sqs

A fully managed message queue service offered by AWS. It provides a reliable, scalable, and cost-effective way to decouple and coordinate distributed software systems and microservices.

Libraries for Amazon SQS (Simple Queue Service)

• GO: sqs, to wrap and simplify aws-sdk-go/service/sqs. Example is at go-amazon-sqs-sample

A common flow to consume a message from a message queue

• The libraries to implement this flow are:
  • mq for GOLANG. Example is at go-amazon-sqs-sample

Use Cases of Amazon SQS (Simple Queue Service)

Decoupling Microservices

• Scenario: Separating different parts of an application to ensure that a failure in one part does not affect others.
• Benefit: Enhances fault tolerance and scalability by allowing asynchronous communication between services.

Asynchronous Processing

• Scenario: Handling tasks that do not need immediate processing, such as batch processing or background tasks.
• Benefit: Improves system efficiency and response times for end-users.

Job Queuing

• Scenario: Managing and distributing jobs to worker processes.
• Benefit: Balances load and ensures all tasks are completed without overloading any single worker.

Order Processing Systems

• Scenario: Processing customer orders, where each order can be handled as a separate task.
• Benefit: Ensures reliable and scalable processing of orders, even during high demand.

Message Buffering

• Scenario: Smoothing out bursty traffic in applications to prevent overload.
• Benefit: Protects the system from spikes in traffic by buffering messages.

Workflow Orchestration

• Scenario: Orchestrating steps in a complex workflow, such as image processing pipelines.
• Benefit: Coordinates different stages of processing in a reliable and scalable manner.

Comparison of Amazon SQS, Google Pub/Sub and Apache Kafka

Amazon SQS

• Type: Managed message queuing service.
• Use Case: Decoupling and scaling microservices, asynchronous tasks.
• Scalability: Automatically scales.
• Delivery Guarantees: At-least-once, FIFO (exactly-once).
• Integration: Deep integration with AWS services.
• Delivery Models: Primarily pull, with long polling.

Google Pub/Sub:

• Type: Managed real-time messaging service.
• Use Case: Event-driven architectures, real-time analytics.
• Scalability: Automatically scales.
• Delivery Guarantees: At-least-once delivery.
• Integration: Tight with Google Cloud services.
• Delivery Models: Push and pull.

Apache Kafka

• Type: Open-source event streaming platform.
• Use Case: High-throughput messaging, event sourcing, log aggregation.
• Scalability: High with partitioned topics.
• Delivery Guarantees: Configurable (at-least-once, exactly-once).
• Integration: Broad ecosystem with various connectors.
• Delivery Models: Pull-based consumer groups.

Key Differences

• Management: Pub/Sub and SQS are managed services, while Kafka is typically self-managed or via managed services like Confluent.
• Use Case Focus: Pub/Sub and Kafka are ideal for real-time processing, whereas SQS is great for decoupling microservices and handling asynchronous tasks.
• Delivery Models: Pub/Sub supports push and pull, SQS supports pull with long polling, and Kafka primarily uses pull with consumer groups.
• Scalability: All three are highly scalable, but Kafka offers the most control over performance tuning.
• Integration: Pub/Sub integrates well with Google Cloud, SQS with AWS, and Kafka has a broad integration ecosystem.

Installation

Please make sure to initialize a Go module before installing core-go/sqs:

go get -u github.com/core-go/sqs

Import:

import "github.com/core-go/sqs"