The book, The Art of Scalability, describes a really useful, three dimension scalability model: the scale cube.
In this model, scaling an application by running clones behind a load balancer is known as X-axis scaling. The other two kinds of scaling are Y-axis scaling and Z-axis scaling. The microservice architecture is an application of Y-axis scaling but let’s also look at X-axis and Z-axis scaling.
X-axis scaling consists of running multiple copies of an application behind a load balancer. If there are N copies then each copy handles 1/N of the load. This is a simple, commonly used approach of scaling an application.
One drawback of this approach is that because each copy potentially accesses all of the data, caches require more memory to be effective. Another problem with this approach is that it does not tackle the problems of increasing development and application complexity.
Unlike X-axis and Z-axis, which consist of running multiple, identical copies of the application, Y-axis axis scaling splits the application into multiple, different services. Each service is responsible for one or more closely related functions. There are a couple of different ways of decomposing the application into services. One approach is to use verb-based decomposition and define services that implement a single use case such as checkout. The other option is to decompose the application by noun and create services responsible for all operations related to a particular entity such as customer management. An application might use a combination of verb-based and noun-based decomposition.
When using Z-axis scaling each server runs an identical copy of the code. In this respect, it’s similar to X-axis scaling. The big difference is that each server is responsible for only a subset of the data. Some component of the system is responsible for routing each request to the appropriate server. One commonly used routing criteria is an attribute of the request such as the primary key of the entity being accessed. Another common routing criteria is the customer type. For example, an application might provide paying customers with a higher SLA than free customers by routing their requests to a different set of servers with more capacity.
Z-axis splits are commonly used to scale databases. Data is partitioned (a.k.a. sharded) across a set of servers based on an attribute of each record. In this example, the primary key of the RESTAURANT table is used to partition the rows between two different database servers. Note that X-axis cloning might be applied to each partition by deploying one or more servers as replicas/slaves. Z-axis scaling can also be applied to applications. In this example, the search service consists of a number of partitions. A router sends each content item to the appropriate partition, where it is indexed and stored. A query aggregator sends each query to all of the partitions, and combines the results from each of them.
Z-axis scaling has a number of benefits.
Z-axis scaling has some drawbacks.
Microservices.io is brought to you by Chris Richardson. Experienced software architect, author of POJOs in Action, the creator of the original CloudFoundry.com, and the author of Microservices patterns.
Chris helps clients around the world adopt the microservice architecture through consulting engagements, and training workshops.
Chris teaches comprehensive workshops for architects and developers that will enable your organization use microservices effectively.
Avoid the pitfalls of adopting microservices and learn essential topics, such as service decomposition and design and how to refactor a monolith to microservices.Learn more
Chris offers numerous other resources for learning the microservice architecture.
Want to see an example? Check out Chris Richardson's example applications. See code
Got a specific microservice architecture-related question? For example:
Consider signing up for a two hour, highly focussed, consulting session.
My virtual bootcamp, distributed data patterns in a microservice architecture, is now open for enrollment!
It covers the key distributed data management patterns including Saga, API Composition, and CQRS.
It consists of video lectures, code labs, and a weekly ask-me-anything video conference repeated in multiple timezones.
The regular price is $395/person but use coupon MECNPWNR to sign up for $120 (valid until May 16th, 2023). There are deeper discounts for buying multiple seats.
Take a look at my Manning LiveProject that teaches you how to develop a service template and microservice chassis.
Engage Chris to create a microservices adoption roadmap and help you define your microservice architecture,
Use the Eventuate.io platform to tackle distributed data management challenges in your microservices architecture.
Eventuate is Chris's latest startup. It makes it easy to use the Saga pattern to manage transactions and the CQRS pattern to implement queries.
Engage Chris to conduct an architectural assessment.
Note: tagging is work-in-process
anti-patterns · application api · application architecture · architecting · architecture documentation · assemblage · beer · containers · dark energy and dark matter · deployment · design-time coupling · development · devops · docker · eventuate platform · glossary · hexagonal architecture · implementing commands · implementing queries · inter-service communication · kubernetes · loose coupling · microservice architecture · microservice chassis · microservices adoption · microservicesio updates · multi-architecture docker images · observability · pattern · refactoring to microservices · resilience · sagas · security · service api · service collaboration · service design · service discovery · service granularity · service template · software delivery metrics · success triangle · tacos · team topologies · transaction management · transactional messaging
Application architecture patterns
Refactoring to microservicesnew
Cross cutting concerns