Hotel Reservation Platform — 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 hotel reservation platform 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 10M searches/s · 100K hotels · zero unauthorized oversell.
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: no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail.

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2. Capacity assumptions

State assumptions before choosing technology. The supplied workload is 10M searches/s · 100K hotels · zero unauthorized oversell. Convert it into peak throughput, retained records, bandwidth, and failure budget; then connect each number to a component.

Dimension	Working estimate	Design implication
Traffic	10M searches/s · 100K hotels · zero unauthorized oversell	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.

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3. API and data model

GET /v1/hotels/search?dates=&guests=

POST /v1/quotesexpiring price

POST /v1/reservationsconditional inventory

POST /v1/reservations/{id}/cancel

Authoritative records and indexes

inventory(hotel_id, room_type, date, available, version)

quote(quote_id, room_type, nights, total, expires_at)

reservation(id PK, guest, nights, state, version)

channel_update(source, external_id, inventory_version)

Command and event envelopes

# Command envelope
operation: GET /v1/hotels/search?dates=&guests=
request_id: req_01J...
idempotency_key: idem_7f2...
aggregate_key: hotel-reservation_123
expected_version: 17
payload: {...}

# Durable event
event_id: evt_01J...
aggregate_key: hotel-reservation_123
event_type: hotel_reservation_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.

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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
Traveler Client	Owns client protocol, request identity, retry, and resume behavior. In this design it participates in: Search eventually consistent catalog/rate/availability projections and return candidates.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents the external caller from also owning the contract and failure behavior of Search Gateway.	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 “One night sells out during multi-night commit” 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 Traveler Client boundary before extracting or scaling it.
Search Gateway	Owns authentication, validation, quotas, deadlines, and routing. In this design it participates in: Create an expiring quote pinned to rate, taxes, room type, and date range.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Traveler Client from also owning the contract and failure behavior of Hotel Catalog.	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 “Channel manager sends stale inventory” to Traveler 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 Search Gateway boundary before extracting or scaling it.
Hotel Catalog	Preserves durable ordering, replay, and consumer checkpoints. In this design it participates in: Conditionally decrement every room-night row and create reservation atomically.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Search Gateway from also owning the contract and failure behavior of Rate Plan Service.	Call every downstream consumer synchronously. That couples user latency and availability to analytics, notification, indexing, and audit dependencies. Here that simpler choice would couple “Payment succeeds after hold expiry” to Search Gateway.	Costs eventual consistency, schema evolution, replay tooling, idempotent consumers, and lag operations. Earn it when bursts or downstream failure must not block acknowledgment. Measure the latency, saturation, lag, or mismatch at the Hotel Catalog boundary before extracting or scaling it.
Rate Plan Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Authorize/capture payment and confirm booking ledger; compensate on terminal failure.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Hotel Catalog from also owning the contract and failure behavior of Availability Projection.	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 “Rate changes after quote” to Hotel Catalog.	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 Rate Plan Service boundary before extracting or scaling it.
Availability Projection	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Publish channel updates and reconcile external inventory versions plus cancellations.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Rate Plan Service from also owning the contract and failure behavior of Search Index.	Read authoritative storage for every request. Simpler initially, but latency and source-of-truth load scale together and a hot key becomes a database incident. Here that simpler choice would couple “Cancellation races check-in” to Rate Plan 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 Availability Projection boundary before extracting or scaling it.
Search Index	Serves a low-latency, disposable projection with authoritative fallback. In this design it participates in: Search eventually consistent catalog/rate/availability projections and return candidates.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Availability Projection from also owning the contract and failure behavior of Quote Service.	Read authoritative storage for every request. Simpler initially, but latency and source-of-truth load scale together and a hot key becomes a database incident. Here that simpler choice would couple “One night sells out during multi-night commit” to Availability Projection.	Costs invalidation, staleness, stampede protection, and memory. Add or split it only after the workload (10M searches/s · 100K hotels · zero unauthorized oversell) proves repeated reads dominate. Measure the latency, saturation, lag, or mismatch at the Search Index boundary before extracting or scaling it.
Quote Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Create an expiring quote pinned to rate, taxes, room type, and date range.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Search Index from also owning the contract and failure behavior of Reservation 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 “Channel manager sends stale inventory” to Search Index.	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 Quote Service boundary before extracting or scaling it.
Reservation API	Owns authentication, validation, quotas, deadlines, and routing. In this design it participates in: Conditionally decrement every room-night row and create reservation atomically.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Quote Service from also owning the contract and failure behavior of Room-Night Inventory.	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 “Payment succeeds after hold expiry” to Quote Service.	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 Reservation API boundary before extracting or scaling it.
Room-Night Inventory	Owns authoritative versioned state or immutable evidence. In this design it participates in: Authorize/capture payment and confirm booking ledger; compensate on terminal failure.	Defines the source of truth and commit boundary. It prevents Reservation API from also owning the contract and failure behavior of Hold Store.	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 “Rate changes after quote” to Reservation API.	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-Night Inventory boundary before extracting or scaling it.
Hold Store	Owns authoritative versioned state or immutable evidence. In this design it participates in: Publish channel updates and reconcile external inventory versions plus cancellations.	Defines the source of truth and commit boundary. It prevents Room-Night Inventory from also owning the contract and failure behavior of Payment Service.	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 “Cancellation races check-in” to Room-Night Inventory.	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 Hold Store boundary before extracting or scaling it.
Payment Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Search eventually consistent catalog/rate/availability projections and return candidates.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Hold Store from also owning the contract and failure behavior of Booking Ledger.	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 “One night sells out during multi-night commit” to Hold Store.	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 Payment Service boundary before extracting or scaling it.
Booking Ledger	Owns authentication, validation, quotas, deadlines, and routing. In this design it participates in: Create an expiring quote pinned to rate, taxes, room type, and date range.	Defines the source of truth and commit boundary. It prevents Payment Service from also owning the contract and failure behavior of Channel Manager Sync.	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 “Channel manager sends stale inventory” to Payment Service.	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 Booking Ledger boundary before extracting or scaling it.
Channel Manager Sync	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Conditionally decrement every room-night row and create reservation atomically.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Booking Ledger from also owning the contract and failure behavior of Cancellation / Refund.	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 “Payment succeeds after hold expiry” to Booking Ledger.	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 Channel Manager Sync boundary before extracting or scaling it.
Cancellation / Refund	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Authorize/capture payment and confirm booking ledger; compensate on terminal failure.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Channel Manager Sync from also owning the contract and failure behavior of Reconciliation.	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 “Rate changes after quote” to Channel Manager Sync.	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 Cancellation / Refund boundary before extracting or scaling it.
Reconciliation	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Publish channel updates and reconcile external inventory versions plus cancellations.	Proves correctness and supports controlled recovery. It prevents Cancellation / Refund from also owning the contract and failure behavior of operators and repair tooling.	Infer correctness from request logs. Logs do not prove durable business state, detect silent drift, or safely repair ambiguous outcomes. Here that simpler choice would couple “Cancellation races check-in” to Cancellation / Refund.	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 Reconciliation boundary before extracting or scaling it.

Decision 01

Chosen boundary vs simpler monolith

Search uses eventually consistent materialized availability, but booking decrements authoritative room-night inventory with a conditional transaction.

A single deployable with one transactional store is the right starting point for a small hotel reservation platform workload. The proposed boundary becomes justified when search uses eventually consistent materialized availability, but booking decrements authoritative room-night inventory with a conditional transaction. Separating it contains the named failure “One night sells out during multi-night commit” without placing unrelated work on the critical path.

Decision 02

Correctness vs availability

Model inventory per room type and date; storing one aggregate hotel count makes multi-night atomic reservation and partial cancellation unsafe.

The design coordinates only where no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail. 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

Use expiring quotes and idempotent booking commands because rates, taxes, and inventory can change between search and commit.

This choice adds dashboards, ownership, schema compatibility, capacity planning, and recovery runbooks. Start with the minimum topology that supports “Conditionally decrement every room-night row and create reservation atomically.” 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 no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail. Durable projections, notifications, indexing, analytics, and repair are isolated so their load or failure cannot corrupt the acknowledged operation.

Correctness boundary

Search uses eventually consistent materialized availability, but booking decrements authoritative room-night inventory with a conditional transaction.

Scale boundary

Model inventory per room type and date; storing one aggregate hotel count makes multi-night atomic reservation and partial cancellation unsafe.

Operational boundary

Use expiring quotes and idempotent booking commands because rates, taxes, and inventory can change between search and commit.

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5. Critical flow and algorithm

Search eventually consistent catalog/rate/availability projections and return candidates.

Create an expiring quote pinned to rate, taxes, room type, and date range.

Conditionally decrement every room-night row and create reservation atomically.

Authorize/capture payment and confirm booking ledger; compensate on terminal failure.

Publish channel updates and reconcile external inventory versions plus cancellations.

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.

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6. Caching, hot keys, and backpressure

Cache policy

Cache immutable or slowly changing reads with versioned keys, TTL jitter, short negative entries, and request coalescing.

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.

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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

no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail. 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.

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8. Failure drills and recovery

Failure	Runtime behavior	Recovery proof
One night sells out during multi-night commit	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.
Channel manager sends stale inventory	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.
Payment succeeds after hold expiry	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.
Rate changes after quote	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.
Cancellation races 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.

Recovery checkpoint

# Recovery checkpoint
scenario: One night sells out during multi-night commit
operation_id: op_01J...
aggregate_key: hotel-reservation_123
last_durable_version: 18
projection_checkpoint: partition-07@offset-482901
retry_policy: same_identity / bounded_backoff
reconciliation_status: pending
owner: Reconciliation

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.

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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: One night sells out during multi-night commit — 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.

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10. Interviewer follow-ups

How do multi-night bookings commit atomically?

Search uses eventually consistent materialized availability, but booking decrements authoritative room-night inventory with a conditional transaction. Walk the concrete lifecycle step “Search eventually consistent catalog/rate/availability projections and return candidates.” Then test it against “One night sells out during multi-night commit”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do cancellations restore inventory?

Model inventory per room type and date; storing one aggregate hotel count makes multi-night atomic reservation and partial cancellation unsafe. Walk the concrete lifecycle step “Create an expiring quote pinned to rate, taxes, room type, and date range.” Then test it against “Channel manager sends stale inventory”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do channel managers synchronize?

Use expiring quotes and idempotent booking commands because rates, taxes, and inventory can change between search and commit. Walk the concrete lifecycle step “Conditionally decrement every room-night row and create reservation atomically.” Then test it against “Payment succeeds after hold expiry”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do overbooking policies work?

Search uses eventually consistent materialized availability, but booking decrements authoritative room-night inventory with a conditional transaction. Walk the concrete lifecycle step “Authorize/capture payment and confirm booking ledger; compensate on terminal failure.” Then test it against “Rate changes after quote”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do you cache search results?

Model inventory per room type and date; storing one aggregate hotel count makes multi-night atomic reservation and partial cancellation unsafe. Walk the concrete lifecycle step “Publish channel updates and reconcile external inventory versions plus cancellations.” Then test it against “Cancellation races check-in”. 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.

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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	no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail.
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 no scarce resource or monetary effect is created twice, lost after acknowledgment, or committed without an audit trail. The online path stays bounded, authoritative state is versioned and partitioned, and asynchronous work is replayable and observable.”

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Traveler Client

Search Gateway

Hotel Catalog

Rate Plan Service

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