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Failure Recovery

Failure Recovery. Checkpointing Undo/Redo Logging. Source: slides by Hector Garcia-Molina. Recovery is very, very SLOW !. Redo log: First T1 wrote A,B Last Record Committed a year ago Record (1 year ago) --> STILL, Need to redo after crash!!. . . . Crash.

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Failure Recovery

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  1. Failure Recovery Checkpointing Undo/Redo Logging Source: slides by Hector Garcia-Molina

  2. Recovery is very, very SLOW ! Redo log: First T1 wrote A,B Last Record Committed a year ago Record (1 year ago) --> STILL, Need to redo after crash!! ... ... ... Crash

  3. Solution: Checkpoint (simple version) Periodically: (1) Do not accept new transactions (2) Wait until all transactions finish (3) Flush all log records to disk (log) (4) Flush all buffers to disk (DB) (do not discard buffers) (5) Write “checkpoint” record on disk (log) (6) Resume transaction processing

  4. <T1,A,16> <T2,B,17> <T1,commit> <T2,commit> Checkpoint <T3,C,21> Example: what to do at recovery? Redo log (disk): Crash ... ... ... ... ... ... Start from last checkpoint and move forward in the log file redoing updates for committed transactions.

  5. Key drawbacks: • Undo logging: data must be written to disk immediately after a transaction finishes, which can increase number of disk I/O's • Redo logging: need to keep all modified blocks in memory until transaction commits and log is flushed, which can increase the number of buffers required

  6. Solution: undo/redo logging! Update record in the log has the format <T, X, new X val, old X val>

  7. Rules • Buffer containing X can be flushed to disk either before or after T commits • Log record must be flushed to disk before corresponding updated buffer is (WAL)

  8. Recovery with Undo/Redo Logging • Redo all committed transactions in order from earliest to latest • handles committed transactions with some changes not yet on disk • Undo all incomplete transactions in order from latest to earliest • handles uncommitted transactions with some chnages already on disk

  9. Non-quiescent Checkpoint • Simple checkpointing scheme requires system to "quiesce" (reach a point with no active transactions), ensured by preventing new transactions from starting for a while • Avoid this behavior with non-quiescent checkpointing: • write a "start checkpoint" record to the log • later write an "end checkpoint" record to the log • Details vary depending on whether undo, redo, or undo/redo logging

  10. Non-quiescent Checkpoint for Undo/Redo • write "start checkpoint" listing all active transactions to log • flush log to disk • write to disk all dirty buffers (contain a changed DB element), whether or not transaction has committed • this implies some log records may need to be written to disk (WAL) • write "end checkpoint" to log • flush log to disk

  11. Non-quiescent checkpoint for undo/redo start ckpt active T's: T1,T2,... end ckpt L O G for undo dirty buffer pool pages flushed ... ... ... ...

  12. Recovery process: • Backwards pass (end of log  latest checkpoint start) • construct set S of committed transactions • undo actions of transactions not in S • Undo pending transactions • follow undo chains for transactions in (checkpoint active list) - S • Forward pass (latest checkpoint start  end of log) • redo actions of S transactions backward pass start check- point forward pass

  13. Exampleswhat to do at recovery time? no T1 commit L O G ... T1,- a ... Ckpt T1 ... Ckpt end ... T1- b  Undo T1 (undo a,b)

  14. Example L O G ... T1 a ... ckpt-s T1 ... T1 b ... ckpt- end ... T1 c ... T1 cmt ...  Redo T1: (redo b,c)

  15. Real world actions E.g., dispense cash at ATM Ti = a1 a2 …... aj …... an $

  16. Solution (1) execute real-world actions after commit (2) try to make idempotent

  17. Media failure (loss of non-volatile storage) A: 16 Solution: Make copies of data!

  18. Example 1Triple modular redundancy • Keep 3 copies on separate disks • Output(X) --> three outputs • Input(X) --> three inputs + vote X3 X1 X2

  19. Example 2 Redundant writes, Single reads • Keep N copies on separate disks • Output(X) --> N outputs • Input(X) --> Input one copy - if ok, done - else try another one  Assumes bad data can be detected

  20. Example 3: DB Dump + Log backup database active database log • If active database is lost, • restore active database from backup • bring up-to-date using redo entries in log

  21. When can log be discarded? last needed undo check- point db dump log time not needed for media recovery not needed for undo after system failure not needed for redo after system failure

  22. Summary • Consistency of data • One source of problems: failures - Logging - Redundancy • Another source of problems: Data Sharing..... next

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