Load Testing and Performance Optimization

1.1 Overview

This section of the Testing and Quality Assurance documentation outlines the methods and tools used for load testing and performance optimization within the CapsureLabs platform. This approach aims to ensure that the platform can scale efficiently under high demand while maintaining optimal response times and resource utilization.

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1.2 Objectives of Load Testing and Performance Optimization

Reliability

Ensure that the platform can handle peak loads without failure or major degradation.

Scalability

Confirm that services scale up or down smoothly based on traffic demands.

Efficiency

Optimize resource usage to prevent overuse of memory, CPU, and network bandwidth.

User Experience

Maintain a smooth and responsive experience across all user interactions.

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1.3 Tools for Load Testing and Performance Optimization

Apache JMeter

Widely used for load testing APIs and web applications.

Gatling

A powerful tool for stress testing, focusing on high-traffic scenarios.

Locust

A Python-based load testing tool for simulating millions of users.

k6

A modern load testing tool specifically designed for automated testing and CI/CD workflows.

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1.4 Load Testing Methodology

Define Scenarios

Identify key workflows to test, such as user registration, login, NFT minting, and asset transfer.

Establish Baseline Performance

Measure the system's performance under normal load conditions.

Determine Peak Load

Simulate the maximum number of users the platform expects during peak usage.

Run Stress Tests

Push the system beyond typical load to determine breaking points and evaluate response handling.

Monitor Resources

Track CPU, memory, disk, and network usage to identify resource bottlenecks.

Analyze and Optimize

Use test results to adjust configurations, optimize code, or reallocate resources as necessary.

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1.5 Load Testing Configurations

1.5.1 API Load Testing with JMeter

<TestPlan>
  <ThreadGroup>
    <stringProp name="ThreadGroup.num_threads">500</stringProp>
    <stringProp name="ThreadGroup.ramp_time">30</stringProp>
    <stringProp name="LoopController.loops">100</stringProp>
  </ThreadGroup>
  <HTTPSamplerProxy>
    <stringProp name="HTTPSampler.domain">api.capsurelabs.com</stringProp>
    <stringProp name="HTTPSampler.path">/api/v1/transaction</stringProp>
    <stringProp name="HTTPSampler.method">POST</stringProp>
    <elementProp name="HTTPsampler.Arguments" elementType="Arguments">
      <collectionProp name="Arguments.arguments">
        <elementProp name="amount" elementType="HTTPArgument">
          <stringProp name="Argument.value">100</stringProp>
        </elementProp>
      </collectionProp>
    </elementProp>
  </HTTPSamplerProxy>
</TestPlan>

1.5.2 Concurrent Users with Locust

from locust import HttpUser, task, between

class LoadTestUser(HttpUser):
    wait_time = between(1, 5)

    @task
    def view_dashboard(self):
        self.client.get("/api/v1/dashboard")

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1.6 Performance Optimization Strategies

1.6.1 Database Indexing and Query Optimization

CREATE INDEX idx_user_id ON transactions (user_id);

1.6.2 Caching Strategies

import redis
cache = redis.StrictRedis(host='localhost', port=6379)
result = cache.get("user_data") if cache.exists("user_data") else db.fetch("user_data")

1.6.3 Load Balancing and Auto-Scaling

{
  "AutoScalingGroupName": "capsurelabs-scaling",
  "LaunchConfigurationName": "capsure-launch-config",
  "MinSize": 1,
  "MaxSize": 10,
  "DesiredCapacity": 5
}

1.6.4 Asynchronous Processing

from celery import Celery
app = Celery('tasks', broker='redis://localhost:6379/0')

@app.task
def process_large_data():