an uncommitted change made by Trans B, which is lost on Rollback. 39

40 Inconsistent Analysis TimeUser 1 (Trans A)User 2 (Trans B) 1Retrieve Acc 1 : Sum = 40 2Retrieve Acc2 : Sum = 90 3Retrieve Acc3 : 4Update Acc3: Retrieve Acc1: 6Update Acc1: commit 8Retrieve Acc3: Sum = 110 (not 120) Initially: Acc 1 = 40; Acc2 = 50; Acc3 = 30; Trans A sees inconsistent DB state after B updated Accumulator => performs inconsistent analysis. 40

41 Why these problems? Retrieve : read (R) Update : write (W). interleaving two transactions => 3 PBS: RR – no problem WW – lost update WR – uncommitted dependency RW – inconsistent analysis 41

42 How to prevent such problems? locking protocol – Other approaches : serializability, time-stamping, and shadow-paging. See books. IF risk of interference = low => two-phase locking ~ common approach – although it requires deadlock avoidance!! Lock applies to a tuple : – exclusive (write; X) or – shared(read; S). 42

43 Lost Update solved Time User 1 (Trans A) User2 (Trans B) 1Retrieve t (get S-lock on t) 2 3Update t (request X-lock on t) 4waitUpdate t (request X-lock on t) 5wait 6 7 No update lost but => deadlock 43

44 Uncommitted Dependency solved TimeUser 1 (Trans A)User 2 (Trans B) 1Update t (get X-lock on t) 2Retrieve t (request S-lock on t)- 3wait Commit / Rollback (releases X-lock on t) 6Resume: Retrieve t (get S-lock on t)

45 Inconsistent Analysis solved TimeUser 1 (Trans A)User 2 (Trans B) 1Retrieve Acc1 : (get S-lock) Sum = 40 2Retrieve Acc2 : (get S-lock) Sum = 90 3Retrieve Acc3: (get S-lock) 4Update Acc3: (get X-lock) Retrieve Acc1: (get S-lock) 6Update Acc1: (request X-lock) wait 7Retrieve Acc3: (request S-lock) wait 45

46 Deadlock Deadlock occurs when 2 or more transaction are in a simultaneous wait state. It is desirable to conceal deadlocks from the user. 46

47 Deadlock Resolution The system must detect and break deadlocks by: 1.Choosing one trans as a victim and rolling it back. 2.Timing out the trans and returning an error. 3.automatically restarting the transaction hoping not to get deadlock again. 4.Return an error code back to the victim and leaving it up to program to handle situation.. 47

48 Topics covered: Integrity – Domains, Constraints, Entity/referential integrity, assertions Security – Discretionary, mandatory, audit trails Recovery – Transactions, ACID properties Concurrency Control – Lost update, uncommitted dependency, inconsistent analysis, deadlock 48

49 Questions? 49

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