v-firecrawl/apps/test-suite/load-test-results/tests-6-7/load-test-8.md

Load Testing Crawl Routes - Test #8

Summary

This load test, conducted over a period of 7 minutes with an extended observation, aimed to evaluate the system's performance under variable loads. The test revealed that while the system managed to handle the initial load effectively, there were issues with autoscaling and resource management that need to be addressed.

Table of Contents

• Load Testing Crawl Routes - Test #8
  • Summary
  • Table of Contents
  • Load Test Configuration
    • Configuration
    • Results
    • Metrics
  • Conclusions and Next Steps
    • Conclusions
    • Next Steps

Load Test Configuration

Configuration

Machine	Size/CPU	Status
73d8dd909c1189 mia (app)	performance-cpu-1x@2048MB	always on
e286de4f711e86 mia (app)	performance-cpu-1x@2048MB	always on
178134db566489 mia (worker)	performance-cpu-1x@2048MB	always on
7811020c91d138 mia (worker)	performance-cpu-1x@2048MB	always on
178134db566489 mia (worker)	performance-cpu-1x@2048MB	stopped
06e825d0da2387 mia (worker)	performance-cpu-1x@2048MB	stopped

fire-engine machines:

Machine	Size/CPU	Status
2874d0db0e5258 mia app	performance-cpu-2x@4096MB	always on
48ed194f7de258 mia app	performance-cpu-2x@4096MB	always on
56830d45f70218 sjc app	performance-cpu-2x@4096MB	always on

phases:
    - duration: 60
      arrivalRate: 1  # Initial load
    - duration: 120
      arrivalRate: 2  # Increased load
    - duration: 180
      arrivalRate: 3  # Peak load
    - duration: 60
      arrivalRate: 1  # Cool down

using fire-engine as default scraping strategy

NUM_WORKERS_PER_QUEUE=12

Results

Date: 14:42:27(-0300)

Metric	Value
errors.Failed capture or match	43
http.codes.200	1757
http.codes.404	43
http.downloaded_bytes	0
http.request_rate	3/sec
http.requests	1800
http.response_time.min	363
http.response_time.max	6065
http.response_time.mean	847.8
http.response_time.median	804.5
http.response_time.p95	1130.2
http.response_time.p99	1353.1
http.responses	1800
vusers.completed	857
vusers.created	900
vusers.created_by_name.Crawl a URL	900
vusers.failed	43
vusers.session_length.min	11598.4
vusers.session_length.max	17005.3
vusers.session_length.mean	11854.1
vusers.session_length.median	11734.2
vusers.session_length.p95	12213.1
vusers.session_length.p99	12459.8

Metrics

CPU Utilization:

• Fire-engine mia machines: Reached 99% after 16 minutes of processing the queue, but dropped to 0% after the queue was fully processed. The sjc machine was not requested during the
• Worker machines: Maintained CPU utilization above 89% during the load testing time. The higher CPU value indicates an accurate maximum number of workers/machine (12). Other worker machines were not autoscaled for solving the queue as spected.

Memory Utilization:

• Fire-engine mia machines: utilization reached 92% after 16 minutes of processing the queue.
• Worker machines: Maintained Memory utilization above 650MiB during the test.

Conclusions and Next Steps

Conclusions

1. Request Handling: The system effectively managed to queue all requests, demonstrating its capability to handle the initial setup of traffic without any failures.
2. Autoscaling Issues: The other worker machines should have been turned on, but the autoscaling strategy did not work as expected.
3. Resource Management: The system maintained high CPU and memory utilization, indicating efficient use of resources, but the autoscaling failure needs to be addressed.

Next Steps

1. Investigate Autoscaling: Investigate why the autoscaling strategy did not work properly and ensure that additional worker machines are turned on as needed.
2. Optimize Autoscaling: Implement and test improvements to the autoscaling strategy to ensure it dynamically adjusts the number of active machines based on the current load.
3. Extended Duration Testing: Conduct further tests with extended durations to evaluate the impact of the improved autoscaling strategy on system stability and performance.
4. Monitor and Optimize: Continuously monitor system performance during the new tests, focusing on the effects of the autoscaling improvements. Use the gathered data to optimize configurations and troubleshoot any new issues that arise.

By following these steps, we can further enhance the system's performance and reliability under varying load conditions.