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Computer Science 425 Distributed Systems

Computer Science 425 Distributed Systems. Lecture 20 Distributed Transactions Chapter 14. Distributed Transactions. A transaction (flat or nested) that invokes operations in several servers. T11. A. A. X. T1. H. T. T12. B. T. B. T21. C. Y. T2. K. D. C. T22. F. D. Z.

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Computer Science 425 Distributed Systems

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  1. Computer Science 425Distributed Systems Lecture 20 Distributed Transactions Chapter 14

  2. Distributed Transactions • A transaction (flat or nested) that invokes operations in several servers. T11 A A X T1 H T T12 B T B T21 C Y T2 K D C T22 F D Z Nested Distributed Transaction Flat Distributed Transaction

  3. Coordination in Distributed Transactions Coordinator join Coordinator Participant Open Transacton Each server has a special participant process. Coordinator process (leader) resides in one of the servers, talks to trans. & participants. TID A T X Close Transaction join Participant Abort Transaction join T B 3 1 Y a.method (TID ) Join (TID, ref) Participant C A Participant D 2 Z Coordinator & Participants The Coordination Process

  4. join openTransaction participant closeTransaction A a.withdraw(4); . . join BranchX T participant b.withdraw(T, 3); Client B b.withdraw(3); T = openTransaction BranchY join a.withdraw(4); c.deposit(4); participant b.withdraw(3); c.deposit(4); d.deposit(3); C closeTransaction D d.deposit(3); Note: the coordinator is in one of the servers, e.g. BranchX BranchZ Distributed banking transaction

  5. I. Atomic Commit Problem • Atomicity principle requires that either all the distributed operations of a transaction complete or all abort. • At some stage, client executes closeTransaction(). Now, atomicity requires that either all participants and the coordinator commit or all abort. • What problem statement is this?

  6. Atomic Commit Protocols • Consensus, but it’s impossible in asynchronous networks! • So, need to ensure safety property in real-life implementation. • In a one-phase commitprotocol, the coordinator communicates either commit or abort, to all participants until all acknowledge. • Doesn’t work when a participant crashes before receiving this message (partial transaction results are lost). • Does not allow participant to abort the transaction, e.g., under deadlock. • In a two-phase protocol • First phase involves coordinator collecting commit or abort vote from each participant (which stores partial results in permanent storage). • If all participants want to commit and no one has crashed, coordinator multicasts commit message • If any participant has crashed or aborted, coordinator multicasts abort message to all participants

  7. Operations for Two-Phase Commit Protocol • canCommit?(trans)-> Yes / No • Call from coordinator to participant to ask whether it can commit a transaction. Participant replies with its vote. • doCommit(trans) • Call from coordinator to participant to tell participant to commit its part of a transaction. • doAbort(trans) • Call from coordinator to participant to tell participant to abort its part of a transaction. • haveCommitted(trans, participant) • Call from participant to coordinator to confirm that it has committed the transaction. (May not be required if getDecision() is used – see below) • getDecision(trans) -> Yes / No • Call from participant to coordinator to ask for the decision on a transaction after it has voted Yes but has still had no reply after some delay. Used to recover from server crash or delayed messages.

  8. The two-phase commit protocol • Phase 1 (voting phase): • 1. The coordinator sends a canCommit? request to each of the participants in the transaction. • 2. When a participant receives a canCommit? request it replies with its vote (Yes or No) to the coordinator. Before voting Yes, it prepares to commit by saving objects in permanent storage. If the vote is No, the participant aborts immediately. • Phase 2 (completion according to outcome of vote): • 3. The coordinator collects the votes (including its own). • (a) If there are no failures and all the votes are Yes, the coordinator decides to commit the transaction and sends a doCommit request to each of the participants. • (b) Otherwise the coordinator decides to abort the transaction and sends doAbort requests to all participants that voted Yes. • 4. Participants that voted Yes are waiting for a doCommit or doAbort request from the coordinator. When a participant receives one of these messages it acts accordingly and in the case of commit, makes a haveCommitted call as confirmation to the coordinator. Recall that server may crash

  9. Coordinator Participant step status step status canCommit? prepared to commit 1 Yes (waiting for votes) 2 prepared to commit (uncertain) doCommit 3 committed haveCommitted 4 committed done Communication in Two-Phase Commit Protocol • To deal with server crashes • Each participant saves tentative updates into permanent storage, right before replying yes/no in first phase. Retrievable after crash recovery. • To deal with canCommit? loss • The participant may decide to abort unilaterally after a timeout (coordinator will eventually abort) • To deal with Yes/No loss, the coordinator aborts the transaction after a timeout (pessimistic!). It must annouce doAbort to those who sent in their votes. • To deal with doCommit loss • The participant may wait for a timeout, send a getDecision request (retries until reply received) – cannot abort after having voted Yes but before receiving doCommit/doAbort!

  10. Two Phase Commit (2PC) Protocol Coordinator Participant CloseTrans() Execute • Execute • Precommit notready ready request • Abort • Send NO to coordinator • Uncertain • Send request to each participant • Wait for replies (time out possible) • Precommit • send YES to coordinator • Wait for decision NO YES Timeout or a NO All YES COMMIT decision ABORT decision • Abort • Send ABORT to each participant • Commit • Send COMMIT to each participant • Commit • Make transaction visible Abort

  11. II. Locks in Distributed Transactions • Each server is responsible for applying concurrency control to its objects. • Servers are collectively responsible for serial equivalence of operations. • Locks are held locally, and cannot be released until all servers involved in a transaction have committed or aborted. • Locks are retained during 2PC protocol • Since lock managers work independently, deadlocks are (very?) likely.

  12. Distributed Deadlocks • The wait-for graph in a distributed set of transactions is held partially by each server • To find cycles in a distributed wait-for graph, one option is to use a central coordinator: • Each server reports updates of its wait-for graph • The coordinator constructs a global graph and checks for cycles • Centralized deadlock detection suffers from usual comm. overhead + bottleneck problems. • In edge chasing, servers collectively make the global wait-for graph and detect deadlocks : • Servers forward “probe” messages to servers in the edges of wait-for graph, pushing the graph forward, until cycle is found.

  13. W ® ® ® W U V W Held by Waits for Deadlock C detected A Z X Initiation ® ® W U V Waits ® W U for V U Held by Waits for B Y Probes Transmitted to Detect Deadlock

  14. Edge Chasing • Initiation: When a server S1 notes that a transaction T starts waiting for another transaction U, where U is waiting to access an object at another server S2, it initiates detection by sending <TU> to S2. • Detection: Servers receive probes and decide whether deadlock has occurred and whether to forward the probes. • Resolution: When a cycle is detected, a transaction in the cycle is aborted to break the deadlock. • Phantom deadlocks=false detection of deadlocks that don’t actually exist • Edges may disappear. So, all edges in a “detected” cycle may not have been present in the system all at the same time.

  15. Transaction Priority • In order to ensure that only one transaction in a cycle is aborted, transactions are given priorities (e.g., inverse of timestamps) in such a way that all transactions are totally ordered. • When a deadlock cycle is found, the transaction with the lowest priority is aborted. Even if several different servers detect the same cycle, only one transaction aborts. • Transaction priorities can be used to limit probe messages to be sent only to lower prio. trans. and initiating probes only when higher prio. trans. waits for a lower prio. trans. • Caveat: suppose edges were created in order 3->1, 1->2, 2->3. Deadlock never detected. • Fix: whenever an edge is created, tell everyone (broadcast) about this edge. May be inefficient.

  16. III. 2PC in Nested Transactions • Each (sub)transaction has a coordinator openSubTransaction(trans-id)  subTrans-id getStatus(trans-id)  (committed / aborted / provisional) • Each sub-transaction starts after its parent starts and finishes before its parent finishes. • When a sub-transaction finishes, it makes a decision to abort or provisionally commit. (Note that a provisional commit is not the same as being prepared – it is just a local decision and is not backed up on permanent storage.) • When the root subtrans completes, it does a 2PC with its subtrans to decide to commit or abort.

  17. Abort No Provisional Yes Yes Provisional Provisional Yes No Yes Abort Provisional Yes Example 2PC in Nested Transactions T11 A T11 T1 H T1 T12 B T12 T T T21 C T21 T2 T2 K D T22 T22 F Nested Distributed Transaction Bottom up decision in 2PC

  18. T abort (at M) 11 T provisional commit (at X) 1 T T provisional commit (at N) 12 provisional commit (at N) T 21 aborted (at Y) T 2 T provisional commit (at P) 22 An Example of Nested Transaction

  19. Information Held by Coordinators of Nested Transactions Coordinator Child Participant Provisional Abort list of… subtrans commit list T T , T yes T , T T , T 1 2 1 12 11 2 T T , T yes T , T T 1 11 12 1 12 11 T T , T no (aborted) T 2 21 22 2 T no (aborted) T 11 11 T T , T T but not T , T 21 12 21 12 21 12 T no (parent aborted) T 22 22 • When each sub-transaction was created, it joined its parent sub-transaction. • The coordinator of each parent sub-transaction has a list of its child sub-transactions. • When a nested transaction provisionally commits, it reports its status and the status of • its descendants to its parent. • When a nested transaction aborts, it reports abort without giving any information about • its descendants. • The top-level transaction receives a list of all sub-transactions, together with their status.

  20. Hierarchic Two-Phase Commit Protocol forNested Transactions • canCommit?(trans, subTrans) -> Yes / No • Call a coordinator to ask coordinator of child subtransaction whether it can commit a subtransaction subTrans. The first argument trans is the transaction identifier of top-level transaction. Participant replies with its vote Yes / No. • The coordinator of the top-level sub-transaction sends canCommit? to the coordinators of its immediate child sub-transactions. The latter, in turn, pass them onto the coordinators of their child sub-transactions. • Each participant collects the replies from its descendants before replying to its parent. • T sends canCommit? messages to T1 (but not T2 which has aborted); T1 sends CanCommit? messages to T12 (but not T11). • If a coord. finds no subtrans. matching 2nd param, then it must have crashed, so it replies NO • Wasteful if coords. shared among subtrans. May take long.

  21. Flat Two-Phase Commit Protocol for Nested Transactions • canCommit?(trans, abortList) -> Yes / No • Call from coordinator to participant to ask whether it can commit a transaction. Participant replies with its vote Yes / No. • The coordinator of the top-level sub-transaction sends canCommit? messages to the coordinators of all sub-transactions in the provisional commit list (e.g., T1 and T12). • If the participant has any provisionally committed sub-transactions that are descendants of the transaction with TID trans: • Check that they do not have any aborted ancestors in the abortList. Then prepare to commit. • Those with aborted ancestors are aborted. • Send a Yes vote to the coordinator giving the good sub-transactions • If the participant does not have a provisionally committed descendent, it must have failed after it performed a provisional commit. Send a NO vote to the coordinator.

  22. IV. Transaction Recovery • Recovery is concerned with: • Object (data) durability: saved permanently • Failure Atomicity: effects are atomic even when servers crash • Recovery Manager’s tasks • To save objects (data) on permanent storage for committed transactions. • To restore server’s objects after a crash • To maintain and reorganize a recovery file for an efficient recovery procedure • Garbage collection in recovery file

  23. The Recovery File Recovery File Transaction Entries T1: committed T1: Prepared T2: Prepared Trans. Status Object Ref Object Ref Intention List Object Ref Object Ref … Object Ref Object Entries name name name name name … … values values values values values

  24. “Logging”=appending • Write entries at prepared-to-commit • and commit points • Writes at commit points are forced C 278 B 242 B 220 A 80 C 300 A 100 B 200 Example: Recovery File 200 300 100 b: c: a: Transaction T1 Transaction T2 balance = b.getBalance() b.setBalance = (balance*1.1) balance = b.getBalance() b.setBalance(balance*1.1) a.withdraw(balance* 0.1) c.withdraw(balance*0.1) 220 b: 242 b: 80 a: 278 c: p1 p3 p4 p7 p2 p5 p6 p0 T2: Preped <B, P4> <C, P6> p5 T1: Preped <A, P1> <B, P2> P0 T1: Commit p3

  25. Using the Recovery File after a Crash • When a server recovers, it sets default initial values for objects and then hands over to recovery manager. • Recovery manager should apply only those updates that are for committed transactions. Prepared-to-commit entries are not used. • Recovery manager has two options: • Read the recovery file forward and update object values • Read the recovery file backwards and update object values • Advantage: each object updated exactly once (hopefully) • Server may crash during recovery • Recovery operations needs to be idempotent

  26. The Recovery File for 2PC Transaction Entries T1: committed T1: Prepared T2: Prepared Trans. Status Object Ref Object Ref Intention List Object Ref Object Ref … Object Ref Object Entries name name name name name … … values values values values values Coor’d: T2 Participants list. Status. Coor’d: T1 Participants list. Status. Par’pant: T1 Coordinator. Status. Par’pant: T1 Coordinator. Status. Coordination Entries …

  27. Summary • Distributed Transactions • More than one server process (each managing different set of objects) • One server process marked out as coordinator • Atomic Commit: 2PC • Deadlock detection: Edge chasing • 2PC commit for nested trans. • Transaction Recovery: Recovery file • Reading for this lecture was: Chapter 14 • Material not Covered from Chapter 14: • Shadow versions format of recovery file • Reorganization of recovery file • Reading for next lecture: Two URL links on website (exciting topic!) • MP2 released – due Nov 11.

  28. Optional Slides

  29. V Held by Wait for C A X Z U  V Wait for Held by T  U  V T U Wait for Held by B Y X: U  V Y: T  U  V A Different Kind of Edge Chasing V Held by Wait for C A X Z Held by Wait for T U B Wait for Held by Y X: U  V Y: T  U Z: V  T LOCAL Wait-for GRAPHS Z: T U V  T deadlock detected

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