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✈️ Travel & Hospitality · Deployment · Pro

Travel Aggregator Deployment

How an online travel aggregator is deployed to handle search spikes: search fan-out to suppliers, caching, and separate booking services.

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Deployment diagrams are part of Pro. Anyone can view this one; generating and editing it needs Pro.

Drawing diagram…

What this diagram shows

  • Search fans out to many airline and hotel suppliers in parallel
  • A fare cache absorbs repeated searches
  • Booking services run separately so search load can't block payments

Prompt used

Deployment for an online travel aggregator on Kubernetes: users hit a CDN and API gateway; the search service fans out requests to supplier connectors (GDS, airline NDC APIs, hotel bed banks) and caches results in Redis for a few minutes. Booking and payment services run in a separate node pool with their own database. Kafka carries booking events to notifications and analytics.

Mermaid code
flowchart TB
  U[Users] --> CDN[CDN]
  CDN --> GW[API Gateway]
  subgraph K8s[Kubernetes Cluster]
    subgraph SearchPool[Search node pool - autoscaling]
      SS[Search Service]
      SC[Supplier Connectors]
    end
    subgraph BookPool[Booking node pool]
      BS[Booking Service]
      PS[Payment Service]
    end
    NOTI[Notification Service]
  end
  REDIS[(Redis Fare Cache)]
  PG[(Bookings DB)]
  KAFKA[Kafka]
  SUP[GDS, airline NDC, hotel bed banks]
  GW --> SS
  SS --> REDIS
  SS --> SC --> SUP
  GW --> BS
  BS --> PS
  BS --> PG
  BS --> KAFKA --> NOTI

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