分布式系统CAP理论
参考
https://jepsen.io/consistency
https://aphyr.com/posts/313-strong-consistency-models
CAP 说明
CAP定理，又称为布鲁尔定理（Brewer’s theorem），是分布式系统理论中的一个重要原理，由计算机科学家Eric Brewer在2000年提出。CAP定理指出，在一个分布式系统中，一致性（Consistency）、可用性（Availability）和分区容错性（Partition tolerance）这三个属性无法同时得到保证，最多只能同时满足其中的两个。
具体来说，CAP定理说明了以下三个特性：
- 一致性（Consistency）：所有的数据备份在同一时间都具有相同的值。即当一个数据副本被更新后，系统保证所有的副本都会被更新，读取操作会返回最新的写入结果。
- 可用性（Availability）：系统保证每个请求在有限的时间内会得到响应，即系统一直处于可用状态，能够接受和处理请求。
- 分区容错性（Partition tolerance）：分布式系统在遭遇节点之间的通信故障或网络分区时仍能继续正常工作，并保持数据的一致性和可用性。
根据CAP定理，分布式系统必须在一致性和可用性之间做出权衡，而无法同时保证两者。当系统面临网络分区或通信故障时，必须选择是保证一致性还是可用性。
根据实际需求，不同的应用可能会选择不同的取舍。例如，对于金融交易这样要求强一致性的场景，在网络分区时可能会选择保证一致性而牺牲可用性；而对于某些互联网应用，可能更重视高可用性，允许一定程度的数据不一致。
强一致性
强一致性（strong consistency）是分布式系统中的一种一致性模型。在强一致性模型下，系统保证在任何时刻，无论是对于单个副本还是多个副本的数据访问，都能保持数据的一致性。
具体来说，强一致性要求在任意操作或更新完成后，系统中的所有节点都能立即看到最新的数据，并保证所有的读操作都返回最新的写入结果。这意味着在进行数据访问操作时，系统提供严格顺序的保证，确保前一个操作完成后，后续操作才能读取到最新的数据。
为了实现强一致性，常用的方法是使用分布式一致性协议，如Paxos、Raft或两阶段提交。这些协议通过约束副本之间的交互、复制和同步机制，确保数据在不同节点之间的一致性。
强一致性的好处是提供了一致和可预测的数据访问。然而，这种模型可能会牺牲一些性能和延迟，因为需要在多个节点之间进行数据的复制和同步，并保证严格的顺序。
在实际应用中，需要权衡一致性和性能之间的关系。对于需要强调一致性的关键应用，如金融交易、订单处理等，强一致性是非常重要的。而对于一些非关键的应用，可能允许一定程度的一致性松弛，以换取更高的性能和可扩展性。
通信问题
在通信时间较长的进程中，如果使用读写强一致性（strong consistency），可能会导致一些性能和延迟方面的问题。读写强一致性要求任何对共享数据的写操作都必须在之后的读操作中立即反映出来，并且读操作必须返回最新的写入结果。
当通信时间较长时，传输数据需要花费更多的时间，并且网络延迟可能增加。在这种情况下，如果使用读写强一致性，会出现以下情况：
延迟增加：由于通信时间变长，读取请求的响应时间会增加，因为需要等待数据传输完成和确认。这可能导致进程的实际响应时间变长，影响用户体验。
吞吐量降低：当等待读取操作返回最新写入的结果时，进程可能会阻塞或等待超时。这会导致系统的吞吐量降低，因为其他操作必须等待读取操作完成。
网络负载增加：要保证读写强一致性，可能需要进行更多的网络通信或增加额外的数据传输，例如进行确认或验证。这会增加网络负载，可能对网络性能造成负面影响。
为了解决这些问题，有时可以考虑使用一致性级别稍低的方法，例如读写偏斜一致性（read-skew consistency）或因果一致性（causal consistency），这些方法放宽了一些一致性要求，从而提高了性能和并发性。
在实际应用中，需要根据应用需求和性能要求综合考虑一致性级别和通信延迟之间的权衡。不同的应用场景可能需要选择不同的一致性模型，以平衡一致性、性能和延迟之间的关系。
Simply put
The CAP theorem, also known as Brewer’s theorem, is a fundamental concept in distributed systems. It states that in the presence of network partitions §, a distributed system can only guarantee either consistency © or availability (A), but not both, at the same time.
Consistency refers to the property that all nodes in a distributed system see the same data simultaneously. Availability, on the other hand, means that every request receives a response, even if the system is experiencing failures or network partitions.
The CAP theorem implies that when a network partition occurs, a distributed system must choose between maintaining consistency or providing availability. In other words, during a partition, the system can either respond to client requests with potentially stale data (maintaining consistency) or respond with the most up-to-date data but risk returning conflicting or inconsistent results (providing availability).
It’s important to note that the CAP theorem assumes that network partitions can occur in a distributed system, which is a realistic assumption for large-scale systems. However, in the absence of network partitions, it is possible to achieve both consistency and availability.
It’s worth mentioning that there have been various approaches and systems designed to handle different trade-offs within the CAP theorem, such as eventual consistency, quorum systems, and distributed consensus algorithms like Paxos and Raft.
Overall, the CAP theorem provides a framework for understanding the inherent trade-offs in distributed systems and helps guide architects and developers in designing systems based on their specific requirements and constraints.
On the other hand
In the distant future, humans have colonized multiple planets throughout the galaxy, establishing interconnected settlements. These settlements rely on a complex network of distributed systems to function and communicate across vast distances. However, the constant advancement of technology has introduced a new challenge – the CAP theorem.
In this futuristic world, renowned scientist Dr. Sophia Evans unveils her groundbreaking invention, a revolutionary computing system named “CAPx”. CAPx is designed to transcend the limitations imposed by the CAP theorem, promising to achieve the impossible: simultaneous Consistency, Availability, and Partition tolerance for distributed systems.
The news of CAPx spreads like wildfire through the galaxy, capturing the attention of governments, corporations, and citizens alike. Hope and excitement fill the air as its potential implications are realized. The once unimaginable prospect of seamless, real-time, and fault-tolerant communication across all settlements becomes tantalizingly close.
As Dr. Sophia continues her research and development, adversaries emerge, skeptical of the ambitious claims made by CAPx. The “Resistors,” a clandestine group of rebels, fear the dominance CAPx could bring, perceiving it as a tool for control and surveillance. They launch a campaign to discredit Dr. Sophia and her invention, igniting a conflict that spreads like wildfire among the settlements.
Caught in the crossfire is Captain Alex Mitchell, an ex-military officer, disillusioned by the endless battles he witnessed during his service. Desperate to find a purpose, he becomes determined to unravel the truth behind CAPx, understanding its potential to either unite or further divide humanity.
Embarking on a perilous journey, Captain Mitchell unravels a web of intrigues, betrayals, and hidden agendas. Along the way, he encounters charismatic hackers, fugitive scientists, and unlikely allies who share his thirst for knowledge and the pursuit of a unified future.
As Captain Mitchell delves deeper, he uncovers the intricate workings of the distributed systems, their vulnerabilities, and the moral dilemmas inherent in balancing Consistency, Availability, and Partition tolerance. It becomes apparent that CAPx is more than just a computing system – it represents the ultimate question of how society can achieve harmony in an ever-expanding universe.
Driven by a desire for truth and justice, Captain Mitchell becomes the voice of reason amidst the chaos. His journey symbolizes the struggle to find a delicate balance between the competing forces of control and freedom.