![]() ![]() This is often the most practical thing to do. Horizontal scaling: More server nodes are added and everything is connected through the computer network. ![]() This is generally more expensive and will hit a limit when the hardware can’t be upgraded anymore. Vertical scaling: More powerful hardware is put to a single server, such as increasing CPU and RAM or moving to a mainframe computer. For these users there are two ways to scale data stores: But for people who need to scale their system with growing requirements for data access, storage capacity, and read/write scaling, this style of architecture quickly falls apart. For most typical users where the processing requirements are kept in a single database server, this model is fine. We are used to having the luxury of ACID transactions whenever we need. This is a clear, straightforward solution, and we will keep writing code like this when required. This avoids inconsistencies such as money being lost from the system if the funds were taken out from one account but not put into the other account. Here, we wrap the individual debit and credit operations in an ACID transaction. We can imagine using ACID transactions as shown below, where we transfer some funds from one account to another. persisted in a disk, so temporary crashes of the database server will not lose data. It should be as the transaction is running in its own isolated environment where other transactions cannot see the changes happening in that duration.ĭurability: After a transaction is committed, the changes are stored durably, e.g. Isolation: Separated transactions running concurrently cannot interfere with each other. Here’s a quick refresher:Ītomicity: All operations are executed successfully or everything fails together.Ĭonsistency: The data in the database is kept in a valid state, as stated by its rules, such as referential integrity. We mainly used ACID transactions, which is what you find in relational database systems. In our good old monolith applications, we did database transactions to implement all-or-nothing data operations, while keeping data consistency. If you are already familiar with the related concepts behind database transactions and the intricacies of data consistency in a distributed system, you can skip to the section on "Data Modelling in Microservice Architecture" where we explore how to model data with a real-world use case. In this article, we will discuss what causes this, the possible solutions, and the best practices in developing safe transactional software systems using MSA. A typical database transaction done in a web application using local transactions is now a complicated distributed transaction problem. Transaction handling is at the front and center of this issue. It’s challenging to handle the complexities that come with a distributed system when moving away from a monolith to microservice architecture (MSA). ![]()
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