Selecting the appropriate message broker for Notification Service is critical for every enterprise that depends on event-triggered frameworks or non-synchronous communication among microservices. In this comprehensive guide, we will see Amazon Kinesis and Amazon Simple Queue Service (SQS), the two widely used choices on the market. Each has its own advantages and disadvantages, but both provide strong capabilities for handling large numbers of messages.
Key Considerations when choosing a Message Broker
When it involves deciding on a message broker for Notification, there are several key factors that you ought to think about; understanding those factors will assist you in making an informed choice and picking the message broker that suits your specific necessities.
Scalability: One important consideration is the scalability of the message broker. You need a message broker that could accommodate your current workload and can quickly scale to handle accelerated traffic in the future.
Performance: Another critical factor to not forget is the performance of the message broker. You need a message broker that can handle excessive throughputs and deliver messages fast and reliably.
Availability: It is critical to have a highly-to-be-had message broker that can assure message transport even during failures or outages.
Integration skills: Consider the integration abilities of the message broker together with your existing structures and technologies. You want a broker who can seamlessly integrate with your contemporary infrastructure.
Cost: Lastly, you want to consider the value implications of using a particular message broker. Evaluate the pricing models, and facts switch fees, and any additional fees related to the usage of the broker.
Amazon Kinesis vs. AWS SQS
Let’s see the difference between Kinesis and SQS.
| Features | Amazon Kinesis | Amazon SQS |
| Deployment | Fully managed by AWS | Fully managed by AWS |
| Message Model | Streams & Shards | Distributed Message Queuing Service |
| Throughput | High | High |
| Latency | Low | Varying |
| Message Ordering | Order Retention with Shard | FIFO Ordering |
| Durability | Data is retained for 1-7 days in streams | Messages are stored for the retention period |
| Scalability | Auto-Scaling by AWS | Auto-Scaling by AWS |
| Complexity | Requires expertise for real-time processing | Simplicity can limit advanced use cases |
| Security | AWS Identity and Access Management | AWS Identity and Access Management |
| Cost Model | PAYG Model | Pay FOR USAGE MODEL |
| Cost Management | Costs can increase with increased throughput | Costs can add up for high message volumes |
| Cost Predictability | Cost can vary with throughput and retention | Cost can be unpredictable with high volumes |
| Management | Managed via AWS Console and APIs | Managed via AWS Console and APIs |
| Data Processing | Offers capabilities for data processing | Minimal processing capabilities |
| Use Cases | Real-time data streaming, analytics | Asynchronous messaging, decoupling |
| Learning Curve | Steeper learning curve for complex use cases | Easy to get started with basic usage |
| Monitoring Tools | CloudWatch metrics and alarms | CloudWatch metrics and alarms |
| Machine Learning Support | Integration with AWS services | Integration with AWS services |
Amazon Kinesis: Pros & Cons
Amazon Kinesis Pros:
Real-Time Data Streaming: Kinesis is motive-built for actual-time statistics streaming, making it best for applications that require processing facts as it arrives.
Scalability: Kinesis can robotically scale primarily based on the incoming records quantity. It makes use of the idea of shards to manage statistics distribution and processing, allowing you to address high throughputs.
Integration with AWS Services: Kinesis seamlessly integrates with different AWS services including Lambda, S3, and analytics services like Kinesis Data Analytics, making it a part of a broader facts processing surroundings.
Data Durability: Data sent to Kinesis is durably saved for a configurable duration (1-7 days), ensuring records availability for batch processing or reprocessing.
Streaming Analytics: Kinesis Data Anaytics permits you to carry out real-time analytics on information streams, taking into account complicated event processing and pattern detection.
Amazon Kinesis Cons:
Complexity: Establishing and overseeing Kinesis streams can be complicated, particularly when dealing with multiple shards and applications.
Cost Expense: Although Kinesis can adjust based on your requirements, this adaptability can also result in variable costs that may be difficult to anticipate, especially for high-throughput use cases.
Learning Curve: Kinesis may have a more difficult learning curve compared to easier messaging services like Amazon SQS, especially if you are new to real-time data streaming concepts.
Restricted Data Retention: Data is kept in Kinesis for a maximum of 7 days by default, which may not be adequate for certain use cases that require longer data retention.
Message Sequencing: While Kinesis can preserve order within a shard, maintaining order across multiple shards can be challenging to handle.
AWS SQS: Pros & Cons
AWS SQS Pros
Fully managed: AWS SQS is a fully managed service, meaning that AWS handles the underlying infrastructure and maintenance tasks, freeing up your time to focus on developing your application.
Easy to use: SQS is relatively easy to use, especially for developers who are already familiar with AWS services. It integrates well with other AWS services, such as AWS Lambda and AWS EC2.
Scalability: SQS can scale automatically to handle large volumes of messages, ensuring that your application remains responsive under heavy loads.
Reliability: SQS provides reliable message delivery, with built-in redundancy and error checking to ensure that messages are not lost or corrupted.
Cost-effective: SQS is a pay-as-you-go service, meaning that you only pay for the resources you use, making it a cost-effective option compared to running your own message broker.
AWS SQS Cons:
Limited message size: SQS has a limited message size of 256KB, which can be a constraint for applications that require larger message sizes.
No guaranteed ordering: SQS does not provide guaranteed message ordering, which can be a problem for applications that rely on strict message sequencing.
Limited number of queues: SQS limits the number of queues you can have per account, which can make it difficult to manage multiple workflows or use cases.
No support for streaming: SQS is designed for batch processing, and it does not support real-time streaming of messages, which can limit its use in certain applications.
Suitability for Notification Services: Kinesis vs SQS
When it comes to high volume notification service, both Amazon Kinesis and Amazon SQS can manage a huge quantity of messages. However, there are some differences between the two that may impact which one is the superior option for a specific use scenario.
Choose Amazon Kinesis If:
Live Data Streaming: You need to input, process, and analyze live streaming data. Kinesis is designed for this use case and excels at it.
Scalability: Your application requires the ability to scale horizontally and handle high-throughput, live data streams.
Streaming Analytics: You intend to perform live analytics, complex event processing, or machine learning on streaming data.
Data Retention: Your use case requires data retention beyond what SQS offers (1-7 days).
Event-Driven Architecture: You're building an event-driven architecture where components communicate via data streams, and you need features like ordering and data durability.
Choose AWS SQS If:
Message Queuing: Your primary use case is asynchronous messaging, decoupling components, and managing the flow of messages between different parts of your application.
Simplicity and Ease of Use: You prefer a simple, fully managed message queuing service with less complexity in setup and operation.
Predictable Costs: You want predictable costs based on the number of messages and operations, without worrying about data retention or shard management.
Message Ordering (FIFO): Your application requires strict message ordering, which is guaranteed by FIFO (First-In-First-Out) queues in SQS.
Standard Use Cases: You're implementing standard queuing scenarios, such as work distribution, job processing, or handling asynchronous tasks.
