container-resource-tuning

DevOps & Security

Size container memory and CPU limits, diagnose OOM kills and CPU throttling, and recommend resource adjustments by ecosystem. Use when containers are being OOM-killed, running slowly, or when setting initial resource limits for a deployment.

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Container Resource Tuning

Default Resource Recommendations

Starting points by ecosystem. Adjust based on actual usage.

Ecosystem	Memory limit	CPU shares	Notes
Node.js	512MB	0.5	V8 GC is memory-hungry; Next.js SSR needs more
Node.js (Next.js SSR)	1024MB	1.0	Server-side rendering is CPU and memory intensive
Python (Django/Flask)	512MB	0.5	Per-worker; multiply by worker count
Python (FastAPI)	256MB	0.5	Async, lower per-process memory
Go	256MB	0.5	Static binary, efficient memory use
Rust	128MB	0.25	Minimal runtime overhead
Java (Spring Boot)	1024MB	1.0	JVM needs headroom; set `-Xmx` to 75% of limit
PHP (FrankenPHP)	512MB	0.5	Per-request memory; depends on payload
Ruby (Rails)	512MB	0.5	Per-worker; Puma workers multiply this
Elixir (Phoenix)	256MB	0.5	BEAM VM is efficient; handles concurrency well
.NET (ASP.NET)	512MB	0.5	Similar to Node.js profile
Static (Caddy/nginx)	64MB	0.25	Minimal; just serving files

Diagnosing OOM Kills

When container_inspect shows oom_killed: true:

Check current limit: container_inspect → memory limit
Check peak usage: container_stats → memory usage and limit
Check what's consuming memory:
- container_exec ["ps", "aux", "--sort=-%mem"] → top processes
- Node.js: container_exec ["node", "-e", "console.log(process.memoryUsage())"]

Common causes

Ecosystem	Cause	Fix
Node.js	V8 heap exceeds limit	Set `NODE_OPTIONS=--max-old-space-size=<MB>` to 75% of container limit
Node.js	Memory leak (heap grows unbounded)	Profile with `--inspect`; check for event listener leaks, unbounded caches
Java	JVM default heap exceeds container limit	Set `-Xmx` to 75% of container memory limit
Python	Large dataset loaded into memory	Use streaming/chunked processing; increase limit if data size is fixed
Any	Too many worker processes	Reduce worker count: Gunicorn `--workers`, Puma `workers`, PM2 instances

Right-sizing after OOM

Increase memory limit by 50% from current value
Deploy and monitor container_stats for 10 minutes
If peak usage is consistently below 60% of limit: limit is right
If peak usage exceeds 80%: increase again or investigate the memory consumer
If peak usage is below 30%: reduce limit to save resources

Diagnosing CPU Throttling

When the app is slow but not OOM-killed:

Check CPU usage: container_stats → CPU percentage
Check host load: get_machine_stats → system load average
Check for CPU-bound work:
- container_exec ["ps", "aux", "--sort=-%cpu"] → top CPU consumers

Common causes

Symptom	Cause	Fix
CPU at 100% of limit	App is compute-bound	Increase CPU shares or optimize hot paths
CPU at 100%, response times spike	Not enough CPU for request volume	Scale horizontally (more instances) or increase CPU
Low CPU but slow responses	Waiting on I/O (database, external API)	Not a CPU issue — check database latency
Host load > 2x cores	Server overloaded	Multiple containers competing — reduce total load or upgrade server

JVM-Specific Tuning

Java apps need explicit JVM flags to respect container limits:

JAVA_TOOL_OPTIONS=-XX:+UseContainerSupport -XX:MaxRAMPercentage=75.0

UseContainerSupport (default since Java 10): JVM reads cgroup memory limits
MaxRAMPercentage=75.0: heap uses 75% of container memory, leaving room for native memory and GC

Node.js-Specific Tuning

NODE_OPTIONS=--max-old-space-size=384

For a 512MB container, set old space to ~75% (384MB). V8 needs headroom for GC, native code, and buffers.

For production, also set:

UV_THREADPOOL_SIZE=4 (default) — increase for I/O-heavy apps
NODE_CLUSTER_WORKERS — if using cluster mode, each worker needs its own memory budget

Python-Specific Tuning

Gunicorn workers multiply memory usage:

gunicorn app:app --workers 2 --worker-class uvicorn.workers.UvicornWorker

Rule of thumb: workers = (2 * CPU cores) + 1, but in containers with limited CPU, use 2-4 workers max.

Each worker uses roughly the same memory as a single process. 4 workers × 256MB = 1GB total.

Compose Resource Limits

services:
  app:
    deploy:
      resources:
        limits:
          memory: 512M
          cpus: '0.5'
        reservations:
          memory: 256M
          cpus: '0.25'

limits: hard ceiling — container is OOM-killed if exceeded
reservations: guaranteed minimum — Docker ensures this is available

Monitoring After Changes

After adjusting resources:

container_stats — check memory and CPU usage over time
get_container_logs — scan for OOM warnings or performance errors
http_probe — verify response times are acceptable
If restart_count drops to 0 and memory stays below 80%: tuning is correct

Related Skills

post-deploy-verification — Check container stability after resource changes
failure-diagnosis — Exit code 137 (OOM kill) diagnosis
compose-setup — Resource limits in docker-compose.yml