Flight API GDS Implementation
Design and optimization of API logic for GDS aggregation, fare management, and resilient search at scale.
Quick Facts
| Item | Details |
|---|---|
| Domain | Multi-GDS flight search (Amadeus, Sabre, Travelport) |
| Objective | Faster, more reliable search; clean fare selection |
| Scope | Request shaping, caching, error normalization, observability |
| Timeline | 1.5w discovery · 3w implementation · 1w rollout |
| Stack | Next.js · Node/TypeScript · Redis · Queues · OpenTelemetry |
Overview
Airlines and OTAs required a faster, more reliable search experience across multiple GDS providers. I redesigned the request/response flows, implemented layered caching, and standardized error handling to reduce latency and improve conversion.
Context
Multi-GDS environment with varying schemas and SLAs
Bursty traffic during promos; strong cost sensitivity on provider calls
Need for transparent observability to debug edge cases and fare mismatches
Challenges
Inconsistent response formats and transient provider errors
Rate limits and quota windows impacting peak-hour reliability
Duplicate results and fare de-duplication across providers
Approach
1) Request shaping and normalization
- Canonical search schema mapped per GDS adapter
- Smart retries with jitter; provider-level circuit breakers
2) Caching and cost control
- Route-level caching for popular O&D and weekend patterns
- Fare rules cache with short TTL; stampede protection
3) Response unification and de-duplication
- Merge itineraries by carrier, legs, and fare basis
- Score by total price, duration, and stop count
4) Observability
- Structured logs with correlation IDs across hops
- Traces for provider latency and error rates
Reference Architecture
- API Gateway (Edge)
- Orchestrator (Node/TypeScript) with provider adapters
- Redis for hot results + fare rules
- Background workers for enrichment and cache refresh
Outcomes
- 23–35% reduction in median search latency (p50)
- 18% fewer provider error surfaces exposed to end users
- Cleaner fare selection; fewer duplicates in top 10 results
Key Metrics
| Metric | Before | After | Delta |
|---|---|---|---|
| p50 latency | 1.40s | 0.95s | −32% |
| p95 latency | 4.80s | 3.60s | −25% |
| Provider retries | — | — | −28% |
| Duplicate fare collapse | — | — | −45% |
Tech Stack
- Next.js (frontend)
- Node/TypeScript (orchestrator)
- Redis · queue workers · OpenTelemetry · structured logging
Delivery Timeline
- Discovery & diagnostics: 1.5 weeks
- Implementation & testing: 3 weeks
- Hardening & rollout: 1 week
Stakeholders
- Product (Search, Conversion)
- Engineering (API, Platform)
- Ops/Support
Need similar GDS optimization? Book a quick call to review your current search and fare pipeline.
