The document discusses the CAP theorem and its implications in modern distributed database system design, emphasizing the trade-offs between consistency, availability, and partition tolerance. It introduces the PACELC model, which addresses trade-offs during network partitioning and non-partitioning scenarios. The conclusion highlights the ongoing relevance of the CAP theorem while suggesting the exploration of new metrics like PACELC in database design.

1. Consistency Tradeoffs in Modern
Distributed Database System Design
Article by Daniel J. Abadi, Yale
University
Presentation 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 partition
High 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 Tradeoff
Modern Database Design
• Dynamo ~ Amazon
• Cassandra ~ Facebook Availability & Latency
• Voldemort ~ LinkedIn are critical!
• PNUTS ~ Yahoo
High Availability -> Replication is needed
Replication is used -> Consistency/Latency Tradeoff
occurs

7. Type of Replication
1. Data updates sent to all replicas at same time
a. Without preprocessing layer ~ latency
b. With preprocessing layer ~ consistency
2. Data updates sent to agreed-upon location first
a. Synchronous ~ consistency
b. Asynchronous ~ latency. Used by PNUTS.
c. Combination ~ configurable

8. Type of Replication
3. 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. PACELC
P ~ when there is partitioning
• Trade off between AC
E ~ else (which is no partitioning)
• Trade off between LC

10. DDBS in PACELC metrics
DBSS A C L C
Dynamo v v
Cassandra v v
Riak v v
VoltDB/H-Store v v
Megastore v v
MongoDB v v
PNUTS v v
Dynamo, 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