FastAPI (Python) vs Node.js Performance

Summary

This video benchmarks FastAPI against Node.js on Kubernetes, comparing latency, throughput, CPU usage, and memory consumption. In the first test, Node.js outperforms FastAPI in request handling, with higher throughput and lower latency. FastAPI struggles with CPU usage, leading to throttling and reduced performance. In the second test, which simulates real-world use cases with database and cache operations, Node.js continues to deliver superior performance, handling more requests and performing more efficiently. The video concludes that Node.js is a better choice for high-performance applications, particularly in Kubernetes environments.

Takeaways

• 😀 Node.js outperforms FastAPI in both latency and throughput when tested in Kubernetes on AWS Graviton instances.
• 😀 FastAPI's higher CPU usage in comparison to Node.js leads to performance throttling and decreased throughput in Kubernetes environments.
• 😀 The first test focuses on static request handling, where Node.js can handle significantly more requests per second than FastAPI.
• 😀 Latency measured at the 90th percentile shows Node.js is more stable, with FastAPI showing significant degradation as CPU usage increases.
• 😀 CPU throttling occurs when FastAPI exceeds its CPU usage limit in Kubernetes, further increasing latency and decreasing throughput.
• 😀 Memory usage does not significantly impact performance in the first test but is higher in FastAPI compared to Node.js.
• 😀 The second test focuses on real-world scenarios, measuring POST request latency, database insert latency, and cache set operation latency.
• 😀 Prometheus metrics are used to track internal application performance, including CPU usage of both PostgreSQL and Memcached.
• 😀 FastAPI's performance drops to 2,500 requests per second in the second test due to high CPU consumption, while Node.js maintains superior throughput.
• 😀 The database (PostgreSQL) and cache (Memcached) performance metrics reveal that both services contribute to the overall performance of the application.
• 😀 The creator uses AWS EKS with Graviton instances to optimize performance, leveraging tools like **Prometheus** and **Grafana** for monitoring application metrics.
• 😀 Despite FastAPI's reputation for speed, in this benchmarking, **Node.js** proves to be more efficient for handling large-scale workloads.

Q & A

What are the main benchmarks used to compare FastAPI and Node.js in this video?

The main benchmarks include measuring latency (90th percentile), throughput (requests per second), CPU and memory usage, availability or error rate, and CPU throttling. Additionally, a second test measures database insert latency and cache operations, specifically with PostgreSQL and Memcached.

Why does FastAPI have higher latency and lower throughput compared to Node.js in the static test?

FastAPI performs worse due to higher CPU usage, which quickly reaches full capacity, leading to throttling in Kubernetes. This throttling increases latency and reduces throughput compared to Node.js.

What role does CPU throttling play in the test results?

CPU throttling occurs when the application attempts to use more CPU resources than allocated by Kubernetes. This limits FastAPI's performance, contributing to higher latency and lower throughput during the test.

How does memory usage affect the performance of FastAPI and Node.js in the static test?

Memory usage is not a significant factor in the static test. Although Python (used by FastAPI) consumes more memory, it does not impact the performance as much as CPU usage, which is the primary bottleneck.

What was the throughput difference between FastAPI and Node.js in the static test?

In the static test, Node.js reached 50,000 requests per second, while FastAPI only managed around 9,000 requests per second, highlighting a significant difference in throughput.

What were the results for CPU usage during the static test?

FastAPI reached full CPU usage within minutes, leading to performance degradation. On the other hand, Node.js maintained better CPU efficiency throughout the test.

What is the significance of the 90th percentile in measuring latency?

The 90th percentile measures the latency experienced by 90% of requests, offering a more stable representation of performance under load. In this case, Node.js had lower and more consistent latency compared to FastAPI.

What additional metrics were tracked during the second test involving database and cache operations?

In addition to overall request latency, the second test tracked metrics such as database insert latency, cache set operation latency, CPU usage for PostgreSQL and Memcached, and connection pool size.

Why does FastAPI struggle to perform well during the second, more realistic test?

FastAPI struggles due to higher CPU usage, which limits its ability to handle a large number of requests per second (around 2,500). This performance is similar to Go, but much lower than Node.js in the same test scenario.

What was the conclusion of the benchmark tests comparing FastAPI and Node.js?

The conclusion of the tests is that Node.js outperforms FastAPI in terms of latency, throughput, and resource efficiency, both in static and realistic test scenarios. The tests indicate that Node.js is a better choice for high-performance applications, while FastAPI may need further optimization.