The document discusses the CAP theorem which states that it is impossible for a distributed computer system to simultaneously provide consistency, availability, and partition tolerance. It defines these terms and explores how different systems address the tradeoffs. Consistency means all nodes see the same data at the same time. Availability means every request results in a response. Partition tolerance means the system continues operating despite network failures. The CAP theorem says a system can only choose two of these properties. The document discusses how different types of systems, like CP and AP systems, handle partitions and trade off consistency and availability. It also notes the CAP theorem is more nuanced in reality with choices made at fine granularity within systems.

1. CAP theorem
and distributed systems
introduction
Nikanovich Yury

2. Distributed Systems
Distributed system is a software system in which
components located on networked computers
communicate and coordinate their actions by passing
messages.

3. Scalability
Scalability is the ability of a system, network, or
process, to handle a growing amount of work in a
capable manner or its ability to be enlarged to
accommodate that growth.
Two particularly relevant aspects
• Performance
• Availability

4. Permformance aspects
• Short response time/low latency for a given piece of
work
• High throughput (rate of processing work)
• Low utilization of computing resource(s)

5. Availability
Availability = uptime / (uptime + downtime)
Availability %
Downtime per
year
90 % ("one nine")
More than one
month
99.9 % ("three nines")
Less than 9
hours
99.9999% ("six nines") 31 seconds

6. Fault tolerance
Fault tolerance ­ability of a system to behave in a
well­defined manner once faults occur.
Failures are norm.

7. Replication
Replication is making copies of the same data on
multiple machines.

8. Replication
Master-Slave Replication
Node B Node B
Multi-Master Replication
Load
Balancer
Master
Slave A Slave B Slave C
Write Operation
Read Operation
Replication
Node D
Node BNode A Node C

9. Replication

10. Consistency
More Consistency Faster reads/writes
Strong
Probabilistic
CRDTs
Red­Blue
Per­key
sequentional
Casual
Eventual

11. Why strong consistency is hard to
achieve
Nodes
• each node executes a program concurrently
• knowledge is local
• global state is potentially out of date
• nodes can fail and recover from failure independently
• messages can be delayed or lost
• clocks are not synchronized across nodes
Links
• Asynchronous system model.
• No timing assumptions.
• No bound on message transmission delay
• Useful clocks do not exist.

12. Distributed Databases

13. ACID
• Atomic
• Consistent
• Isolated
• Durable

14. BASE
• Basic Availability
• Soft­state
• Eventual consistency

15. CAP theorem
It is impossible for a distributed computer system to
simultaneously provide all three of the following guarantees:
• Consistency (all nodes see the same data at the same time)
• Availability (every request received by a non­failing
[database] node in the system must result in a [non­error]
response)
• Partition tolerance (the system continues to operate despite
arbitrary partitioning due to network failures)

16. CAP theorem
Consistency
Availability
Partition
tolerance
Pick
2

17. CAP theorem and Internet
ConsistencyAvailability
Partition
tolerance

18. CP systems
Available Available

19. CP systems (Partition)
Partition
Unavailable
Available

20. CP systems
Protocols:
• Strict quorum protocols (paxos, raft, zab)
• 2PC
Storages:
• MongoDB
• HBase
• Zookeeper

21. AP systems
Available
(consistent
reads/writes)
Available
(consistent
reads/writes)

22. AP systems (Partition)
Partition
Available
(inconsistent
reads/writes)
Available
(inconsistent
reads/writes)

23. AP systems
Protocols:
• Partial quorum protocols
Storages:
• Couch DB
• Cassandra
• Amazon Dynamo

24. CAP fifteen years later
●
Partitions are rare, there is little reason to forfeit C or
A when the system is not partitioned.
●
Choice between C and A can occur many times
within the same system at very fine granularity.
●
All three properties are more continuous than binary.
●
Most software doesn’t neatly fit CP/AP definition.

25. Thank You!
Questions?