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Version: 3.9

Production checklist for ScalarDB Cluster

This checklist provides recommendations when deploying ScalarDB Cluster in a production environment.

Before you begin​

In this checklist, we assume that you are deploying ScalarDB Cluster on a managed Kubernetes cluster, which is recommended.

Production checklist: ScalarDB Cluster​

The following is a checklist of recommendations when setting up ScalarDB Cluster in a production environment.

Number of pods and Kubernetes worker nodes​

To ensure that the Kubernetes cluster has high availability, you should use at least three worker nodes and deploy at least three pods spread across the worker nodes. You can see the sample configurations of podAntiAffinity for making three pods spread across the worker nodes.

note

If you place the worker nodes in different availability zones (AZs), you can withstand an AZ failure.

Worker node specifications​

From the perspective of commercial licenses, resources for one pod running ScalarDB Cluster are limited to 2vCPU / 4GB memory. In addition, some pods other than ScalarDB Cluster pods exist on the worker nodes.

In other words, the following components could run on one worker node:

  • ScalarDB Cluster pod (2vCPU / 4GB)
  • Envoy proxy (if you use indirect client mode or use a programming language other than Java)
  • Your application pods (if you choose to run your application's pods on the same worker node)
  • Monitoring components (if you deploy monitoring components such kube-prometheus-stack)
  • Kubernetes components
note

You do not need to deploy an Envoy pod when using direct-kubernetes mode.

With this in mind, you should use a worker node that has at least 4vCPU / 8GB memory resources and use at least three worker nodes for availability, as mentioned in Number of pods and Kubernetes worker nodes.

However, three nodes with at least 4vCPU / 8GB memory resources per node is the minimum for a production environment. You should also consider the resources of the Kubernetes cluster (for example, the number of worker nodes, vCPUs per node, memories per node, ScalarDB Cluster pods, and pods for your application), which depend on your system's workload. In addition, if you plan to scale the pods automatically by using some features like Horizontal Pod Autoscaling (HPA), you should consider the maximum number of pods on the worker node to decide on the worker node resources.

Network​

You should create the Kubernetes cluster on a private network since ScalarDB Cluster does not provide any services to users directly via internet access. We recommend accessing ScalarDB Cluster via a private network from your applications.

Monitoring and logging​

You should monitor the deployed components and collect their logs. For details, see Monitoring Scalar products on a Kubernetes cluster and Collecting logs from Scalar products on a Kubernetes cluster.

Backup and restore​

You should enable the automatic backup feature and point-in-time recovery (PITR) feature in the backend database. For details, see Set up a database for ScalarDB/ScalarDL deployment.

Production checklist: Client applications that access ScalarDB Cluster​

The following is a checklist of recommendations when setting up a client application that accesses ScalarDB Cluster in a production environment.

Client mode (Java client library only)​

When using Java for your application, you can use an official Java client library. In this case, you can choose one of the two client modes: direct-kubernetes mode or indirect mode.

From the perspective of performance, we recommend using direct-kubernetes mode. To use direct-kubernetes mode, you must deploy your application pods on the same Kubernetes cluster as ScalarDB Cluster pods. In this case, you don't need to deploy Envoy pods.

If you can't deploy your Java application pods on the same Kubernetes cluster as ScalarDB Cluster pods for some reason, you must use indirect mode. In this case, you must deploy Envoy pods.

note

The client mode configuration is dedicated to the Java client library. If you use a programming language other than Java for your application (essentially, if you use the gRPC API or gRPC SQL API directly from the programming language), no such configuration exists. In this case, you must deploy Envoy pods.

Transaction manager configuration (Java client library only)​

The client application must always access the database through ScalarDB Cluster. To ensure requests are running properly, check the properties file for your client application and confirm that scalar.db.transaction_manager=cluster is configured when using the CRUD API.

SQL connection configuration (Java client library only)​

The client application must always access the database through ScalarDB Cluster. To ensure requests are running properly, check the properties file for your client application and confirm that scalar.db.sql.connection_mode=cluster is configured when using the SQL API.

Deployment of the client application when using direct-kubernetes client mode (Java client library only)​

If you use direct-kubernetes client mode, you must deploy your client application on the same Kubernetes cluster as the ScalarDB Cluster deployment.

Also, when using direct-kubernetes client mode, you must deploy additional Kubernetes resources to make your client application work properly. For details, see Deploy your client application on Kubernetes with direct-kubernetes mode.

Transaction handling (Java client library and gRPC API)​

You must make sure that your application always runs commit() or rollback() after you begin() a transaction. If the application does not run commit() or rollback(), your application might experience unexpected issues or read inconsistent data from the backend database.

note

If you use the gRPC API or SQL gRPC API, your application should call a Commit or Rollback service after you call a Begin service to begin a transaction.

Exception handling (Java client library and gRPC API)​

You must make sure that your application handles transaction exceptions. For details, see the document for the API that you are using: