Catalog
method#Software engineering#Reliability#Analytics#Observability

Benchmarking

Concept for the systematic measurement of performance and reliability of software, hardware and processes.

Benchmarking is the systematic measurement and analysis of the performance of software, hardware, or processes under reproducible conditions.
Established
Medium

Classification

  • Medium
  • Technical
  • Technical
  • Intermediate

Technical context

CI/CD systems (e.g. Jenkins, GitHub Actions)Monitoring and observability tools (e.g. Prometheus, Grafana)Load generators and test frameworks (e.g. k6, JMeter, hyperfine)

Principles & goals

Reproducibility before ad-hoc optimizationUse defined metrics and representative workloadsInterpret measurements in context, not in isolation
Build
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Wrong conclusions from incorrect test selection
  • Over-optimizing for synthetic benchmarks instead of user behavior
  • High operational effort without clear benefit
  • Automated, regularly recurring benchmarks in CI
  • Combine micro- and end-to-end benchmarks
  • Contextualize metrics and align with SLAs/SLOs

I/O & resources

  • Defined workloads and scenarios
  • Measurable metrics and acceptance criteria
  • Reproducible test environment or container images
  • Benchmark reports with metrics and percentiles
  • Comparison tables against baselines
  • Recommendations for optimization or scaling

Description

Benchmarking is the systematic measurement and analysis of the performance of software, hardware, or processes under reproducible conditions. It provides quantitative comparisons, identification of bottlenecks and baselines for optimisation. Results inform architecture, technology and capacity decisions and guide continuous performance improvements. Methodically it requires defined metrics and representative workloads.

✔

  • Objective basis for technology and architecture decisions
  • Early detection of performance bottlenecks
  • Informed capacity planning and cost estimation

✖

  • Lab conditions cannot fully replicate real production load
  • Effort-intensive setup of representative test environments
  • Results are only as good as the defined workloads and metrics

  • Latency (median / p95 / p99)

    Measures response times; relevant percentiles show worst-case behavior.

  • Throughput (requests per second)

    Indicates how many operations a system processes per time unit.

  • Resource utilization (CPU, RAM, I/O)

    Shows resource usage of infrastructure during tests.

Database comparison for write workload

A company ran benchmarks to compare write throughput and latency of two DB engines and selected the suitable engine.

Optimizing frontend load times

Benchmarks identified render-path bottlenecks; targeted optimizations improved TTFB and time-to-interactive.

Testing microservice scaling

Load tests showed a CPU limit under increasing traffic, prompting an architectural change and horizontal scaling.

1

Define goals and KPIs, set acceptable thresholds

2

Build representative workloads and test environment

3

Create measurement scripts, automate and integrate into CI

4

Run measurements, collect and evaluate data

5

Document results, update baselines and derive actions

⚠️ Technical debt & bottlenecks

  • Missing automation of benchmark runs
  • No historical baselines and trend data
  • Insufficient test data or test environments
CPU utilizationI/O and memory latencyNetwork throughput and latency
  • Comparing systems without identical test conditions
  • Basing decisions solely on short-term benchmarks
  • Overinterpreting minor measurement differences without statistical significance
  • Using non-representative workloads
  • Test environment unintentionally shared with production
  • Lack of reproducibility due to non-versioned artifacts
Performance analysis and profilingScripting and automationStatistical evaluation and interpretation
Scalability under loadPredictability of performanceCost and resource optimization
  • • Availability of representative test data
  • • Limited test environments compared to production
  • • Time and personnel resources for recurring measurements