GitLab CI comparison in Jenkins - Time & Space Complexity

Time Complexity: GitLab CI comparison

O(n)

Understanding Time Complexity

We want to understand how the time it takes to run Jenkins pipelines grows as we add more jobs or stages.

How does Jenkins handle more work compared to GitLab CI?

Scenario Under Consideration

Analyze the time complexity of this Jenkins pipeline snippet:

pipeline {
  agent any
  stages {
    stage('Build') {
      steps {
        echo 'Building...'
      }
    }
    stage('Test') {
      steps {
        echo 'Testing...'
      }
    }
    stage('Deploy') {
      steps {
        echo 'Deploying...'
      }
    }
  }
}

This pipeline runs three stages sequentially: Build, Test, and Deploy.

Identify Repeating Operations

Look at what repeats as the pipeline grows.

• Primary operation: Each stage runs one after another.
• How many times: Number of stages determines how many steps run.

How Execution Grows With Input

As you add more stages, the total time grows roughly by how many stages you have.

Input Size (n = stages)	Approx. Operations (steps run)
3	3 steps
10	10 steps
100	100 steps

Pattern observation: The time grows directly with the number of stages.

Final Time Complexity

Time Complexity: O(n)

This means if you double the number of stages, the total time roughly doubles.

Common Mistake

[X] Wrong: "Adding more stages won't affect total time because they run in parallel."

[OK] Correct: In Jenkins, stages run one after another by default, so more stages mean more total time.

Interview Connect

Understanding how pipeline steps add up helps you design efficient CI/CD workflows and explain your choices clearly.

Self-Check

What if we changed the pipeline to run stages in parallel? How would the time complexity change?