Backup restore #33
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Backup restore #33
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This adds support for the horizontal pod autoscaler (HPA) to work with
VerticaDB. With this integration, you can have k8s automatically scale a
VerticaDB based on metrics from your workload.
We want flexibility in how we scale. Depending on the use case we are going to
look at providing two types:
- Subcluster: Scale by adding or removing subclusters. This is the preferred
approach for "dashboard" style of queries, which typically run in a short
amount of time. Multiple subclusters will all share the same service object.
This means the client connections do not have to change their endpoints.
- Pod: Scale by adding or removing pods to an existing subcluster. This is the
preferred approach for longer running analytic queries. We recommend a shard
to node ration of 2:1 or 3:1. If the ratio in the subcluster is smaller, it
could benefit from increasing the size.
To allow either of these use cases to be used, or to add support for a
different type of scaling, we are introducing a new custom resource that will
manage autoscaling of a set of subclusters. The new CR is called
VerticaAutoscaler.
We created a separate package to handle reconciliation of the new CR. It is
handle by the same operator though.
A webhook was added for this new CR.
Sample usage:
1. Create your VerticaDB as normal. Be sure to include a CPU resource setting
if scaling by CPU percent. Note, to scale beyond 3 nodes be sure to include
your license.
kubectl apply -f config/samples/v1beta1_verticadb.yaml
2. Create the VerticaAutoscaler to indicate how we want to scale. The default
in this file will scale by adding/removing subclusters.
kubectl apply -f config/samples/v1beta1_verticaautoscaler.yaml
3. Create the HPA object to tell k8s to autoscale.
kubectl autoscale verticaautoscaler/verticaautoscaler-sample --cpu-percent=70 --min=3 --max=6
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This PR is just to stay in sync with main branch.