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Data Concurrency, Consistency and Integrity

Data Concurrency, Consistency and Integrity. My goals. Managing Data Keep it valid Let people access it Start from simple models Build more complex ones High-Level Abstraction Oracle implementation No Code. You. Programming Databases Concurrency SQL Oracle. A Simple Model. Read

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Data Concurrency, Consistency and Integrity

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  1. Data Concurrency, Consistency and Integrity

  2. My goals • Managing Data • Keep it valid • Let people access it • Start from simple models • Build more complex ones • High-Level Abstraction • Oracle implementation • No Code

  3. You • Programming • Databases • Concurrency • SQL • Oracle

  4. A Simple Model Read Process Write Read Process Write … Processor Read Write Memory

  5. Increase a counter: Single User 1+1 ⇒ 2 2+1 ⇒ 3 1 2 2 3 Time

  6. Increase a counter: Multi User 1+1 ⇒ 2 1 1 2 2 Time 1+1 ⇒ 2

  7. Increase a counter: Multi User 1+1 ⇒ 2 1 1 2 2 3 2 Time 1+1 ⇒ 2 2+1 ⇒ 3

  8. Locking 1+1 ⇒ 2 1 1 2 2 Time Read Lock Write Lock 1+1 ⇒ 2

  9. Concurrency Data Concurrency means that many users can access data at the same time Locks limit concurrency

  10. Transfer Funds: Consistency Transfer 50€: (100,100)⇒(50,150) A:50€ B:100€ A:50€ B:150€ A:100€ B:100€ T:200€ T:150€ T:200€

  11. Transaction A transaction comprises a unit of work treated in a coherent and reliable way independent of other transactions.

  12. Transfer Funds: Failures Transfer 50€: (100,100)⇒(50,150) A:50€ B:100€ A:100€ B:100€ Anything can go wrong at any time

  13. Transaction Properties • Atomicity (all or nothing) • Consistency (From Valid to Valid) • Isolation (No interference) • Durability (Changes are reliably persisted) ACID

  14. Commit 1+1 ⇒ 2 Transaction #1 Commit 1 2 Transaction #2 1 1 2 Multiple versions must be kept

  15. Transfer Funds: Commit Transfer 50€ T#1 Commit A:50€ B:100€ A:50€ B:150€ A:100€ B:100€ T#2 T:200€ T:200€ T:200€

  16. Transfer Funds: Rollback Transfer 50€ Rollback A:50€ B:100€ A:100€ B:100€ A:100€ B:100€ Automatic or user initiated

  17. Transaction Isolation Levels • Read Phenomena • Dirty Reads: read uncommitted data of another transaction • Nonrepeatable & Phantom Reads: read changes committed by another transaction

  18. Oracle Read Consistency Statement-Level Read Consistency is always guaranteed Session-Level Read Consistency is guaranteed in only serializable mode

  19. Oracle Read Consistency • Same read ⇒ 100€ • Multiple reads • Read committed ⇒ 150€ • Serializable ⇒ 100€ T#1 Transfer 50€ A:100€ B:100€ A:50€ B:150€ Same read? A:100€ B:100 or 150€? T#2

  20. Multiversion Concurrency Control System Change Number (SCN): the Oracle database “clock”, incremented at every insert, update, delete. A query made at (SCN) time T return the most recent (highest SCN) records whose SCN is less or equal to T Read SCN = 1 Readers never block Writers Writers never block Readers Read SCN = 10 * *except when they do

  21. Updates: Locks Write 1 T#1 Commit 1 2 Wait T#2 Commit Write 2

  22. Locks: Implicit Vs Explicit • Implicit Locks • Automatic for every operation • Locks as little as possible for highest concurrency • Explicit Locking • transaction-level read consistency (repeatable reads) • Cannot afford to wait once it has started

  23. Increase a Counter 1 Read Compute Update Commit 1 Read Compute Update Commit 1 2 2 2 Wait 2

  24. Increase a Counter 1 Lock Read Compute Update Commit Lock Read Compute Update 5. Commit 1 2 Wait 2 2 3 3

  25. Optimistic Locking A|1 Read Value & Version Compute Lock Check if version changed. If no (1=1) then Update Value & Version Commit Read Value & Version Compute Lock Check version ⇒ Exception A|1 A|1 B C B|2

  26. Deadlocks Avoid by always locking in the same order. Transaction #1 Update A Update B Transaction #2 Update B Update A Wait #2 Wait #1 Deadlock!

  27. Data Integrity • Enforce Business Rules • Player • have a last name • have age > 0 • have a unique login name • belong to a team (reference)

  28. Unique Constraint • Insert new player with login “chierico” • Check that no other “chierico” exists • Insert new “chierico” record • Make sure no one insert it between your “check” and your “insert” ⇒ • Lock whole table ⇒ Bad for Concurrency • Check • Insert • Commit DBs offer a proper “unique” constraint.

  29. Lessons Learned • Always think about how others might use the data • No “one solution fits all” • Databases offer valuable abstractions • Flexible • Safe • Declarative • Not all databases behave the same way

  30. Q&A

  31. спасибо Globe of Science and Innovation, CERN

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