Mastering MAANG System Design Interviews: A Complete Guide with Real-World Case Study
What You'll Learn: This comprehensive guide distills lessons from dozens of MAANG- level system design interview sessions into a structured framework you can apply immediately. We'll walk through a complete ride-share app design case study, examine what separates strong candidates from great ones, and provide the mathematical rigor and technical depth that top-tier interviewers expect. Whether you're preparing for Google, Meta, Amazon, or Uber, this guide covers structure, calculations, architecture patterns, and expert insights to help you succeed.
What You'll Learn: This comprehensive guide distills lessons from dozens of MAANG- level system design interview sessions into a structured framework you can apply immediately. We'll walk through a complete ride-share app design case study, examine what separates strong candidates from great ones, and provide the mathematical rigor and technical depth that top-tier interviewers expect. Whether you're preparing for Google, Meta, Amazon, or Uber, this guide covers structure, calculations, architecture patterns, and expert insights to help you succeed.
Part 1: The Big Picture — What Is a System Design Interview?
A system design interview is a 45–60 minute session where you are expected to architect a scalable, reliable, and maintainable system from scratch. Unlike coding interviews, there is no single correct answer. Interviewers are evaluating your thought process, communication, trade- off reasoning, and technical depth.
1.1 What Interviewers Are Actually Evaluating
1.2 SWE vs. Data Engineer System Design
The ride-share app mock interview with Sirisha falls primarily in the SWE system design category — designing services, APIs, WebSocket connections, and stateful session management. However, the principles of structured thinking, math, and trade-off reasoning apply universally.
Part 2: The 4-Step Framework for System Design Interviews
Every strong system design answer follows a consistent structure. Use this as your mental checklist during any interview.
1.Step 1 — Ask Clarifying Questions (3–5 minutes)
Identify functional requirements (what the system must do)
Identify non-functional requirements (scale, latency, availability, consistency)
Call out assumptions explicitly
Ask about data sources, outputs, and how data will be consumed
2.Step 2 — Design High-Level Architecture (10–15 minutes)
Identify 1–2 key metrics to anchor your design
Outline main components and data flow end-to-end
Define storage strategy: batch vs. streaming, ETL vs. ELT
Sketch a block diagram on the whiteboard
3.Step 3 — Drill Down on Critical Components (15–20 minutes)
Pick 1–2 components to go deep on
Address bottlenecks, race conditions, failure modes
Discuss trade-offs between alternatives
Handle real-world issues: late data, duplicates, data quality
4.Step 4 — Bring It All Together (5 minutes)
Revisit requirements — does your design meet them?
Summarize key decisions and trade-offs
Highlight how the system handles failure and scales
Define success metrics
Part 3: Case Study — A Real Ride-Share App System Design Interview
3.1 What Strong Candidates Do Well
3.2 Common Areas for Improvement
Part 4: The Ride-Share App — Full System Design Walkthrough
4.1 Functional Requirements
Rider books a cab via mobile app; driver receives and accepts the request
One driver assigned to one booking — no double assignment
Real-time location tracking of driver during the ride
Payment processed after ride completion
Rider rates driver; tipping options provided (10%, 15%, custom)
Support for solo rides and shared rides along the same route
Rider preferences: car size, luggage, gender of driver
4.2 Non-Functional Requirements
Scale: 500,000 ride requests per minute globally
Availability: 99.99% uptime (≈ 52 minutes downtime/year)
Latency: Driver match delivered to rider in < 3 seconds
Consistency: Strong consistency for driver assignment (no double booking); eventual consistency for location updates
Durability: No ride data lost even during partial system failure
Real-time: Driver location updated every 3–5 seconds
4.3 Back-of-Envelope Calculations
Scale Estimation
Storage Estimation
Server Estimation
4.4 System Architecture Block Diagram
4.5 Core Services Deep Dive
Service 1: Location Service (GPS Ingestion)
The Location Service is the most write-intensive component. Every active driver emits a GPS ping every 3–5 seconds.
Protocol: WebSocket (persistent, bidirectional) — not REST, because polling would create 2.5M HTTP requests/second
Storage: Redis with H3 Spatial Index — drivers are bucketed into hexagonal grid cells (H3 resolution 8 ≈ 0.7 km² per cell)
TTL: Driver location entries expire after 30 seconds of no update (driver went offline)
Write path: Driver App → WebSocket Gateway → Kafka → Location Service → Redis H3 Index
Service 2: Ride Matching Engine (H3 Spatial Search)
When a rider requests a ride, the matching engine finds the 6 nearest available drivers using the H3 hexagonal spatial index.
Why H3? H3 (Uber's open-source grid system) divides the Earth into hexagons. Hexagons have equal distance to all neighbors — unlike squares, which have diagonal distortion. This makes proximity searches faster and more accurate.
Search radius: Start at H3 resolution 9 (≈ 0.1 km²), expand outward if fewer than 6 drivers found
Filtering: Apply rider preferences (car type, shared/solo, luggage) to the candidate set
Output: Ranked list of up to 6 drivers with ETA, car type, and rating
Service 3: Trip & State Management
This is the source of truth for active rides. It manages the full lifecycle of a trip.
Service 4: Payment Service
Stateless REST API calls (not WebSocket) — payment is a one-time event, not a stream
Integrates with Stripe/PayPal via tokenized payment credentials (never store raw card numbers)
Stores only last 4 digits + auth token in the transactional DB
Handles tipping: fixed percentages (10%, 15%, 20%) or custom amount
Maintains ride history for both rider and driver
4.6 Preventing Race Conditions — Double Assignment Problem
Solution: Optimistic Locking with Conditional Write in Redis
4.7 WebSocket Failure Handling
Recommended Answer:
1.Redundant WebSocket Pods: Deploy N WebSocket gateway pods behind the NLB. If one pod fails, the NLB routes new connections to healthy pods. Existing connections on the failed pod reconnect automatically (client-side retry with exponential backoff).
2.Session Registry Replication: Redis is deployed as a Redis Cluster (3 primary + 3 replica nodes across availability zones). If one node fails, a replica is promoted within seconds.
3.Persistent Fallback: Trip state is asynchronously written to PostgreSQL/DynamoDB every 10 seconds. If Redis is completely lost, the Trip & State Management service can reconstruct active trips from the DB (with up to 10 seconds of staleness).
4.Client-Side Reconnection: Both rider and driver apps implement WebSocket reconnection with exponential backoff (1s, 2s, 4s, 8s…). Upon reconnect, the client sends its last known trip_id and the server restores state from the session registry or DB.
5.Multi-Region Deployment: Each geographic region has its own WebSocket cluster. A full regional outage triggers DNS failover to the nearest healthy region (Route 53 health checks or equivalent).
Part 5: MAANG-Level Interview Questions & Model Answers
Below are the types of questions senior interviewers at Google, Meta, Amazon, or Uber frequently ask, along with model answers that demonstrate the depth and precision expected at this level.
Q1: What is the source of truth for trip state — the message bus, the cache, or the database?
Model Answer: For active trips, the source of truth is the Session Registry (Redis) and the Spatial Index (H3/Redis). These are low-latency, in-memory stores that reflect the current real-time state. The Transactional DB (PostgreSQL/DynamoDB) is the source of truth for completed trips — it stores immutable records of payments, ratings, and ride history. The message bus (Kafka) is not a source of truth; it is a durable event log used for decoupling services and enabling replay. If Redis goes down, we reconstruct state from Kafka's event log or the DB's last checkpoint.
Q2: How does your system handle a driver whose car breaks down mid-trip?
Model Answer: The Trip & State Management service monitors driver location updates via WebSocket heartbeats. If no location update is received for > 3 minutes AND the driver's last known speed was 0, the service transitions the trip to a DEGRADED state. It then: (1) sends a push notification to the driver asking for a status update, (2) notifies the rider of a potential delay, (3) if no response within 5 minutes, cancels the trip, issues a full refund to the rider, and re-enters the rider into the matching queue with priority. The driver's session is marked UNAVAILABLE in Redis.
Q3: What is the session registry schema and what is the TTL?
Model Answer: The session registry is a Redis hash. Key: session:{trip_id}. Fields: driver_id, rider_id, driver_status (AVAILABLE/LOCKED/IN_TRIP), last_location (lat/lon), last_heartbeat_ts, trip_state, websocket_node_id. TTL: 2 hours (maximum trip duration). For driver availability entries (not yet in a trip), TTL is 30 seconds — refreshed with each GPS ping. If the TTL expires without a refresh, the driver is considered offline and removed from the spatial index.
Q4: How do you handle 500,000 ride requests per minute without overwhelming the matching engine?
Model Answer: Requests are first buffered in Kafka (or NATS) before reaching the Ride Matching Engine. Kafka acts as a shock absorber — it can absorb millions of events per second and deliver them to consumers at a controlled rate. The matching engine is horizontally scaled (auto-scaling pods). Geographic partitioning means each regional cluster handles only a fraction of global traffic. Additionally, the H3 spatial index lookup is O(1) for a given cell, making each match operation extremely fast (< 10ms). With 8,333 requests/second and 10ms per match, we need ~84 concurrent matching threads — easily handled by 10–20 pods.
Q5: How do you prevent a shared ride from being assigned to incompatible routes?
Model Answer: When a rider opts into a shared ride, the Ride Matching Engine checks the driver's current route (stored in the session registry) against the new rider's pickup and dropoff coordinates. It uses a route deviation threshold — if adding the new rider increases the driver's total route by more than X% (e.g., 20%), the driver is not offered as a shared option. Only drivers whose existing route passes within Y meters of the new rider's pickup are eligible. This is computed using the H3 grid — riders and drivers in the same or adjacent H3 cells are candidates for route overlap analysis.
Part 6: Common System Design Topics — Quick Reference
6.1 Load Balancers: Layer 4 vs. Layer 7
6.2 WebSocket vs. REST API — When to Use Which
6.3 Kafka as an Event Buffer — Why It Matters
Decoupling: Producers (apps) and consumers (services) are independent — a slow matching engine doesn't block GPS ingestion
Durability: Messages are persisted to disk — if a service crashes, it can replay from its last offset
Conclusion: What Makes a Great System Design Interview
After coaching hundreds of engineers through system design interviews at Grounow, I've seen a clear pattern: the candidates who succeed aren't always the ones with the most years of experience — they're the ones who can think out loud, justify their decisions, and anchor every design choice in real numbers.
The mock interview that inspired this guide was no different. The candidate demonstrated strong technical instincts — they correctly identified the need for WebSockets, spatial indexing, and distributed state management. But what stood out most was their willingness to pause, think, and respond thoughtfully under pressure. That's a skill you can practice.
At Grounow, we run sessions like this regularly because we believe that structured practice, honest feedback, and repeated exposure to hard questions are what bridge the gap between knowing the theory and performing confidently in the room. This guide is an artifact of that process — a real session, real feedback, and real lessons distilled into a framework you can use.
If there's one thing I want you to take away, it's this: System design interviews are not about memorizing architectures — they're about demonstrating structured thinking, mathematical rigor, and trade-off reasoning. Use the 4-step framework. Write the math on the whiteboard. Ask clarifying questions. Control the scope. And remember: the interviewer is not testing your ability to build Uber in 45 minutes — they're testing your ability to think like a senior engineer.
— Suraj Ethirajan, Coach, Grounow
A Final Word: Your Next Step
If you're reading this, you're already ahead of most candidates. You're investing time in learning the craft, not just cramming patterns the night before.
Here's my advice: Don't just read this guide — practice it. Pick a system you use every day (Instagram, Netflix, Slack) and design it on a whiteboard. Do the math. Draw the block diagram. Explain it out loud to a friend, a rubber duck, or a mirror. Record yourself. Watch it back. Notice where you hesitate, where you skip details, where you lose structure.
Then do it again.
The first time will feel messy. The tenth time will feel mechanical. The twentieth time will feel natural. That's when you're ready.
Trust the process. Every great engineer I've worked with — whether they came from Stanford or a bootcamp, whether they had 2 years of experience or 20 — got there the same way: repetition, feedback, and a willingness to be uncomfortable in the learning zone.
You've got this. And if you ever want a second pair of eyes on your designs, you know where to find me.
— Suraj
Acknowledgments
This guide would not exist without the courage and openness of the talented engineers who participated in the mock interview session that inspired it. They showed up ready to learn, accepted feedback with grace, and engaged in the kind of real-time problem-solving that makes these sessions so valuable.
To the candidate who designed the ride-share system: thank you for your thoughtfulness, your resilience under pressure, and your willingness to let this session become a teaching moment for the broader community. To the peer reviewers who asked the hard questions and offered constructive insights: your contributions sharpened this guide immensely.
I've anonymized all names and details to protect your privacy, but your energy, curiosity, and collaborative spirit are what made this work come alive. Thank you for trusting the process — and for letting me share what we built together.
— Suraj
•Replay: Every message has an offset — services can replay events to rebuild state or reprocess data after a bug fix
•Scalability: Kafka partitions messages by rider_id or driver_id — different services can consume different partitions in parallel
•Observability: The event log becomes an audit trail — you can trace every ride request, match, and state transition for debugging and compliance
Part 1: The Big Picture — What Is a System Design Interview?
A system design interview is a 45–60 minute session where you are expected to architect a scalable, reliable, and maintainable system from scratch. Unlike coding interviews, there is no single correct answer. Interviewers are evaluating your thought process, communication, trade- off reasoning, and technical depth.
1.1 What Interviewers Are Actually Evaluating
1.2 SWE vs. Data Engineer System Design
The ride-share app mock interview with Sirisha falls primarily in the SWE system design category — designing services, APIs, WebSocket connections, and stateful session management. However, the principles of structured thinking, math, and trade-off reasoning apply universally.
Part 2: The 4-Step Framework for System Design Interviews
Every strong system design answer follows a consistent structure. Use this as your mental checklist during any interview.
1.Step 1 — Ask Clarifying Questions (3–5 minutes)
Identify functional requirements (what the system must do)
Identify non-functional requirements (scale, latency, availability, consistency)
Call out assumptions explicitly
Ask about data sources, outputs, and how data will be consumed
2.Step 2 — Design High-Level Architecture (10–15 minutes)
Identify 1–2 key metrics to anchor your design
Outline main components and data flow end-to-end
Define storage strategy: batch vs. streaming, ETL vs. ELT
Sketch a block diagram on the whiteboard
3.Step 3 — Drill Down on Critical Components (15–20 minutes)
Pick 1–2 components to go deep on
Address bottlenecks, race conditions, failure modes
Discuss trade-offs between alternatives
Handle real-world issues: late data, duplicates, data quality
4.Step 4 — Bring It All Together (5 minutes)
Revisit requirements — does your design meet them?
Summarize key decisions and trade-offs
Highlight how the system handles failure and scales
Define success metrics
Part 3: Case Study — A Real Ride-Share App System Design Interview
3.1 What Strong Candidates Do Well
3.2 Common Areas for Improvement
Part 4: The Ride-Share App — Full System Design Walkthrough
4.1 Functional Requirements
Rider books a cab via mobile app; driver receives and accepts the request
One driver assigned to one booking — no double assignment
Real-time location tracking of driver during the ride
Payment processed after ride completion
Rider rates driver; tipping options provided (10%, 15%, custom)
Support for solo rides and shared rides along the same route
Rider preferences: car size, luggage, gender of driver
4.2 Non-Functional Requirements
Scale: 500,000 ride requests per minute globally
Availability: 99.99% uptime (≈ 52 minutes downtime/year)
Latency: Driver match delivered to rider in < 3 seconds
Consistency: Strong consistency for driver assignment (no double booking); eventual consistency for location updates
Durability: No ride data lost even during partial system failure
Real-time: Driver location updated every 3–5 seconds
4.3 Back-of-Envelope Calculations
Scale Estimation
Storage Estimation
Server Estimation
4.4 System Architecture Block Diagram
4.5 Core Services Deep Dive
Service 1: Location Service (GPS Ingestion)
The Location Service is the most write-intensive component. Every active driver emits a GPS ping every 3–5 seconds.
Protocol: WebSocket (persistent, bidirectional) — not REST, because polling would create 2.5M HTTP requests/second
Storage: Redis with H3 Spatial Index — drivers are bucketed into hexagonal grid cells (H3 resolution 8 ≈ 0.7 km² per cell)
TTL: Driver location entries expire after 30 seconds of no update (driver went offline)
Write path: Driver App → WebSocket Gateway → Kafka → Location Service → Redis H3 Index
Service 2: Ride Matching Engine (H3 Spatial Search)
When a rider requests a ride, the matching engine finds the 6 nearest available drivers using the H3 hexagonal spatial index.
Why H3? H3 (Uber's open-source grid system) divides the Earth into hexagons. Hexagons have equal distance to all neighbors — unlike squares, which have diagonal distortion. This makes proximity searches faster and more accurate.
Search radius: Start at H3 resolution 9 (≈ 0.1 km²), expand outward if fewer than 6 drivers found
Filtering: Apply rider preferences (car type, shared/solo, luggage) to the candidate set
Output: Ranked list of up to 6 drivers with ETA, car type, and rating
Service 3: Trip & State Management
This is the source of truth for active rides. It manages the full lifecycle of a trip.
Service 4: Payment Service
Stateless REST API calls (not WebSocket) — payment is a one-time event, not a stream
Integrates with Stripe/PayPal via tokenized payment credentials (never store raw card numbers)
Stores only last 4 digits + auth token in the transactional DB
Handles tipping: fixed percentages (10%, 15%, 20%) or custom amount
Maintains ride history for both rider and driver
4.6 Preventing Race Conditions — Double Assignment Problem
Solution: Optimistic Locking with Conditional Write in Redis
4.7 WebSocket Failure Handling
Recommended Answer:
1.Redundant WebSocket Pods: Deploy N WebSocket gateway pods behind the NLB. If one pod fails, the NLB routes new connections to healthy pods. Existing connections on the failed pod reconnect automatically (client-side retry with exponential backoff).
2.Session Registry Replication: Redis is deployed as a Redis Cluster (3 primary + 3 replica nodes across availability zones). If one node fails, a replica is promoted within seconds.
3.Persistent Fallback: Trip state is asynchronously written to PostgreSQL/DynamoDB every 10 seconds. If Redis is completely lost, the Trip & State Management service can reconstruct active trips from the DB (with up to 10 seconds of staleness).
4.Client-Side Reconnection: Both rider and driver apps implement WebSocket reconnection with exponential backoff (1s, 2s, 4s, 8s…). Upon reconnect, the client sends its last known trip_id and the server restores state from the session registry or DB.
5.Multi-Region Deployment: Each geographic region has its own WebSocket cluster. A full regional outage triggers DNS failover to the nearest healthy region (Route 53 health checks or equivalent).
Part 5: MAANG-Level Interview Questions & Model Answers
Below are the types of questions senior interviewers at Google, Meta, Amazon, or Uber frequently ask, along with model answers that demonstrate the depth and precision expected at this level.
Q1: What is the source of truth for trip state — the message bus, the cache, or the database?
Model Answer: For active trips, the source of truth is the Session Registry (Redis) and the Spatial Index (H3/Redis). These are low-latency, in-memory stores that reflect the current real-time state. The Transactional DB (PostgreSQL/DynamoDB) is the source of truth for completed trips — it stores immutable records of payments, ratings, and ride history. The message bus (Kafka) is not a source of truth; it is a durable event log used for decoupling services and enabling replay. If Redis goes down, we reconstruct state from Kafka's event log or the DB's last checkpoint.
Q2: How does your system handle a driver whose car breaks down mid-trip?
Model Answer: The Trip & State Management service monitors driver location updates via WebSocket heartbeats. If no location update is received for > 3 minutes AND the driver's last known speed was 0, the service transitions the trip to a DEGRADED state. It then: (1) sends a push notification to the driver asking for a status update, (2) notifies the rider of a potential delay, (3) if no response within 5 minutes, cancels the trip, issues a full refund to the rider, and re-enters the rider into the matching queue with priority. The driver's session is marked UNAVAILABLE in Redis.
Q3: What is the session registry schema and what is the TTL?
Model Answer: The session registry is a Redis hash. Key: session:{trip_id}. Fields: driver_id, rider_id, driver_status (AVAILABLE/LOCKED/IN_TRIP), last_location (lat/lon), last_heartbeat_ts, trip_state, websocket_node_id. TTL: 2 hours (maximum trip duration). For driver availability entries (not yet in a trip), TTL is 30 seconds — refreshed with each GPS ping. If the TTL expires without a refresh, the driver is considered offline and removed from the spatial index.
Q4: How do you handle 500,000 ride requests per minute without overwhelming the matching engine?
Model Answer: Requests are first buffered in Kafka (or NATS) before reaching the Ride Matching Engine. Kafka acts as a shock absorber — it can absorb millions of events per second and deliver them to consumers at a controlled rate. The matching engine is horizontally scaled (auto-scaling pods). Geographic partitioning means each regional cluster handles only a fraction of global traffic. Additionally, the H3 spatial index lookup is O(1) for a given cell, making each match operation extremely fast (< 10ms). With 8,333 requests/second and 10ms per match, we need ~84 concurrent matching threads — easily handled by 10–20 pods.
Q5: How do you prevent a shared ride from being assigned to incompatible routes?
Model Answer: When a rider opts into a shared ride, the Ride Matching Engine checks the driver's current route (stored in the session registry) against the new rider's pickup and dropoff coordinates. It uses a route deviation threshold — if adding the new rider increases the driver's total route by more than X% (e.g., 20%), the driver is not offered as a shared option. Only drivers whose existing route passes within Y meters of the new rider's pickup are eligible. This is computed using the H3 grid — riders and drivers in the same or adjacent H3 cells are candidates for route overlap analysis.
Part 6: Common System Design Topics — Quick Reference
6.1 Load Balancers: Layer 4 vs. Layer 7
6.2 WebSocket vs. REST API — When to Use Which
6.3 Kafka as an Event Buffer — Why It Matters
Decoupling: Producers (apps) and consumers (services) are independent — a slow matching engine doesn't block GPS ingestion
Durability: Messages are persisted to disk — if a service crashes, it can replay from its last offset
Conclusion: What Makes a Great System Design Interview
After coaching hundreds of engineers through system design interviews at Grounow, I've seen a clear pattern: the candidates who succeed aren't always the ones with the most years of experience — they're the ones who can think out loud, justify their decisions, and anchor every design choice in real numbers.
The mock interview that inspired this guide was no different. The candidate demonstrated strong technical instincts — they correctly identified the need for WebSockets, spatial indexing, and distributed state management. But what stood out most was their willingness to pause, think, and respond thoughtfully under pressure. That's a skill you can practice.
At Grounow, we run sessions like this regularly because we believe that structured practice, honest feedback, and repeated exposure to hard questions are what bridge the gap between knowing the theory and performing confidently in the room. This guide is an artifact of that process — a real session, real feedback, and real lessons distilled into a framework you can use.
If there's one thing I want you to take away, it's this: System design interviews are not about memorizing architectures — they're about demonstrating structured thinking, mathematical rigor, and trade-off reasoning. Use the 4-step framework. Write the math on the whiteboard. Ask clarifying questions. Control the scope. And remember: the interviewer is not testing your ability to build Uber in 45 minutes — they're testing your ability to think like a senior engineer.
— Suraj Ethirajan, Coach, Grounow
A Final Word: Your Next Step
If you're reading this, you're already ahead of most candidates. You're investing time in learning the craft, not just cramming patterns the night before.
Here's my advice: Don't just read this guide — practice it. Pick a system you use every day (Instagram, Netflix, Slack) and design it on a whiteboard. Do the math. Draw the block diagram. Explain it out loud to a friend, a rubber duck, or a mirror. Record yourself. Watch it back. Notice where you hesitate, where you skip details, where you lose structure.
Then do it again.
The first time will feel messy. The tenth time will feel mechanical. The twentieth time will feel natural. That's when you're ready.
Trust the process. Every great engineer I've worked with — whether they came from Stanford or a bootcamp, whether they had 2 years of experience or 20 — got there the same way: repetition, feedback, and a willingness to be uncomfortable in the learning zone.
You've got this. And if you ever want a second pair of eyes on your designs, you know where to find me.
— Suraj
Acknowledgments
This guide would not exist without the courage and openness of the talented engineers who participated in the mock interview session that inspired it. They showed up ready to learn, accepted feedback with grace, and engaged in the kind of real-time problem-solving that makes these sessions so valuable.
To the candidate who designed the ride-share system: thank you for your thoughtfulness, your resilience under pressure, and your willingness to let this session become a teaching moment for the broader community. To the peer reviewers who asked the hard questions and offered constructive insights: your contributions sharpened this guide immensely.
I've anonymized all names and details to protect your privacy, but your energy, curiosity, and collaborative spirit are what made this work come alive. Thank you for trusting the process — and for letting me share what we built together.
— Suraj
•Replay: Every message has an offset — services can replay events to rebuild state or reprocess data after a bug fix
•Scalability: Kafka partitions messages by rider_id or driver_id — different services can consume different partitions in parallel
•Observability: The event log becomes an audit trail — you can trace every ride request, match, and state transition for debugging and compliance
