Message queues and event-driven work in Kolkata

+91 94329 43298 WhatsApp

Message queues and event-driven work

The difference between an order that quietly fails and one that retries until it succeeds is usually a queue.

Messaging

Chosen per project

Maintainable handover

Plenty of work does not belong in the request that triggered it. Generating a PDF, pushing an invoice to an accounting system, sending a batch of messages — do it inline and the user waits, and a failure anywhere in the chain loses the work.

A queue makes the work durable. The request finishes immediately, the task is recorded, and a worker picks it up — retrying on failure, and parking anything that cannot succeed where a human can see it rather than silently discarding it.

What we build with Message queues

Integrations with third-party systems that are slow or occasionally down

Bulk operations — statements, notifications, imports, report generation

Smoothing traffic spikes so the database is not hit all at once

Passing events between services without them calling each other directly

Where it fits — and where it does not

Good fit when

Work that must not be lost if a process restarts

Anything that depends on a system you do not control

Slow tasks that currently make a user wait

Consider something else when

Work whose result the user needs on the same screen, immediately

Very small systems where a database-backed job table is simpler

Message lifecycle

What happens to a job that fails

The point of a queue is that the unhappy path is designed rather than accidental.

Published

Request returns immediately; work is durable.

Consumed

A worker takes the message with a visibility timeout.

Retried

Transient failure? Redelivered on a backoff schedule.

Dead-lettered

Still failing? Parked, alerted, inspectable.

How we work with Message queues

Idempotent handlers

Every consumer is written so processing the same message twice is harmless — because eventually it will happen.

Retry with backoff

Transient failures retried on a widening delay rather than hammering a system that is already struggling.

Dead-letter queues

Messages that cannot be processed are parked and alerted on, never dropped.

Visible depth

Queue length and consumer lag are monitored, so a backlog is noticed before customers notice it.

Our typical Message queues setup

Typical Message queues stack choices and what we use for each
ConcernWhat we use
BrokersRabbitMQ, Azure Service Bus, Amazon SQS, Kafka where ordering and replay matter
DeliveryAt-least-once, with idempotent consumers to make that safe
Poison messagesDead-letter queue with alerting and a replay path
SchedulingDelayed and recurring jobs where the broker supports them
MonitoringQueue depth, age of oldest message, consumer lag

Frequently asked questions

For modest volumes a jobs table with a worker process is genuinely simpler and easier to debug. A broker earns its place when you need fan-out to several consumers, ordering guarantees, or throughput a database polling loop cannot sustain.

Nothing, if the handler is written to be idempotent — which is how we write them. Duplicate delivery is normal in every at-least-once system, so it is designed for rather than hoped against.

Yes. Failed messages land in a dead-letter queue with the original payload and the error, so they can be inspected, fixed and replayed instead of vanishing.

Topics people search for

Services built with Message queues

Tell us what you are trying to build

Describe the problem in plain language and we will tell you what it would take to solve it — the approach, the moving parts and the sensible order to build them in. No obligation either way.

WhatsApp
Call now Enquire