![Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/9
Fault Tolerance Measures
Reliability
R(t) = Pr{0 failures in time [0,t] | no failures at t=0}
If occurrence of failures is Poisson
R(t) = Pr{0 failures in time [0,t]}
Then
where
z(x) is known as the hazard function which gives the time-dependent failure
rate of the component
k!
Pr(k failures in time [0,t] =
e-m(t)[m(t)]k](https://image.slidesharecdn.com/12-reliability-140404013510-phpapp02/85/Database-12-Reliability-9-320.jpg)
![Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/10
Fault-Tolerance Measures
Reliability
The mean number of failures in time [0, t] can be computed as
and the variance can be be computed as
Var[k] = E[k2] - (E[k])2 = m(t)
Thus, reliability of a single component is
R(t) = e-m(t)
and of a system consisting of n non-redundant components as
E [k] =
k =0
∞
k k!
e-m(t )[m(t )]k
= m(t )
Rsys(t) =
i =1
n
Ri(t)](https://image.slidesharecdn.com/12-reliability-140404013510-phpapp02/85/Database-12-Reliability-10-320.jpg)
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Database , 12 Reliability
- 1. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/1 Outline • Introduction • Background • Distributed Database Design • Database Integration • Semantic Data Control • Distributed Query Processing • Distributed Transaction Management ➡ Transaction Concepts and Models ➡ Distributed Concurrency Control ➡ Distributed Reliability • Data Replication • Parallel Database Systems • Distributed Object DBMS • Peer-to-Peer Data Management • Web Data Management • Current Issues
- 2. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/2 Reliability Problem: How to maintain atomicity durability properties of transactions Ch.10/2
- 3. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/3 Fundamental Definitions • Reliability ➡ A measure of success with which a system conforms to some authoritative specification of its behavior. ➡ Probability that the system has not experienced any failures within a given time period. ➡ Typically used to describe systems that cannot be repaired or where the continuous operation of the system is critical. • Availability ➡ The fraction of the time that a system meets its specification. ➡ The probability that the system is operational at a given time t.
- 4. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/4 Fundamental Definitions • Failure ➡ The deviation of a system from the behavior that is described in its specification. • Erroneous state ➡ The internal state of a system such that there exist circumstances in which further processing, by the normal algorithms of the system, will lead to a failure which is not attributed to a subsequent fault. • Error ➡ The part of the state which is incorrect. • Fault ➡ An error in the internal states of the components of a system or in the design of a system.
- 5. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/5 Faults to Failures Fault Error Failure causes results in
- 6. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/6 Types of Faults • Hard faults ➡ Permanent ➡ Resulting failures are called hard failures • Soft faults ➡ Transient or intermittent ➡ Account for more than 90% of all failures ➡ Resulting failures are called soft failures
- 7. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/7 Fault Classification Permanent fault Incorrect design Unstable environment Operator mistake Transient error System Failure Unstable or marginal components Intermittent error Permanent error
- 8. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/8 Failures Fault occurs Error caused Detection of error Repair Fault occurs Error caused MTBF MTTRMTTD Multiple errors can occur during this period Time
- 9. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/9 Fault Tolerance Measures Reliability R(t) = Pr{0 failures in time [0,t] | no failures at t=0} If occurrence of failures is Poisson R(t) = Pr{0 failures in time [0,t]} Then where z(x) is known as the hazard function which gives the time-dependent failure rate of the component k! Pr(k failures in time [0,t] = e-m(t)[m(t)]k
- 10. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/10 Fault-Tolerance Measures Reliability The mean number of failures in time [0, t] can be computed as and the variance can be be computed as Var[k] = E[k2] - (E[k])2 = m(t) Thus, reliability of a single component is R(t) = e-m(t) and of a system consisting of n non-redundant components as E [k] = k =0 ∞ k k! e-m(t )[m(t )]k = m(t ) Rsys(t) = i =1 n Ri(t)
- 11. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/11 Fault-Tolerance Measures Availability A(t) = Pr{system is operational at time t} Assume ✦ Poisson failures with rate ✦ Repair time is exponentially distributed with mean 1/μ Then, steady-state availability
- 12. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/12 Fault-Tolerance Measures MTBF Mean time between failures MTTR Mean time to repair Availability MTBF MTBF + MTTR
- 13. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/13 Types of Failures • Transaction failures ➡ Transaction aborts (unilaterally or due to deadlock) ➡ Avg. 3% of transactions abort abnormally • System (site) failures ➡ Failure of processor, main memory, power supply, … ➡ Main memory contents are lost, but secondary storage contents are safe ➡ Partial vs. total failure • Media failures ➡ Failure of secondary storage devices such that the stored data is lost ➡ Head crash/controller failure (?) • Communication failures ➡ Lost/undeliverable messages ➡ Network partitioning
- 14. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/14 Local Recovery Management – Architecture • Volatile storage ➡ Consists of the main memory of the computer system (RAM). • Stable storage ➡ Resilient to failures and loses its contents only in the presence of media failures (e.g., head crashes on disks). ➡ Implemented via a combination of hardware (non-volatile storage) and software (stable-write, stable-read, clean-up) components. Secondary storage Stable database Read Write Write Read Main memoryLocal Recovery Manager Database Buffer Manager Fetch, Flush Database buffers (Volatile database)
- 15. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/15 Update Strategies • In-place update ➡ Each update causes a change in one or more data values on pages in the database buffers • Out-of-place update ➡ Each update causes the new value(s) of data item(s) to be stored separate from the old value(s)
- 16. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/16 In-Place Update Recovery Information Database Log Every action of a transaction must not only perform the action, but must also write a log record to an append-only file. New stable database state Database Log Update Operation Old stable database state
- 17. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/17 Logging The log contains information used by the recovery process to restore the consistency of a system. This information may include ➡ transaction identifier ➡ type of operation (action) ➡ items accessed by the transaction to perform the action ➡ old value (state) of item (before image) ➡ new value (state) of item (after image) …
- 18. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/18 Why Logging? Upon recovery: ➡ all of T1's effects should be reflected in the database (REDO if necessary due to a failure) ➡ none of T2's effects should be reflected in the database (UNDO if necessary) 0 t time system crash T1Begin End Begin T2
- 19. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/19 REDO Protocol • REDO'ing an action means performing it again. • The REDO operation uses the log information and performs the action that might have been done before, or not done due to failures. • The REDO operation generates the new image. Database Log REDO Old stable database state New stable database state
- 20. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/20 UNDO Protocol • UNDO'ing an action means to restore the object to its before image. • The UNDO operation uses the log information and restores the old value of the object. New stable database state Database Log UNDO Old stable database state
- 21. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/21 When to Write Log Records Into Stable Store Assume a transaction T updates a page P • Fortunate case ➡ System writes P in stable database ➡ System updates stable log for this update ➡ SYSTEM FAILURE OCCURS!... (before T commits) We can recover (undo) by restoring P to its old state by using the log • Unfortunate case ➡ System writes P in stable database ➡ SYSTEM FAILURE OCCURS!... (before stable log is updated) We cannot recover from this failure because there is no log record to restore the old value. • Solution: Write-Ahead Log (WAL) protocol
- 22. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/22 Write–Ahead Log Protocol • Notice: ➡ If a system crashes before a transaction is committed, then all the operations must be undone. Only need the before images (undo portion of the log). ➡ Once a transaction is committed, some of its actions might have to be redone. Need the after images (redo portion of the log). • WAL protocol : Before a stable database is updated, the undo portion of the log should be written to the stable log When a transaction commits, the redo portion of the log must be written to stable log prior to the updating of the stable database.
- 23. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/23 Logging Interface Read WriteWrite Read Main memory Local Recovery Manager Database Buffer Manager Fetch, Flush Secondary storage Stable log Stable database Database buffers (Volatile database) Log buffers
- 24. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/24 Out-of-Place Update Recovery Information • Shadowing ➡ When an update occurs, don't change the old page, but create a shadow page with the new values and write it into the stable database. ➡ Update the access paths so that subsequent accesses are to the new shadow page. ➡ The old page retained for recovery. • Differential files ➡ For each file F maintain ✦ a read only part FR ✦ a differential file consisting of insertions part DF+ and deletions part DF- ✦ Thus, F = (FR DF+) – DF- ➡ Updates treated as delete old value, insert new value
- 25. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/25 Execution of Commands Commands to consider: begin_transaction read write commit abort recover Independent of execution strategy for LRM
- 26. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/26 Execution Strategies • Dependent upon ➡ Can the buffer manager decide to write some of the buffer pages being accessed by a transaction into stable storage or does it wait for LRM to instruct it? ✦ fix/no-fix decision ➡ Does the LRM force the buffer manager to write certain buffer pages into stable database at the end of a transaction's execution? ✦ flush/no-flush decision • Possible execution strategies: ➡ no-fix/no-flush ➡ no-fix/flush ➡ fix/no-flush ➡ fix/flush
- 27. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/27 No-Fix/No-Flush • Abort ➡ Buffer manager may have written some of the updated pages into stable database ➡ LRM performs transaction undo (or partial undo) • Commit ➡ LRM writes an “end_of_transaction” record into the log. • Recover ➡ For those transactions that have both a “begin_transaction” and an “end_of_transaction” record in the log, a partial redo is initiated by LRM ➡ For those transactions that only have a “begin_transaction” in the log, a global undo is executed by LRM
- 28. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/28 No-Fix/Flush • Abort ➡ Buffer manager may have written some of the updated pages into stable database ➡ LRM performs transaction undo (or partial undo) • Commit ➡ LRM issues a flush command to the buffer manager for all updated pages ➡ LRM writes an “end_of_transaction” record into the log. • Recover ➡ No need to perform redo ➡ Perform global undo
- 29. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/29 Fix/No-Flush • Abort ➡ None of the updated pages have been written into stable database ➡ Release the fixed pages • Commit ➡ LRM writes an “end_of_transaction” record into the log. ➡ LRM sends an unfix command to the buffer manager for all pages that were previously fixed • Recover ➡ Perform partial redo ➡ No need to perform global undo
- 30. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/30 Fix/Flush • Abort ➡ None of the updated pages have been written into stable database ➡ Release the fixed pages • Commit (the following have to be done atomically) ➡ LRM issues a flush command to the buffer manager for all updated pages ➡ LRM sends an unfix command to the buffer manager for all pages that were previously fixed ➡ LRM writes an “end_of_transaction” record into the log. • Recover ➡ No need to do anything
- 31. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/31 Checkpoints • Simplifies the task of determining actions of transactions that need to be undone or redone when a failure occurs. • A checkpoint record contains a list of active transactions. • Steps: Write a begin_checkpoint record into the log Collect the checkpoint dat into the stable storage Write an end_checkpoint record into the log
- 32. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/32 Media Failures – Full Architecture Read WriteWrite Read Main memory Local Recovery Manager Database Buffer Manager Fetch, Flush Archive log Archive database Secondary storage Stable log Stable database Database buffers (Volatile database) Log buffers Write Write
- 33. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/33 Distributed Reliability Protocols • Commit protocols ➡ How to execute commit command for distributed transactions. ➡ Issue: how to ensure atomicity and durability? • Termination protocols ➡ If a failure occurs, how can the remaining operational sites deal with it. ➡ Non-blocking : the occurrence of failures should not force the sites to wait until the failure is repaired to terminate the transaction. • Recovery protocols ➡ When a failure occurs, how do the sites where the failure occurred deal with it. ➡ Independent : a failed site can determine the outcome of a transaction without having to obtain remote information. • Independent recovery non-blocking termination
- 34. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/34 Two-Phase Commit (2PC) Phase 1 : The coordinator gets the participants ready to write the results into the database Phase 2 : Everybody writes the results into the database ➡ Coordinator :The process at the site where the transaction originates and which controls the execution ➡ Participant :The process at the other sites that participate in executing the transaction Global Commit Rule: The coordinator aborts a transaction if and only if at least one participant votes to abort it. The coordinator commits a transaction if and only if all of the participants vote to commit it.
- 35. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/35 Centralized 2PC ready? yes/no commit/abort?commited/aborted Phase 1 Phase 2 C C C P P P P P P P P
- 36. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/36 2PC Protocol Actions ParticipantCoordinator No Yes VOTE-COMMIT Yes GLOBAL-ABORT No write abort in log Abort Commit ACK ACK INITIAL write abort in log write ready in log write commit in log Type of msg WAIT Ready to Commit? write commit in log Any No? write abort in log ABORTCOMMIT COMMITABORT write begin_commit in log write end_of_transaction in log READ Y INITIAL Unilateralabort
- 37. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/37 Linear 2PC Prepare VC/VA Phase 1 Phase 2 GC/GA VC/VA VC/VA VC/VA VC: Vote-Commit, VA: Vote-Abort, GC: Global-commit, GA: Global-abort 1 2 3 4 5 N GC/GA GC/GA GC/GA GC/GA ≈≈
- 38. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/38 Distributed 2PC prepare vote-abort/ vote-commit global-commit/ global-abort decision made independently Phase 1 Coordinator Participants Participants Phase 2
- 39. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/39 State Transitions in 2PC INITIAL WAIT Commit command Prepare Vote-commit (all) Global-commit INITIAL READY Prepare Vote-commit Global-commit Ack Prepare Vote-abort Global-abort Ack Coordinator Participants Vote-abort Global-abort ABORT COMMIT COMMITABORT
- 40. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/40 Site Failures - 2PC Termination • Timeout in INITIAL ➡ Who cares • Timeout in WAIT ➡ Cannot unilaterally commit ➡ Can unilaterally abort • Timeout in ABORT or COMMIT ➡ Stay blocked and wait for the acks COORDINATOR INITIAL WAIT Commit command Prepare Vote-commit Global-commit ABORT COMMIT Vote-abort Global-abort
- 41. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/41 Site Failures - 2PC Termination • Timeout in INITIAL ➡ Coordinator must have failed in INITIAL state ➡ Unilaterally abort • Timeout in READY ➡ Stay blocked INITIAL READY Prepare Vote-commit Global-commit Ack Prepare Vote-abort Global-abort Ack ABORT COMMIT PARTICIPANTS
- 42. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/42 Site Failures - 2PC Recovery • Failure in INITIAL ➡ Start the commit process upon recovery • Failure in WAIT ➡ Restart the commit process upon recovery • Failure in ABORT or COMMIT ➡ Nothing special if all the acks have been received ➡ Otherwise the termination protocol is involved COORDINATOR INITIAL WAIT Commit command Prepare Vote-commit Global-commit ABORT COMMIT Vote-abort Global-abort
- 43. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/43 Site Failures - 2PC Recovery • Failure in INITIAL ➡ Unilaterally abort upon recovery • Failure in READY ➡ The coordinator has been informed about the local decision ➡ Treat as timeout in READY state and invoke the termination protocol • Failure in ABORT or COMMIT ➡ Nothing special needs to be done INITIAL READY Prepare Vote-commit Global-commit Ack Prepare Vote-abort Global-abort Ack ABORT COMMIT PARTICIPANTS
- 44. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/44 2PC Recovery Protocols – Additional Cases Arise due to non-atomicity of log and message send actions • Coordinator site fails after writing “begin_commit” log and before sending “prepare” command ➡ treat it as a failure in WAIT state; send “prepare” command • Participant site fails after writing “ready” record in log but before “vote- commit” is sent ➡ treat it as failure in READY state ➡ alternatively, can send “vote-commit” upon recovery • Participant site fails after writing “abort” record in log but before “vote- abort” is sent ➡ no need to do anything upon recovery
- 45. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/45 2PC Recovery Protocols – Additional Case • Coordinator site fails after logging its final decision record but before sending its decision to the participants ➡ coordinator treats it as a failure in COMMIT or ABORT state ➡ participants treat it as timeout in the READY state • Participant site fails after writing “abort” or “commit” record in log but before acknowledgement is sent ➡ participant treats it as failure in COMMIT or ABORT state ➡ coordinator will handle it by timeout in COMMIT or ABORT state
- 46. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/46 Problem With 2PC • Blocking ➡ Ready implies that the participant waits for the coordinator ➡ If coordinator fails, site is blocked until recovery ➡ Blocking reduces availability • Independent recovery is not possible • However, it is known that: ➡ Independent recovery protocols exist only for single site failures; no independent recovery protocol exists which is resilient to multiple-site failures. • So we search for these protocols – 3PC
- 47. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/47 Three-Phase Commit • 3PC is non-blocking. • A commit protocols is non-blocking iff ➡ it is synchronous within one state transition, and ➡ its state transition diagram contains ✦ no state which is “adjacent” to both a commit and an abort state, and ✦ no non-committable state which is “adjacent” to a commit state • Adjacent: possible to go from one stat to another with a single state transition • Committable: all sites have voted to commit a transaction ➡ e.g.: COMMIT state
- 48. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/48 State Transitions in 3PC INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Global-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit INITIAL READY Prepare Vote-commit Prepared-to-commit Ready-to-commit Prepare Vote-abort Global-abort Ack Participants COMMIT ABORT PRE- COMMIT Global commit Ack
- 49. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/49 Communication Structure C P P P P C P P P P C ready? yes/no pre-commit/ pre-abort? commit/abort Phase 1 Phase 2 P P P P C yes/no ack Phase 3
- 50. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/50 Site Failures – 3PC Termination • Timeout in INITIAL ➡ Who cares • Timeout in WAIT ➡ Unilaterally abort • Timeout in PRECOMMIT ➡ Participants may not be in PRE- COMMIT, but at least in READY ➡ Move all the participants to PRECOMMIT state ➡ Terminate by globally committing INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Global-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit
- 51. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/51 Site Failures – 3PC Termination • Timeout in ABORT or COMMIT ➡ Just ignore and treat the transaction as completed ➡ participants are either in PRECOMMIT or READY state and can follow their termination protocols INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Global-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit
- 52. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/52 Site Failures – 3PC Termination • Timeout in INITIAL ➡ Coordinator must have failed in INITIAL state ➡ Unilaterally abort • Timeout in READY ➡ Voted to commit, but does not know the coordinator's decision ➡ Elect a new coordinator and terminate using a special protocol • Timeout in PRECOMMIT ➡ Handle it the same as timeout in READY state INITIAL READY Prepare Vote-commit Prepared-to-commit Ready-to-commit Prepare Vote-abort Global-abort Ack Participants COMMIT ABORT PRE- COMMIT Global commit Ack
- 53. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/53 Termination Protocol Upon Coordinator Election New coordinator can be in one of four states: WAIT, PRECOMMIT, COMMIT, ABORT Coordinator sends its state to all of the participants asking them to assume its state. Participants “back-up” and reply with appriate messages, except those in ABORT and COMMIT states. Those in these states respond with “Ack” but stay in their states. Coordinator guides the participants towards termination: ✦ If the new coordinator is in the WAIT state, participants can be in INITIAL, READY, ABORT or PRECOMMIT states. New coordinator globally aborts the transaction. ✦ If the new coordinator is in the PRECOMMIT state, the participants can be in READY, PRECOMMIT or COMMIT states. The new coordinator will globally commit the transaction. ✦ If the new coordinator is in the ABORT or COMMIT states, at the end of the first phase, the participants will have moved to that state as well.
- 54. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/54 Site Failures – 3PC Recovery • Failure in INITIAL ➡ start commit process upon recovery • Failure in WAIT ➡ the participants may have elected a new coordinator and terminated the transaction ➡ the new coordinator could be in WAIT or ABORT states transaction aborted ➡ ask around for the fate of the transaction • Failure in PRECOMMIT ➡ ask around for the fate of the transaction INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Global-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit
- 55. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/55 Site Failures – 3PC Recovery • Failure in COMMIT or ABORT ➡ Nothing special if all the acknowledgements have been received; otherwise the termination protocol is involved INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Global-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit
- 56. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/56 Site Failures – 3PC Recovery • Failure in INITIAL ➡ unilaterally abort upon recovery • Failure in READY ➡ the coordinator has been informed about the local decision ➡ upon recovery, ask around • Failure in PRECOMMIT ➡ ask around to determine how the other participants have terminated the transaction • Failure in COMMIT or ABORT ➡ no need to do anything INITIAL READY Prepare Vote-commit Prepared-to-commit Ready-to-commit Prepare Vote-abort Global-abort Ack Participants COMMIT ABORT PRE- COMMIT Global commit Ack
- 57. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/57 Network Partitioning • Simple partitioning ➡ Only two partitions • Multiple partitioning ➡ More than two partitions • Formal bounds: ➡ There exists no non-blocking protocol that is resilient to a network partition if messages are lost when partition occurs. ➡ There exist non-blocking protocols which are resilient to a single network partition if all undeliverable messages are returned to sender. ➡ There exists no non-blocking protocol which is resilient to a multiple partition.
- 58. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/58 Independent Recovery Protocols for Network Partitioning • No general solution possible ➡ allow one group to terminate while the other is blocked ➡ improve availability • How to determine which group to proceed? ➡ The group with a majority • How does a group know if it has majority? ➡ Centralized ✦ Whichever partitions contains the central site should terminate the transaction ➡ Voting-based (quorum)
- 59. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/59 Quorum Protocols • The network partitioning problem is handled by the commit protocol. • Every site is assigned a vote Vi. • Total number of votes in the system V • Abort quorum Va, commit quorum Vc ➡ Va + Vc > V where 0 ≤ Va , Vc ≤ V ➡ Before a transaction commits, it must obtain a commit quorum Vc ➡ Before a transaction aborts, it must obtain an abort quorum Va
- 60. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/60 State Transitions in Quorum Protocols INITIAL WAIT Commit command Prepare Vote-commit Prepare-to-commit Coordinator Vote-abort Prepare-to-abort ABORT COMMIT PRE- COMMIT Ready-to-commit Global commit INITIAL READY Prepare Vote-commit Prepare-to-commit Ready-to-commit Prepare Vote-abort Global-abort Ack Participants COMMITABORT PRE- COMMIT Global commit Ack PRE- ABORT Prepared-to-abortt Ready-to-abort PRE- ABORT Ready-to-abort Global-abort
- 61. Distributed DBMS © M. T. Özsu & P. Valduriez Ch.12/61 Use for Network Partitioning • Before commit (i.e., moving from PRECOMMIT to COMMIT), coordinator receives commit quorum from participants. One partition may have the commit quorum. • Assumes that failures are “clean” which means: ➡ failures that change the network's topology are detected by all sites instantaneously ➡ each site has a view of the network consisting of all the sites it can communicate with