Shopping Cart & Checkout — 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 shopping cart & checkout 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 1M carts/s · flash-sale contention · global catalog.
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.

↑ top

2. Capacity assumptions

State assumptions before choosing technology. The supplied workload is 1M carts/s · flash-sale contention · global catalog. Convert it into peak throughput, retained records, bandwidth, and failure budget; then connect each number to a component.

Dimension	Working estimate	Design implication
Traffic	1M carts/s · flash-sale contention · global catalog	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

PUT /v1/carts/{id}/itemsidempotent item mutation

POST /v1/carts/{id}/quoteauthoritative price

POST /v1/checkoutsstart saga

GET /v1/orders/{id}durable result

Authoritative records and indexes

cart(cart_id PK, owner_id, items, version, expires_at)

quote(quote_id, cart_version, lines, totals, expires_at)

reservation(reservation_id, sku, quantity, expires_at, state)

order(order_id PK, quote_id, payment_intent, state)

Command and event envelopes

# Command envelope
operation: PUT /v1/carts/{id}/items
request_id: req_01J...
idempotency_key: idem_7f2...
aggregate_key: shopping-cart_123
expected_version: 17
payload: {...}

# Durable event
event_id: evt_01J...
aggregate_key: shopping-cart_123
event_type: shopping_cart_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
Buyer Client	Owns client protocol, request identity, retry, and resume behavior. In this design it participates in: Store cart intent with optimistic versioning; merge guest/authenticated carts explicitly.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents the external caller from also owning the contract and failure behavior of Cart 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 “Price changes after item added” 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 Buyer Client boundary before extracting or scaling it.
Cart API	Owns authentication, validation, quotas, deadlines, and routing. In this design it participates in: At checkout recompute catalog, promotions, tax, shipping, and produce an expiring quote.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Buyer Client from also owning the contract and failure behavior of Cart Store.	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 “Two checkouts consume same inventory” to Buyer 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 Cart API boundary before extracting or scaling it.
Cart Store	Owns authoritative versioned state or immutable evidence. In this design it participates in: Reserve inventory per SKU with idempotent tokens and start the checkout saga.	Defines the source of truth and commit boundary. It prevents Cart API from also owning the contract and failure behavior of Catalog 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 “Payment succeeds but order write times out” to Cart 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 Cart Store boundary before extracting or scaling it.
Catalog Service	Preserves durable ordering, replay, and consumer checkpoints. In this design it participates in: Confirm payment and create order; compensate stock/payment on terminal failure.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Cart Store from also owning the contract and failure behavior of Pricing Engine.	Call every downstream consumer synchronously. That couples user latency and availability to analytics, notification, indexing, and audit dependencies. Here that simpler choice would couple “Coupon budget races across carts” to Cart Store.	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 Catalog Service boundary before extracting or scaling it.
Pricing Engine	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Publish order events through an outbox for fulfillment, email, and analytics.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Catalog Service from also owning the contract and failure behavior of Promotion 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 “Reservation expires during payment challenge” to Catalog 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 Pricing Engine boundary before extracting or scaling it.
Promotion Engine	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Store cart intent with optimistic versioning; merge guest/authenticated carts explicitly.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Pricing Engine from also owning the contract and failure behavior of Tax / Shipping.	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 “Price changes after item added” to Pricing 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 Promotion Engine boundary before extracting or scaling it.
Tax / Shipping	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: At checkout recompute catalog, promotions, tax, shipping, and produce an expiring quote.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Promotion Engine from also owning the contract and failure behavior of Inventory 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 checkouts consume same inventory” to Promotion 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 Tax / Shipping boundary before extracting or scaling it.
Inventory Service	Owns authoritative versioned state or immutable evidence. In this design it participates in: Reserve inventory per SKU with idempotent tokens and start the checkout saga.	Defines the source of truth and commit boundary. It prevents Tax / Shipping from also owning the contract and failure behavior of Reservation 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 “Payment succeeds but order write times out” to Tax / Shipping.	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 Inventory Service boundary before extracting or scaling it.
Reservation Store	Owns authoritative versioned state or immutable evidence. In this design it participates in: Confirm payment and create order; compensate stock/payment on terminal failure.	Defines the source of truth and commit boundary. It prevents Inventory Service from also owning the contract and failure behavior of Checkout Saga.	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 “Coupon budget races across carts” to Inventory 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 Reservation Store boundary before extracting or scaling it.
Checkout Saga	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Publish order events through an outbox for fulfillment, email, and analytics.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Reservation Store from also owning the contract and failure behavior of Order 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 “Reservation expires during payment challenge” to Reservation 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 Checkout Saga boundary before extracting or scaling it.
Order Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Store cart intent with optimistic versioning; merge guest/authenticated carts explicitly.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Checkout Saga from also owning the contract and failure behavior of Payment 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 “Price changes after item added” to Checkout Saga.	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 Order Service boundary before extracting or scaling it.
Payment Service	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: At checkout recompute catalog, promotions, tax, shipping, and produce an expiring quote.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Order Service from also owning the contract and failure behavior of Outbox Events.	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 checkouts consume same inventory” to Order 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 Payment Service boundary before extracting or scaling it.
Outbox Events	Preserves durable ordering, replay, and consumer checkpoints. In this design it participates in: Reserve inventory per SKU with idempotent tokens and start the checkout saga.	Decouples bursts and failure from user acknowledgment. It prevents Payment Service from also owning the contract and failure behavior of Fulfillment.	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 but order write times out” to Payment Service.	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 Outbox Events boundary before extracting or scaling it.
Fulfillment	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Confirm payment and create order; compensate stock/payment on terminal failure.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Outbox Events from also owning the contract and failure behavior of operators and repair tooling.	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 “Coupon budget races across carts” to Outbox Events.	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 Fulfillment boundary before extracting or scaling it.

Decision 01

Chosen boundary vs simpler monolith

Treat carts as durable but eventually consistent user intent; recompute authoritative price at checkout.

A single deployable with one transactional store is the right starting point for a small shopping cart & checkout workload. The proposed boundary becomes justified when treat carts as durable but eventually consistent user intent; recompute authoritative price at checkout. Separating it contains the named failure “Price changes after item added” without placing unrelated work on the critical path.

Decision 02

Correctness vs availability

Reserve inventory with expiring tokens and use a saga to compensate payment or stock failures.

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

Make order creation idempotent and preserve a timeline of state transitions for support and recovery.

This choice adds dashboards, ownership, schema compatibility, capacity planning, and recovery runbooks. Start with the minimum topology that supports “Reserve inventory per SKU with idempotent tokens and start the checkout saga.” 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

Treat carts as durable but eventually consistent user intent; recompute authoritative price at checkout.

Scale boundary

Reserve inventory with expiring tokens and use a saga to compensate payment or stock failures.

Operational boundary

Make order creation idempotent and preserve a timeline of state transitions for support and recovery.

↑ top

5. Critical flow and algorithm

Store cart intent with optimistic versioning; merge guest/authenticated carts explicitly.

At checkout recompute catalog, promotions, tax, shipping, and produce an expiring quote.

Reserve inventory per SKU with idempotent tokens and start the checkout saga.

Confirm payment and create order; compensate stock/payment on terminal failure.

Publish order events through an outbox for fulfillment, email, and analytics.

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

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.

↑ top

8. Failure drills and recovery

Failure	Runtime behavior	Recovery proof
Price changes after item added	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.
Two checkouts consume same inventory	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.
Payment succeeds but order write times out	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.
Coupon budget races across carts	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.
Reservation expires during payment challenge	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.

Recovery checkpoint

# Recovery checkpoint
scenario: Price changes after item added
operation_id: op_01J...
aggregate_key: shopping-cart_123
last_durable_version: 18
projection_checkpoint: partition-07@offset-482901
retry_policy: same_identity / bounded_backoff
reconciliation_status: pending
owner: Fulfillment

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: Price changes after item added — 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

Where do coupons get validated?

Treat carts as durable but eventually consistent user intent; recompute authoritative price at checkout. Walk the concrete lifecycle step “Store cart intent with optimistic versioning; merge guest/authenticated carts explicitly.” Then test it against “Price changes after item added”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do guest carts merge?

Reserve inventory with expiring tokens and use a saga to compensate payment or stock failures. Walk the concrete lifecycle step “At checkout recompute catalog, promotions, tax, shipping, and produce an expiring quote.” Then test it against “Two checkouts consume same inventory”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

What happens when price changes?

Make order creation idempotent and preserve a timeline of state transitions for support and recovery. Walk the concrete lifecycle step “Reserve inventory per SKU with idempotent tokens and start the checkout saga.” Then test it against “Payment succeeds but order write times out”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do partial shipments work?

Treat carts as durable but eventually consistent user intent; recompute authoritative price at checkout. Walk the concrete lifecycle step “Confirm payment and create order; compensate stock/payment on terminal failure.” Then test it against “Coupon budget races across carts”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do flash sales avoid overselling?

Reserve inventory with expiring tokens and use a saga to compensate payment or stock failures. Walk the concrete lifecycle step “Publish order events through an outbox for fulfillment, email, and analytics.” Then test it against “Reservation expires during payment challenge”. 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	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.”

↑ top

Buyer Client

Cart API

Cart Store

Catalog 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