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Architectural fitness functions

An overview of architectural fitness functions as a way to check if your architecture matches your needs

Basic idea​

Architectural fitness functions:

  • Allow us to evaluate to what extent our architecture has the properties we want it to have
  • Assess goals like performance, reliability, security, operability, coding standards, ...
    • Focus on what is really important for the specific system based on business requirements, technical capabilities, scale, ...
      • This also means not spending time and money on things that are not really important!
    • For every system, a crucial early architecture decision is to define the most important dimensions (scalability, performance, security, ...) to take into account
      • Can help to prioritize riskier work to get the risky stuff out of the way as soon as possible
      • Can lead to an architecture that makes certain concerns explicit instead of having them scattered across the codebase
      • This can still evolve! some fitness functions will emerge during development of the system
  • Provide guidance in evolving our architecture
    • Help finding areas that need improvement
    • Evaluate how certain changes affect how well the architecture satisfies our goals
    • Allow us to make tradeoffs between different goals if needed

Important building block of evolutionary architectures

  • "An evolutionary architecture supports guided, incremental change as a first principle across multiple dimensions"
  • Guidance is provided by fitness functions

Categorizing fitness functions​

Atomic versus holistic​

Atomic fitness functions:

  • Test one particular aspect of the architecture
  • Example: unit test checking for cyclic dependencies in a certain package

Holistic fitness functions:

  • Test a combination of architectural aspects
  • Useful for assessing the interactions between different architectural concerns
  • Lots of combinations possible, need to focus on most important ones
  • Example:
    • Data freshness
    • Scalability: number of concurrent users within a certainly latency range
      • Developers make this work by implementing caching, but this influences freshness of data
    • Very helpful to have a fitness function that tests for freshness of data with the caching enabled

Triggered (batch) versus continuous​

Triggered fitness functions:

  • Executed in response to some particular event
  • Examples:
    • A developer executing a unit test
    • A QA person performing exploratory testing

Continuous fitness functions:

Static versus dynamic​

Static fitness function:

  • Fixed predefined acceptable values: binary, a certain number range, ...
  • Example: binary pass/fail of a unit test

Dynamic fitness function:

  • Acceptable values depend on context
  • Example: acceptable latency might depend on actual scale of the system

Automated versus manual​

Automated fitness function:

  • Automated unit tests, deployment pipelines, stress tests, ...
  • Ideally as much automation as possible!

Manual fitness functions:

  • Some things impossible to automate, for example when certain changes need manual approval for legal reasons
  • Some things may not be properly automated yet, for example QA
  • For some tests, it might be more efficient to determine success/failure manually

Temporal​

Temporal fitness functions:

  • Have a particular time component
  • Example: temporal fitness function as a reminder to check to see if important security updates have been performed

Example fitness functions​

Atomic and triggered​

Typically run by developers and in deployment pipeline

See also Static analysis

Holistic and triggered​

Typically run by developers and in deployment pipeline

Example: testing what kind of impact tighter security has on scalability

Atomic and continuous​

Run as part of the deployed architecture

Example: monitoring

Example: logging

Holistic and continuous​

Run as part of the deployed architecture

Example: Netflix's Chaos Monkey

  • Designed to test resilience to instance failures
  • Runs in production and randomly terminates instances
  • Forces teams to build resilient services while still taking into account other architectural goals

Resources​

  • Building Evolutionary Architectures (book by Neal Ford, Rebecca Parsons and Patrick Kua) (summary slides )

View this note on GitHub

Last modified: 2021-01-03