CAP Theorem (Consistency, Availability, Partition Tolerance)
The CAP Theorem, also known as Brewer's Theorem, is a theory in the field of distributed database systems. It was proposed by Eric Brewer in 2000. The theorem states that in a distributed database system, it is impossible to simultaneously guarantee the following three properties:

Consistency: Every read in a database returns the most recent value written to it or an error. In other words, the data is always in a consistent state and reflects all updates made.

Availability: Every read or write request to the database receives a response, either successful or indicating a failure. The system is always operational and responds to all requests.

Partition Tolerance: The system continues to function even in the presence of communication failures between database nodes. Partitioning refers to the situation where the communication network between nodes is interrupted or slow.

The theorem suggests that you can only choose two out of the three properties to be guaranteed in a distributed database system. Therefore, when designing a distributed system, you need to make decisions about which aspects are most important depending on the system's requirements.

For example, in a scenario where availability is crucial (such as real-time Internet services where quick responses are essential), you can sacrifice data consistency in some cases. This means that during concurrent updates, different parts of the system may see different versions of the data for a short period.

On the other hand, if consistency is paramount (as in financial systems where data errors can be catastrophic), you can sacrifice availability during failures or partitioning to ensure consistency.

It's important to note that since the proposal of the CAP Theorem, many advances have been made in distributed database systems, and different solutions and techniques have been employed to achieve different levels of consistency, availability, and partition tolerance depending on specific application needs.

Examples of Industries and Their Priorities Regarding CAP Theorem Properties:
Social Networks (e.g., Facebook, Twitter):

Priority: AP (Availability and Partition Tolerance)
Reasoning: Social networks need to be always available to their users, even if communication failures occur between the servers hosting them. During traffic spikes and concurrent updates, it's acceptable to temporarily sacrifice data consistency to ensure users can access and interact with the platform.
Financial Systems (e.g., Banks, Brokerages):

Priority: CP (Consistency and Partition Tolerance)
Reasoning: In financial systems, it's crucial to ensure all operations are consistent and that there are no data integrity issues. Sacrificing availability in some cases during failures or partitioning is preferable to ensure data correctness and reliability.
E-commerce Applications (e.g., Amazon, Alibaba):

Priority: AP (Availability and Partition Tolerance)
Reasoning: In e-commerce platforms, it's essential for users to access products and complete purchases without interruptions. Although consistency is important, it's acceptable that in exceptional situations, there may be slight delays in updating product stocks or information to ensure availability.
Real-Time Monitoring Systems (e.g., Industry Sensors):

Priority: CA (Consistency and Availability)
Reasoning: In real-time monitoring systems like sensors in industries or medical devices, it's crucial to ensure data consistency and availability. Communication failures or inconsistent data can lead to serious problems, so partition tolerance may be sacrificed in exchange for greater consistency and availability.
Online Games (e.g., Fortnite, PUBG):

Priority: AP (Availability and Partition Tolerance)
Reasoning: In online games, it's crucial to provide a seamless experience to players, even in high competition or network instability situations. Temporarily sacrificing data consistency, such as real-time player positions, may be acceptable to ensure uninterrupted gameplay.
It's worth noting that priorities can vary depending on the specific needs of each system and the design choices of developers. Additionally, systems often employ hybrid techniques and intelligent strategies to balance the three properties of the CAP Theorem and meet the requirements of each application.