Kafka - Uber-Style Trip Event Pipeline Example

📚 Table of Contents

1. Purpose
2. Event Lifecycle
3. Event Streams (Topics)
   • events.raw (ingestion layer)
   • events.cleaned (standardized stream)
   • trip.events (business-critical stream)
   • driver.location (high-frequency updates)
   • dispatch.commands
   • billing.events
   • analytics.events (optional fan-out)
4. Processing Patterns
5. Fault Tolerance & Reliability
6. Trade Offs
7. Summary Flow Example

Uber Style Trip Event Pipeline (Detailed)

🎯 Purpose

• Handle millions of trip lifecycle events per second.
• Support real-time matching, billing, surge pricing, ETA calculations, fraud detection, and analytics.
• Guarantee low latency, reliability, and correct billing even with failures.

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Event Lifecycle

Imagine a rider books a trip → driver accepts → trip starts → trip ends → payment → receipt.
Each step generates events, and Kafka is the backbone that transports and processes them.

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Event Streams (Topics)

1. events.raw (ingestion layer)

• What it is:

  • All raw events (user requests, driver pings, trip updates, payment events, logs) land here first.
  • Similar to a staging area.

• Why:

  • Raw events may be incomplete, malformed, duplicated, or carry noise (e.g., multiple GPS pings per second).
  • By first writing to events.raw, you don’t lose anything.
  • Acts as a data lake inside Kafka — good for replay, debugging, reprocessing.

• Partitioning:

  • Usually partition by eventType (trip, location, billing).
  • Keeps ingestion scalable across multiple consumers.

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2. events.cleaned (standardized stream)

• What it is:

  • Events from events.raw get validated, deduplicated, schema-validated, enriched → written here.
  • Example:
  • GPS events consolidated to 1 ping per X seconds.
  • Trip request events checked for valid rider/driver IDs.
  • Payment events tagged with transaction IDs.

• Why:

  • Ensures downstream systems get consistent, structured data.
  • Clean separation of dirty data handling vs business logic.

• Partitioning:

  • By entity ID (e.g., tripId, driverId) → preserves ordering for that entity.

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3. trip.events (business-critical stream)

• What it is:

  • Cleaned trip lifecycle events only (request → accept → start → end → cancel).
  • Every trip’s state transitions must be ordered correctly.

• Why:

  • Dispatch engine uses this for matching riders ↔ drivers.
  • Billing service uses this to compute fare.
  • Analytics uses this to understand demand/supply.

• Partitioning:

  • By tripId → all trip updates land in same partition, preserving ordering.

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4. driver.location (high-frequency updates)

• What it is:

  • Continuous driver GPS updates (every few seconds).

• Why:

  • Needed for:
  • Nearest driver search.
  • ETA calculation.
  • Surge pricing (supply-demand ratio).

• Partitioning:

  • By driverId (to keep updates for one driver ordered).
  • Or by geoHash region (group drivers by location cells → helps load balance).

• Additional Handling:

  • Raw driver pings → cleaned → then aggregated (e.g., one location every 5 sec instead of every 1 sec).

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5. dispatch.commands

• What it is:

  • Commands from dispatch/matching system → drivers.
  • Example: “Driver 123, go pick up Rider 456”.

• Why:

  • Ensures low-latency, reliable delivery of matching decisions.
  • Acts as the control plane of the system.

• Partitioning:

  • By driverId → ensures commands to a driver are in correct order.

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6. billing.events

• What it is:

  • Financially critical events → trip started, trip ended, payment initiated, payment confirmed.

• Why:

  • Exactly-once processing required here (no double charges).
  • Must be atomic: offset commit + DB update + event publishing.

• Partitioning:

  • By tripId or paymentId.
  • Keeps billing updates for one trip ordered.

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7. analytics.events (optional fan-out)

• What it is:

  • Non-critical stream of enriched events → BI dashboards, ML pipelines.
  • Example: demand trends, heat maps, revenue reports.

• Why:

  • Offloads heavy analytical processing from core dispatch/billing topics.
  • Can tolerate slight delays.

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Processing Patterns

1. Validation & Cleaning

• events.raw → events.cleaned.
• Deduplication, schema checks, enrichment.

1. Matching & Dispatch

• events.cleaned + driver.location → dispatch system.
• Kafka Streams KTable to store active drivers.
• Rider requests matched to nearest available driver.

1. Trip Lifecycle Management

• trip.events consumed by trip state machine.
• Ensures transitions are valid (no “trip.end” without “trip.start”).

1. Billing & Payments

• trip.events + billing.events.
• Kafka transactions ensure exactly-once writes to DB + Kafka.

1. Analytics & ML

• analytics.events used for demand prediction, surge pricing, fraud detection.

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Fault Tolerance & Reliability

• Replication Factor = 3 for critical topics (trip.events, billing.events).
• Idempotent Producers = avoid duplicates.
• Kafka Transactions = atomic billing writes.
• Multi-region replication = MirrorMaker 2 for cross-region failover.

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Trade Offs

• Low latency vs Cost → more partitions & brokers give speed but cost infra.
• Exactly-once for billing only → rest can tolerate at-least-once.
• Driver location noise → must downsample to avoid flooding Kafka.

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✅ Summary Flow Example:

• Rider requests → events.raw → cleaned → trip.events.
• Driver sends GPS → events.raw → cleaned → driver.location.
• Matching system consumes both → produces dispatch.commands.
• When trip ends → billing.events → payment service.
• All events mirrored into analytics.events for BI/ML.

Please comment Netflix’s if you want:

Design Netflix’s recommendation system with Kafka?

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