ARCHITECTURAL BLUEPRINTS

Distributed Systems & Architecture Lab

Empirical architectural patterns engineered by Muhammad Asadullah Nadeem. High throughput, low latency, and zero data loss under network partitions.

// 02. SYSTEM DESIGN

Designing systems that scale.

Explore practical system-design examples covering architecture, data flow, scalability, reliability, asynchronous processing, caching, messaging, and failure handling.

DESIGN EXAMPLE 01 — JOB QUEUE & ASYNC WORKER

Reliable background task execution

A general-purpose asynchronous processing architecture that separates user-facing HTTP request handling from long-running background work.

HIGH-LEVEL ARCHITECTURE FLOW

CLICK NODE TO INSPECT

// DESIGN ASSUMPTIONS (ILLUSTRATIVE PARAMETERS)

• Design Assumption: 10,000 jobs/min peak workload

• Design Assumption: Up to 500 concurrent worker threads

• Design Assumption: Job pickup lag < 500ms under normal queue load

Architecture Components:

END-TO-END DATA PIPELINE:

WEB / MOBILE CLIENT→API SERVICE→QUEUE→WORKER POOL→DATABASE & STORAGE→AUDIT LOG

// COMPONENT INSPECTOR

PERSISTENT QUEUE

Decoupled Task Buffer

Technology Options

Redis StreamsRabbitMQAmazon SQS

Responsibilities

•Persist job payloads durably before worker consumption
•Enforce visibility timeouts while jobs are in flight
•Buffer traffic spikes to prevent database overload
•Provide dead-letter routing after max retries

Engineering Why

Decouples producer throughput from consumer processing rates and enables worker pools to scale independently.

Scaling Signals

❖Queue backlog depth (unclaimed jobs)

❖Oldest job age in queue

❖Enqueue vs Dequeue rate delta

Failure Considerations

Producers must apply client-side backpressure or fail gracefully if queue durability guarantees are breached.

// IDEMPOTENCY STRATEGY

Duplicate Delivery is Normal

In distributed queueing architectures, network partitions can cause duplicate job deliveries. Workers must execute jobs idempotently by checking completion state before running business logic.

// Idempotency Pattern
async function processJob(job) {
  const isCompleted = await redis.get(`job:done:${job.id}`);
  if (isCompleted) {
    console.log(`Job ${job.id} already processed. Skipping.`);
    return;
  }
  
  await executeBusinessLogic(job.payload);
  await redis.set(`job:done:${job.id}`, 'TRUE', 'EX', 86400);
}

// FAILURE & RETRY PIPELINE

Exponential Backoff & DLQ Flow

JOB EXECUTION→TRANSIENT FAILURE

1. Exponential Backoff with Random Jitter (e.g., 2s, 4s, 8s, 16s)

2. Enforce Max Retry Count Limit (e.g., 5 attempts)

3. Enforce Visibility Timeout to recover silent worker crashes

MAX RETRIES EXCEEDED→DEAD LETTER QUEUE (DLQ)

// ENGINEERING TRADE-OFFS

Queue & Messaging Engine Selection

Redis Streams

PROS:

Sub-millisecond operations, simple in-memory log, consumer groups.

CONS:

Requires careful RAM sizing & persistence configuration (AOF/RDB).

RabbitMQ

PROS:

Rich AMQP routing capabilities, native priority queues, mature ACK semantics.

CONS:

Additional operational complexity managing cluster nodes.

Amazon SQS

PROS:

Fully managed cloud service, infinite auto-scaling, zero server overhead.

CONS:

Vendor lock-in, short-polling vs long-polling cost considerations.

// RESILIENCE ANALYSIS

What happens when components fail?

DATABASE FAILURE

Jobs remain buffered in persistent queue streams while storage recovers.

WORKER CRASH

Unacknowledged jobs automatically become visible again after visibility timeout expires.

QUEUE STORAGE OUTAGE

Producers apply client-side backpressure or fail safely rather than accepting unqueueable work.

NETWORK PARTITION

Timeouts, exponential backoff retries with random jitter, and idempotent completion keys.

THIRD-PARTY DEPENDENCY FAIL

Circuit breakers open to prevent cascading failures when external API vendors are down.

CONSISTENCY MODEL

At-least-once delivery with eventual consistency across read replicas.

// COMPLETE SYSTEM SPECIFICATION MODEL

Job Queue & Async Worker Deep Dive Specifications

01. PROBLEM STATEMENT

Separating user HTTP requests from compute-heavy background execution (such as image transcoding, report generation, or bulk data processing) without losing jobs during component crashes or sudden traffic spikes.

02. REQUIREMENTS

Functional:

• At-least-once task delivery guarantee across distributed workers

• Delayed task scheduling and configurable visibility timeouts

• Automatic exponential backoff retry policy for transient failures

• Dead-Letter Queue (DLQ) isolation for poison pill payloads

05. STORAGE STRATEGY

Engine: PostgreSQL for job metadata & Amazon S3 for file outputs

Schema: jobs (job_id VARCHAR PRIMARY KEY, payload JSONB, status VARCHAR, attempts INT, created_at TIMESTAMP)

06. CACHING STRATEGY

Redis lock keys for worker heartbeats & idempotency completion flags

Why: Fast atomic checks prevent two workers from processing duplicate jobs simultaneously.

12. OBSERVABILITY

• Traces: OpenTelemetry trace context passed through queue message metadata headers (`traceparent`).

• Alerting: Alert triggers when DLQ depth > 100 or oldest job age > 15 minutes.

11. SECURITY & ACCESS

Payload field-level encryption for sensitive user data; IAM role scoping for worker containers.

// 05. ENGINEERING PRINCIPLES

How I think about systems.

01 —

Design for failure

Assume every network call will drop, every database will delay, and every server will eventually crash. Build resilience into the architecture from day one.

02 —

Keep boundaries explicit

Define clear domain contexts, clean API contracts, and isolated data ownership. Blurred boundaries breed untangleable system coupling.

03 —

Prefer observable systems

If you cannot measure latency, queue depth, and error rates in real time, you are running blind. Systems must explain their state continuously.

04 —

Automate repetitive work

Build infrastructure as code, continuous deployment pipelines, and automated recovery loops. Human intervention during incidents should be the exception.

05 —

Make scaling predictable

Scale horizontally by keeping processing stateless and partitioning data deterministically. Avoid hidden bottlenecks in central lock hubs.

06 —

Keep complexity intentional

Every architectural abstraction introduces operational overhead. Introduce microservices or event streams only when simple monoliths hit verified constraints.

07 —

Data decisions matter

Database selection, indexing strategy, and partition key choice dictate system performance bounds far more than application code optimization.

08 —

Reliability is a feature

Performance is meaningless if the system is unavailable. Treat error budgets, SLOs, and graceful degradation as core product requirements.

09 —

Measure before optimizing

Base architectural refactoring on empirical telemetry, profiling, and distributed trace evidence rather than intuition or premature assumptions.

10 —

Build for maintainability

Write clean, readable code and maintain concise architecture decision records (ADRs). Systems are read and maintained far more than they are written.