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Lecture XII: Replication

Lecture XII: Replication. CMPT 401 Summer 2007 Dr. Alexandra Fedorova. Replication. Why Replicate? (I) . Fault-tolerance / High availability As long as one replica is up, the service is available Assume each of n replicas has same independent probability p to fail.

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Lecture XII: Replication

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  1. Lecture XII: Replication CMPT 401 Summer 2007 Dr. Alexandra Fedorova

  2. Replication

  3. Why Replicate? (I) • Fault-tolerance / High availability • As long as one replica is up, the service is available • Assume each of n replicas has same independent probability p to fail. • Availability = 1 - pn Fault-Tolerance: Take-Over

  4. Why Replicate? (II) • Fast local access (WAN replication) • client can always send requests to closest replica • Goal: no communication to remote replicas necessary during request execution • Goal: client experiences location transparency since all access is fast local access Fast local access Rome Toronto Montreal

  5. Why Replicate? • Scalability and load distribution (LAN replication) • Requests can be distributed among replicas • Handle increasing load by adding new replicas to the system cluster instead of bigger server

  6. Challenges: Data Consistency • We will study systems that use data replication • It is hard, because data must be kept consistent • Users submit operations against the logical copies of data • These operations must be translated into operations against one, some, or all physical copies of data • Nearly all existing approaches follow a ROWA(A) approach: • Read-one-write-all-(available) • Update has to be (eventually) executed at all replicas to keep them consistent • Read can be performed at any replica

  7. Challenges: Fault Tolerance • The goal is to have data available despite failures • If one site fails others should continue providing service • How many replicas should we have? • It depends on: • How many faults we want to tolerate • The types of faults we expect • How much we are willing to pay

  8. Roadmap • Replication architectures • Active replication • Primary-backup (passive, master-slave) replication • Design considerations for replicated services • Surviving failures

  9. Active Replication Replicated Servers A A B A Client C A

  10. Active Replication

  11. Active Replication • The client send request to the servers using totally ordered reliable multicast (logical clocks or vector clocks) • Server coordination is given by the total order property (assumption: synchronous system) • All replicas execute the request in the order they are delivered • No additional coordination necessary (Assumption: determinism) • All replicas produce the same result • All replicas send result to the client; client waits for the first answer

  12. Fault Tolerance: Failstop Failures • As long as at least one replica survives the client will continue receiving service • Assuming there are no partitions! • Suppose B and C are partitioned, so the cannot communicate • They cannot agree on how to order client’s requests Replicated Servers A A B A Client C A

  13. Fault Tolerance: Byzantine Failures • Can survive Byzantine failures (assuming no partitions) • The system must have n ≥ 2f + 1 replicas (f is the number of failures) • The client will compare results of all replicas, will choose the result returned by the majority f + 1 non-faulty replicas • This is the idea used in LOCKSS (Lots of Copies Keep Stuff Safe)

  14. Primary-Backup Replication (PB) Replicated Servers Client A A primary If the primary fails, a backup takes over, becomes the primary B A C A backup backup Also known as passive replication

  15. System Requirements • How do we want the system to behave? • Just like a single-server system? • Must ensure that there is only one primary at a time • Data is kept consistent: • If a client received a response from an update operation and then the system crashed, the client should find the data reflecting that update • Results of operations should be the same as they would be if executed on a single-server system • Can we tolerate loose data consistency? • The client eventually gets the consistent data, but not right away

  16. Example of Data Inconsistency • Client operations: write(x = 5) read (x) // should return 5 on a single-server system • On a replicated system: write (x = 5) Primary responds to client Primary crashed before propagating update to other replicas A new primary is selected read (x) // may return x ≠ 5, the new primary does not know about the update to x

  17. Design Considerations for Replicated Services • Where to submit updates? • A designated server or any server? • When to propagate updates? • Eager or lazy? • How many replicas to install?

  18. Where to Submit Updates? • Primary Copy: • Each object has a primary copy • Often there is a designated primary - it holds primary copies for all objects • Updates on object x have to be submitted to the primary copy of x • Primary propagates changes on x to secondary copies • Secondary copies are read-only • Also called master/slave approach

  19. Where to Submit Updates • Update Everywhere: • Both read and write operations can be submitted to any server • This server takes care of the execution of the operation and the propagation of updates to the other copies T1:r(x)w(y) T2:r(y)w(y)

  20. When to Propagate Updates? • Eager: • Within the boundaries of the transaction for replicated databases • Before response is sent to client for non-transactional services • Lazy: • After the commit of the transaction for replicated databases • After the response is sent to client for non-transactional services

  21. PB Replication with Eager Updates • The client sends the request to the primary • There is no initial coordination • The primary executes the request • The primary coordinates with the other replicas by sending the update information to the backups • The primary (or another replica) sends the answer to the client Updates are propagated eagerly, before we respond to client

  22. Eager Update Propagation

  23. Eager Update Propagation For Transactional Services On every update At the end of transaction

  24. When Can a Failure Occur? • F1: Primary fails beforereplica coordination • Client receives no response. It will retry. Eventually will get data from new primary. • F2: Primary fails during replica coordination • Replicas may or may not have reached agreement w.r.t. client’s transaction. Client may receive a response after system recovers.The system may fail to recover (if the agreement protocol blocks). • F3: Primary fails after replica coordination • A new primary responds F1 F2 F3 Phase 1:Client Request Phase 3:Execution Phase 4:Replica Coordination Phase 5:Client response

  25. Lazy Update Propagation (Transactional Services) • Primary Copy: • Upon read: read locally and return to user • Upon write: write locally and return to user • Upon commit/abort: terminate locally • Sometime after commit: multicast changed objects in a single message to other sites (in FIFO) • Secondary copy: • Upon read: read locally • Upon message from primary copy: install all changes (FIFO) • Upon write from client: refuse (writing clients must submit to primary copy) • Upon commit/abort request (only for read-only txn): local commit • Note: existing systems allow different objects to have different primary copies • A transaction that wants to write X (primary copy is site S1) and Y (primary copy on site S2) is usually disallowed

  26. Lazy Update Propagation A client may end up with an inconsistent view of the system

  27. Lazy Propagation: Discussion • Lazy replication has no server/agreement coordination within response time • Faster • Transactions might be lost in case of primary crash • Weak data consistency • Simple to achieve • Secondary copies only need to apply updates in FIFO order • Data at secondary copies might be stale • Multiple Primaries possible (multi-master replication) • More locality

  28. How Many Replicas? • Properties of correct PB protocol • Property 1: There is at most one primary at any time • Property 2: Each client maintains the identity of the primary, and sends its requests only to the primary • Property 3: If a client update arrives at a backup, it is not processed • When a primary fails, we must elect a new one • Network partitions may cause election of more than one primary • We can avoid network by choosing the right number of replicas (under certain failure assumptions) • How many replicas do we need to tolerate failures?

  29. System Model • Synchronous system (useful for deriving theoretical results) • Fully connected network (exactly one FIFO link between any two processes) • Failure model: • Crash failures: also known as failstop failures • Crash+Link failures: A server may crash or a link may lose messages (but links do not delay, duplicate or corrupt messages) • Receive-Omission failures: A server may crash and also omit to receive some of the messages send over a non-faulty link • Send-Omission failures: A server may fail not only by crashing but also by omitting to send some messages over a non-faulty link • General-Omission failures: A server may exhibit send-omission and receive-omission failures

  30. Lower Bounds on Replication • How many replicas n do you need to tolerate f failures?

  31. Crash Failures, Send-Omission Failures: n > fReplicas FAILED(crashed or fail to send) Becomes primary

  32. Other Failure Models • The rest of the failure models may create partitions • Partitions: Servers are divided into mutually non-communicating partitions • A primary may emerge in each partition, so we’ll have more than one primary – against the rules • To avoid partitions, we use more replication

  33. Crash+Link Failures: n > f+1Replicas Scenario 1: f servers fail Scenario 2: f links fail UNREACHABLE BUT ALIVE FAILED Becomes primary Becomes primary Becomes primary Problem! 2 primaries!!!

  34. Crash+Link Failures: n > f+1Replicas • We need another correct node that would serve as a link between the two partitions • We can assume that its links will be correct, because we allow no more than f failures UNREACHABLE BUT ALIVE Becomes primary Becomes primary

  35. Omission Failures • Precise definitions of omission failures[Perry-Toueg86] • Notation: sent(Pj, Pi) – a message sent from Pj to Pi received(Pi, Pj) – a message received by Pi fromPj • Receive-omission failure of Pi with respect to Pj: sent(Pj, Pi) ≠ received(Pi, Pj) • Send-omission failure of Pi with respect to Pj: Pi fails to send a message prescribed by a protocol to Pj • General-omission failure of Piw.r.t. Pj Pi commits both receive-omission and send-omission w.r.t. Pj

  36. Receive-Omission Failures: n > 3f/2 Replicas f/2 f/2 A B FAIL C f servers in B and C fail Server in A becomes primary FAIL f/2

  37. Receive-Omission Failures: n > 3f/2 Replicas f/2 f/2 A B FAIL C Server in B becomes primary f servers in A and C fail FAIL f/2

  38. Receive-Omission Failures: n > 3f/2 Replicas Server in A becomes primary f/2 Servers in B: receive-omission failures B A f/2 Servers in A: receive-omission failures w.r.t. processes outside their partition C Server in B becomes primary Problem! 2 primaries!!! From A servers’ perspective, everyone else has crashed: partition! Need another non-failed server that links the partitions f/2

  39. General-Omission Failures: n>2f Replicas B A f f FAIL Becomes primary B A f f FAIL Becomes primary

  40. General-Omission Failures: n>2f Replicas • A commits general-omission failures w.r.t. servers in B • A’s servers think all servers in B failed – one of them becomes primary • B’s servers think all servers in A failed – one of them becomes primary • A server in A becomes a primary, a server in B becomes a primary:We have two primaries • To fix this, we need another non-faulty server that will link the two partitions B A f f Becomes primary Becomes primary

  41. How Many Replicas? Summary • We showed how many replicas are needed to preventpartitions in the face of f failures • However partitions do happen due to router failures, for example • So having extra replicas won’t help, because they will also be on one of the sides of the faulty router • Next we’ll talk aboutsurviving failures despitenetwork partitions

  42. Surviving Network Partitions • Most systems operate under assumption that a partition will eventually be repaired • Optimistic approach: • Allow updates in all partitions • When the partition is repaired, eventually synchronize the data • OK for a distributed file system (think about your laptop in disconnected mode) • Pessimistic approach: • Allow updates only in a single partition – used where strong consistency is required (flight reservation system) • Which partition? This is usually decided by quorum consensus • After partition is repaired update copies of data in the other partition

  43. Quorum Consensus • Quorum is a sub-group of servers whose size gives it the right to carry out the operation • Usually the majority gets the quorum • Design/implementation challenges: • Replicas must agree that they are behind a partition – must rely on timeouts, failuredetectors (special devices?) • If the quorum set does not containthe primary, the replicas must electthe new primary • Cost consideration: to tolerate one partition, musthave at least three servers. Implement one as a simple witness? Quorum

  44. Bringing Replicas Up-to-Date • Version numbers: • Each copy has a version number (or a timestamp) • Only copies that are up-to-date have the current version number • Operations should be applied only to copies with the current version number • How does a failed server finds out that its not up-to-date? • Periodically compare all version numbers? • Log sequence numbers: • Each operation is written to a log (like a transactional log) • Each log record has a log sequence number (LSN) • Replica managers compare LSN’s to find out if they are not up-to-date • Used by Berkeley DB replication system

  45. Summary • Discussed replication • Used for performance, high availability • Active replication • Client sends updates to all replicas • Replicas co-ordinate amongst themselves, apply updates in order • Passive replication (primary copy, primary-backup) • Eager/lazy update propagation • Number of replicas to prevent partitions • Handling partitions • Optimistic • Pessimistic (quorum consensus) • Next time we will look at real systems that use replication

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