Load Testing

Simulate football matches with 100K+ concurrent viewers to test system performance.

Overview

The load testing tools simulate two types of events:

Game Events (on the field)

• pass, shot, goal, foul, yellow_card, red_card
• corner, free_kick, penalty, offside, substitution
• interception, tackle, save, var_review

Engagement Events (viewer reactions)

• Reactions: cheers, boos, emoji reactions
• Comments: match commentary, player discussions
• Video Actions: pause, rewind, replay, camera switch
• Shares: Twitter, Facebook, WhatsApp, in-app
• Predictions: score predictions, polls, player ratings
• Clicks: stats views, player profiles, merchandise

Key Concept: Engagement Correlation

Viewer engagement correlates with game events. A goal triggers 15x more reactions than baseline activity:

Viewer Personas

The simulation models different viewer behavior patterns:

Quick Start

1. Set Up Python Environment

cd tests/load
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

2. Configure API

export API_URL="http://localhost:8080"
export API_KEY="your-api-key"

3. Run Simulation

# Quick test (1,000 viewers, 5 minutes)
python match_simulator.py --viewers 1000 --duration 5

# Full simulation (100K viewers, 90 minutes)
python match_simulator.py --viewers 100000 --duration 90

Scripts

match_simulator.py - Full Match Simulation

Simulates a complete match with game events and viewer engagement.

python match_simulator.py \
  --match-id "match_abc123" \
  --api-url "http://localhost:8080" \
  --api-key "your-api-key" \
  --viewers 100000 \
  --duration 90 \
  --batch-size 500 \
  --concurrency 50

Options:

simulate_match.py - Game Events Only

Simulates only game events (no viewer engagement).

python simulate_match.py

viewer_simulator.py - Engagement Only

Simulates only viewer engagement events.

python viewer_simulator.py \
  --match-id "match_123" \
  --viewers 100000 \
  --duration 90

VS Code Tasks

Pre-configured tasks for load testing:

Run via Command Palette: Tasks: Run Task

Sample Output

============================================================
MATCH SIMULATION: AlHilal vs AlNassr
Match ID: match_8f3a2bc1
Target Viewers: 100,000
Duration: 90 minutes
============================================================

Ramping up to 100,000 viewers over 120s...
  Added 10,000/100,000 viewers
  Added 20,000/100,000 viewers
  ...
Ramp-up complete: 100,000 viewers active

Min  0 | Score: 0-0 | Viewers: 100,000 | Engagements: 12,453
Min  5 | Score: 0-0 | Viewers: 99,234 | Engagements: 8,123
...
Min 23 | Score: 0-0 | Viewers: 98,456 | Engagements: 9,876
Minute 23: GOAL - Score: 1-0
Min 23 | Score: 1-0 | Viewers: 98,456 | Engagements: 145,234  <-- SPIKE!
...
Min 90 | Score: 2-1 | Viewers: 87,654 | Engagements: 125,000

======================================================================
MATCH SIMULATION COMPLETE
======================================================================

FINAL SCORE:         AlHilal 2 - 1 AlNassr
Match ID:            match_8f3a2bc1

----------------------------------------------------------------------
GAME EVENTS ON FIELD
----------------------------------------------------------------------
Total Events:        3,245

By Type:
  pass                 2,100
  shot                    89
  foul                    78
  goal                     2
  ...

----------------------------------------------------------------------
VIEWER ENGAGEMENT
----------------------------------------------------------------------
Peak Viewers:        100,000
Total Engagements:   2,456,789
Eng/Viewer:          24.6

Engagement by Type:
  reaction           1,523,456 (62.0%)
  comment              368,518 (15.0%)
  video_action         245,679 (10.0%)
  click                196,543 ( 8.0%)
  share                 73,593 ( 3.0%)
  prediction            49,000 ( 2.0%)

Peak Engagement: Minute 23 (145,234 engagements)

----------------------------------------------------------------------
API PERFORMANCE
----------------------------------------------------------------------
Total Calls:         5,234
Errors:                  12
Error Rate:           0.23%
Avg Latency:          45.2ms
======================================================================

Engagement Event Types

Reaction Subtypes

cheer, boo, emoji_goal, emoji_fire, emoji_clap, emoji_cry, emoji_angry, emoji_heart, emoji_laugh, emoji_wow

Comment Subtypes

match_commentary, player_discussion, team_support, trash_talk, question

Video Action Subtypes

pause, play, rewind, replay, camera_switch, quality_change, fullscreen

Share Subtypes

twitter, facebook, whatsapp, instagram, in_app, copy_link

Prediction Subtypes

score_prediction, next_goal, player_rating, poll_vote, man_of_match

Click Subtypes

stats_view, player_profile, team_info, lineup, ad_click, merchandise, ticket

Performance Tips

1. Start Small: Begin with --viewers 1000 to verify setup
2. Batch Size: Use --batch-size 500 for optimal throughput
3. Concurrency: Adjust --concurrency 50 based on API capacity
4. Gradual Ramp-up: Viewers are added gradually to avoid overwhelming the API
5. Monitor Grafana: Watch dashboards during tests for bottlenecks

Monitoring During Tests

Grafana Dashboards

1. Open http://localhost:3005 (dev) or https://grafana.your-domain.com (prod)
2. Navigate to Dashboards
3. Watch for:
   • Request rate and latency
   • Kafka consumer lag
   • ClickHouse write throughput
   • System resources (CPU, memory)

ClickHouse Queries

-- Events per minute
SELECT
  toStartOfMinute(timestamp) as minute,
  count() as events
FROM engagement_events
WHERE match_id = 'match_8f3a2bc1'
GROUP BY minute
ORDER BY minute;

-- Engagement by type
SELECT
  engagement_type,
  count() as cnt
FROM engagement_events
WHERE match_id = 'match_8f3a2bc1'
GROUP BY engagement_type;

Team Data

The simulation uses player rosters from CSV files in tests/load/:

• alhilal.csv - Al Hilal player data
• alnassr.csv - Al Nassr player data

CSV format:

Name,Position,Number
Player Name,MF,10

Dependencies

• aiohttp>=3.9.0 - Async HTTP client
• tqdm>=4.66.0 - Progress bar
• requests>=2.31.0 - Sync HTTP client
• python-dotenv>=1.0.0 - Environment variable loading