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Distributed transactions

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

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Kafka

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

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ShardingSphere

Table of contents [ hide ] ShardingSphere The distributed SQL transaction & query engine for data sharding, scaling, encryption, and more - on any database. ShardingJDBC ShardingSphere-JDBC is a lightweight Java framework that provides additional services at Java’s JDBC layer. ShardingProxy ShardingSphere-Proxy is a transparent database proxy, providing a database server that encapsulates database binary protocol to support heterogeneous languages. Core Concept 1. Virtual Database Provides a virtual database with sharding capabilities, allowing applications to be easily used as a single database 2. Real Database The database that stores real data in the shardingShereDatasource instance for use by ShardingSphere 3. Logic Table Tables used by the application 4. Real Table In the table that stores real data, the data structure is the same as the logical table. The application maintains the mapping between the logical table and the real table. All real tables map to ShardingSpher

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Mongo DB

Table of contents [ hide ] Mongo DB An open-source NoSQL file database. Due to the use of JSON document data storage structure, MongoDB has three advantages: flexibility, easy expansion, and high performance. Compared with traditional relational databases, users do not need to define the data structure in advance when using MongoDB to store data, and can freely add the Key/Value of the data. Basic concept SQL vs MongoDB Database vs Database Table vs Collection Row vs Document Column vs Field Index vs Index Primary key vs _id View vs View Table Joins vs $lookup Query Syntax Example SQL vs MongoDB a = 1 vs {a: 1} a<> 1 vs {a: {$ne: 1}} a > 1 vs {a: {$gt: 1}} a >= 1 vs {a: {$gte: 1}} a < 1 vs {a: {$lt: 1}} a <= 1 vs {a: {$lte: 1}} Aggregation Pipeline Stages Description: Mongo DB vs SQL Filter criteria: $match vs where Projection: $project vs as Left outer join: $lookup vs left outer join Sort: $sort vs order by Group: $group vs group by Pagination: $skip/$limit vs

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