name: optimization
router_kit: FullStackKit
description: System and flow optimization guide. 2025 AI-driven optimization, bottleneck detection and user flow improvements.
metadata:
skillport:
category: operations
tags: [architecture, automation, best practices, clean code, coding, collaboration, compliance, debugging, design patterns, development, documentation, efficiency, git, optimization, productivity, programming, project management, quality assurance, refactoring, software engineering, standards, testing, utilities, version control, workflow] - observability

Optimization Skill - System & Flow Optimization

Methodology for achieving highest performance and efficiency in software systems and user flows.
2025 AI-assisted observability and systematic improvement techniques.

📋 Contents

1. Optimization Principles (2025)
2. Bottleneck Identification
3. AI-Driven Optimization
4. Modern Observability
5. Frontend & User Experience Optimization
6. Backend & Database Optimization
7. Systematic Improvement Cycle
8. Checklist
9. Don't Do List
10. Must Do List

1. Optimization Principles (2025)

2. Bottleneck Identification

2.1 Systematic Error & Slowness Detection

Follow these steps to identify bottlenecks:

1. Profiling: Determine which parts of the code consume CPU/Memory.
2. Tracing: Trace the journey of requests between systems.
3. Log Analysis: Find error and slowness patterns.
4. User Flow Analysis: Identify "steps" where the user gets stuck or slows down.

2.2 Signs of Bottlenecks

• CPU Spikes: Intense mathematical operation or infinite loop.
• Memory Leaks: Memory usage increasing over time and not dropping.
• High Latency: Database queries or external API dependencies.
• Lock Contention: Resource waiting times in concurrent operations.

3. AI-Driven Optimization

In 2025, optimization has evolved from manual analysis to AI-assisted analysis:

• AI Profiling: AI tools inspecting the codebase and predicting potential performance problems in advance.
• Automated Refactoring: AI replacing performance-reducing code blocks with "cleaner & faster" versions.
• Predictive Scaling: Predicting future load and preparing infrastructure in advance.

4. Modern Observability

4.1 OpenTelemetry and Full-Stack Visibility

It is not enough to just "monitor" the system, it is necessary to "understand" (observability):

• Metrics: Time-based numeric data (e.g., requests per second).
• Traces: The entire trail of a single request in the system (Microservices/Distributed tracing).
• Logs: Detailed event records.

4.2 Gözlemlenebilirlik Araçları

• Prometheus & Grafana: Metrik görselleştirme.
• Jaeger/Zipkin: Distributed tracing.
• New Relic / Datadog: Full-stack APM (Application Performance Monitoring).

5. Frontend & User Experience Optimization

5.1 Core Web Vitals (2025)

• LCP (Largest Contentful Paint): Loading time of the largest content.
• INP (Interaction to Next Paint): Response speed to user interaction (Replaced FID).
• CLS (Cumulative Layout Shift): Shifts in page layout (unpleasant jumps).

5.2 Techniques

• Static Replacement: Server-Side Rendering (SSR) or Static Site Generation (SSG) instead of JavaScript render.
• Image Optimization: WebP/AVIF formats, responsive sizes and lazy loading.
• Bundle Splitting: Not loading unused JavaScript (Tree shaking).

6. Backend & Database Optimization

6.1 Database Query Optimization

• Indexing: Adding indexes to correct columns can increase query speed by 100x.
• N+1 Avoidance: Using "include" or "join" instead of querying one by one.
• Query Caching: Keeping data that doesn't change frequently in memory-stores like Redis.

6.2 Conventional Techniques

• Connection Pooling: Keeping database connections open and reusing them.
• Asynchronous Processing: Moving time-consuming tasks (email sending etc.) to the background (background jobs).

7. Systematic Improvement Cycle

graph LR
    A[Measure] --> B[Analyze]
    B --> C[Optimize]
    C --> D[Verify]
    D --> A

1. Measure: Measure current state (Baseline).
2. Analyze: Find bottleneck and cause.
3. Optimize: Make the change that will create the biggest impact.
4. Verify: Verify the effect of change on performance (Regression test).

8. Checklist

• [ ] Is bottleneck verified with technical data (metrics)?
• [ ] Are you sure no premature optimization is done?
• [ ] Is rollback plan ready for the change?
• [ ] Is "perceived performance" measured after change?
• [ ] Tested that no new bottleneck is created?

9. Don't Do List

❌ Do not change code without measuring.
❌ Do not decide based only on benchmark results (without real user data).
❌ Do not try to optimize too many parameters at once.
❌ Do not sacrifice code readability for "faster".

10. Must Do List

✅ Focus on 80/20 rule; improve most used flows.
✅ Make your system transparent using OpenTelemetry standards.
✅ Use AI tools for data analysis and pattern detection.
✅ Prioritize fixing "perceived" slowness via user flow analysis.
✅ Run regression tests after every optimization.

Last Update: December 2025
Optimization v1.1 - Enhanced

🔄 Workflow

Source: Google SRE Book - Performance & Brendan Gregg's Methodology

Phase 1: Baseline & Profiling

• [ ] Metric Selection: What will you optimize? (Latency, Throughput, Saturation, Error Rate).
• [ ] Baseline Measurement: Record "Before" data before optimization (Don't start without numbers).
• [ ] Profiling: Perform CPU (Flamegraph), Memory (Heap dump) or I/O analysis.

Phase 2: Optimization Cycle

• [ ] Hypothesis: Establish hypothesis "If I change X, it will speed up by Y".
• [ ] Small Steps: Make one change at a time (Atomic commit).
• [ ] Verification: Measure again after change ("After" data). Is target reached?

Phase 3: Prevention

• [ ] Regression Test: Add performance test to CI process (Load testing).
• [ ] Alerting: Set up alarms to notify if metrics get worse again.
• [ ] Scalability: Evaluate Vertical vs Horizontal scaling options.

Checkpoints