Consistency Tradeoffs in Modern Distributed Database System Design

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  • 1. Consistency Tradeoffs in ModernDistributed Database System DesignArticle by Daniel J. Abadi, YaleUniversityPresentation by Arinto Murdopo
  • 2. Outline• CAP Theorem• What’s wrong with CAP?• Consistency/Latency Tradeoff• Type of Replication• PACELC• DDBS in PACELC metrics• Conclusion
  • 3. CAP theorem Consistency Availability CA CP AP Partition Tolerance
  • 4. What’s wrong with CAP? Consistency CA Availability Reduced Consistency, CP AP let’s justify it! Partition Tolerance(?)Tolerant to network partitionHigh availability Sacrifice consistency
  • 5. What’s wrong with CAP?The P in CAP is combination of:• Partition tolerance ~ commonly used as justification• Existence of a network partition itself ~ often forgotten
  • 6. Consistency/Latency TradeoffModern Database Design• Dynamo ~ Amazon• Cassandra ~ Facebook Availability & Latency• Voldemort ~ LinkedIn are critical!• PNUTS ~ YahooHigh Availability -> Replication is neededReplication is used -> Consistency/Latency Tradeoffoccurs
  • 7. Type of Replication1. Data updates sent to all replicas at same time a. Without preprocessing layer ~ latency b. With preprocessing layer ~ consistency2. Data updates sent to agreed-upon location first a. Synchronous ~ consistency b. Asynchronous ~ latency. Used by PNUTS. c. Combination ~ configurable
  • 8. Type of Replication3. Data updates sent to an arbitrary location• Location for data item is not always same a. Synchronous ~ consistency b. Asynchronous ~ latency• Used by Dynamo, Cassandra, and Riak -> combined with 2c
  • 9. PACELCP ~ when there is partitioning• Trade off between ACE ~ else (which is no partitioning)• Trade off between LC
  • 10. DDBS in PACELC metricsDBSS A C L CDynamo v vCassandra v vRiak v vVoltDB/H-Store v vMegastore v vMongoDB v vPNUTS v vDynamo, Cassandra and Riak have user-adjustable settings in LC tradeoff!
  • 11. Conclusion• CAP is still important• Exploring new metrics is good• PACELC metrics are worth to consider