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Dangers of Replication

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Dangers of Replication Materials taken from J. Gray, P. Helland, P. O Neil, and D. Shasha. The Dangers of Replication and a Solution. SIGMOD, 2006. – PowerPoint PPT presentation

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Title: Dangers of Replication


1
  • Dangers of Replication

Materials taken from J. Gray, P. Helland, P.
ONeil, and D. Shasha. The Dangers of Replication
and a Solution. SIGMOD, 2006. http//research.mic
rosoft.com/gray/replicas.ps
2
Whats the danger?
  • Replication of transactional data results in
    unstable system performance
  • For consistent replication
  • Waits and deadlocks
  • For update-anywhere-anytime replication
  • Reconciliations
  • Both grow polynomially (w/ meaningful exponents)
    in the number of clients
  • Based on simple, lower bounds derived from
    mean-value analysis

3
Whats the point?
  • This theme is predicated on the knowledge that
    globally consistent replication does not scale

4
Replication Policies
  • Eager replication
  • Copies are updated as part of the original
    transaction.
  • Lazy replication
  • One replica is updated. Other copies are updated
    asynchronously
  • Update policy
  • Group any node can update its replica.
  • Master only master updates its replica. The rest
    replicas are read only.

5
Representing Writes
6
Mastered and Group Replication
7
The Scale-up Pitfall
  • Replication works well on small, prototype
    systems
  • But, at deployment, replication is unstable
  • At larger scales
  • Messages propagation delay increases
  • Higher transaction rates
  • For eager replication
  • More transactions with each txn taking longer
  • For lazy transactions
  • Delays in reconciliation leads to system delusion

8
Analysis of Eager Group Replication
  • Scaling laws
  • Third power of the number of nodes
  • Fifth power of the of operations per
    transaction
  • Problems with eager replication
  • Cannot be used by disconnected nodes
  • Probability of deadlocks (failed transactions)
    increases with systems size

9
Analysis of Lazy Group Replication
  • Scaling laws
  • Third power of the number of nodes
  • third power of the of operations per
    transaction
  • Better than eager, but not so good

10
Analysis of Lazy Master Replication
  • Scaling laws
  • second power of the number of nodes
  • fifth power of the of operations per transaction

11
Status of Replication
  • Negative scaling results
  • Dont account for message delays (so its worse)
  • Cant escape these via lazy vs eager options
  • No reason for group replication
  • Master is the same (eager) or better (lazy)
  • So, what do we do
  • Avoid scale, keep systems small

12
Two-Tier Replication
  • Two node types
  • Base nodes Always connected, store replica,
    master most objects
  • Mobile nodes often disconnected, store a
    replica, issues tentative transactions
  • Two version types
  • Master version
  • Exists at the object owner, other may have older
    versions
  • Tentative version
  • Local version is updated by tentative
    transactions

13
Pictures to Entertain
14
System Principles
  • Hierarchies to reduce scale
  • Nodes (Master Mobile-disconnected)
  • Transactions (Tentative and Eager/Consistent)
  • Techniques
  • Convergence (Bayou-like eventual consistency)
  • Idempotence encode writes in non-conflicting
    ways
  • Does it fix any of Bayous semantic problems?

15
Conclusions
  • Eager waits and deadlocks
  • Lazy converts waits and deadlocks into
    reconciliations
  • Both do not scale.
  • Two tier replication
  • Supports mobile nodes
  • Combine eager-master-replication with local
    updates
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