Scale your Cluster

How to scale Hydrolix with Kubernetes

Basic Scaling

Scaling a component's resources or replica count is done within the scale: section of your hydrolixcluster.yaml file. Hydrolix has both stateful and stateless components, and the scaling of these components need to take this into account.

Rather than require you to scale each of the many components by hand, we provide pre-made Scale Profiles that work for various throughput levels. For instance, scale_profile: prod provides components scaled for a typical 1-4 TB/daily workload.

You can always override a component's scale profile setting by providing your own setting in the scale section of the hydrolixcluster.yaml file.

Stateful Components

These components have a data_storage scale key:

prometheusReporting and Control

Stateless Components

These components all have cpu, memory & storage scale keys along with replicas:

alter-peerAlter Jobsquery-headQuery
batch-peerIngestreaperData Lifecycle
decayData Lifecyclestream-headIngest
operatorKubernetes Operator

To edit the file, you can use this command

kubectl edit hydrolixcluster --namespace="$HDX_KUBERNETES_NAMESPACE"


Stateful Persistent Volume changes

You can only increase persistent volume storage, decreasing the PVC size will not work.

Scale Settings

The following settings can be used to set the resource values of a pod

cpuAmount of CPU to use for the pod/containercpu: 2
cpu: 2.5
memoryThe amount of RAM to use for the pod/containermemory: 500Gi
storageThe amount of ephemeral storage to be used: note this type of storage is not statefulstorage: 10Gi
data_storageFor those pods that use PVC's, to scale the PVC you can use the data_storage keydata_storage: 1TB

Specifying Pods and Their Settings

To modify a pod's individual settings in your hydrolixcluster.yaml, the pod component name can be given and the setting you wish to apply can be added.

For example, this modifies the stream-head pods to have 2 CPUs and 10 Gibibytes of RAM allocated:

       cpu: 2
       memory: 10Gi

Some components have multiple containers within a pod. Typically these containers will be seen as <service>-peer and a turbine container. For example, stream peers have a container called stream-peer and a container called turbine. Turbine is the "Indexer" component that executes some transformation and indexes the content.
Any setting you use with the default name (e.g. stream-peer) will only define the stream-peer component. It will not edit the turbine component.

Specify the turbine component in your hydrolixcluster.yaml file using the name <component>-indexer. For example, for stream, use stream-indexer. A list of the different pods can be found here.

Scale Profiles

Specify a scale_profile: key in your hydrolixcluster.yaml file with a value of prod or mega.

  • prod - a fully resilient production deployment (1-4 TB/day)

  • mega - a fully resilient large scale production deployment (10-50 TB/day)

An example of this setting in the hydrolixcluster.yaml file is shown below.

kind: HydrolixCluster
  name: hdxcli-xxxyyyy
  namespace: hdxcli-xxxyyyy
  admin_email: [email protected]
  kubernetes_namespace: hdxcli-xxxyyyy
  kubernets_profile: gcp
  db_bucket_region: us-central1
  scale_profile: prod  <--- For the Prod Profile

Once you kubectl apply -f this change the system will be automatically scaled.

You can view details about each of these scale profiles.

Scale Overriding

You will most likely need to override some of the components scale settings. You may need more instances of a type of component (horizontally scaling), or you may need more resources (vertical scaling). Both needs are served by the scale: section of your hydrolixcluster.yaml file.

Lets say you needed to scale the batch-peer component, as your task requires five instances and more memory that the scale profile provided. The prod scale profile provides two Gi memory and one replica. Edit the hydrolixcluster.yaml to add this override:

  scale_profile: prod
      memory: 5G
    	replicas: 5

Once ready, the configuration is applied as follows:

kubectl apply -f hydrolixcluster.yaml


Services with PVC Storage

Some of the services Hydrolix uses need to maintain state in order to provide high availability and redundancy. The postgres and redpanda services use PVC storage, so when setting storage overrides you should specify storage using the data_storage key.

PVC changes are also significant changes, so we recommend talking to us before making them.


Don't forget to apply your changes

kubectl apply -f hydrolixcluster.yaml

Scaling to Zero

If you want to turn everything in your clsuter off, you can do that by adding the following into the top level spec:
scale_off : true

  kubernets_profile: gcp
  db_bucket_region: us-central1
  scale_off : true  <--- Turn everything off

This will turn all stateless components off that can be turned off. It will take a couple of minutes for all those components to shut down. This should be used with care.

Configuring Query Peer Pools

Hydrolix has the capability to create pools of services for specific workloads. Each pool has a ring-fenced capacity so that a workload will not be affected by other processes or users querying data or ingesting data.

For example, a query-pool can be created for a subset of users that is accessible only by them, with another pool being used by everyone else. Similarly you can configure a specific pool of Kafka servers which be used solely for ingesting server logs, and another Kafka pool used for performance logs.

This capability allows independent access and scaling of workload components without the need to overprovision a single architecture. In addition to query server components, Role Based Performance Control gives users access to different levels of performance based on their role within a company.

To create a pool, specify the pool name in the hydrolixcluster.yaml configuration file:

apiVersion: v1
- apiVersion:
  kind: HydrolixCluster
    - name: demo-pool
      replicas: 1
      service: query-peer
      cpu: "1.0"

Within the pools: configuration, specify the scale similarly to the scale: configuration above.