Handling Large Traffic In Short Period Of Time
In the previous post about Probability in migration new services to prevent traffic flood , I try to apply Probability to solve problem.
This post explains more about how I handle large-n-heavy traffics in short period of time. Btw, I called it’s heavy because it needs cpu and memory resize image on cloud (download original image => load to memory => transform format => resize).
So if I spin 2, or 4, or even 10 servers to handle this workload, these are some cons:
- Devops Costs : you have to know to manage large of VMs and put it behind Load Balancer
- Timing Costs : manual scaling take time (certainly)
- Resource Costs : you have to pay for idle time
- Complexity Costs : your system will be a mess
All challenges your met mainly because you have to have many servers, so solution is
Yah right, serverless is born to solve above these problems. Basicly, you only have to build 1 node template and give everything else to the Cloud Platforms.
To be more specific, I use CloudRun - a service of Google Cloud Platforms (which built on KNative I guess).
Some side effects of CloudRun :
- Un-predictable costs if you have DDOS => I setting maximum containers and GCP Billing Alert
- Traffic costs from GCP is expensive => I proxied it through Cloudflare and cache on CF Edge - hit rate is 95%. But CF has weird routing system (which I will write later in another article), so I plan to selfhost NginX Reverve Proxy And Cache server on DigitalOcean or Vultr (this server only need good networking and disk speed)
- Cold start (called waiting latency) is not small - acceptable => Try to keep at least 2 containers alive (use free web cron to fetch healthcheck endpoint). Why is 2 ? (Because two is better than one, haha I will write about this in Computer Science)
Figure 1 : Infrastructure design
SERVERLESS is good, like everything else is useful in moderation ! :D