Complete Guide to AWS SQS: Pros/Cons, Getting Started and Pro Tips

What Is AWS SQS? 

Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables message storage and retrieval. It is designed to decouple and scale microservices, distributed systems, and serverless applications. SQS offers a reliable, highly scalable, hosted queue for storing messages in transit between applications or microservices.

SQS provides a secure platform where messages are stored redundantly across multiple servers. Messages can contain text in any format. It is useful for sending order information, performing tasks asynchronously, and buffering data. SQS handles the transmission of messages with an emphasis on preventing losses and minimizing administrative overhead.

Amazon SQS Pros 

SQS offers the following key capabilities:

• Standard queues: Offer maximum throughput, best-effort ordering, and at least-once delivery. They are suitable for applications where the volume of transactions is critical, but the order is not. 
• FIFO (First-In-First-Out) queues: Ensure that messages are processed once and in the exact order they are sent, making them suitable for tasks like bank transactions.
• At least once delivery: Guarantees a message will be delivered at least once. However, in some scenarios, a message might be delivered multiple times due to network issues or software constraints. 
• Visibility timeout: Enables control over how long a message remains invisible to other consumers after a consumer retrieves a message. This helps manage scenarios where a consumer takes longer to process a message or fails to process and delete it. After the visibility timeout expires, the message becomes visible again in the queue.
• Availability: It replicates messages across multiple servers and data centers to ensure low latency and high throughput. This redundancy enables reliable queuing for critical systems that require continuous uptime and robust performance.

AWS SQS Cons

While SQS is useful for ensuring messages are sent, it also has some potential drawbacks.

• Message ordering for standard queues: AWS SQS only guarantees that the message order will be retained in FIFO queues. For standard queues, the order of message delivery is not guaranteed, meaning that messages can be delivered in any order. This makes standard queues unsuitable for applications where the order is critical.

• Throughput limits for FIFO queues: While SQS enables almost unlimited transactions per second for standard queues, it enforces throughput limits for FIFO queues. With batching, the limit is 3,000 messages per second. Without batching, it is 300 messages per second. For some applications, these limits can create a bottleneck.

• No dead letter handling for FIFO queues: In SQS, a dead letter queue (DLQ) can sideline messages that failed to be processed even after multiple attempts. This feature is available for standard queues, but FIFO queues do not support dead letter handling by default. If a message in a FIFO queue consistently fails to be processed, developers must handle it manually.

AWS SQS vs. AWS SNS vs. AWS MQ 

SQS can be easily confused with other AWS messaging services like SNS (Simple Notification Service) and AWS MQ. 

• SQS is a message queuing service primarily used to decouple applications or microservices. 
• SNS is a scalable notification service that enables organized, push-based messaging across distributed systems, including mobile notifications and email.
• AWS MQ, tailored for migrating existing message brokers, such as RabbitMQ and ActiveMQ, to the cloud, simplifies the management of message brokers. 

While SQS and SNS are cloud-native services, AWS MQ comes in handy when users need a dedicated message broker that supports industry-standard APIs and protocols.

AWS SQS Use Cases

SQS is useful for the following scenarios.

Decoupling Components

When using SQS, different components of an application can operate independently, enhancing overall application fault tolerance. Components no longer communicate directly, sending messages via SQS, which handles the delivery to the appropriate component at the right time.

This decoupling allows independent scaling of different components and mitigates the impact of a component’s failure on the entire system. For example, if a processing module fails, the messages continue to queue without affecting the input module’s operation.

Handling Traffic Spikes 

SQS effectively manages sudden spikes in traffic, preventing server overloads by queuing excess requests. When a component receives more requests than it can process, SQS stores incoming messages until it can handle them. This prevents data loss and maintains quality of service during traffic peaks.

This capability ensures that applications respond gracefully to varying load levels, maintaining performance stability without provisioning excess compute resources. It aids in optimizing costs and resource utilization.

Asynchronous Processing

Asynchronous processing involves components pushing tasks to an SQS queue and proceeding without waiting to complete the tasks. The tasks are processed later, either by the same system or a different one, which retrieves the messages from the queue.

Asynchronous processing enables tasks requiring significant processing time without stalling the primary application processes. It helps ensure application responsiveness.

Serverless Automation with Lambda

Integrating Amazon Lambda with SQS can enhance application functionality by allowing serverless computing services to respond to messages more efficiently. A Lambda function can automatically execute code in response to messages in an SQS queue, processing data as it arrives without managing server clusters.

This integration simplifies the messaging architecture and reduces maintenance efforts and costs. Lambda only runs when triggered by messages and scales automatically with the workload.

AWS SQS Pricing 

SQS offers a pricing model based on usage, ensuring you pay only for the services you use, without any minimum fees. New users can start with AWS SQS at no cost under the AWS Free Tier, which includes 1 million monthly SQS requests. This makes it accessible for small projects or applications with minimal queuing requirements.

Beyond the free tier, SQS pricing varies between Standard and FIFO queues. For Standard queues, the first 1 million requests each month are free, after which the price is $0.40 per million requests up to 100 billion requests. If you exceed 100 billion requests, the price decreases to $0.30 per million requests up to 200 billion, then drops to $0.24 for additional requests over 200 billion per month.

The pricing structure for FIFO queues is slightly higher due to their added features such as message ordering and exactly-once processing. The first 1 million requests each month are free. The price then increases to $0.50 per million requests up to 100 billion, reduces to $0.40 per million from 100 billion to 200 billion, and further increases to $0.35 per million for over 200 billion requests per month.

SQS also charges for data transfers. All incoming data to SQS is free, while data transfer out rates start at $0.09 per GB for the first 10 TB per month and decrease incrementally as volume increases. Data transfers between SQS and other AWS services like EC2 or Lambda within the same region are free, which can help reduce costs for AWS-based applications.

Tutorial: Create an Amazon SQS Standard Queue and Send a Message 

Here’s an overview of how to create a standard queue and send a message in SQS.

Creating a Queue

To create an Amazon SQS standard queue, go to the Amazon SQS console:

1. Access the SQS console at https://console.aws.amazon.com/sqs/.
2. Click on Create queue.
3. Ensure the Type is set to your preferred option. The default queue type set in the console is Standard, and you cannot change it after the queue is created. 
4. Enter a unique name for your queue.
5. You can customize your queue settings in the Configuration section, where the console offers default values, but you have the option to adjust them:

• Visibility Timeout: This can be set between 0 seconds and 12 hours, with the default at 30 seconds.
• Message Retention Period: Define how long messages are kept in the queue, from 1 minute to 14 days, with a default of 4 days.
• Delivery Delay: This option allows you to set a delay for message delivery, from 0 seconds to 15 minutes; the default is 0 seconds.
• Maximum Message Size: Set this from 1 KB to 256 KB, with the default 256 KB.
• Receive Message Wait Time: This is the polling interval, which can be from 0 to 20 seconds. The default of 0 seconds indicates short polling.

For more advanced settings:

• Access Policy: You can define who can send and receive messages. Options include basic settings for user roles or an advanced mode where you can edit the JSON policy directly.
• Redrive Policy: Configure to enable and manage messages sent to a dead-letter queue.
• Encryption: Managed server-side encryption is enabled by default, but you can choose the encryption key or disable this setting.
• Dead Letter Queue: Set this up to handle undeliverable messages.
• Tags: Optionally, add tags to help organize and identify your queue.

1. After configuring these settings, click Create queue. The system will then confirm the creation, and you will be redirected to the queue’s Details page.

Sending a Message

Once your queue is created, you can send messages to it:

1. On the left navigation pane in the SQS console, select Queues.
2. Choose the queue you just created.
3. Click on Send and receive messages among the Actions options.
4. Under Message body, type your message.
5. If it’s a standard queue, you might set a Delivery delay; for example, type 60 and select seconds for a one-minute delay.
6. Click Send message.

Upon sending, the console will display a confirmation message and provide details about the message sent. This way, you can verify that the message has been correctly queued in your SQS standard queue.

Pro Tips for Making the Most of AWS SQS

Here are some recommended measures for ensuring the best use of Amazon SQS.

Ensure Efficient Polling

AWS provides two types of polling: short and long. Short polling responds immediately, sometimes even when messages are available in the queue. Long polling waits for a message to arrive or the timeout to lapse, reducing the number of empty responses and lowering costs. Implementing long polling with proper timeout settings can enhance the efficiency and cost-effectiveness of message processing in SQS.

Manage Visibility Timeouts

Visibility timeouts control the period a message remains invisible in SQS after a reader picks it up. If processing isn’t completed before the timeout expires, the message becomes visible again and might be reprocessed, leading to duplicates. Properly configuring the visibility timeout minimizes this risk and improves the processing reliability of transactions.

Implement Error Handling and Retry Mechanisms

If a message cannot be processed after several attempts, SQS can redirect it to a dead letter queue (DLQ). A DLQ is a collector for problematic messages, which can be examined and reprocessed manually or programmatically. Configuring retry policies and dead letter queues ensures that messages are not lost and errors are properly managed.

Use Dead Letter Queues (DLQ)

DLQs help manage messages that fail to process multiple times. By storing these messages aside, developers can later analyze why they weren’t processed and decide corrective measures without affecting the overall system performance. Implementing DLQs ensures that even if a message consistently fails, it does not disappear.

Implement Monitoring and Alerts

Monitoring messaging performance and setting up alerts for SQS can help maintain high system reliability. AWS CloudWatch offers monitoring tools that provide real-time visibility into SQS operations. Alarms for key metrics like message age, queue length, and processing failures help manage queues and prevent system overloads or interruptions.

Ensure Idempotency

Idempotency mechanisms guarantee that a particular operation yields the same result no matter how often it is performed. This is important in environments where message duplication might occur, ensuring consistency and reliability while reducing duplicates and potential conflicts within applications. It is particularly useful for financial transactions and operations that alter system state.

Monitoring AWS SQS with Lumigo

Lumigo is a cloud-native observability and troubleshooting tool. Lumigo automatically enriches traces with complete in-context request and response payloads and correlates them to the appropriate logs and metrics. This unified view of all troubleshooting data enables users to solve cloud native issues 80% faster than similar tools. Monitoring AWS SQS with Lumigo:

• Distributed tracing—One of the key drivers behind Lumigo is the distributed tracing capabilities. These allow you to follow the path of the message through the system from the AWS SQS queue all the way through and into the Node.js container and beyond. This allows you to identify how these errors are occurring and help you replicate them in a controlled environment.
• Debug code errors—With Lumigo’s detailed error analysis, you can see the call stack, the exact location of the error in the system, and how it may be being thrown. This helps alleviate the manual process of logging and debugging the code, helping to optimise yourself and other developers in the team.
• Monitor key metrics you set—By getting into the nitty-gritty of your system, Lumigo can also help monitor strategic key metrics within your infrastructure, such as invocation counts, execution times, and message throughput. By allowing you to see all of this and more, it gives you a holistic view of how your system is working from the inside out.
• Analyse the logs—In Lumigo, you can aggregate logs from Lambda functions and other AWS services, allowing you to quickly identify bottlenecks and failure points. This can help with this scenario by allowing you to see the logs from the Lambda function and the Node.js container and see where the errors are being thrown from.

Get started with a free trial of Lumigo for your microservice applications.