Distributed Lock Service — 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 distributed lock service 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 lock ops/s · lease precision in seconds · 5-node quorum.
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: each accepted distributed lock service operation has one stable result and retries do not repeat its business effect.

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2. Capacity assumptions

State assumptions before choosing technology. The supplied workload is 100K lock ops/s · lease precision in seconds · 5-node quorum. 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 lock ops/s · lease precision in seconds · 5-node quorum	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

POST /v1/locks/{name}/acquirelease and fencing token

POST /v1/leases/{id}/renewquorum renewal

DELETE /v1/leases/{id}best-effort release

WATCH /v1/locks/{name}?revision=ordered ownership

Authoritative records and indexes

lease(lock_name PK, owner_session, fencing_token, expires_revision)

session(session_id, principal, last_renew_revision, state)

log_entry(index, term, command, checksum)

watch(lock_name, start_revision, subscriber)

Command and event envelopes

# Command envelope
operation: POST /v1/locks/{name}/acquire
request_id: req_01J...
idempotency_key: idem_7f2...
aggregate_key: distributed-lock_123
expected_version: 17
payload: {...}

# Durable event
event_id: evt_01J...
aggregate_key: distributed-lock_123
event_type: distributed_lock_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
Lock Client	Owns client protocol, request identity, retry, and resume behavior. In this design it participates in: Client opens an authenticated session and proposes acquire to the consensus leader.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents the external caller from also owning the contract and failure behavior of Session Library.	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 “Old owner resumes after long pause” 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 Lock Client boundary before extracting or scaling it.
Session Library	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Committed acquire increments fencing counter and returns lease plus token.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Lock Client from also owning the contract and failure behavior of Leader Endpoint.	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 “Leader changes during renewal” to Lock Client.	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 Session Library boundary before extracting or scaling it.
Leader Endpoint	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Protected resource requires token on every mutation and rejects values below its maximum.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Session Library from also owning the contract and failure behavior of Consensus Leader.	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 “Client reaches resource after lease loss” to Session Library.	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 Leader Endpoint boundary before extracting or scaling it.
Consensus Leader	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Renewals extend the lease only after quorum commit; client stops work before uncertainty margin.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Leader Endpoint from also owning the contract and failure behavior of Consensus Followers.	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 “Watch misses compaction window” to Leader Endpoint.	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 Consensus Leader boundary before extracting or scaling it.
Consensus Followers	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Expiry/release commits a new revision and notifies ordered watchers.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Consensus Leader from also owning the contract and failure behavior of Replicated Log.	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 “Administrator force-releases active lock” to Consensus Leader.	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 Consensus Followers boundary before extracting or scaling it.
Replicated Log	Preserves durable ordering, replay, and consumer checkpoints. In this design it participates in: Client opens an authenticated session and proposes acquire to the consensus leader.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Consensus Followers from also owning the contract and failure behavior of Lease Table.	Call every downstream consumer synchronously. That couples user latency and availability to analytics, notification, indexing, and audit dependencies. Here that simpler choice would couple “Old owner resumes after long pause” to Consensus Followers.	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 Replicated Log boundary before extracting or scaling it.
Lease Table	Owns authoritative versioned state or immutable evidence. In this design it participates in: Committed acquire increments fencing counter and returns lease plus token.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Replicated Log from also owning the contract and failure behavior of Fencing Counter.	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 “Leader changes during renewal” to Replicated Log.	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 Lease Table boundary before extracting or scaling it.
Fencing Counter	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Protected resource requires token on every mutation and rejects values below its maximum.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Lease Table from also owning the contract and failure behavior of Watch Registry.	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 “Client reaches resource after lease loss” to Lease Table.	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 Fencing Counter boundary before extracting or scaling it.
Watch Registry	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Renewals extend the lease only after quorum commit; client stops work before uncertainty margin.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Fencing Counter from also owning the contract and failure behavior of Clock / Lease Monitor.	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 “Watch misses compaction window” to Fencing Counter.	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 Watch Registry boundary before extracting or scaling it.
Clock / Lease Monitor	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Expiry/release commits a new revision and notifies ordered watchers.	Proves correctness and supports controlled recovery. It prevents Watch Registry from also owning the contract and failure behavior of Protected Database.	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 “Administrator force-releases active lock” to Watch Registry.	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 Clock / Lease Monitor boundary before extracting or scaling it.
Protected Database	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Client opens an authenticated session and proposes acquire to the consensus leader.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Clock / Lease Monitor from also owning the contract and failure behavior of Protected Object Store.	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 “Old owner resumes after long pause” to Clock / Lease Monitor.	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 Protected Database boundary before extracting or scaling it.
Protected Object Store	Owns authoritative versioned state or immutable evidence. In this design it participates in: Committed acquire increments fencing counter and returns lease plus token.	Defines the source of truth and commit boundary. It prevents Protected Database from also owning the contract and failure behavior of Admin Recovery.	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 “Leader changes during renewal” to Protected Database.	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 Protected Object Store boundary before extracting or scaling it.
Admin Recovery	Owns one bounded domain decision, transition, or operational capability. In this design it participates in: Protected resource requires token on every mutation and rejects values below its maximum.	Separates a distinct scale, ownership, policy, or failure boundary. It prevents Protected Object Store from also owning the contract and failure behavior of Audit Stream.	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 “Client reaches resource after lease loss” to Protected Object 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 Admin Recovery boundary before extracting or scaling it.
Audit Stream	Preserves durable ordering, replay, and consumer checkpoints. In this design it participates in: Renewals extend the lease only after quorum commit; client stops work before uncertainty margin.	Proves correctness and supports controlled recovery. It prevents Admin Recovery from also owning the contract and failure behavior of operators and repair tooling.	Call every downstream consumer synchronously. That couples user latency and availability to analytics, notification, indexing, and audit dependencies. Here that simpler choice would couple “Watch misses compaction window” to Admin Recovery.	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 Audit Stream boundary before extracting or scaling it.

Decision 01

Chosen boundary vs simpler monolith

Back locks with a consensus system and represent ownership as a lease that expires without renewal.

A single deployable with one transactional store is the right starting point for a small distributed lock service workload. The proposed boundary becomes justified when back locks with a consensus system and represent ownership as a lease that expires without renewal. Separating it contains the named failure “Old owner resumes after long pause” without placing unrelated work on the critical path.

Decision 02

Correctness vs availability

Issue monotonically increasing fencing tokens; the resource must reject operations from stale owners.

The design coordinates only where each accepted distributed lock service operation has one stable result and retries do not repeat its business effect. 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

Never rely on wall-clock equality or a client releasing correctly; design for pauses and partitions.

This choice adds dashboards, ownership, schema compatibility, capacity planning, and recovery runbooks. Start with the minimum topology that supports “Protected resource requires token on every mutation and rejects values below its maximum.” 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 each accepted distributed lock service operation has one stable result and retries do not repeat its business effect. Durable projections, notifications, indexing, analytics, and repair are isolated so their load or failure cannot corrupt the acknowledged operation.

Correctness boundary

Back locks with a consensus system and represent ownership as a lease that expires without renewal.

Scale boundary

Issue monotonically increasing fencing tokens; the resource must reject operations from stale owners.

Operational boundary

Never rely on wall-clock equality or a client releasing correctly; design for pauses and partitions.

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5. Critical flow and algorithm

Client opens an authenticated session and proposes acquire to the consensus leader.

Committed acquire increments fencing counter and returns lease plus token.

Protected resource requires token on every mutation and rejects values below its maximum.

Renewals extend the lease only after quorum commit; client stops work before uncertainty margin.

Expiry/release commits a new revision and notifies ordered watchers.

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

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.

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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

each accepted distributed lock service operation has one stable result and retries do not repeat its business effect. 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
Old owner resumes after long pause	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.
Leader changes during renewal	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.
Client reaches resource after lease loss	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.
Watch misses compaction window	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.
Administrator force-releases active lock	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: Old owner resumes after long pause
operation_id: op_01J...
aggregate_key: distributed-lock_123
last_durable_version: 18
projection_checkpoint: partition-07@offset-482901
retry_policy: same_identity / bounded_backoff
reconciliation_status: pending
owner: Audit Stream

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: Old owner resumes after long pause — 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

Why are fencing tokens required?

Back locks with a consensus system and represent ownership as a lease that expires without renewal. Walk the concrete lifecycle step “Client opens an authenticated session and proposes acquire to the consensus leader.” Then test it against “Old owner resumes after long pause”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

What if a client pauses past its lease?

Issue monotonically increasing fencing tokens; the resource must reject operations from stale owners. Walk the concrete lifecycle step “Committed acquire increments fencing counter and returns lease plus token.” Then test it against “Leader changes during renewal”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How do fair locks work?

Never rely on wall-clock equality or a client releasing correctly; design for pauses and partitions. Walk the concrete lifecycle step “Protected resource requires token on every mutation and rejects values below its maximum.” Then test it against “Client reaches resource after lease loss”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

How does leader election differ?

Back locks with a consensus system and represent ownership as a lease that expires without renewal. Walk the concrete lifecycle step “Renewals extend the lease only after quorum commit; client stops work before uncertainty margin.” Then test it against “Watch misses compaction window”. State the retry identity, durable checkpoint, recovery signal, and cost of the alternative.

When should you avoid distributed locks?

Issue monotonically increasing fencing tokens; the resource must reject operations from stale owners. Walk the concrete lifecycle step “Expiry/release commits a new revision and notifies ordered watchers.” Then test it against “Administrator force-releases active lock”. 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	each accepted distributed lock service operation has one stable result and retries do not repeat its business effect.
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 each accepted distributed lock service operation has one stable result and retries do not repeat its business effect. The online path stays bounded, authoritative state is versioned and partitioned, and asynchronous work is replayable and observable.”

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Lock Client

Session Library

Leader Endpoint

Consensus Leader

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