The default configurations of openwhisk deployment, support low concurrency-limit which can only be used for testing purposes. This document outlines how this concurrency-limit can be increased to scale-up openwhisk deployment for more practical use, on custom-built-kubernetes cluster. Also, provides information regarding some issues one might encounter while scaling-up.
By default, openwhisk deployment is configured to provide a bare-minimum working platform for testing and exploration. For your specialized workloads, you can scale-up your openwhisk deployment by defining your deployment configurations in your
mycluster.yaml which overrides the defaults in
helm/openwhisk/values.yaml. Some important parameters to consider (for other parameters, check
helm/openwhisk/values.yaml and configurationChoices):
actionsInvokesPerminute: limits the maximum number of invocations per minute.
actionsInvokesConcurrent: limits the maximum concurrent invocations.
containerPool.userMemory: total memory available per
Invokeruses this memory to create containers for user-actions. The concurrency-limit (actions running in parallel) will depend upon the total memory configured for
containerPooland memory allocated per action (
default:256mb per container).
triggersFiresPerminute: limits the maximum triggers invoked per minute.
Modifying the above mentioned parameters, one can easily increase the concurrency-limit (
default: 8) to
200 without affecting the runtime performance (may vary based on the running functions). To further increase the concurrency-limit, check
Large scale-up below.
In order to further increase the scale-up beyond
Small Scale, one needs to modify the following additional configurations appropriately (on top of the above mentioned):
invoker:jvmHeapMB: jvmHeap memory available to each invoker instance. May or may not require increase based on running functions. For more information check
invoker:containerFactory:_:replicaCount: number of invoker instances that will be used to handle the incoming workload. By default, there is only one invoker instance which can become overwhelmed if workload goes beyond a certain threshold.
controller:replicaCount: number of controller instances that will be used to handle the incoming workload. Same as invoker instances.
invoker:options: Log processing at the invoker can become a bottleneck for the KubernetesContainerFactory. One might try disabling invoker log processing by setting it to
-Dwhisk.spi.LogStoreProvider=org.apache.openwhisk.core.containerpool.logging.LogDriverLogStoreProvider. In general, one needs to offload log processing from the invoker to a node-level log store provider if one is trying to push a large load through the system.
On the client-side, the most frequently received error:
"error": "The server is currently unavailable (because it is overloaded or down for maintenance).
The above mentioned error occurs when controller is unable to find any healthy invoker instance to serve the incoming requests. To resolve this issue, one needs to debug the
Deployment-side to figure-out the cause for unhealth invoker instances.
For debugging, one needs to identify the
controller pods and check their logs for further details. Few known errors:
class io.fabric8.kubernetes.client.KubernetesClientTimeoutException - Timed out waiting for  milliseconds for [Pod] with name
The above error occurs when one has configured too large a
containerPool to match the incoming workloads, without configuring the scale-up for the invoker instance(s) to keep up with the serving rate.
java.lang.OutOfMemoryError: Java heap space
The above error occurs when the configured
invoker:jvmHeapMB memory is insufficient for the faced workload.
OpenWhisk treats blackbox (docker) actions differently when compared to regular actions. By default, OpenWhisk loadbalancer is configured to use only
10% (only 1 invoker-instance if total invoker-instances are less than 10) of invoker instances for
blackbox actions. This behavior can be configured by modifying