Personal project

BookLK: Event-Driven Service Booking Marketplace

Customers book time slots with salons, clinics and repair shops, vendors get paid out, and the platform keeps a commission. It is four services plus a job worker, joined by a transactional outbox to Kafka, with a Go WebSocket service for live updates. Payments run on Stripe test mode and the PayHere sandbox, and they are reconciled every night.

BookLK showcase on GitHub Contact Janindu Gayanga

The problem

When many customers press "book" on the same slot at the same moment, the system must sell it exactly once.
Booking changes must reach every connected browser in real time without losing or reordering events.
Money must stay correct when payment webhooks arrive twice, late, or never.

Architecture and key decisions

NestJS API with 45 REST handlers and a public GraphQL endpoint, a Next.js 14 PWA, a Go realtime service on gorilla/websocket, an Express webhooks service, and a BullMQ job worker.
Double booking is blocked four times over: an idempotency key, a Redis SET NX lock, SELECT … FOR UPDATE inside a READ COMMITTED transaction, and a unique active-slot constraint as the database backstop.
Transactional outbox: the booking and its event commit in one Postgres transaction, and a relay publishes them to Kafka (Redpanda) using FOR UPDATE SKIP LOCKED. Consumers dedupe on eventId. Failures land in a per-topic DLQ that an admin can replay.
Payments sit behind a provider port (Stripe, PayHere and a deterministic fake). Webhooks are verified against the raw body. Money is in integer cents and commission in integer basis points; the ledger is append-only, and a refund is a reversal entry.
A nightly job compares provider transactions, payments and the ledger, records each gap, and can repair a missing webhook from the provider's own record.
Read side: MongoDB read models fed from Kafka, and GraphQL for public catalog reads with DataLoader batching, depth and cost limits, and persisted queries.
Kubernetes on a local k3d cluster: one Helm chart covering all six workloads, ArgoCD (app of apps) syncing from an in-cluster Gitea, and Prometheus and Grafana with multi-window SLO burn-rate alerts.
Terraform for a single EC2 + K3s node with a USD 10 budget alarm. It is tested with a mocked provider and applied to LocalStack; it has never touched real AWS.

Results

An integration test fires 20 parallel bookings at one slot and asserts that exactly one wins.
k6 booking spikes (six runs of 200 virtual users on 20 slots, one of 500 on 50): zero double bookings in all seven, checked directly in Postgres, and no 5xx errors.
Serializable isolation was aborting bookings for different slots (264 conflicts in one run) and leaving slots unsold. READ COMMITTED with row locks took conflicts to 0 and the median from 468–530 ms down to about 247 ms.
The latency target is not met. p95 is about 3.2 s at 200 virtual users against a 300 ms goal. cgroup CPU stats showed the API pinned at one CPU while Postgres sat mostly idle, so I moved to two API pods (85–92 req/s became 126–140). Profiling a single request is the next step.
A crash test kills the outbox relay before and after it publishes. No event is lost.

Tech stack

TypeScript
Next.js 14
NestJS
Go
PostgreSQL
Redis
Kafka (Redpanda)
MongoDB
GraphQL
BullMQ
Stripe
PayHere
Kubernetes (k3d)
Helm
ArgoCD
Terraform
Prometheus
Grafana
k6
Playwright

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