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Build a WebSocket Presence Server

WebSocket Presence Server — a complete walkthrough of real-time presence, covering connection registry, room router, presence tracker, and testing.

websocket_presence_server/
├── src/index.ts
├── src/connection-registry.ts
├── src/room-router.ts
├── tests/websocket_presence_server.test.ts
└── package.json

// Connection registry: real-time presence core
export function connectionregistry(input: ConnectionregistryInput): ConnectionregistryResult {
  // Build the connection registry that anchors real-time presence and defines the core data shape.
  return processConnectionregistry(input);
}

// Room router — handles real-time presence Add the room router so
export class Roomrouter {
  constructor(private opts: RoomrouterOptions) {}
  async run(ctx: Context): Promise<RoomrouterOutput> {
    return this.execute(ctx);
  }
}

// Presence tracker: real-time presence stage
export const presencetracker = (next: Next) => async (req: Request) => {
  const out = await transform(req);
  return next(out);
};

// Message history for real-time presence
export async function messagehistory(stream: AsyncIterable<Chunk>): Promise<Aggregate> {
  let acc = init();
  for await (const c of stream) acc = merge(acc, c);
  return acc;
}

// Backpressure guard: real-time presence finalization
export function backpressureguard(state: State): Result {
  validate(state);
  return finalize(state);
}

Project overview

This project builds a real-time presence system whose reason for existing is broadcasting presence updates to room members while keeping per-connection memory bounded under backpressure. The temptation in real-time presence is to bolt the interesting part onto a working prototype and discover the hard cases in production; this build inverts that order and treats a slow client whose send buffer fills and blocks the broadcast loop for the whole room as the first thing designed, not the last.

You will implement Connection registry, Room router, Presence tracker, Message history, and Backpressure guard, each as a module with one responsibility and a test that proves it in isolation. The real-time presence pipeline is then the wiring that composes them, and the integration test is where the composition is shown to survive a slow client whose send buffer fills and blocks the broadcast loop for the whole room.

The walkthrough is written for a developer who has used a real-time presence system but has not had to make one honest. It favors explicit data shapes over clever abstractions, and it names the failure each design choice prevents rather than presenting the choice as a style preference.

Each phase ends with something observable — a connection registry that returns the right shape, a room router that rejects bad input, an integration test that goes green — so you can stop at any point and still have a real-time presence system that works for the cases covered so far.

What you will build

The finished real-time presence system exposes five capabilities, each with a focused test:

• Connection registry
• Room router
• Presence tracker
• Message history
• Backpressure guard

Connection registry is the foundation that every later stage assumes; Room router is where real-time presence correctness lives; Presence tracker handles a slow client whose send buffer fills and blocks the broadcast loop for the whole room; Message history makes the system observable; and Backpressure guard is the graceful-degradation layer that keeps overload from becoming corruption.

The build keeps the real-time presence logic in ordinary modules you can read in one sitting, and pulls in dependencies only for the parts that are genuinely tedious to hand-roll. That keeps the diff between the tutorial and a real codebase small, and keeps the failure modes visible in code rather than hidden behind a framework.

Architecture and data model

The system is organized around Connection registry, Room router, and Presence tracker, with Message history tying them together. Data flows as a map of rooms to member connection sets, with a ring buffer of recent messages per room for replay on join.

Each component is a module with a pure interface where practical: the same input produces the same output, and side effects are pushed to the edges. That discipline is what makes the phases independently testable, because a test for Presence tracker does not need to stand up Connection registry and Room router to exercise the interesting branch.

The data model is deliberately explicit. A map of rooms to member connection sets, with a ring buffer of recent messages per room for replay on join Keeping the shape written down — in a schema, a type, or a fixture — means a change to the shape is a visible diff rather than a silent runtime shift, and it means the integration test can assert against a concrete shape instead of guessing.

Configuration and code are kept apart: the configuration says what real-time presence should do, the modules say how. A reviewer can change a threshold or a policy without reading the implementation, which keeps the risky changes small and lets the diff stand as the review.

The failure path through Presence tracker is designed before the happy path is finished, because a slow client whose send buffer fills and blocks the broadcast loop for the whole room is the case that distinguishes a real-time presence system from a demo. Every component declares what it does when its inputs are wrong, and the integration test asserts each of those declarations.

Prerequisites and dependencies

Have these ready before you install anything, so the real-time presence setup is not blocked by a missing tool:

• Node.js 20 LTS or later
• Familiarity with real-time presence fundamentals
• A local environment to run the test suite

The runtime dependencies are chosen to keep the real-time presence datapath legible:

• ws (^8.18.0) — WebSocket server
• pino (^9.0.0) — Connection logging

# setup for websocket-presence-server
pnpm init
pnpm add ws pino
pnpm add -D typescript tsx vitest

Versions are pinned to a major range so the real-time presence install is reproducible; commit the lockfile. The connection registry reads its operational parameters from the environment and validates them at startup, so a missing value fails the boot rather than the first real-time presence request.

Directory structure

Keep each real-time presence component in its own file so it can be tested without starting the others:

websocket_presence_server/
├── src/index.ts
├── src/connection-registry.ts
├── src/room-router.ts
├── tests/websocket_presence_server.test.ts
└── package.json

Phase 1: Connection registry

Build the connection registry that anchors real-time presence and defines the core data shape.

// Connection registry: real-time presence core
export function connectionregistry(input: ConnectionregistryInput): ConnectionregistryResult {
  // Build the connection registry that anchors real-time presence and defines the core data shape.
  return processConnectionregistry(input);
}

After this phase the connection registry works in isolation and is covered by a unit test. The connection registry defines the data shape that every later stage assumes, so getting it right first means the later phases compile against a stable contract.

Treat the connection registry as the single source of truth for the real-time presence data shape. Validate inputs at the boundary and reject early, so a malformed value never reaches Room router or Presence tracker where its effect would be harder to localize.

Phase 2: Room router

Add the room router so real-time presence can broadcasting presence updates to correctly.

// Room router — handles real-time presence Add the room router so
export class Roomrouter {
  constructor(private opts: RoomrouterOptions) {}
  async run(ctx: Context): Promise<RoomrouterOutput> {
    return this.execute(ctx);
  }
}

After this phase the room router works in isolation and is covered by a unit test. This is where real-time presence correctness lives: the room router is what makes broadcasting presence updates to room members while keeping per-connection memory bounded under backpressure true, and its test is the proof.

Keep the room router pure where you can. A function that takes an input and returns an output is trivial to test and impossible to mock wrong; a class with hidden state is the opposite. Push side effects to the caller and keep the room router about the transformation.

Phase 3: Presence tracker

Wire the presence tracker to handle a slow client whose without losing data.

// Presence tracker: real-time presence stage
export const presencetracker = (next: Next) => async (req: Request) => {
  const out = await transform(req);
  return next(out);
};

After this phase the presence tracker works in isolation and is covered by a unit test. The presence tracker is the difference between a real-time presence system that works on the happy path and one that survives a slow client whose send buffer fills and blocks the broadcast loop for the whole room.

Design the failure before the success. Decide what presence tracker does when a slow client whose send buffer fills and blocks the broadcast loop for the whole room: reject, retry, shed, or degrade — and write that test first. A real-time presence system whose failure behavior is unspecified will pick one at random under pressure, and it is usually the wrong one.

Phase 4: Message history

Integrate the message history to make real-time presence observable and testable end to end.

// Message history for real-time presence
export async function messagehistory(stream: AsyncIterable<Chunk>): Promise<Aggregate> {
  let acc = init();
  for await (const c of stream) acc = merge(acc, c);
  return acc;
}

After this phase the message history works in isolation and is covered by a unit test. Observability is not a finishing touch; it is how you know the real-time presence system is doing what it claims in production.

Emit one structured event per meaningful action, with enough context to localize a failure without a second round-trip. The message history is what turns “something is slow” into “the room router is slow for inputs matching this shape,” which is the difference between an alert and an investigation.

Phase 5: Backpressure guard

Finalize the backpressure guard so the real-time presence system degrades gracefully under overload.

// Backpressure guard: real-time presence finalization
export function backpressureguard(state: State): Result {
  validate(state);
  return finalize(state);
}

After this phase the backpressure guard works in isolation and is covered by a unit test. The backpressure guard is what keeps overload from becoming corruption: when the real-time presence system is asked for more than it can do, it degrades in a defined way instead of failing in an undefined one.

Pick a degradation strategy for backpressure guard — shed, queue, or backpressure — and make it explicit in code and in a test. Leaving the behavior to chance is how a real-time presence traffic spike becomes a data loss incident.

Testing

Each phase has a focused test: connection registry, room router, and presence tracker are exercised in isolation, then an integration test drives the full real-time presence pipeline and asserts the a slow client whose case is handled.

Start with unit tests that pin each component’s contract: the connection registry returns the documented shape, the room router transforms correctly, and the presence tracker fails in the documented way. These tests are fast and they localize regressions to the component that broke.

Then add an integration test that composes the components and drives a realistic real-time presence scenario end to end. Assert the happy path first, then deliberately trigger a slow client whose send buffer fills and blocks the broadcast loop for the whole room and assert the system degrades rather than corrupts. Because the components are pure where practical, the integration test stays deterministic rather than flaky.

A real-time presence suite must also prove the negative: bad input is rejected at the boundary, overload is shed rather than queued indefinitely, and a partial failure does not leave the system in an inconsistent state. These are the tests that catch the regressions a happy-path suite never would.

The entire real-time presence suite runs with no external service; every dependency is either in-process or stubbed, so every case is reproducible in CI. That is what lets the presence tracker failure-mode assertions live as tests rather than as a manual checklist that drifts out of date.

# run the real-time presence test suite
pnpm vitest run

Deployment and operations

Run at least two real-time presence instances behind a load balancer and share state in Redis. Set resource limits so real-time presence overload surfaces as backpressure rather than failure.

Run at least two stateless instances behind a load balancer, and move any shared state into a shared store so the real-time presence behavior is global rather than per instance. Size limits so the system sheds load before it runs out of memory, because a real-time presence system that OOMs under load fails in a way that is hard to reason about.

Drain on deploy: stop accepting new work, let in-flight real-time presence operations finish, then exit. Because the system holds no state that cannot be reconstructed, a rollout is a matter of cycling instances one at a time while watching the error rate.

When a dependency is unavailable, decide explicitly whether the real-time presence system fails closed or degrades. Leaving that choice to chance is how a dependency outage becomes a silent data loss, so make the choice visible in code and in a test.

Security considerations

Validate all real-time presence inputs at the boundary, reject oversized payloads before buffering them, and keep secrets in the environment rather than the repository.

The real-time presence system is a choke point, so it is the right place to reject malformed input before it reaches room router or presence tracker. Validate the shape and the size at the edge, and let the inner components assume what they receive is well-formed.

Never log secrets or full payloads; log enough to localize a failure without recording data that should not be on disk. Treat any external call as untrusted and validate its response before using it, because a real-time presence system that trusts its inputs blindly is one malformed response away from corruption.

Keep dependencies pinned and run an audit in CI. A transitive dependency with a known vulnerability is still a vulnerability in your real-time presence system, and the lockfile is what makes the audit meaningful.

Observability

Emit one structured event per real-time presence operation with the inputs’ shape, the outcome, and a correlation id. Track error rate and latency per component and alert on a step change rather than a single spike.

The correlation id is the thread that ties a user-visible symptom to the real-time presence decision and to the component log entry for the same operation. Without it, an investigation starts from a timestamp and a guess.

Watch the presence tracker failure rate separately from the overall error rate. A jump in presence tracker failures usually means a slow client whose send buffer fills and blocks the broadcast loop for the whole room, not a bug in room router, and the two have different owners and different fixes.

Chart the share of real-time presence operations that shed, queue, or succeed. A change in the mix points at the cause faster than an aggregate latency can, because each class has a different root.

Troubleshooting

A slow client whose send buffer fills and blocks the broadcast loop for the whole room. Reproduce with a minimal fixture, then add a guard test so the real-time presence system rejects the condition loudly.

Performance regresses under real-time presence load. Profile with the sampling analyzer, locate the hot path in room router, and add a targeted benchmark to lock the regression.

Keep the real-time presence dashboards separate from dependency dashboards. When an operation fails, the correlation id lets you decide in seconds whether the fault is connection registry, room router, or presence tracker, instead of guessing from a timeout.

Limitations and next steps

• Single-region; multi-region replication is out of scope.
• The connection registry is in-process and not horizontally shared by default.
• No auth layer; assume deployment behind an edge that enforces identity.

The real-time presence system is single-region by design; extending to multi-region means making connection registry shareable across regions, which is a real change to the consistency model rather than a config flag. The presence tracker is in-process, which is correct for one instance but must move to a shared store before scaling out.

There is no authentication or authorization layer; the system assumes it is deployed behind an edge that enforces identity. Adding one is straightforward but out of scope for this build, and doing it here would distract from the real-time presence correctness that is the point.

The natural next step, once the real-time presence pipeline is trusted in production, is to add a short-TTL cache for idempotent reads and a richer message history that exports histograms rather than counters. Both are additive and neither changes the contracts this build establishes.