A Minimal TLA+ Workflow for Distributed Protocols

Monthly research note. Theme: Distributed Systems Under Failure.

TL;DR

A focused memo on A Minimal TLA+ Workflow for Distributed Protocols: define the model, state the properties, then design the system so those properties remain true under failure and adversaries.

Key takeaways

• Expose protocol state (epoch/term/commit index) as first-class telemetry.
• Write the safety property first; liveness is always conditional on timing assumptions.
• Mixed-version operation is the default; upgrades must preserve invariants.
• Treat retries, reordering, and partial failure as default conditions.
• Prefer protocols and APIs that make invalid states hard to express.

Why this matters

• Mixed-version operation is the default state of real deployments.
• Most protocol bugs hide in timeouts, retries, and membership changes.
• State compaction and snapshots are where correctness goes to die quietly.
• If your protocol isn’t testable under reordering, it isn’t deployable.

Key questions

• Which components require determinism for reproducibility?
• What is the compaction story (snapshots, log truncation, state transfer)?
• What is the failure model (crash, byzantine, partitions, reordering)?
• Where do you pay for liveness (timeouts, leader election, reconfiguration)?
• How do clients discover leaders safely (and what happens during flaps)?
• How do you prevent overload from becoming inconsistency?

Assumptions

• Partitions happen at multiple layers (network, DNS, LB, service mesh).
• Delays are unbounded during incidents; timeouts are guesses.
• Clocks drift; leases can be violated under GC pauses or VM stalls.
• Nodes restart with partial state unless you prove durability.

Non-goals

• Treating membership as static or human-managed only.
• Relying on global time for ordering without strong synchronization assumptions.

Model & invariants

For quorum-based protocols, the intersection property is the backbone of safety:

Treat membership changes as protocol events, not control-plane side effects.

Write down the safety property first. If it’s not written, it’s not implemented.

Security properties

• Authenticity: actions are bound to identity and purpose.
• Replay resistance: duplicated inputs do not change outcomes.
• Least authority: privileges are scoped by purpose and time.
• Evidence: critical actions emit verifiable audit events.

Failure modes

• Observability gaps during incidents (missing evidence).
• Mixed-version behavior that violates assumptions silently.
• Config drift that weakens security posture over time.
• Recovery paths that only work when nothing is broken.

Design sketch

sequenceDiagram
  participant C as Client
  participant L as Leader
  participant F1 as Follower 1
  participant F2 as Follower 2
  C->>L: propose(cmd)
  L->>F1: appendEntries
  L->>F2: appendEntries
  F1-->>L: ack
  F2-->>L: ack
  L-->>C: commit(result)

Implementation notes

Your protocol is an interface between failures and invariants. Encode both.

Operational invariants to monitor:
- leader_changes_per_minute
- commit_index_monotonic
- snapshot_install_failures
- quorum_acks_latency_p99
- rejected_requests_due_to_admission_control

Verification strategy

• Model checking the smallest core (timeouts, election, reconfiguration).
• Stress + skew tests: hot keys, slow disks, noisy neighbors.
• Upgrade tests: mixed versions and rolling deploy invariants.
• Jepsen-style fault injection: partitions + reordering + client retries.
• Linearizability checks for read/write APIs that claim it.

Operational notes

• Treat compaction and snapshot install as first-class SLOs.
• Rate-limit retries and apply admission control before saturation.
• Rehearse region failover and reconfiguration under load.
• Prefer monotonic time sources for leases; alert on clock discontinuities.
• Expose protocol state: term/epoch, leader, commit index, config version.

What to monitor

• Authz failures and policy denials (unexpected spikes).
• Invariant violation rate (should be ~0).
• Admission-control / rate-limit rejections (by reason).
• Rollback events and the conditions that triggered them.
• Error budget burn + tail latency under load.

Rollback plan

• Keep dual-write / dual-verify windows where appropriate.
• Use canaries and staged rollout; stop early when signals degrade.
• Prefer backward-compatible changes; avoid “flag day” upgrades.
• Preserve evidence (configs, artifacts, audit logs) to reconstruct what changed.
• Define an explicit rollback trigger (metrics + thresholds).

Evidence

• Site Reliability Engineering (Google) (1) — Error budgets, incident response, and reliability as an engineering discipline.
  • Evidence: Error budgets and incident response are correctness controls; tie monitoring and rollback triggers to SLO burn.
• Time, Clocks, and the Ordering of Events (Lamport) (2) — Causality, ordering, and why clocks are tricky.
  • Evidence: Use this as the baseline for happens-before vs wall-clock; avoid embedding clock assumptions into safety properties.

Open questions

• How do you prevent “operator fixes” from changing safety properties?
• What is the worst-case recovery time after a leader + disk failure?
• Which invariants are violated first under overload: latency, availability, or correctness?
• Where does your protocol assume synchrony without admitting it?

Checklist

• Telemetry captures correctness signals.
• Assumptions listed and reviewed.
• Safety properties stated as invariants.
• Failure modes enumerated with mitigations.
• Rollback plan rehearsed and automated.
• Costs bounded (CPU/memory/bandwidth) under adversarial inputs.

Further reading

• Paxos Made Simple (Lamport) — Agreement basics and the invariants that matter.
• Jepsen — Testing correctness under partitions and faults.
• In Search of an Understandable Consensus Algorithm (Raft) — Consensus with explicit state machines and practical tradeoffs.
• Time, Clocks, and the Ordering of Events (Lamport) — Causality, ordering, and why clocks are tricky.
• Learn TLA+ — Practical entry point for specification and model checking.
• Site Reliability Engineering (Google) — Error budgets, incident response, and reliability as an engineering discipline.