Description

CPS216: Data-intensive Computing Systems Failure Recovery Shivnath Babu 1 Integrity or correctness of data Would like data to be “accurate” or “correct” at all times EMP Name Age White 52 Green 3421 Blue 1 2 Integrity or consistency constraints Predicates data must satisfy Examples: x is key of relation R x → y holds in R (functional dependency) Domain(x) = {Red, Blue, Green} a is valid index for attribute x of R no employee should make more than twice the average salary 3 Definition: Consistent state: satisfies all constraints Consistent DB: DB in consistent state 4 Constraints (as we use here) may not capture “full correctness” Example 1 Transaction constraints When salary is updated, new salary > old salary When account record is deleted, balance = 0 5 Note: could be “emulated” by simple constraints, e.g., account Acct # …. balance deleted? 6 Constraints (as we use here) may not capture “full correctness” Example 2 DB Database should reflect real world Reality 7 in any case, continue with constraints... Observation: DB cannot be consistent always! Example: a1 + a2 +…. an = TOT (constraint) Deposit $100 in a2: a2  a2 + 100 TOT  TOT + 100 8 Example: a1 + a2 +…. an = TOT (constraint) Deposit $100 in a2: a2  a2 + 100 TOT  TOT + 100 a2 TOT . . 50 . . 1000 . . 150 . . 1000 . . 150 . . 1100 9 Transaction: collection of actions that preserve consistency Consistent DB T Consistent DB’ 10 Assumption: If T starts with DB in consistent state + T executes in isolation  T leaves DB in consistent state 11 Correctness (informally) If we stop running transactions, DB left consistent Each transaction sees a consistent DB 12 How can constraints be violated? Transaction bug DBMS bug Hardware failure e.g., disk crash alters balance of account Data sharing e.g.: T1: give 10% raise to programmers T2: change programmers  systems analysts 13 How can we prevent/fix violations? Due to failure

AI summary