PApaulfurtado·Dec 24, 2019
That said, as an operator of a number of fairly large kubernetes clusters (100-500 nodes), I might prefer java's memory tuneability to Go's garbage collector simply not caring about the max memory a system can offer.
At scale, we need to heavily oversize the kubernetes masters to deal with the spikiness of go memory consumption. It's possible for a kube apiserver to average 2GB of memory use and then jump to 18GB after suddenly handling more requests than usual and get OOM-killed. I'd much rather it simply slow down a bit than behave so erratically.
This is a common thing across all Go programs that handle data: since Go's garbage collector doesn't try to keep memory use within some upper bound, if you allocate and throw away a bunch of objects you'll quickly run out of memory on any low-memory system or container even though there's a to
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n of memory ready to be reclaimed.
Even a major library like the official AWS SDK's S3 client had this wrong as recently as 2017 (solution was to use sync.pool to avoid throwing away buffers): https://github.com/aws/aws-sdk-go/pull/1784
Python should perform much better from a memory perspective in these situations: its weakness would be the deserialized size that objects blow up to in memory, but its use would be bounded. Java would also handle this pretty effortlessly, though with a high enough rate of garbage production might hit a few short GC pauses that last far less time than a process restart.
Really wish go were better about this. Imagine that a simple Go cronjob that uploads a backup to s3 at under a
160MB/sec could risk you OOM killing your server if you don't set cgroup memory caps on all your go processes.