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Multi-Cluster Deployment Requirements and Limitations⚓︎

Multi-cluster deployment extends Kubernetes scalability on Linode infrastructure. This page helps determine if multi-cluster architecture is right for your Linode deployment.

When to use multi-cluster deployment⚓︎

Use Hydrolix multi-cluster deployment when approaching Kubernetes scaling limits on Linode infrastructure. The primary indicator is node count: when reaching 125 or more nodes on Linode LKE or planning growth beyond 250 nodes, multi-cluster architecture becomes necessary. At this scale, the Kubernetes API becomes unresponsive under load, causing Traefik service discovery failures and system-wide instability.

High data ingestion rates also drive multi-cluster adoption. Sustained data ingest rates of 4 GB/second or more typically require the workload isolation that multi-cluster provides. Deployments with multiple high-volume data streams benefit from distributing intake across dedicated clusters. This allows separate tuning and minimizes partition fragmentation, which helps query performance.

Linode's infrastructure characteristics make multi-cluster beneficial for large-scale deployments. The maximum node size of 48 CPU and 96GB RAM defines vertical scaling options. Multi-cluster architecture, combined with workload isolation between intake, query, and merge operations, supports large-scale Linode deployments.

When not to use multi-cluster⚓︎

Multi-cluster deployment adds operational complexity. For Linode deployments under 125 nodes, try these alternatives first:

  • Optimize a single cluster by tuning intake pools, query pools, and resource allocation
  • Use vertical scaling with larger Linode node types (up to 48 CPU and 96GB RAM)
  • Configure dedicated resource pools for different workload types to achieve isolation without managing multiple clusters

Why Linode requires multi-cluster⚓︎

Multi-cluster deployment is specifically designed for Linode LKE infrastructure to extend platform capabilities:

  • Kubernetes stability threshold: Linode LKE clusters can become unstable above 250 nodes. The Kubernetes API becomes unresponsive at this scale, causing system-wide failures that can take hours to recover from. Since Traefik relies on the Kubernetes API for service discovery, API failures cascade throughout the system, taking down ingress routing and making the cluster effectively unusable.
  • Resource constraints: The maximum node size of 48 CPU and 96GB RAM provides less vertical scaling headroom than larger instance types available on other platforms.

Benefits and tradeoffs⚓︎

Multi-cluster deployments on Linode offer scaling advantages but introduce operational complexity.

Benefits⚓︎

Multi-cluster architecture extends beyond Linode's 250-node single-cluster configuration, enabling deployments that support data ingestion rates of 10 GB/second or more and handle billions of events each hour. Distributing workloads across multiple clusters maintains Kubernetes API responsiveness and system stability at scale.

Workload isolation improves both performance and reliability. Dedicated clusters for each major data stream minimize partition fragmentation across intake clusters and allow separate tuning for intake, merge, and query workloads. Query workloads remain isolated from high-throughput intake operations, preventing resource contention and maintaining consistent query performance.

Performance optimization benefits include larger partitions created at intake time, better real-time query performance, and reduced partition fragmentation. Each workload type can scale independently based on its specific requirements without affecting other components of the system.

Tradeoffs⚓︎

Operational complexity increases significantly with multiple Linode LKE clusters. Managing and monitoring separate clusters, each with its own Prometheus instance, requires more effort. Alerting and troubleshooting become more complex across clusters, requiring coordination across infrastructure teams and more sophisticated monitoring strategies.

Component coordination introduces distributed system complexity. Keycloak cache synchronization across clusters can create authentication inconsistencies. Running a RabbitMQ in each cluster that serves batch, autoingest, or data lifecycle services increases infrastructure overhead. Cross-cluster service discovery adds complexity to the networking layer. These distributed system challenges result in longer troubleshooting cycles when issues span multiple clusters.

Next steps⚓︎

For architectural details about how multi-cluster works, see Multi-Cluster Deployment Overview.

For deployment configuration and setup on Linode, contact Hydrolix support.