CAP theorem and distributed systems
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.
![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)](https://image.slidesharecdn.com/distributedsystemsintroandcaptheorem16x9-170303102955/85/CAP-theorem-and-distributed-systems-15-320.jpg)
CAP theorem and distributed systems
- 1. CAP theorem and distributed systems introduction Nikanovich Yury
- 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)
- 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
- 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is hard to achieve Nodes • each node executes a program concurrently • knowledge is local • global state is potentially out of date • nodes can fail and recover from failure independently • messages can be delayed or lost • clocks are not synchronized across nodes Links • Asynchronous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.
- 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)
- 17. CAP theorem and Internet ConsistencyAvailability Partition tolerance
- 20. CP systems Protocols: • Strict quorum protocols (paxos, raft, zab) • 2PC Storages: • MongoDB • HBase • Zookeeper
- 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.