Meeting Room Scheduler — System Design Academy

Complete System Design Tutorial

The design separates the latency-sensitive online path, the correctness-critical durable transition, and asynchronous projection or repair work. Each chapter below explains one decision shown in the diagrams.

1. Requirements and scope

Functional requirements

Execute the primary meeting room scheduler workflow and return an observable result.
Expose the concrete operations shown below with stable retry identities.
Allow operators to inspect, disable, repair, or replay ambiguous state.
Keep derived analytics and notifications off the correctness-critical path.

Non-functional requirements

Design for 100K rooms · recurring meetings · timezone aware.
Protect the named business invariant under concurrency and partial failure.
Scale by an explicit aggregate/partition key without one global mutex.
Degrade intentionally when cache, queue, store, or regional ownership is unavailable.

Primary invariant: every object transition is legal and atomic at one aggregate boundary.

↑ top

2. Capacity assumptions

State assumptions before choosing technology. The supplied workload is 100K rooms · recurring meetings · timezone aware. Convert it into peak throughput, retained records, bandwidth, and failure budget; then connect each number to a component.

Dimension	Working estimate	Design implication
Traffic	100K rooms · recurring meetings · timezone aware	Separate online compute, durable state, and asynchronous consumers.
Peak factor	5–10× average	Admission control, autoscaling headroom, and hot-key protection.
Stored state	write rate × retention × record bytes	Partition count, indexes, compaction, backup, and archival tier.
Network	peak operations × payload size	Batching, compression, edge placement, and egress budget.
Availability	99.9–99.99%	Multi-zone replicas, tested failover, and dependency error budgets.
Recovery	RPO/RTO by invariant	Ack boundary, replay retention, reconciliation, and operator runbooks.

Do not cargo-cult the numbers: show the arithmetic, name the uncertain variable, and say which production metric will replace the interview estimate.

↑ top

3. API and data model

GET /v1/rooms/search?start=&end=&capacity=

POST /v1/bookingsconditional reservation

PATCH /v1/series/{id}recurrence/exception

POST /v1/bookings/{id}/check-in

Authoritative records and indexes

room(room_id PK, capacity, equipment, timezone, state)

booking(booking_id, room_id, start_utc, end_utc, version, series_id)

series(series_id, rrule, timezone, horizon, version)

exception(series_id, occurrence, override_or_cancel)

Command and event envelopes

# Command envelope
operation: GET /v1/rooms/search?start=&end=&capacity=
request_id: req_01J...
idempotency_key: idem_7f2...
aggregate_key: meeting-room-scheduler_123
expected_version: 17
payload: {...}

# Durable event
event_id: evt_01J...
aggregate_key: meeting-room-scheduler_123
event_type: meeting_room_scheduler_committed
schema_version: 3
trace_id: tr_8ac...

The authoritative aggregate and idempotency record share a transactional or conditional-write boundary. Event streams, search indexes, caches, analytics, and dashboards are rebuildable projections.

↑ top

4. Why this architecture and what it costs

The boxes are not a shopping list. Each boundary exists because it owns a different invariant, scaling dimension, data lifecycle, security policy, or failure domain. The table makes those choices explicit and shows when the design should remain simpler.

Component	Owned responsibility	Why separate it	Rejected alternative	Cost and evolution trigger
Calendar Client	Owns client protocol, request identity, retry, and resume behavior. In this design it participates in: Normalize requested local time to UTC while retaining timezone/recurrence intent.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents the external caller from also owning the contract and failure behavior of Availability API.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Two users book same interval” to the external caller.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Calendar Client boundary before extracting or scaling it.
Availability API	Owns authentication, validation, quotas, deadlines, and routing. In this design it participates in: Filter candidate rooms by static constraints, then query interval availability.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Calendar Client from also owning the contract and failure behavior of Identity / Directory.	Put protocol, authentication, quotas, and business transitions in one process. Acceptable at low scale, but it couples external traffic risk to domain releases. Here that simpler choice would couple “DST changes recurrence wall time” to Calendar Client.	Costs another hop, policy distribution, and a high-blast-radius fleet. Introduce it when many clients or services need one protocol and protection boundary. Measure the latency, saturation, lag, or mismatch at the Availability API boundary before extracting or scaling it.
Identity / Directory	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Insert booking with exclusion constraint/serializable overlap check on room calendar.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Availability API from also owning the contract and failure behavior of Room Search.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Room equipment becomes unavailable” to Availability API.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Identity / Directory boundary before extracting or scaling it.
Room Search	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Write attendee events asynchronously and preserve room booking as source of truth.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Identity / Directory from also owning the contract and failure behavior of Constraint Filter.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “No-show auto-release races late check-in” to Identity / Directory.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Room Search boundary before extracting or scaling it.
Constraint Filter	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Expand recurring occurrences within a horizon; exceptions override one occurrence without rewriting series.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Room Search from also owning the contract and failure behavior of Booking Service.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Series edit conflicts with occurrence override” to Room Search.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Constraint Filter boundary before extracting or scaling it.
Booking Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Normalize requested local time to UTC while retaining timezone/recurrence intent.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Constraint Filter from also owning the contract and failure behavior of Conflict Detector.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Two users book same interval” to Constraint Filter.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Booking Service boundary before extracting or scaling it.
Conflict Detector	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Filter candidate rooms by static constraints, then query interval availability.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Booking Service from also owning the contract and failure behavior of Room Calendar DB.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “DST changes recurrence wall time” to Booking Service.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Conflict Detector boundary before extracting or scaling it.
Room Calendar DB	Owns authoritative versioned state or immutable evidence. In this design it participates in: Insert booking with exclusion constraint/serializable overlap check on room calendar.	Defines the source of truth and commit boundary. It prevents Conflict Detector from also owning the contract and failure behavior of Attendee Calendar Adapter.	Let each service persist its own interpretation. That removes one clear commit boundary and makes retries, support, and reconciliation ambiguous. Here that simpler choice would couple “Room equipment becomes unavailable” to Conflict Detector.	Costs schema migration, replication, backup, and shard ownership. Split storage only when access patterns, retention, or compliance boundaries materially differ. Measure the latency, saturation, lag, or mismatch at the Room Calendar DB boundary before extracting or scaling it.
Attendee Calendar Adapter	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Write attendee events asynchronously and preserve room booking as source of truth.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Room Calendar DB from also owning the contract and failure behavior of Recurrence Engine.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “No-show auto-release races late check-in” to Room Calendar DB.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Attendee Calendar Adapter boundary before extracting or scaling it.
Recurrence Engine	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Expand recurring occurrences within a horizon; exceptions override one occurrence without rewriting series.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Attendee Calendar Adapter from also owning the contract and failure behavior of Timezone Service.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Series edit conflicts with occurrence override” to Attendee Calendar Adapter.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Recurrence Engine boundary before extracting or scaling it.
Timezone Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Normalize requested local time to UTC while retaining timezone/recurrence intent.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Recurrence Engine from also owning the contract and failure behavior of Notification Service.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Two users book same interval” to Recurrence Engine.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Timezone Service boundary before extracting or scaling it.
Notification Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Filter candidate rooms by static constraints, then query interval availability.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Timezone Service from also owning the contract and failure behavior of Check-In Sensor.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “DST changes recurrence wall time” to Timezone Service.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Notification Service boundary before extracting or scaling it.
Check-In Sensor	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Insert booking with exclusion constraint/serializable overlap check on room calendar.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Notification Service from also owning the contract and failure behavior of Auto-Release Worker.	Keep this behavior inside an adjacent service. Prefer that simpler option until independent scale, ownership, policy, or failure isolation is measurable. Here that simpler choice would couple “Room equipment becomes unavailable” to Notification Service.	Costs an interface, deployment, telemetry, ownership, and failure mode. Keep it in-process first; extract when independent scale or reliability pays for the boundary. Measure the latency, saturation, lag, or mismatch at the Check-In Sensor boundary before extracting or scaling it.
Auto-Release Worker	Executes idempotent asynchronous work under lease and retry policy. In this design it participates in: Write attendee events asynchronously and preserve room booking as source of truth.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Check-In Sensor from also owning the contract and failure behavior of operators and repair tooling.	Perform the work before acknowledging the caller. This gives simpler consistency but unbounded tail latency and fragile retries. Here that simpler choice would couple “No-show auto-release races late check-in” to Check-In Sensor.	Costs leases, checkpoints, retries, DLQs, and lag operations. Separate once execution time or burstiness no longer fits the request deadline. Measure the latency, saturation, lag, or mismatch at the Auto-Release Worker boundary before extracting or scaling it.

Decision 01

Chosen boundary vs simpler monolith

Store intervals with an exclusion constraint or conditional overlap check, not check-then-insert.

A single deployable with one transactional store is the right starting point for a small meeting room scheduler workload. The proposed boundary becomes justified when store intervals with an exclusion constraint or conditional overlap check, not check-then-insert. Separating it contains the named failure “Two users book same interval” without placing unrelated work on the critical path.

Decision 02

Correctness vs availability

Expand recurrence lazily within a horizon and preserve the recurrence rule plus exceptions.

The design coordinates only where every object transition is legal and atomic at one aggregate boundary. Everywhere else it accepts bounded staleness or asynchronous propagation. The alternative—strong coordination for every projection—simplifies the mental model but increases tail latency and turns partitions into broad write unavailability.

Decision 03

Operational cost and evolution

Separate attendee calendars from room-resource calendars; the room is the scarce invariant.

This choice adds dashboards, ownership, schema compatibility, capacity planning, and recovery runbooks. Start with the minimum topology that supports “Insert booking with exclusion constraint/serializable overlap check on room calendar.” and extract another service only when measured throughput, blast radius, data lifecycle, or team ownership changes independently.

Why this design: the synchronous path contains only the decisions required to protect every object transition is legal and atomic at one aggregate boundary. Durable projections, notifications, indexing, analytics, and repair are isolated so their load or failure cannot corrupt the acknowledged operation.

Correctness boundary

Store intervals with an exclusion constraint or conditional overlap check, not check-then-insert.

Scale boundary

Expand recurrence lazily within a horizon and preserve the recurrence rule plus exceptions.

Operational boundary

Separate attendee calendars from room-resource calendars; the room is the scarce invariant.

↑ top

5. Critical flow and algorithm

Normalize requested local time to UTC while retaining timezone/recurrence intent.

Filter candidate rooms by static constraints, then query interval availability.

Insert booking with exclusion constraint/serializable overlap check on room calendar.

Write attendee events asynchronously and preserve room booking as source of truth.

Expand recurring occurrences within a horizon; exceptions override one occurrence without rewriting series.

Retry-safe mutation skeleton

identity = authenticate(request)
prior = idempotency.get(identity, request.key)
if prior: return prior.result

current = authoritative_store.read(partition_key)
next_state, events = domain.transition(current, request)
authoritative_store.compare_and_set(current.version, next_state)
idempotency.commit(request.key, next_state.result)
outbox.append(events)
return next_state.result

Exactly-once business effect: the network can duplicate requests. Stable identities, conditional state transitions, immutable event IDs, and reconciliation make the user-visible effect happen once.

↑ top

6. Caching, hot keys, and backpressure

Cache policy

Add a cache only for a measured repeated read; do not place mutable correctness state in a disposable layer.

Hot partition or tenant

Measure per-key and per-tenant skew. Replicate safe reads, isolate the offender, salt only when ordering permits, and enforce budgets.

Backpressure

Bound queues, buffers, connection pools, and concurrency. Shed low-priority work and scale from lag/oldest-item age rather than CPU alone.

Stampede and retry storms

Use single-flight/coalescing, jittered expiry, bounded retries, exponential backoff, and circuit breakers so one dependency failure does not multiply traffic.

Cache outage rule: never allow every miss to fall through simultaneously. Use local protection, request coalescing, circuit breakers, bounded pools, and controlled fallback.

↑ top

7. Partitioning and consistency

partition = stable_hash(primary_or_aggregate_key) → shard owner
write = conditional_update(expected_version)
read_projection = versioned and rebuildable

Strong boundary

every object transition is legal and atomic at one aggregate boundary. Use a uniqueness constraint, serializable/conditional write, expected version, or fencing token at this boundary.

Eventual boundary

Analytics, notifications, search, read models, and operational dashboards may lag when freshness is observable and replay exists.

Operation	Consistency	Reason
Authoritative mutation	Conditional / serializable	Protect uniqueness, ownership, money, inventory, order, or legal state transition.
Read after own write	Primary, session token, or cache warm	The caller must observe the result it just committed.
Ordinary read projection	Bounded stale where safe	Replicas and caches improve latency when version/freshness is visible.
Analytics and operations	Eventual + replay	Freshness is less important than isolating the online path.

Route every mutation for one aggregate to one owner/partition epoch.
Use cursors rather than offsets for changing ordered collections.
Retain tombstones until replication and repair windows close.
Move shard ownership through versioned maps and dual-read verification.

↑ top

8. Failure drills and recovery

Failure	Runtime behavior	Recovery proof
Two users book same interval	Resolve at source of truth with uniqueness, expected version, lease generation, or monotonic cursor.	Reconciliation returns zero invariant gaps; lag/replica health and user-visible SLO return to baseline.
DST changes recurrence wall time	Stop unsafe progress, serve only explicitly safe degraded behavior, and recover from durable state.	Reconciliation returns zero invariant gaps; lag/replica health and user-visible SLO return to baseline.
Room equipment becomes unavailable	Stop unsafe progress, serve only explicitly safe degraded behavior, and recover from durable state.	Reconciliation returns zero invariant gaps; lag/replica health and user-visible SLO return to baseline.
No-show auto-release races late check-in	Resolve at source of truth with uniqueness, expected version, lease generation, or monotonic cursor.	Reconciliation returns zero invariant gaps; lag/replica health and user-visible SLO return to baseline.
Series edit conflicts with occurrence override	Resolve at source of truth with uniqueness, expected version, lease generation, or monotonic cursor.	Reconciliation returns zero invariant gaps; lag/replica health and user-visible SLO return to baseline.

Recovery checkpoint

# Recovery checkpoint
scenario: Two users book same interval
operation_id: op_01J...
aggregate_key: meeting-room-scheduler_123
last_durable_version: 18
projection_checkpoint: partition-07@offset-482901
retry_policy: same_identity / bounded_backoff
reconciliation_status: pending
owner: Auto-Release Worker

Every dependency needs a timeout, retry budget, circuit policy, and explicit fail-open, fail-closed, stale, or read-only mode. “Highly available” is incomplete until degraded behavior is named.

↑ top

9. Security, regions, and observability

Security and abuse

Authenticate at ingress and authorize the exact resource/tenant on every operation.
Encrypt transport and durable data; tokenize or isolate sensitive material.
Rate-limit by tenant, actor, resource, and network; detect enumeration and anomalous fan-out.
Redact secrets and personal data before logs or analytical storage.
Audit privileged mutations, support access, policy changes, and forced recovery.

Signals that prove correctness

User-visible success rate and p50/p95/p99 latency.
Timeouts, circuit state, saturation, and replica health by dependency.
Queue lag, oldest-item age, retries, and dead-letter growth.
Conflict, duplicate-suppression, and reconciliation mismatch counts.
Business drill: Two users book same interval — alert before the user-visible invariant is violated.

Regional evolution

Begin multi-zone with tested failover, backups, and reconciliation.
Add global stateless ingress and regional read projections.
Keep one write owner per aggregate or partition where coordination is expensive.
Move ownership with epochs/fencing; avoid silent simultaneous writers.
Use expand/contract schemas, canaries, shadow reads, and reversible cutovers.

↑ top

10. Interviewer follow-ups

How do recurring meetings handle exceptions?

Store intervals with an exclusion constraint or conditional overlap check, not check-then-insert. Walk the concrete lifecycle step “Normalize requested local time to UTC while retaining timezone/recurrence intent.” Then test it against “Two users book same interval”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do you find the smallest suitable room?

Expand recurrence lazily within a horizon and preserve the recurrence rule plus exceptions. Walk the concrete lifecycle step “Filter candidate rooms by static constraints, then query interval availability.” Then test it against “DST changes recurrence wall time”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

What happens during concurrent booking?

Separate attendee calendars from room-resource calendars; the room is the scarce invariant. Walk the concrete lifecycle step “Insert booking with exclusion constraint/serializable overlap check on room calendar.” Then test it against “Room equipment becomes unavailable”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do timezones and DST work?

Store intervals with an exclusion constraint or conditional overlap check, not check-then-insert. Walk the concrete lifecycle step “Write attendee events asynchronously and preserve room booking as source of truth.” Then test it against “No-show auto-release races late check-in”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do you reschedule one occurrence?

Expand recurrence lazily within a horizon and preserve the recurrence rule plus exceptions. Walk the concrete lifecycle step “Expand recurring occurrences within a horizon; exceptions override one occurrence without rewriting series.” Then test it against “Series edit conflicts with occurrence override”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

Answer pattern: state the invariant, choose a mechanism, walk one failure, name the recovery signal, and explain the cost of the alternative.

↑ top

11. How to present this in 45 minutes

Time	What to cover
0–5 min	Clarify scope, workload, SLOs, and the correctness invariant.
5–10 min	Estimate throughput/storage and define concrete APIs and records.
10–20 min	Draw online, durable-write, and asynchronous/repair paths.
20–30 min	Explain the exact lifecycle, partition key, and concurrency control.
30–38 min	Discuss caching, hot keys, replication, and backpressure.
38–43 min	Run failure drills, security, observability, and regional evolution.
43–45 min	Summarize the five most important choices and trade-offs.

Final decision summary

Invariant	every object transition is legal and atomic at one aggregate boundary.
Partitioning	Stable aggregate/key hash with versioned ownership and measured hot-key isolation.
Commit	Expected version or uniqueness at the authoritative state boundary.
Async work	Durable outbox/log, idempotent consumers, checkpoints, and replay.
Recovery	Same operation identity, durable truth lookup, and business reconciliation.

Closing summary

CLOSING “The design protects every object transition is legal and atomic at one aggregate boundary. The online path stays bounded, authoritative state is versioned and partitioned, and asynchronous work is replayable and observable.”

↑ top

Calendar Client

Availability API

Identity / Directory

Room Search

Complete System Design Tutorial

1. Requirements and scope

Functional requirements

Non-functional requirements

2. Capacity assumptions

3. API and data model

Authoritative records and indexes

Command and event envelopes

4. Why this architecture and what it costs

Chosen boundary vs simpler monolith

Correctness vs availability

Operational cost and evolution

5. Critical flow and algorithm

Retry-safe mutation skeleton

6. Caching, hot keys, and backpressure

7. Partitioning and consistency

Strong boundary

Eventual boundary

8. Failure drills and recovery

Recovery checkpoint

9. Security, regions, and observability

Security and abuse

Signals that prove correctness

Regional evolution

10. Interviewer follow-ups

11. How to present this in 45 minutes

Final decision summary

Closing summary