Dangers of Replication

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• 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
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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

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Whats the point?

• This theme is predicated on the knowledge that
  globally consistent replication does not scale

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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.

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Representing Writes
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Mastered and Group Replication
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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

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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

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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

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Analysis of Lazy Master Replication

• Scaling laws
• second power of the number of nodes
• fifth power of the of operations per transaction

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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

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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

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Pictures to Entertain
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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?

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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