Install CloudPrem on AWS EKS

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Overview

This document walks you through the process of installing CloudPrem on AWS EKS.

Prerequisites

Before getting started with CloudPrem, ensure you have:

AWS account with necessary permissions
Kubernetes 1.25+ (EKS recommended)
AWS Load Balancer Controller installed (optional)
PostgreSQL database (RDS recommended)
S3 bucket for log storage
Datadog Agent
Kubernetes command line tool (kubectl)
Helm command line tool (helm)

Prepare your AWS environment

Before installing CloudPrem on EKS, ensure your AWS environment is properly configured. For detailed AWS configuration instructions, see the AWS Configuration guide.

Key requirements:

AWS credentials configured (IAM role or access keys)
Appropriate IAM permissions for S3 access
EKS cluster with AWS Load Balancer Controller installed
RDS PostgreSQL instance or compatible database

Create an RDS database

You can create a micro RDS instance with the following command. For production environments, a small instance deployed across multiple Availability Zones (multi-AZ) is enough.

# Micro RDS instance for testing purposes. Takes around 5 min.
aws rds create-db-instance --db-instance-identifier cloudprem-postgres --db-instance-class db.t3.micro --engine postgres --engine-version 16.3 --master-username cloudprem --master-user-password 'FixMeCloudPrem' --allocated-storage 20 --storage-type gp2 --db-subnet-group-name <VPC-ID> --vpc-security-group-ids <VPC-SECURITY-GROUP-ID> --db-name cloudprem --backup-retention-period 0 --no-multi-az

You can retrieve RDS info by executing the following shell commmands:

# Get RDS instance details
RDS_INFO=$(aws rds describe-db-instances --db-instance-identifier cloudprem-demo-postgres --query 'DBInstances[0].{Status:DBInstanceStatus,Endpoint:Endpoint.Address,Port:Endpoint.Port,Database:DBName}' --output json 2>/dev/null)

STATUS=$(echo $RDS_INFO | jq -r '.Status')
ENDPOINT=$(echo $RDS_INFO | jq -r '.Endpoint')
PORT=$(echo $RDS_INFO | jq -r '.Port')
DATABASE=$(echo $RDS_INFO | jq -r '.Database')

echo ""
echo "🔗 Full URI:"
echo "postgres://cloudprem:FixMeCloudPrem@$ENDPOINT:$PORT/$DATABASE"
echo ""

Install the CloudPrem Helm chart

Add and update the Datadog Helm repository:

helm repo add datadog https://helm.datadoghq.com
helm repo update

Create a Kubernetes namespace for the chart:
```
kubectl create namespace <NAMESPACE_NAME>
```

Store your Datadog API key as a Kubernetes secret:

kubectl create secret generic datadog-secret \
-n <NAMESPACE_NAME> \
--from-literal api-key="<DD_API_KEY>"

Store the PostgreSQL database connection string and your Datadog API key as a Kubernetes secret:

kubectl create secret generic cloudprem-metastore-uri \
-n <NAMESPACE_NAME> \
--from-literal QW_METASTORE_URI="postgres://<USERNAME>:<PASSWORD>@<ENDPOINT>:<PORT>/<DATABASE>"

Customize the Helm chart

Create a datadog-values.yaml file to override the default values with your custom configuration. This is where you define environment-specific settings such as the image tag, AWS account ID, service account, ingress setup, resource requests and limits, and more.

Any parameters not explicitly overridden in datadog-values.yaml fall back to the defaults defined in the chart’s values.yaml.

# Show default values
helm show values datadog/cloudprem

Here is an example of a datadog-values.yaml file with such overrides:

aws:
  accountId: "123456789012"

# Environment variables
# Any environment variables defined here are available to all pods in the deployment
environment:
  AWS_REGION: us-east-1

# Datadog configuration
datadog:
   # The Datadog [site](https://docs.datadoghq.com/getting_started/site/) to connect to. Defaults to `datadoghq.com`.
   # site: datadoghq.com
   # The name of the existing Secret containing the Datadog API key. The secret key name must be `api-key`.
   apiKeyExistingSecret: datadog-secret

# Service account configuration
# If `serviceAccount.create` is set to `true`, a service account is created with the specified name.
# The service account will be annotated with the IAM role ARN if `aws.accountId` and serviceAccount.eksRoleName` are set.
# Additional annotations can be added using serviceAccount.extraAnnotations.
serviceAccount:
  create: true
  name: cloudprem
  # The name of the IAM role to use for the service account. If set, the following annotations will be added to the service account:
  # - eks.amazonaws.com/role-arn: arn:aws:iam::<aws.accountId>:role/<serviceAccount.eksRoleName>
  # - eks.amazonaws.com/sts-regional-endpoints: "true"
  eksRoleName: cloudprem
  extraAnnotations: {}

# CloudPrem node configuration
config:
  # The root URI where index data is stored. This should be an S3 path.
  # All indexes created in CloudPrem are stored under this location.
  default_index_root_uri: s3://<BUCKET_NAME>/indexes

# Internal ingress configuration for access within the VPC
# The ingress provisions an Application Load Balancers (ALBs) in AWS which is created in private subnets.
#
# Additional annotations can be added to customize the ALB behavior.
ingress:
  # The internal ingress is used by Datadog Agents and other collectors running outside
  # the Kubernetes cluster to send their logs to CloudPrem.
  internal:
    enabled: true
    name: cloudprem-internal
    host: cloudprem.acme.internal
    extraAnnotations:
      alb.ingress.kubernetes.io/load-balancer-name: cloudprem-internal

# Metastore configuration
# The metastore is responsible for storing and managing index metadata.
# It requires a PostgreSQL database connection string to be provided by a Kubernetes secret.
# The secret should contain a key named `QW_METASTORE_URI` with a value in the format:
# postgresql://<username>:<password>@<host>:<port>/<database>
#
# The metastore connection string is mounted into the pods using extraEnvFrom to reference the secret.
metastore:
  extraEnvFrom:
    - secretRef:
        name: cloudprem-metastore-uri

# Indexer configuration
# The indexer is responsible for processing and indexing incoming data it receives data from various sources (for example, Datadog Agents, log collectors)
# and transforms it into searchable files called "splits" stored in S3.
#
# The indexer is horizontally scalable - you can increase `replicaCount` to handle higher indexing throughput.
# Resource requests and limits should be tuned based on your indexing workload.
#
# The default values are suitable for moderate indexing loads of up to 20 MB/s per indexer pod.
indexer:
  replicaCount: 2

  resources:
    requests:
      cpu: "4"
      memory: "8Gi"
    limits:
      cpu: "4"
      memory: "8Gi"

# Searcher configuration
# The searcher is responsible for executing search queries against the indexed data stored in S3.
# It handles search requests from Datadog's query service and returns matching results.
#
# The searcher is horizontally scalable - you can increase `replicaCount` to handle more concurrent searches.
# Resource requirements for searchers are highly workload-dependent and should be determined empirically.
# Key factors that impact searcher performance include:
# - Query complexity (for example, number of terms, use of wildcards or regex)
# - Query concurrency (number of simultaneous searches)
# - Amount of data scanned per query
# - Data access patterns (cache hit rates)
#
# Memory is particularly important for searchers as they cache frequently accessed index data in memory.
searcher:
  replicaCount: 2

  resources:
    requests:
      cpu: "4"
      memory: "16Gi"
    limits:
      cpu: "4"
      memory: "16Gi"

Install or upgrade the Helm chart

helm upgrade --install <RELEASE_NAME> datadog/cloudprem \
-n <NAMESPACE_NAME> \
-f datadog-values.yaml

Verification

Check deployment status

Verify that all CloudPrem components are running:

kubectl get pods -n <NAMESPACE_NAME>
kubectl get ingress -n <NAMESPACE_NAME>
kubectl get services -n <NAMESPACE_NAME>

Uninstall

To uninstall CloudPrem:

helm uninstall <RELEASE_NAME>

Next step

Set up log ingestion with Datadog Agent - Configure the Datadog Agent to send logs to CloudPrem