Hey everyone! I’m back on track!
This means that with a new fortnight there’s a brand new blog post! In today’s, we’ll cover the key points that you need to master if you want to know the differences between SQS Queues and Kafka Topics. Even though they look pretty similar, they have distinctions and can be applied for different purposes.
What is Amazon SQS?
Amazon Simple Queue Service, more commonly known as Amazon SQS, is a fully managed message queuing service (Amazon MQ) that allows you to connect distributed systems and serverless applications with ease, speeding up processes and reducing complexity with the help of a powerful messaging queue.
Using AWS SQS, you can effortlessly send, store, and receive messages across your systems at any volume, without fear of missed messages or service downtime.
With Amazon SQS, you can choose from standard queues and FIFO queues. Both ensure your message reaches a single consumer and offer similar functionality.
Standard queues offer better throughput and best-effort ordering while guaranteeing at least one delivery. FIFO queues guarantee that messages are processed exactly once and in the same order they were put into the queue.
What is Apache Kafka?
Apache Kafka is a distributed data store that’s ideal for processing real-time data, known as streaming data. Streaming data is data that is generated non-stop by countless data sources (often thousands) that are sending data in simultaneously. Streaming platforms need to be able to handle this massive, constant data while processing it incrementally using Kafka topics and Kafka clusters.
Kafka allows users to store streams of records in the same order they were generated, publish-subscribe to record streams, and process streams in real-time. As such, Kafka is primarily used when you need to build real-time pipelines and applications that process data streams.
By combining messaging, storing, and stream processing into one application, Kafka gives you better insights and processing capabilities in the form of a fault-tolerant managed service.
Amazon SQS vs Kafka: What are the differences?
As we’ve just seen, either Amazon SQS or Kafka can be categorized as “Message Queue” tools.
With Amazon SQS, developers are able to transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use. Besides, a queue can be created in any region.
On the other hand, Kafka is detailed as a “Distributed, fault-tolerant, high throughput pub-sub messaging system”. Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design. It is used by LinkedIn to offload processing of all pages and other views.
Uber Technologies, Spotify, and Slack are some of the popular companies that use Kafka, whereas Amazon SQS is used by Medium, Lyft, and Coursera. Kafka has a broader approval compared to Amazon SQS. But don’t get us wrong here – some companies use both – and that’s totally fine!
Pros and Cons of Amazon SQS
The pros and cons of Amazon SQS are plentiful. Let’s start with the pros:
- Reduce overhead. If you already use Amazon AWS to manage your operations and the infrastructure that powers them, SQS promises a highly available service that has no upfront cost and no need to acquire or configure third-party software. SQS queues are created dynamically and scale up or down automatically, depending on your needs.
- Reliable delivery. You can transmit any amount of data using SQS without the fear of losing messages or seeing processes halt because one service is unavailable. SQS decouples the components of your applications so they can run and fail, independent of one another, improving your system’s overall fault tolerance.
- Secure sensitive data. With Amazon SQS, you can securely exchange sensitive data with the help of server-side encryption.
- Elastic and cost-effective. Leverage the AWS cloud to scale on-demand, making your system more elastic and cost-effective. There’s no need to plan resources or provision them in advance, as Amazon will handle that automatically.
The cons? Other than high costs when operating at scale, SQS lacks some basic features, like the ability for a message to trigger a Lambda function. Reduced control over the MQs performance and the lack of control over messages themselves can hold you back when using SQS (remember that you can use a dead-letter queue to avoid this!).
Pros and Cons of Apache Kafka
Apache Kafka also has a long list of pros and cons to consider. The pros include the following:
- Scalable solution. Kafka uses a partitioned log model, allowing large amounts of data to be distributed across multiple servers. This makes your system scalable beyond a single server’s capacity.
- Fast streams. Reduce latency by decoupling your data streams with the help of Kafka, leading to faster processing and more reliable results.
- Durable partitions. Since partitions are distributed and replicated across multiple servers, Kafka is durable. All data is written to disk, protecting against server outages and failures and improving the fault tolerance of your data.
Kafka also comes with some cons, with no complete set of monitoring tools and sometimes some issues with message edits.
To summarize: When to use which
Kafka is a pretty scalable system and it does fit on high workloads when you want to send messages in batches (to have a good message throughput).
A Kafka topic consists of a number of partitions that can be read completely in parallel by different consumers in one consumer group and that give us a very good performance. So if for example, you need to build a high loaded streaming system, Kafka is really suitable for it.
SQS is an Amazon managed service (so you do not have to support infrastructure by yourself). It is better for eventing when you need to catch some message (event) by some client which will then be automatically popped out from the queue. SQS is not so fast as Kafka and it doesn’t fit a high workload, it’s much more suitable for eventing where the count of events per second is not so much. For example, if you want to react on some S3 file upload (to start some processing of this file) SQS is very good. Another possible use case might be sending an email notification for a customer’s reminder to do some action.
Hope you found this post insightful.
Made with 🖤 from Patricia!
