Company

Experience

Netflix

Has 600+ services in production. Deploys 100 times per day.

Uber

Has 1,000+ services in production. Deploys several thousand times each week.

WeChat

Has 3,000+ services in production. Deploys 1,000 times a day.

Factor

Explanation

1 - Code Base

A single code base for each microservice, stored in its own repository. Tracked with version control, it can deploy to multiple environments (QA, Staging, Production).

2 - Dependencies

Each microservice isolates and packages its own dependencies, embracing changes without impacting the entire system.

3 - Configurations

Configuration information is moved out of the microservice and externalized through a configuration management tool outside of the code. The same deployment can propagate across environments with the correct configuration applied.

4 - Backing Services

Ancillary resources (data stores, caches, message brokers) should be exposed via an addressable URL. Doing so decouples the resource from the application, enabling it to be interchangeable.

5 - Build, Release, Run

Each release must enforce a strict separation across the build, release, and run stages. Each should be tagged with a unique ID and support the ability to roll back. Modern CI/CD systems help fulfill this principle.

6 - Processes

Each microservice should execute in its own process, isolated from other running services. Externalize required state to a backing service such as a distributed cache or data store.

7 - Port Binding

Each microservice should be self-contained with its interfaces and functionality exposed on its own port. Doing so provides isolation from other microservices.

8 - Concurrency

When capacity needs to increase, scale out services horizontally across multiple identical processes (copies) as opposed to scaling-up a single large instance on the most powerful machine available. Develop the application to be concurrent making scaling out in cloud environments seamless.

9 - Disposability

Service instances should be disposable. Favor fast startup to increase scalability opportunities and graceful shutdowns to leave the system in a correct state. Docker containers along with an orchestrator inherently satisfy this requirement.

10 - Dev/Prod Parity

Keep environments across the application lifecycle as similar as possible, avoiding costly shortcuts. Here, the adoption of containers can greatly contribute by promoting the same execution environment.

11 - Logging

Treat logs generated by microservices as event streams. Process them with an event aggregator. Propagate log data to data-mining/log management tools like Azure Monitor or Splunk and eventually to long-term archival.

12 - Admin Processes

Run administrative/management tasks, such as data cleanup or computing analytics, as one-off processes. Use independent tools to invoke these tasks from the production environment, but separately from the application.

Tasks

Explanation

Scheduling

Automatically provision container instances.

Affinity/anti-affinity

Provision containers nearby or far apart from each other, helping availability and performance.

Health monitoring

Automatically detect and correct failures.

Failover

Automatically reprovision a failed instance to a healthy machine.

Scaling

Automatically add or remove a container instance to meet demand.

Networking

Manage a networking overlay for container communication.

Service Discovery

Enable containers to locate each other.

Rolling Upgrades

Coordinate incremental upgrades with zero downtime deployment. Automatically roll back problematic changes