Performance Tales of Serverless

on YouTube ↗37m

" summary

Compares AWS Lambda, Azure Functions and Google Cloud Functions with real load tests and a working itinerary app backed by DynamoDB/Cosmos DB and message buses (Kinesis/Event Hub/Pub/Sub). Finds cold starts cause 400 ms–4 s spikes and stay warm ~20–30 minutes; CPU scales with configured memory size; batch import of 1 million items finishes in about 13 minutes on Azure and 22 minutes on Google while Lambda scales linearly limited by Kinesis partitions (32). Lambda absorbs an 800 requests per second TV spike best after reusing TCP connections, Google initially errors until connections are reused.

AI-written summary · about

" chapters (31)

1. 0:00Start
2. 2:34What did we want?
3. 6:31What do we want?
4. 7:50Function-as-a-Service in one picture
5. 8:56Function-as-a-Service Cloud Offerings
6. 10:22Application Overview
7. 11:16Ideal Scalability: Throughput
8. 11:31Ideal Scalability: Latency
9. 12:07Visitors during sample 5 hours
10. 12:37Dependency Call Duration (ms)
11. 13:00Target End-to-End Latency (ms)
12. 13:50AWS Lambda Latency (ms)
13. 14:15Azure Functions Latency (ms)
14. 15:43Subsequent Cold Starts after quiet periods
15. 17:15Load Testing Setup
16. 18:27Serverless artillery
17. 19:35Requests/sec during 5 hours of load test
18. 22:24Serverless ETL
19. 23:59Google Cloud Functions + Pub/Sub Processing 1 Million Points
20. 24:54Azure Functions + Event Hub Processing 1 Million Data Points
21. 25:33AWS Lambda + Kinesis Processing 1 Million Data Points
22. 26:02AWS Processing Speed per Reserved RAM
23. 28:50Requests/sec during the load test
24. 29:24Azure Functions Response Percentiles (ms)
25. 30:06Azure Functions Response 99º Percentile (ms)
26. 30:18AWS Lambda Response Percentiles (ms)
27. 30:48Google Cloud Functions: Throttled
28. 31:06Google Cloud Functions: Persist Connections
29. 32:12Google Functions Response Percentiles (ms)
30. 33:25Is Serverless Good for My Next Project?
31. 34:27Perfect Scenarios for Serverless

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