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High Availability & Disaster Recovery

The VDA enterprise deployment architecture is engineered for 99.99% availability, zero-data-loss failover, and linear horizontal scalability across stateless application pods and polyglot persistence engines.

1. High Availability (HA) Architecture

flowchart TD
    subgraph LB["Enterprise High-Availability Load Balancers"]
        ALB1["Active Load Balancer"]
        ALB2["Standby Load Balancer"]
    end

    subgraph StatelessCompute["Stateless Application Cluster (K8s / OpenShift)"]
        UI["VDA Web Portal<br/>(Stateless Web Tier)"]
        Srv["VDA Control Plane<br/>(Active-Active Replicas)"]
        Meta["Core Intelligence Engine<br/>(Horizontal Pod Autoscaling)"]
        W["Distributed Worker Fleet<br/>(Dynamic Task Scaling)"]
    end

    subgraph ClusteredState["Clustered Persistence Tier"]
        PGPrimary[("Relational State Primary")]
        PGReplica[("Relational State Standby<br/>(Streaming Replication)")]
        GraphCluster[("Operational Graph Cluster<br/>(High-Availability Consensus)")]
        VectorCluster[("Vector Search Cluster<br/>(Replicated Shards)")]
        OrchestratorCluster[("Durable Orchestration Cluster<br/>(Clustered State Engine)")]
    end

    ALB1 --> UI
    ALB1 --> Srv
    ALB1 --> Meta

    Srv --> PGPrimary
    PGPrimary -->|Replication| PGReplica

    Meta --> GraphCluster
    Meta --> VectorCluster
    Srv & W --> OrchestratorCluster

Stateless Service Redundancy

  • VDA Web Portal & Control Plane: Stateless containers managed by Kubernetes Deployments with Horizontal Pod Autoscalers (HPA) configured to scale based on CPU (>70%) and incoming request rates.
  • Core Intelligence Engine: Scales horizontally behind internal service load balancers. Sessions and state are offloaded to distributed caching, relational state, and client-side verified tokens.
  • Distributed Workers: Background queues dynamically balance task execution across active worker nodes without duplicate execution or task loss.

2. Persistence Clustering & Failover

Relational State High Availability

  • Primary-Standby Replication: Continuous streaming replication to warm standby nodes.
  • Automated Failover: Managed via automated clustering and cloud-native database operators (Multi-AZ, Patroni, CloudNativePG).
  • Connection Pooling: Enterprise connection pooling sits between application services and relational datastores to handle high-concurrency workloads.

Vector Search Clustering

  • Sharding & Replication: Vector indexes are partitioned across multiple nodes with multi-way replication, guaranteeing continuous query availability even during node maintenance.
  • Snapshot Persistence: Automated disk persistence ensures vector representations are retained across pod lifecycles.

Operational Graph Engine

  • Consensus Storage: Deployed across distributed multi-node consensus clusters to maintain transactional consistency across graph edges and lineage traversals.

3. Disaster Recovery (DR) & Backup Strategy

Target Metric Enterprise Target Mechanism
Recovery Point Objective (RPO) < 15 Minutes Continuous WAL archiving for relational state, distributed write quorum for graph and vector datastores.
Recovery Time Objective (RTO) < 1 Hour Automated Kubernetes manifest redeployment via GitOps (ArgoCD / Flux) and persistent volume re-attachment.

Automated Backup Procedures

  1. Relational State:
  2. Continuous Write-Ahead Log (WAL) archiving.
  3. Daily automated logical backups encrypted with AES-256 and replicated to off-site object storage.
  4. Operational Graph Lineage:
  5. Nightly immutable snapshot backups of the complete dependency and lineage graph.
  6. Vector Search Snapshots:
  7. Scheduled hourly collection snapshots stored in durable enterprise object storage.

4. Hardware Sizing & Capacity Planning

Tier Dataset Scale Workload Profile Recommended Compute Recommended Memory Recommended Storage
Standard Up to 500 Tables
(< 5M Rows)		 Up to 25 Concurrent Users
Standard Profiling & Search		 8 vCPU 32 GB RAM 250 GB NVMe
Enterprise Up to 5,000 Tables
(< 100M Rows)		 Up to 150 Concurrent Users
Continuous Lineage & GX		 16-32 vCPU 64-128 GB RAM 1 TB NVMe
Large Bank 10,000+ Tables
(Billions of Rows)		 500+ Concurrent Users
Real-Time Kafka + OpenLineage		 64+ vCPU 256+ GB RAM 3+ TB NVMe