Originally published at woitzik.dev
Renovate kept getting OOMKilled on the BMAX Mini PC* running my entire k3s cluster. The Kubernetes CronJob would start, run for a while, and then die with exit code 137. The natural assumption: not enough memory. Bump the limit. Problem solved.
Three rounds of bumps later — 2 GiB, then 3 GiB, then 4 GiB — it was still dying. The fix had nothing to do with memory limits. It had to do with what Renovate was actually doing with the memory it had.
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The Escalation
Round 1: the CronJob was OOMKilled at 2 GiB. Every job, 3 retries each, all dying. kubectl describe pod confirmed exit code 137, reason OOMKilled. Bumped to 3 GiB container limit / 2304Mi V8 heap, keeping the same ~25% non-heap headroom ratio as the previous configuration.
Round 2: OOMKilled again at 3 GiB. Same pattern. Checked for regressions — no commits landed between the last clean run and the first failure that meaningfully grew the dependency count. The internal-docs relocation merged after the failures started, and Renovate doesn't scan .md files anyway. Read as ordinary run-to-run variance tipping an already-marginal limit over, not a new regression.
Round 3: bumped to 4 GiB. This time, it landed at 3 GiB instead of 4 GiB — the edit was described but never actually made before merge. A separate PR corrected it to the intended 3 GiB → 4 GiB / 2304Mi → 3072Mi heap.
Still OOMKilled.
The Diagnostic Run
At this point, guessing at memory limits wasn't working. The approach shifted to isolating the spike instead of outrunning it.
I ran Renovate off-cluster in an isolated diagnostic run with 12 GiB of headroom, a real GitHub token, real branches, and user authorization. The goal: measure the actual peak instead of guessing at it.
The diagnostic run finished clean at exit 0. Peak memory usage: 3.99 GiB.
That confirmed this was never unbounded growth. The 2 GiB and then 4 GiB production kills both landed mid-spike, ~30-40 MiB past the ceiling. The cgroup limit sat too close to a single transient spike for GC to reclaim before the kill.
The Root Cause: Terraform Hash Concurrency
The spike wasn't random. It was Renovate's Terraform manager SHA256-hashing all 14 platform builds of a major hashicorp/aws provider bump (5.x → 6.53.0) to keep .terraform.lock.hcl valid.
At the default concurrentRequestLimit of 16, that's up to 16 provider zips buffered in memory simultaneously — each one a large binary being hashed for lock-file integrity. The spike is predictable: any Terraform provider upgrade that touches multiple platforms hits this.
The fix wasn't more memory. It was flattening the spike at the source:
{
"terraform": {
"concurrentRequestLimit": 2
}
}
Cap concurrent Terraform hash operations at 2 for releases.hashicorp.com. The spike shrinks because fewer provider zips are buffered simultaneously. The 4 GiB limit becomes genuine headroom instead of a near-miss ceiling, and the 6 GiB / 4608Mi heap becomes defense in depth — sized from the actual measured 3.99 GiB peak with real GC margin, not a blind guess.
resources:
limits:
memory: 6Gi
env:
- name: V8_FLAG_INITIAL_HEAP_SIZE
value: "4608"
The CronJob stays suspended — this fix doesn't re-enable it. That's a separate decision once the fix is reviewed and validated in production.
The Lesson
The first two bumps were the obvious response to OOMKilled: more memory. They were also wrong, because they treated the symptom (process needs more memory) instead of the cause (process is buffering too many large objects simultaneously).
The diagnostic run with 12 GiB of headroom was the turning point. Instead of guessing at limits, I isolated the real peak. 3.99 GiB. Then the question became: why is the peak 3.99 GiB, and can I flatten it instead of accommodating it?
concurrentRequestLimit=2 flattens it. The memory limit becomes a safety net, not the fix.
This is the same pattern as tuning Kubernetes resource requests: you don't set requests based on what the app uses at peak, you set them based on what it needs to function normally, and you use limits as a ceiling for the spikes. In this case, the "normal" was far below the spike, and the spike was artificially inflated by a configuration knob nobody had touched.
Right-sizing infrastructure isn't just a Kubernetes problem — Azure VM sizing, AKS node pool autoscaling, and Cosmos DB RU allocation all face the same trap: responding to peak usage with more capacity instead of understanding what's driving the peak.
