Low Level Design: Travel Itinerary Service

What Is a Travel Itinerary Service?

A travel itinerary service lets users plan and manage multi-component trips: flights, hotels, car rentals, and activities stitched together in a timeline. The low-level design must handle heterogeneous booking types, time-zone-aware scheduling, conflict detection, and collaborative sharing between travelers.

Data Model

-- Core tables (SQL pseudocode)

CREATE TABLE itineraries (
  itinerary_id    BIGINT PRIMARY KEY,
  owner_user_id   BIGINT,
  title           VARCHAR(255),
  start_date      DATE,
  end_date        DATE,
  status          VARCHAR(32),  -- 'DRAFT', 'CONFIRMED', 'COMPLETED', 'CANCELLED'
  currency        CHAR(3),
  created_at      TIMESTAMP,
  updated_at      TIMESTAMP
);

CREATE TABLE itinerary_items (
  item_id         BIGINT PRIMARY KEY,
  itinerary_id    BIGINT REFERENCES itineraries(itinerary_id),
  item_type       VARCHAR(32),  -- 'FLIGHT', 'HOTEL', 'CAR', 'ACTIVITY'
  external_ref_id VARCHAR(128), -- booking ID from external service
  title           VARCHAR(255),
  start_datetime  TIMESTAMP WITH TIME ZONE,
  end_datetime    TIMESTAMP WITH TIME ZONE,
  location        VARCHAR(255),
  cost            DECIMAL(10,2),
  currency        CHAR(3),
  status          VARCHAR(32),  -- 'PENDING', 'CONFIRMED', 'CANCELLED'
  notes           TEXT,
  sort_order      INT
);

CREATE TABLE itinerary_collaborators (
  itinerary_id    BIGINT REFERENCES itineraries(itinerary_id),
  user_id         BIGINT,
  role            VARCHAR(16),  -- 'VIEWER', 'EDITOR', 'OWNER'
  invited_at      TIMESTAMP,
  accepted_at     TIMESTAMP,
  PRIMARY KEY (itinerary_id, user_id)
);

CREATE TABLE itinerary_documents (
  doc_id          BIGINT PRIMARY KEY,
  itinerary_id    BIGINT REFERENCES itineraries(itinerary_id),
  doc_type        VARCHAR(32),  -- 'TICKET', 'VOUCHER', 'PASSPORT'
  storage_url     VARCHAR(512),
  uploaded_at     TIMESTAMP
);

Core Algorithm: Conflict Detection

When a user adds or moves an itinerary item, the service checks for time overlaps against existing confirmed items. The overlap condition for two intervals A and B is: A.start < B.end AND A.end > B.start. All datetime comparisons are performed in UTC after normalizing from local time zones using the item location’s IANA timezone.

On item insert or update, load all confirmed items for the itinerary.
Convert all timestamps to UTC.
Check the new item against each existing item for overlap.
Return a list of conflicts (not a hard block, since layovers and quick transfers are valid in some cases); flag them as warnings in the UI.

Aggregation Workflow: Building the Timeline

The itinerary timeline is assembled by:

Fetching all itinerary_items for the given itinerary_id, ordered by start_datetime ASC, sort_order ASC.
Grouping items by local calendar date at the destination timezone to produce a day-by-day view.
Computing gaps between consecutive items and surfacing them as free time slots in the UI.
Aggregating total cost across all items grouped by currency, with an optional FX conversion to the itinerary base currency using a daily exchange rate lookup.

Failure Handling

Partial booking failures: If a flight books but the hotel call fails, the itinerary item for the hotel is left in PENDING status and a retry job is enqueued. The user is notified of the partial failure and can manually retry or substitute.
Cancellation propagation: Cancelling a flight that is the only way to reach a hotel destination triggers a warning that downstream hotel and activity items may need to be cancelled too. The system does not auto-cancel dependents; it surfaces a checklist.
Collaborative edit conflicts: Two editors moving the same item simultaneously use optimistic locking via an updated_at version check. The second writer receives a conflict error and must reload before saving.
Time-zone edge cases: All stored timestamps are UTC. Display conversion happens client-side using the item location timezone, preventing DST ambiguity in stored data.

Scalability Considerations

Read-heavy profile: Itinerary reads (viewing the timeline) vastly outnumber writes. Cache the assembled timeline in Redis, keyed by itinerary_id:version, and invalidate on any item write.
Document storage: Tickets and vouchers are stored in object storage (S3-compatible) with signed URLs; the database holds only the metadata reference.
Event-driven updates: External booking status changes (flight delay, hotel confirmation) arrive as webhook events and are processed by a consumer that updates the relevant itinerary_items row and pushes a real-time notification via WebSocket or push notification.
Sharing at scale: Public share links generate a read-only token stored alongside the itinerary. Shared views are served from a CDN-cached snapshot refreshed on write, eliminating database load for viral itinerary shares.
Search: Users search their past and upcoming itineraries by destination or date range. Index itineraries in Elasticsearch with denormalized location and date fields to support fast full-text and range queries without scanning the main database.

Summary

A travel itinerary service is a timeline management problem at its core. The critical design choices are: store all times in UTC and convert at display time, detect conflicts via interval overlap checks, use optimistic locking for collaborative edits, and decouple external booking status updates via an event-driven webhook consumer. Caching the assembled timeline and serving shared views from a CDN keeps the read path fast even as the number of collaborators and public shares grows.

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“text”: “Email parsing uses a combination of rule-based extractors for known sender formats (e.g., airline confirmation templates) and ML-based NER models for freeform text. Emails are routed through a dedicated inbox, parsed asynchronously by a worker service, and structured data is stored in a normalized booking schema. Confidence scores gate automatic vs. manual review.”
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“text”: “Real-time flight status is ingested from providers like FlightAware or OAG via webhooks or polling. A change detection service diffs incoming status against stored itinerary segments and publishes change events to a queue. Downstream consumers send push notifications, update the itinerary timeline, and trigger rebooking suggestions if the delay causes a missed connection.”
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