Introduction
As digital repositories grow, organizations increasingly need DSpace environments that can support large collections, high user concurrency, continuous ingestion, enterprise integrations, and high availability.
Traditionally, DSpace can be deployed on a small number of servers with the application, search engine, database, and storage managed together. This approach can work well for smaller repositories, but larger deployments can benefit from a more flexible architecture.
With DSpace 10, organizations can combine the DSpace platform with modern container technologies such as Docker and Kubernetes to create an architecture where individual components can be deployed, managed, and scaled independently.
This approach is particularly relevant for enterprise and institutional repositories where availability, performance, and scalability are important considerations.
A Containerized DSpace Architecture
A DSpace deployment can be divided into several logical components:
DSpace Angular frontend
DSpace backend and REST API
Apache Solr search
PostgreSQL database
Persistent digital-object storage
Load balancer / ingress
Monitoring and logging
A simplified architecture can look like:
The important concept is that these components do not have to run inside a single server or container.
They can be independently managed within a container orchestration platform.
Scaling the DSpace Application Layer
The DSpace frontend and backend can be deployed as multiple container replicas.
For example:
When the number of users or API requests increases, additional replicas can be deployed.
This provides horizontal scalability rather than depending only on a larger physical or virtual server.
For example, an environment could run two or three backend replicas during normal operation and increase the number of replicas during periods of higher demand.
The actual number of replicas and resources should be determined through performance and load testing based on the expected repository workload.
Separating Search, Database and Storage
One of the major advantages of a containerized architecture is the ability to treat DSpace's supporting services as separate infrastructure layers.
Apache Solr
Solr can be deployed independently from the DSpace backend.
For larger repositories, multiple Solr instances can be considered to improve search availability and capacity.
This also means that search resources can be scaled independently when search traffic becomes a significant part of the workload.
PostgreSQL
The PostgreSQL database should be treated as a persistent service rather than an ordinary application container.
Enterprise deployments can use a highly available PostgreSQL architecture or a managed PostgreSQL service, depending on the infrastructure environment.
Digital Object Storage
Repository files should also be maintained on persistent storage.
Separating metadata, search indexes, and digital objects provides greater flexibility for backup, scaling, and disaster recovery.
Deploying DSpace 10 on Kubernetes Platforms
The containerized architecture can be deployed on different Kubernetes-based platforms without fundamentally changing the DSpace application architecture.
OpenShift Container Platform
Organizations using Red Hat OpenShift Container Platform (OCP) can deploy the DSpace frontend, backend and supporting workloads as Kubernetes/OpenShift resources.
OpenShift can provide:
Container orchestration
Service discovery
Ingress and routing
Resource management
Health checks
Rolling deployments
Monitoring and logging
Role-based access control
This makes OCP suitable for organizations that already operate enterprise Kubernetes infrastructure.
Amazon EKS
The same architecture can also be deployed on Amazon Elastic Kubernetes Service (EKS).
In an AWS environment, Kubernetes can manage the DSpace application containers while cloud services can be used for supporting infrastructure such as load balancing, persistent storage, monitoring, backup, and managed databases.
The key benefit is that the application architecture remains portable, while the underlying infrastructure can change according to organizational requirements.
Resource Allocation and Independent Scaling
Containerization also makes it possible to allocate resources independently.
For example, the frontend may require relatively fewer resources but several replicas, while the DSpace backend may require more CPU and memory per replica.
Similarly, Solr and PostgreSQL can be provisioned according to their specific workloads.
This allows an organization to define:
CPU requests and limits
Memory requests and limits
Minimum and maximum replicas
Persistent storage requirements
Scaling policies
The result is more efficient resource utilization than simply allocating a large server to the entire DSpace application.
For stateless components such as the frontend and application layer, Kubernetes-based horizontal scaling can be used where appropriate.
Designing for High Availability
Scalability and high availability should be considered together.
A production DSpace architecture should avoid relying on a single frontend instance, backend instance, or infrastructure node.
Multiple application replicas can be distributed across available worker nodes, while the database, search layer, and storage can use their respective high-availability and replication mechanisms.
A high-level production architecture could therefore look like:
The exact implementation will depend on the organization's availability requirements, infrastructure standards, data volume, and disaster recovery objectives.
From DSpace Deployment to DSpace Platform
The biggest benefit of this approach is a change in how DSpace can be viewed.
Instead of treating DSpace simply as an application installed on a server, organizations can build a repository platform around DSpace.
The platform can combine:
DSpace 10 + Containers + Kubernetes/OpenShift/EKS + Load Balancing + Persistent Storage + Monitoring + Backup/DR
This architecture also makes future infrastructure changes easier. An organization can move between on-premises Kubernetes, OpenShift, or cloud-based Kubernetes services while retaining the same fundamental DSpace application architecture.
Conclusion
DSpace 10 can be deployed effectively in a modern containerized environment by separating its major application and infrastructure components and managing them through a Kubernetes-based platform.
Deploying the frontend, backend, Solr, database, and storage as independent layers provides greater flexibility to scale resources according to workload.
Platforms such as OpenShift Container Platform and Amazon EKS can provide the orchestration layer required for highly available and scalable deployments.
For organizations managing growing digital collections and increasing numbers of users, this approach provides a practical path from a traditional DSpace installation toward a scalable, highly available and cloud-ready repository architecture.
The goal is not simply to containerize DSpace, but to design an infrastructure in which DSpace can scale with the organization and its digital repository requirements.
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