1. V. Megalooikonomou Concurrency control (based on slides by C. Faloutsos at CMU and on notes by Silberchatz,Korth, and Sudarshan) Temple University – CIS Dept. CIS331– Principles of Database Systems

2. General Overview Relational model - SQL Functional Dependencies & Normalization Physical Design &Indexing Query optimization Transaction processing concurrency control recovery

3. Transactions - dfn = unit of work, e.g., move $10 from savings to checking Atomicity (all or none) Consistency Isolation (as if alone) Durability recovery concurrency control

4. Concurrency – overview why we want it? what does it mean ‘correct’ interleaving? precedence graph how to achieve correct interleavings automatically? concurrency control

5. Problem concurrent access to data (consider ‘lost update’ problem) how to solve it?

6. Read(N) Lost update problem – no locks time

7. Solution – part 1 locks! (most popular solution) lock manager: grants/denies lock requests

8. Lost update problem – with locks time T1 lock(N) Read(N) N=N-1 Write(N) Unlock(N) T2 lock(N) lock manager grants lock denies lock T2: waits grants lock to T2 Read(N)...

9. Locks but, what if we all just want to read ‘N’?

10. Solution – part 1 Locks and their flavors X-locks: exclusive (or write-) locks S-locks: shared (or read-) locks compatibility matrix T2 wants T1 has SX STF XFF

11. Solution – part 1 transactions request locks (or upgrades) lock manager grants or blocks requests transactions release locks lock manager updates lock-table

12. Solution – part 1 A transaction is granted a lock on an item if the requested lock is compatible with locks already held on the item Any number of transactions can hold shared locks on an item If any transaction holds an exclusive on the item no other transaction may hold any lock on the item If a lock cannot be granted, the requesting transaction is made to wait till all incompatible locks held by other transactions have been released

13. Solution – part 2 locks are not enough – e.g., ‘inconsistent analysis’

14. ‘Inconsistent analysis’ Precedence graph? time

15. ‘Inconsistent analysis’ – w/ locks time T1 L(A) Read(A)... U(A) T2 L(A).... L(B).... the problem remains! Solution??

16. General solution: Protocol(s) A locking protocol is a set of rules followed by all transactions while requesting and releasing locks. Locking protocols restrict the set of possible schedules. Most popular protocol: 2 Phase Locking (2PL)

17. 2PL (2 Phase Locking ) Phase 1: Growing Phase transaction may obtain locks transaction may not release locks Phase 2: Shrinking Phase transaction may release locks transaction may not obtain locks The protocol assures serializability The transactions can be serialized in the order of their lock points (i.e. the point where a transaction acquired its final lock)

18. 2PL X-lock version: transactions issue no lock requests, after the first ‘unlock’ THEOREM: if all transactions obey 2PL  all schedules are serializable

19. 2PL – example ‘inconsistent analysis’ – why not 2PL? how would it be under 2PL?

20. 2PL – X/S lock version transactions issue no lock/upgrade request, after the first unlock/downgrade In general: ‘growing’ and ‘shrinking’ phase

21. 2PL – observations - limits concurrency - may lead to deadlocks - 2PLC (keep locks until ‘commit’) strict two-phase locking. Here a transaction must hold all its exclusive locks till it commits/aborts. Rigorous two-phase locking is even stricter: here all locks are held till commit/abort.

22. Concurrency – overview what does it mean ‘correct’ interleaving? precedence graph how to achieve correct interleavings automatically?  concurrency control locks + protocols 2PL, 2PLC graph protocols multiple granularity locks

23. Other protocols than 2-PL – graph-based - Assumption: we have prior knowledge about the order in which data items will be accessed - (hierarchical) ordering on the data items, like, e.g., pages of a B-tree A B C

24. Other protocols than 2-PL – graph-based Graph-based protocols are an alternative to 2PL Impose a partial ordering  on the set D = {d 1, d 2,..., d h } of all data items If d i  d j then any transaction accessing both d i and d j must access d i before accessing d j. Implies that the set D may now be viewed as a directed acyclic graph, called a database graph. The tree-protocol is a simple kind of graph protocol

25. E.g., tree protocol (X-lock version) - an xact can request any item, on its first lock request - from then on, it can only request items for which it holds the parent lock - it can release locks at any time - it can NOT request an item twice

26. Tree protocol - example T1 T2 L(B) L(D) L(H) U(D) L(E) U(E) L(D) U(B) U(H) L(G) U(D) U(G) GIH FED CB A -2PL? -follows tree protocol? -‘correct’?

27. Tree protocol - equivalent to 2PL? - deadlocks? - Pros and cons

28. Tree protocol The tree protocol ensures conflict serializability and no deadlocks Unlocking may occur earlier in the tree-locking protocol than in the two-phase locking protocol shorter waiting times, increase in concurrency protocol is deadlock-free, no rollbacks are required the abort of a transaction can still lead to cascading rollbacks However, in the tree-locking protocol, a transaction may have to lock data items that it does not access increased locking overhead, and additional waiting time potential decrease in concurrency Schedules not possible under two-phase locking are possible under tree protocol, and vice versa

29. More protocols - lock granularity – field? record? page? table? - Pros and cons? - (Ideally, each transaction should obtain a few locks)

30. Multiple granularity Example: attr1 attr2 record-nrecord2record1 Table2 Table1 DB

31. Multiple granularity Allows data items to be of various sizes Defines a hierarchy of data granularities Can be represented graphically as a tree (but don't confuse with tree-locking protocol) When a transaction locks a node in the tree explicitly, it implicitly locks all the node's descendents in the same mode Locking granularity (level in tree where locking is done): fine granularity (lower in tree) high concurrency, high locking overhead coarse granularity (higher in tree) low locking overhead, low concurrency

32. What types of locks? X/S locks for leaf level + ‘intent’ locks, for higher levels IS: intent to obtain S-lock underneath IX: intent to obtain X-lock underneath S: shared lock for this level X: ex- lock for this level SIX: shared lock here; + IX

33. Protocol - each xact obtains appropriate lock at highest level - proceeds to desirable lower levels - intention locks allow a higher level node to be locked in S or X mode without having to check all descendent nodes.

34. Protocol Transaction T i can lock a node Q, using the following rules: 1. The lock compatibility matrix must be observed. 2. The root of the tree must be locked first, and may be locked in any mode. 3. A node Q can be locked by T i in S or IS mode only if the parent of Q is currently locked by T i in either IX or IS mode. 4. A node Q can be locked by T i in X, SIX, or IX mode only if the parent of Q is currently locked by T i in either IX or SIX mode. 5. T i can lock a node only if it has not previously unlocked any node (that is, T i is two-phase). 6. T i can unlock a node Q only if none of the children of Q are currently locked by T i. Observe that locks are acquired in root-to-leaf order, whereas they are released in leaf-to-root order.

35. Compatibility matrix T2 wants T1 has ISIXSSIXX ISttttf IXttfff Stftff SIXtffff Xfffff

36. Conclusions - ‘ACID’ for transactions - concurrency: - serializability (precedence graph) - one (popular) solution: locks + - 2PL(C) protocol - graph protocols; multiple granularity