File streaming @ 237MB/s on $8/m droplet in 507 lines of Python

Multipart Streaming Axon returns multiple files through a single HTTP connection using a Python generator to dispense data with multipart boundaries via stream. This approach maintains constant memory usage while CPU utilization scales with request volume and payload size. 64KB chunks optimize throughput for mixed file sizes. System memory remained under 225MB while CPU utilization was maxed throughout all test scenarios.

Backpressure Handling The iterator works like a revolver - each chunk is 'chambered' on request. The iterator is called by WSGI on client demand, preventing memory buildup. This demand-driven mechanism explains why 16 threads proved optimal - threads only activate when releasing data. Thread count likely scales with network latency to better manage concurrent I/O waits, while processes scale with CPU cores.

Performance testing was conducted using identical Digital Ocean droplets ($8/month, 1GB RAM, 1 Intel CPU, 35GB NVMe SSD) deployed in the NYC3 datacenter to minimize network latency between Axon and the stress testing client. Axon used 2 processes and 16 threads per process based on 25 preliminary optimization tests that determined this configuration was likely optimal for the hardware, with 2 processes providing a slight advantage over 1 process while improving error recovery. Chunk size was set to 64KB across Axon, uWSGI, and nginx layers after testing 8KB-64KB chunks.

Each test iteration involved deploying a fresh Axon instance via script, transferring test files with SCP, and executing wrk with 50 concurrent connections for a 1-minute warmup period followed by 10 minutes of sustained testing. Eight test scenarios were designed as four payload-size pairs (8KB, 64KB, 256KB, 512KB) with each pair testing single-file versus multi-file handling to isolate file processing overhead from payload size effects. The testing droplet was destroyed after each iteration to ensure consistent baseline conditions.

Process

1. I deployed a new droplet
2. I ran deploy-axon.sh with:
   chmod +x deploy-axon.sh && ./deploy-axon.sh
3. I sent the required test file to the server with:
   scp ./[FILE_NAME].[EXTENSION] root@[DROPLET_IP]:/var/www/axon-api/examples/
4. I checked the endpoint with:
   curl http://[DROPLET_IP]/api/health
5. I ran the 1-minute burst test with:
   wrk -t10 -c50 -d60s --latency "http://[DROPLET_IP]/api/stream-files?file1=examples/[FILE_NAME].[EXTENSION]&file2=examples/[FILE_NAME].[EXTENSION]"
6. I ran the 10-minute sustained test with:
   wrk -t10 -c50 -d600s --latency "http://[DROPLET_IP]/api/stream-files?file1=examples/[FILE_NAME].[EXTENSION]&file2=examples/[FILE_NAME].[EXTENSION]"
7. I recorded my results and destroyed the droplet

Computational overhead from multipart boundary generation and header construction constrains throughput rates. Generator-based streaming maintains bounded memory under load achieving zero socket errors across all test scenarios.

Baseline tests measured the framework's maximum throughput (on this hardware) using two scenarios: a health endpoint returning JSON and invalid file requests triggering error responses.