* Retrieve pod metrics only if metrics server is running
Currently we query pod metrics every 30 sec which floods executor logs,
added check which confirms if metrics server is running then only we start
querying pod metrics for identifying CPU utilization.
Signed-off-by: Sanket Sudake <sanketsudake@gmail.com>
* Fixed couple of typos and misspells with Go CI
Signed-off-by: Sanket Sudake <sanketsudake@gmail.com>
* Remove unnecessary conversions with Go CI
Signed-off-by: Sanket Sudake <sanketsudake@gmail.com>
* Add a SharedIndexInformer for services and deployments to NewDeploy executor.
This brings the NewDeploy executor behaviour into sync with GenericPoolManager behaviour
by caching Kubernetes services and deployments used in per-request function validation.
* Create informers in executer by executor label
Signed-off-by: Sanket Sudake <sanketsudake@gmail.com>
Co-authored-by: James Sinclair <irgeek@btc.com.au>
Adding once only execution pattern to pool manager based functions. This is for use cases where you don't want to share the samne pod for another execution instance!
Websocket event support for cleaning up pods only after WS connection is terminated. The support for websocket is right now in the environment itself and router simply acts as a proxy for WS communication!
Increase the default concurrency to 500 from 5 set in the function. Usually, it is good to error on the higher side and if needed, users can always fine-tune it to their needs.
This feature enables routing more than one request to a pod at the same time. This is the first draft of the work and might involve more optimizations later.
Passing information of function from router to executor is more efficient than calling the K8S API. This change does that instead of passing only metadata and then executor calling the K8S API again.
A ready pod which can be specialized was fetched for every function earlier, this has been changed to a queue and cache implementation in client-go to improve performance.
Poolmanager when tested at high load had some issues and this PR fixes one set of them which were found so far.
Co-authored-by: Vishal <vishal-biyani@users.noreply.github.com>
Concurrency in the pool manager allows specializing pods concurrently based on a specified limit.
Co-authored-by: Vishal <vishal-biyani@users.noreply.github.com>
Attribute enableServiceLinks can be now configured in PodSpec so that environment variables are not injected in function pods and DNS is used for discovery
Executor wrongly deletes role bindings if the user
creates an environment in the reserved namespaces. This
PR is a quick fix to solve the problem by checking if
an environment is under reserved namespaces.
The root cause of the problem is that eagerPoolCreator
tries to create the deployment when the poolmanager
is trying to delete it. To avoid this, start eager pool
creator after executor starts serving requests.
The pod template is embedded inside the deployment. So if
the pod annotation contains instance-id, the deployment
will get updated and thus triggers a rolling update whenever
a new executor starts which is unwanted.
After this PR, poolmanager will patches instance-id when a
pod is chosen for a function.
For newdeploy, unlike poolmanager manages the lifecycle
of function pod directly, newdeploy is only responsible
to create the deployment so we append instance-id to top-
level controller (deployment) only.
We used to update timestamp in the deployment environment field
in order to trigger a rolling update when the function referenced
resources get updated. However, use timestamp means we are not
able to avoid triggering a rolling update when executor tries to adopt
orphaned deployment due to timestamp changed which is unwanted.
In order to let executor adopt deployment without triggering a rolling
update, we need an identical way to get a value that can reflect res-
ources changed without affecting by time.
To achieve this goal, the sum of the resource version of all referenced
resources is a good fit for our scenario since the sum of the resource
version is always the same as long as no resources changed.
When a new executor starts up, it adopts the orphan kubernetes resources created
by the old executor instance. However, the adopted resource won't reflect the changes
come with the new executor, for example, the fetcher image inside won't be changed.
To solve this, executor updates the resource spec (HPA/Deployment/Service) with the
latest resources spec. By doing this, we can prevent the inconsistency between resources
created by different executor instance, also minimizes the impact on users.
Previously, once the executor is deleted for reasons (like upgrade or cluster scale-in),
the new executor deletes all existing resources created by the old executor and creates
new one. This mechanism becomes a problem when there are requests connecting to the
existing pods. Also in the worst case, the cluster may not have enough resources to create
new pods and cause service downtime.
This PR let each executor type adopts existing resources before starting the executor
API services, and so the alive connections won't experience failure. However, the requests
send to the function that doesn't have alive function pods will still fail due to the
executor is in bootstrapping.
The pool manager keeps terminating function pod periodically even there are
traffic to the function. The root cause is that executor, poolmgr, newdeploy
manage their own functionServiceCache separately. And when router taps a
function, executor updates the access time of the function service entry in its
own cache without notifying executor types to do the update as well. Hence,
the access time of function service entry in poolmanager cache never gets updated.
Due to the access time never gets updated, the idle pod reaper in poolmanager
then thinks the function pod is in idle state and recycle it.
This PR removes the cache in executor itself, and when router tries to tap a function,
executor will call executor type to tap the function and update access time.
The router taps function service one by one which is inefficient and
increases the burden of executor. This PR aggregates all requests into
one to solve the problem mentioned above.
When a function is created before the creation of the environment it's used, the newdeploy will not be able to create kube objs. Hence no function service record is inserted into the cache.
When getFuncSvc is called, the newdeploy tries to find the record in service cache in order to create kube objs with the same name used in previous kubeobjs creation. However, due to no record in the cache, a NotFound error is returned directly and causes the problem. To solve this, we use fn meta UID to ensure we always get the same obj name instead of getting it from the cache.
