What is CAP Theorem?
The CAP theorem, also known as Brewer's theorem, is a fundamental concept in distributed computing that states it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees:
- Consistency
- Availability
- Partition Tolerance
Consistency
Every node in a distributed system sees the same data at the same time. Once a write operation is completed, any subsequent read operation will return the updated value.
Consistency means all parts of the system see the same data at the same time. This is done using methods like consensus algorithms, which make sure all parts agree on the data. It's important for things like banking or inventory systems where accurate data is crucial.
⚠️ Keeping data consistent can slow down the system and make it less available, especially if parts of the network fail. The system might need to wait for all parts to agree, which can cause delays.
Availability
Every request (read or write) receives a response, regardless of the state of any individual node in the system
Availability means the system always responds to requests, even if some parts fail. This is achieved by copying data across multiple parts and having backup plans. It's vital for services like social media or online games where being always online is important
⚠️ Focusing on availability can mean sometimes showing old data, especially if network problems occur. The system might prioritize staying online over having the latest data.
Partition Tolerance
The system continues to operate despite network partitions, where communication between nodes is disrupted
Partition tolerance means the system keeps working even if parts of the network are disconnected. This is done by handling network failures gracefully. It's essential for apps used in places with poor network connections, like mobile apps
⚠️ A system that handles network failures must choose between consistency and availability. This choice can affect how reliable and up-to-date the data is during network problems.
The CAP Triangle: Trade-offs
1. Consistency vs Availability
In the event of a network partition, a system must decide whether to maintain consistency (ensuring all nodes have the same data) or availability (ensuring the system remains operational). This trade-off is critical because it directly impacts how the system behaves when parts of the network are disconnected. Choosing consistency can lead to better data integrity but may result in downtime or slower responses during network issues. Conversely, prioritizing availability ensures the system stays responsive but might serve stale or inconsistent data.
2. Availability vs Partition Tolerance
A system that prioritizes availability and partition tolerance (AP) remains operational during network partitions but may return stale or inconsistent data. This trade-off is common in systems where uptime is more important than immediate data consistency. While this approach ensures high availability, it can lead to temporary data inconsistencies. Users might see outdated information, which can be acceptable in some applications but problematic in others, like financial systems.
🤓 The most frequent trade-off is between consistency and availability, especially in systems that must handle network partitions. This trade-off is crucial because network partitions are inevitable in distributed systems. Many modern distributed databases and applications opt for eventual consistency to balance this trade-off, ensuring data will become consistent over time as the network stabilizes.
Consistency(CAP) vs Consistency(ACID)
CAP consistency ensures that all nodes in a distributed system always return the most recent data, focusing on synchronization across replicas, even at the cost of availability during network failures. In contrast, ACID consistency ensures that a database remains in a valid state by enforcing rules and constraints within transactions, preventing partial updates or invalid data. While CAP consistency is crucial for distributed systems like Google Spanner or Zookeeper, ACID consistency is fundamental for relational databases like PostgreSQL and MySQL, ensuring correctness but potentially impacting performance.
Helpful Links 🤓
Text resources:
- What is the CAP theorem?
- CAP Theorem Explained: Consistency, Availability & Partition Tolerance
- Navigating the CAP Theorem: A Guide to Selecting the Right Database
- Understanding the CAP Theorem: Balancing Consistency, Availability, and Partition
- Demystifying the CAP Theorem: Understanding Consistency, Availability, and Partition Tolerance
Video resources:
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