02 // SELECTED WORK // ENGINEERING LAB
QueueForge.
What happens when a backend service faces concurrency, failure, and scale?
QueueForge is a progressively developed job-processing platform at the center of my Backend & Distributed Systems Engineering course. Each lab explores an engineering failure mode, implements a possible solution, and tests the assumptions behind it.
This is an active learning and engineering project. Described later-stage capabilities are planned, not claims of completed implementation.
Working code is not the same as correct behavior.
A simple job API can appear reliable until two workers claim the same task, a dependency slows down, or a retry causes an unintended second operation. QueueForge provides a controlled environment for investigating those failures rather than treating distributed system patterns as a checklist.
The objective is to understand when each design is useful, what it costs, how it can fail, and which tests would demonstrate that it works.
Reproduce. Explain. Verify.
Each laboratory starts with an observable problem and finishes with a measured result and documented limitations.
Introduce the failure
Build a small example that reproduces a specific concurrency, performance, or reliability defect.
Evaluate the mechanism
Compare implementation choices, including database constraints, transactions, coordination, and operational tradeoffs.
Gather evidence
Use tests, logs, SQL plans, traces, and repeatable load experiments to evaluate the result.
Document the limits
Record the design rationale, failure assumptions, remaining weaknesses, and recovery behavior.
One platform. Increasing complexity.
The 16-week course introduces components progressively, starting with Python and PostgreSQL before adding caching, messaging, scaling, and observability.
Implementation milestones
Five progressive checkpoints from reproducing concurrency defects to assessing operational readiness.
Reproduce concurrency failures
Build a small PostgreSQL-backed job model and demonstrate how overlapping claims can cause incorrect state transitions.
Establish safe job claims
Compare transactional locking and optimistic concurrency, then verify behavior with concurrent tests.
Measure before optimizing
Profile database queries, capture execution plans, and compare repeatable performance baselines.
Build resilient processing
Introduce caching, retries, queues, and workers, with evidence collected from controlled failures.
Assess operational readiness
Document architecture, test failure recovery, instrument service boundaries, and evaluate capacity.
Engineering domains
The technical disciplines developed throughout the course, from transactional correctness to observability and production operations.
Concurrency & correctness
Race conditions, transactions, isolation, locking, and deadlock recovery.
Data engineering
PostgreSQL query plans, indexes, constraints, migrations, replication, and partitioning.
Caching & reliability
Redis caching, invalidation, safe retries, idempotency, rate limiting, and circuit breakers.
Distributed workflows
RabbitMQ workers, durable messaging, transactional outbox, and eventual consistency.
Production operations
Health checks, graceful shutdown, tracing, metrics, load testing, and fault injection.
Show what the system actually does.
The initial repository contains early Python concurrency experiments, a database schema, dependencies, and a Docker Compose configuration. These are the foundations for the broader capstone, not a finished distributed platform.
As labs are completed, I intend to publish the reproduction steps, automated tests, benchmark results, engineering notes, and architecture decisions alongside the implementation.
From isolated experiments to a defensible system.
Follow the source repository for implementation history, test evidence, and changes to the architecture as each module progresses.