Table of contents [ hide ] Basic theory CAP States that any distributed data store can provide only two of the following three guarantees. Consistency Every read receives the most recent write or an error. Availability Every request receives a (non-error) response, without the guarantee that it contains the most recent write. Partition tolerance The system continues to operate despite an arbitrary number of messages being dropped (or delayed) by the network between nodes. Typical architecture of distributed systems When a network partition failure happens, it must be decided whether to do one of the following: CP: cancel the operation and thus decrease the availability but ensure consistency AP: proceed with the operation and thus provide availability but risk inconsistency. BASE Basically-available, soft-state, eventual consistency. Base theory is the practical application of CAP theory, that is, under the premise of the existence of partitions and copies, through certain syste
Table of contents [ hide ] Kafka Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications. Why use a Message Queue like Kafka 1. Asynchronous The program can continue execution without waiting for I/O to complete, increasing throughput. 2. Decoupling It refers to reducing the dependencies between different parts of the system so that each component of the system can be developed, maintained, and evolved relatively independently. The main goal of decoupling is to reduce tight coupling between components to improve system flexibility, maintainability, and scalability. 3. Peak clipping Peak clipping is essential to delay user requests more, filter user access needs layer by layer, and follow the principle of "the number of requests that ultimately land on the database is as small as possible". Basic Concept 1. Client Includi