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c12s-kubespray/docs/large-deployments.md
Bogdan Dobrelya 6e1c0cdd15 Systemd units, limits, and bin path fixes 
* Add restart for weave service unit
* Reuse docker_bin_dir everythere
* Limit systemd managed docker containers by CPU/RAM. Do not configure native
  systemd limits due to the lack of consensus in the kernel community
  requires out-of-tree kernel patches.

Signed-off-by: Bogdan Dobrelya <bdobrelia@mirantis.com>
2016-12-28 15:49:42 +01:00

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Large deployments of K8s

For a large scaled deployments, consider the following configuration changes:

  • Tune ansible settings for forks and timeout vars to fit large numbers of nodes being deployed.

  • Override containers' foo_image_repo vars to point to intranet registry.

  • Override the download_run_once: true and/or download_localhost: true. See download modes for details.

  • Adjust the retry_stagger global var as appropriate. It should provide sane load on a delegate (the first K8s master node) then retrying failed push or download operations.

  • Tune parameters for DNS related applications (dnsmasq daemon set, kubedns replication controller). Those are dns_replicas, dns_cpu_limit, dns_cpu_requests, dns_memory_limit, dns_memory_requests. Please note that limits must always be greater than or equal to requests.

  • Tune CPU/memory limits and requests. Those are located in roles' defaults and named like foo_memory_limit, foo_memory_requests and foo_cpu_limit, foo_cpu_requests. Note that 'Mi' memory units for K8s will be submitted as 'M', if applied for docker run, and cpu K8s units will end up with the 'm' skipped for docker as well. This is required as docker does not understand k8s units well.

For example, when deploying 200 nodes, you may want to run ansible with --forks=50, --timeout=600 and define the retry_stagger: 60.