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Quantifying Isolation Anomalies

Quantifying Isolation Anomalies. Alan Fekete Shirley Goldrei Jorge Perez Asenjo. At VLDB’09, Lyon, August 2009. Overview. The Issue The Microbenchmark Predicting the rate of integrity violation Validating the Predictions Finding an Inversion Conclusion. Isolation.

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Quantifying Isolation Anomalies

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  1. Quantifying Isolation Anomalies Alan Fekete Shirley Goldrei Jorge Perez Asenjo At VLDB’09, Lyon, August 2009

  2. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  3. Isolation • Transactions should be ACID • Atomic • Consistent • Isolated • Durable • Isolation achieved by concurrency control

  4. Weaker isolation • Traditional dbms offer application developer a choice among isolation levels • Eg Read Committed: read locks are held only till the read is completed, not till transaction completion

  5. The tradeoff Data integrity, poor performance Serializable Weaker isolation Loss of data integrity, better performance

  6. Quantification • We know how to capture performance gains in numbers • Eg Txn Per Minute • Standard benchmarks • Analytical models • But how to similarly understand the loss of data integrity from weak isolation?

  7. A Microbenchmark • Goal: explore the design space • How configuration, isolation mechanism etc impact on the amount of data corruption • Not: be realistic • Not: compare platforms

  8. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  9. Style • Like performance benchmarks • A particular schema, random data of given size • A mix of particular transaction types • Run concurrent clients, submitting transactions of randomly chosen types with random parameters • Warmup, then measurement interval, then collect data • What to measure is different: the number of cases where integrity constraint is violated, in the data state after the run

  10. The Data • TableA(int id, int valueA, varchar(100) description) • TableB(int id, int valueB, varchar(100) description) • Integrity: for given id, TableA.valueA+TableB.valueB between 0 and 99 inclusive

  11. The transaction types • ChangeA (id): • Read TableA.valueA • Long pause • Read TableB.valueB • Long pause • Update TableA.valueA, adding delta • If sum seen as 0..49, delta = 50 • If sum seen as 50..99, delta = -50 • If sum violates integrity, or during warmup, delta = 0 • Similar ChangeB(id), ChangeAB(id) • ChangeAB applies delta/2 to TableA.valueA, delta/2 to TableB.valueB

  12. Configuration choices • Which Isolation level • Especially SI (Snapshot isolation) and RC_MV (multiversion with each select seeing the most recent data that had committed at the time when the select statement was executed) • MPL • Size of hot-spot with contention on data • Mix of transaction types • Length of pauses

  13. Complexities • If integrity violation is seen, make no change • Otherwise, hard to understand situation at low isolation levels, where inconsistent reads can lead to unjustified appearance of violation • Runs short enough to limit impact of cases of no change • But long enough to give reasonable confidence interval on measurements • Solution: super-runs!

  14. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  15. Simplistic model • Probability that a given transaction introduces an anomaly • Chance of bad overlap with another transaction

  16. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  17. Violation Rate • In our study, the main dependent variable (Y-axis in graphs) is violation rate • The number of rows where integrity condition is false at the end of the run, divided by the number of committed transactions that were performed • Typical values around 1%

  18. Violation Rate vs MPL Measured Predictions from our model

  19. Violation rate vs Txn Mix

  20. Violation Rate vs Pauses

  21. Trends • Violation rate proportional to (MPL-1) • So number of violations proportional to MPL2 • Violation rate proportional to 1/hotspot • At SI, violations only from A vs B • At RC_MV, violations from other combinations as well • At RC_MV, more violations with longer pause from read(B) to updates

  22. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  23. Conventional Wisdom • SI is a rather strong isolation level, and RC_MV is rather weak • More violations of integrity at RC_MV than at SI • Oracle etc use SI for serializable, and RC_MV for Read Committed • But it is known that RC_MV is not strictly weaker than SI • There exist interleavings which are serializable under RC_MV (read latest committed data) but not when executed under SI (read data committed at time txn started)

  24. An inversion • Configuration found from predictive model

  25. Overview • The Issue • The Microbenchmark • Predicting the rate of integrity violation • Validating the Predictions • Finding an Inversion • Conclusion

  26. Conclusion • We can understand the extent of integrity loss from weak isolation • Quantitative • Trends with configuration parameters • Further work: include phantoms • Further work: extend predictive model to other transaction types and data schemas

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